Although, severe acute respiratory syndrome coronavirus – 2 (SARS-CoV 2) represents one of the biggest challenges in the world today, the exact immunopathogenic mechanism that leads to severe or critical Coronavirus Disease 2019 (COVID-19) has remained incompletely understood. Several studies have indicated that high systemic plasma levels of inflammatory cytokines result in the so-called “cytokine storm”, with subsequent development of microthrombosis, disseminated intravascular coagulation, and multiorgan-failure. Therefore, we reasoned that elevated inflammatory cytokine might act as prognostic factors. Here, we analyzed 245 serum samples of patients with COVID-19, collected at hospital admission. We assessed the levels of heat shock protein 27 (HSP27), soluble suppressor of tumorigenicity- 2 (sST2), caspase cleaved cytokeratin 18 (cCK18), 20S proteasome, and tumor necrosis factor receptor 1 (TNFR-1) and explored their associations with overall-, 30-, 60-, 90-day- and in-hospital mortality. Moreover, we investigated their association with the risk of ventilation. We demonstrated that increased serum sST2 was uni- and multivariably associated with all endpoints. However, we also identified 20S proteasome as independent prognostic factor for in-hospital mortality. Furthermore, elevated HSP27, sST2, and 20S proteasome levels at hospital admission were univariably associated with higher risk of invasive ventilation. These findings could help to identify high-risk patients early in the course of COVID-19.

Objective: To investigate the use of blood levels of N-(2 cyanoethyl)valine (CEVal), a haemoglobin adduct of acrylonitrile, to assess lack of compliance with smoking cessation. Methods: We report CEVal concentrations and percentiles over time for 2 cessation groups, compliance was established using NNAL concentrations. CEVal half-life was estimated. Optimal thresholds were calculated based on receiver operating characteristics analysis. Results: At 180 days, among 67 participants in the continued smoking group and 159 assigned to smoking cessation or sole THP use. CEVal half-life was estimated to be approximately 30 days and the optimal thresholds were for NNAL at 40 pg/mL and CEVal at 35 pmol/g globin (81% sensitivity and specificity). Conclusions: A new generation of biomarkers of compliance is required, specific to the new generation of nicotine products. Methodological validation and standardisation could allow robust assessment of effects across clinical and observational studies while promoting comparability between studies. CEVal could play an important role as biomarker of compliance for smoking cessation and switching studies.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a devastating illness whose biomedical basis is now beginning to be elucidated. We reported previously that, after recovery from frozen storage, lymphocytes (peripheral blood monocytic cells, PBMCs) from ME/CFS patients die faster in culture medium than those from healthy controls. We also found that lymphoblastoid cell lines (lymphoblasts) derived from these PBMCs exhibit multiple abnormalities in mitochondrial respiratory function and signalling activity by the cellular stress-sensing kinase TORC1. These differences were correlated with disease severity, as measured by the Richardson and Lidbury Weighted Standing Test. The clarity of the differences between these cells derived from ME/CFS patient blood and those from healthy controls suggested that they may provide useful biomarkers for ME/CFS. Here we report a preliminary investigation into that possibility using a variety of analytical classification tools, including linear discriminant analysis, logistic regression and Receiver Operating Characteristic (ROC) curve analysis. We found that results from three different tests, lymphocyte death rate, mitochondrial respiratory function and TORC1 activity could each individually serve as biomarker with better than 90% sensitivity but only modest specificity vís a vís healthy controls. However, in combination they provided a cell-based biomarker with sensitivity and specificity approaching 100% in our sample. This level of sensitivity and specificity was almost equalled by a suggested protocol in which the frozen lymphocyte death rate was used as a highly sensitive test to triage positive samples to the more time consuming and expensive tests measuring lymphoblast respiratory function and TORC1 activity. This protocol provides a promising biomarker that could assist in more rapid and accurate diagnosis of ME/CFS.

Soil pollution has been estimated using soil analysis and leaching test. These methods could show different data from reality due to effects by soil properties such as grain size and mineral composition. Therefore, this study advocates a new assessment and monitoring method of heavy metal polluted soil using fruticose lichens. Lichens growing at abandoned mine sites and unpolluted areas in southwest Japan and their substrata were analyzed using inductively coupled plasma-mass spectrometry and X-ray fluorescence spectrometry to clarify the relationships between the heavy metal concentrations in lichens and soils, and their heavy metal absorption properties. Concentrations of Cu, Zn, As, and Pb in the lichens were positively correlated with those in the soil. Variability of the relationships did not depend on the lichen species, location, habitat, or the conditions of soils. The analyzed lichens had neither competitive nor antagonistic properties in their heavy metal absorption, which make them good biomarkers of heavy metal pollution of soil. The distribution maps of average heavy metal concentrations at each sampling region detected almost all of the Cu, Zn, and As pollution of soil. Therefore, lichens could be used in practical applications to assess Cu, Zn, and As pollution of soils.

The diagnosis and the pathology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections. In this work, we took advantage of a large public dataset on the IgG antibodies to 3,054 EBV peptides to understand whether these immune responses could be used as putative biomarkers for disease diagnosis and triggers of pathological autoimmunity in ME/CFS patients using healthy controls (HCs) as a comparator cohort. We then aimed at predicting disease status of study participants using a Super Learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets. When we compared data of all ME/CFS patients or data of a subgroup of these patients with non-infectious or unknown disease trigger to the dataset of HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset. In contrast, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from HCs with 100% and 90% accuracies on the train and test sets, respectively. We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies. We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies. In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis of a subset of patients, but they are less likely to trigger pathological autoimmune responses that could explain the pathogenesis of ME/CFS.

Objective: We determined, in stable ambulatory heart failure with reduced ejection fraction (HFrEF) subjects and matched controls, the capability of a novel blood based cardiac-specific cBIN1 Score (CS), which assesses the health of cardiac muscle, to identify patients with known heart failure (HF) and to prognosticate future hospitalization. Background: Limited clinical tools are available in assessing cardiac muscle health in stable ambulatory patients. Cardiac bridging integrator 1 (cBIN1) is a cardiomyocyte t-tubule membrane scaffolding protein which regulates calcium signaling in cardiomyocytes, decreases in failing muscle, and is present in plasma in levels that correlate with cardiac content. We hypothesize that CS, a normalized index of plasma cBIN1 concentration, can function as a diagnostic and prognostic biomarker of HF. Methods: Plasma cBIN1 concentration is measured by an ELISA test, and CS is calculated as the natural log of the ratio of a constant population mean cBIN1 to measured cBIN1 concentration. We determined CS among 125 clinically stable individuals with HFrEF (LVEF ≤ 40%) (mean age 56 ± 10 years old, 79% men) and 125 age, sex matched volunteers with no known history of HF. We obtained plasma concentrations of NT-proBNP, a marker of volume status, as comparison. Baseline co-morbidities and 18-month longitudinal clinical information were obtained through electronic medical records. Results: CS follows a normal distribution with a median of 0 in the control population and median is significantly increased among HFrEF patients to 1.8 (IQR 1.4 – 2.1, p < 0.0001). CS diagnosed HFrEF with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.93 (AUC is 0.98 for NT-proBNP, and combined CS and NT-proBNP AUC is 0.99). Unlike NT-proBNP, CS does not correlate with body mass index (BMI) in either the control or HFrEF population (Pearson’s r = -0.15, p = 0.12; Pearson’s r = 0.003, p = 0.97, respectively). NT-proBNP significantly correlates with renal function (Pearson’s r = -0.37, p = 0.001), while CS also has no correlation (Pearson’s r = 0.03, p = 0.71). During an 18-month follow-up, a high CS ≥ 1.8 at the initial visit predicted future cardiovascular hospitalizations (38% vs. 21%, p = 0.04, hazard ratio 2.0). NT-proBNP did not predict future cardiovascular hospitalizations. Conclusions: Plasma cBIN1 based CS is insensitive to BMI and renal function and differentiates myocardial health between patients with HFrEF versus matched controls. An abnormally high CS reflected poor intrinsic myocardial health and can predict future 18-month cardiac hospitalization in stable ambulatory patients.

To screen and validate immunosuppressive factors in luminal- and basal-like breast cancer cell lines and tissue samples associated with malignant phenotypes. The mRNA microarray datasets, GSE40057 and GSE1561, were downloaded and remodelled. Differentially expressed genes (DEGs) were identified. Enrichment analyses performed and the online resources, GOBO and Kaplan-Meier Plotter, were employed to screen for immunosuppressive factors associated with breast cancer malignant phenotypes. qRT-PCR and western blot were used to validate the expression of CD274 and IL8 in cell lines and immunohistochemical detected the MIF and VEGFA on tissue microarrays. The results showed that CD274 and IL8 were both upregulated in basal-like cell lines. That MIF expression was dramatically increased in patients with breast cancer metastases (p<0.05) and that VEGFA expression positively correlates with breast cancer pathologic grade (p<0.05).During the formation and development of breast cancer, immune-related genes are always activated, and immunosuppressive factors CD274, IL8, MIF and VEGFA are upregulated. Such molecules could be used as biomarkers for breast cancer prognosis. However, because individual immune-related factors can play several biological roles, the mechanistic relationship between immunosuppressive factors and breast cancer malignant phenotypes and the feasibility of their application as drug targets require further investigation.

The purpose of current study was to investigate the expression characteristic of circular RNAs (circRNAs) in peripheral blood of type 2 diabetes mellitus (T2DM) patients and their potentials as diagnostic biomarkers for pre-diabetes and T2DM. In present study, the circRNAs in the peripheral blood from 6 healthy individuals and 6 T2DM patients were collected for microarray analysis. The results indicated that there were 489 differentially expressed circRNAs, of which 78 were upregulated and 411 were downregulated in the T2DM group. Then we selected 5 circRNAs as the candidate biomarkers under a stricter screening criteria and further verified them in another cohort (control group, n=20; pre-diabetes group, n =20; T2DM group; n=20). 3 of the 5 circRNAs presented upregulated expression in the experimental groups, including 2 circRNAs of the T2DM group that had higher expression than the pre-diabetes group. Hsa_circ_0054633 was identified to have the largest area value under the carve (AUC). In another independent cohort (control group, n=60; pre-diabetes group, n=63; T2DM group, n=64), the diagnostic capacity of hsa_circ_0054633 was tested. The results showed that the AUC for the diagnosis of pre-diabetes was 0.751(95% confidence interval=[0. 666-0.835], P＜0.001) while it was 0.793 ([0.716-0.871], P＜0.001) for the diagnosis of T2DM. After including the risk factors of T2DM, the AUC increased to 0.841 ([0.773-0.910], P <0.001) and 0.834 ([0.762-0.905], P <0.001), respectively. Hsa_circ_0054633 presented a certain diagnostic capability for pre-diabetes and T2DM.

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one’s health, there is an urgent need: (1) to find a broadly acceptable and useful definition of health, based on a list of features (which may or may not be molecular); (2) to operationalize features of health so that it can be measured; (3) to identify predictive biomarkers and (molecular) pathways of health, and (4) to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of (a) physiological function, (b) cognitive function and (c) physical function, amended, specifically in case of humans, by (d) lack of disease, and by (e) reproductive function. Often used concepts such as lack of frailty, allostatic load, or self-reported health (in case of human), and indices such as the Healthy Aging Index can be viewed as projections or surrogates of our definition. We further define aging as the set of all processes in an individual that reduce its “wellbeing”, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other, specifically by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts that are a key to reducing the need for medical and nursing care.

Neuro-rehabilitation services are essential in reducing post-stroke impairments, enhancing independence, and improving recovery in hospital and post-discharge. However these services are therapist-dependent and resource intensive. Patients’ disengagement and boredom in stroke units are common which adversely affect functional and psychological outcomes. Novel techniques such as use of iPads™ are increasingly researched to overcome such challenges. The aim of this review is to determine the feasibility, effectiveness, acceptability, and barriers to the use of iPads™ in stroke neuro-rehabilitation. Four databases and manual literature search were used to identify published studies using the terms “iPad”, “Stroke”, and “neuro-rehabilitation”. Studies were included in accordance with the review selection criteria. A total of 16 articles were included in the review. The majority of the studies focused on iPads use in speech and language therapy. Although of small scale, the studies highlighted that iPads are feasible, have the potential to improve rehabilitation outcomes, and can improve patient’s social isolation. Patients’ stroke severity and financial limitations are some of the barriers highlighted in this review. This review presents preliminary data supportive for the use of iPad technology in stroke neuro-rehabilitation. However, further research is needed to determine impact on rehabilitation goals acquisition, clinical efficacy, and cost-efficiency.

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to support lifelong learning. We provide our ideas about how to combine this optimization method with evolutionary algorithms in order to boost the development of specialized Artificial Neural Networks, which define the proprioceptive configuration of particular robotic units that are part of a swarm. We consider how optimization of the free-energy can promote the homeostasis of the swarm system, i.e. ensures that the system remains within its sensory boundaries throughout its active lifetime. We will show how complex distributed cognitive systems can be build in the form of hierarchical modular system, which consists of specialized micro-intelligent agents connected through information channels. We will also consider the co-evolution of various robotic swarm units, which can result in development of proprioception and a comprehensive awareness of the properties of the environment. And finally, we will give a brief outline of how this system can be implemented in practice and of our progress in this area.

Despite the increasing number of individuals affected by Alzheimer’s disease (AD) every year, no effective therapy has been developed to treat this neurodegenerative disease yet. The current methods for AD diagnosis are effective for clinical confirmation of the disease only when symptoms become apparent, years after molecular damage started within the patients’ brains. As higher expression of a conformationally altered p53 has been correlated with AD, we developed a mass spectrometry-based method for highly sensitive, specific, and reproducible quantification of a p53 conformational variant in plasma samples of patients with known clinical outcome. In particular, we tested the prognostic performance of an AD-specific 2D3A8-immunoselected p53 peptide (AZ 284™) in different sets of individuals progressing from both cognitively unimpaired (CU) and mild cognitive impairment (MCI) patients progressing to AD dementia. Our data showed that quantitative analysis of AZ 284™ is a reliable tool for predicting AD progression up to 6 years prior to dementia onset with AUC >90%. Taken together, these results support the implementation of p53 conformational variant quantification as an affordable and powerful diagnostic tool for early, non-invasive AD diagnosis.

Circulating tumor DNA (ctDNA) in fluids has gained attention because ctDNA seems to identify tumor-specific abnormalities, which could be used for diagnosis, follow-up of treatment, and prognosis: the so-called liquid biopsy. Liquid biopsy is a minimally invasive approach and presents the sum of ctDNA from primary and secondary tumor sites. It has been possible not only to quantify the amount of ctDNA but also to identify (epi)genetic changes. Specific mutations in genes have been identified in the plasma of patients with several types of cancer, which highlights ctDNA as a possible cancer biomarker. However, achieving detectable concentrations of ctDNA in body fluids is not an easy task. ctDNA fragments present a short half-life, and there are no cut-off values to discriminate high and low ctDNA concentrations. Here, we discuss the use of ctDNA as a cancer biomarker, the main methodologies, the inherent difficulties, and the clinical predictive value of ctDNA.

The development of reliable Food Frequency Questionnaires (FFQs) requires detailed information about the level and variation of dietary food intake of the target population. However, these data are often limited. To facilitate the development of new high quality FFQs and validation of existing FFQs, we developed a comprehensive National Dietary Assessment Reference Database (NDARD) detailing information about the level and variation in dietary food intake of people 20-70 years old in the general Dutch population. This paper describes the methods and characteristics of the population included in the NDARD database. 1063 men and 985 women agreed to participate in this research. Dietary intake data were collected using different FFQs, web-based and telephone-based 24-hour recalls, as well as blood and urine-based biomarkers. The baseline FFQ was completed by 1647 participants whose mean BMI was 26±4 kg/m2; 1117 participants completed telephone-based recalls and 1781 participants completed web-based recalls. According to the baseline FFQ, the mean energy intake was 2051±605 kcal/day. The percentage of total energy intake from protein was 15±2 En%, from carbohydrates was 43±6 En%, and from fat was 36±5 En%. This database will enable researchers to validate existing FFQs and to develop new high quality dietary assessment methods.

Dietary assessment methods recognized as appropriate for the general population are usually applied in a similar manner to athletes, despite knowledge that sport-specific factors can complicate assessment and impact accuracy in unique ways. As dietary assessment methods are used extensively within the field of sports nutrition, there is concern the validity of methodologies have not undergone more rigorous evaluation in this unique population sub-group. The purpose was to systematically review studies comparing two or more methods of dietary assessment, including dietary intake measured against biomarkers or reference measures of energy expenditure, in athletes. Six electronic databases were searched for English-language, full-text articles published from January 1980 until June 2016. The search strategy combined the following keywords: diet, nutrition assessment, athlete and validity; where the following outcomes are reported but not limited to: energy intake, macro and/or micronutrient intake, food intake, nutritional adequacy, diet quality, or nutritional status. Meta-analysis was performed on studies with sufficient methodological similarity, with between-group standardized mean differences (or effect size) and 95 % confidence intervals (CI) calculated. Of the 1624 studies identified, 18 were eligible for inclusion. Studies comparing self-reported energy intake (EI) to energy expenditure assessed via doubly labelled water were grouped for comparison (n=11) and demonstrated mean EI was under-estimated by 19 % (- 2793 ± 1134 kJ/d). Meta-analysis revealed a large pooled effect size of - 1.006 (95% CI: -1.3 to -0.7; p<0.001). The remaining studies (n=7) compared a new dietary tool or instrument to a reference method(s) (e.g. food record, 24-h dietary recall, biomarker) as part of a validation study. This systematic review revealed there are limited robust studies evaluating dietary assessment methods in athletes. Existing literature demonstrates substantial variability between methods, with under and misreporting of intake frequently observed. There is a clear need for careful validation of dietary assessment methods, including emerging technical innovations, among athlete populations.

Background: Research continues to search for blood-based biomarkers sensitive to Alzheimer’s disease (AD) pathology during the initial stages when symptoms of cognitive decline are not yet apparent. A blood-based biomarker candidate is metalloprotein p53, the conformation of which was previously found to be altered in peripheral cells from individuals with mild cognitive impairment (MCI) and AD, presenting as an unfolded p53 (U-p53) conformational variant. Methods: Plasma samples from the well-characterized Australian Imaging, Biomarkers, and Lifestyle (AIBL) cohort were used to identify the clinically relevant AZ 284^® peptide, specifically present in samples from individuals with symptomatic AD. The AZ 284^® peptide, which is a marker of the U-p53 conformational variant (U-p53^AZ), was identified by immunoprecipitation (IP) with a novel U-p53 conformational variant-specific antibody followed by liquid chromatography (LC) tandem mass spectrometry (MS/MS) and protein sequencing. Using IP-LC surface-activated chemical ionization (SACI) MS/MS analysis, the prognostic and diagnostic performance of U-p53^AZ were examined in the longitudinal AIBL cohort, including 252 plasma samples derived from 214 elderly individuals. For the prognostic analyses, U-p53^AZ levels were assessed at 36, 72, and 90 months after baseline assessment. Results: The prognostic performance of U-p53^AZ to predict the progression to AD from preclinical or prodromal AD was high, with area under the receiver operating characteristic curve (AUC) values close to or above 0.90. Furthermore, U-p53^AZ predicted the progression to AD more than 6 years prior to symptom onset with positive and negative predictive values of about 90%. Additionally, the estimated prognostic performance of U-p53^AZ was superior to other main risk factors (i.e., age, sex, and either alone or in combination with amyloid status. Furthermore, U-p53^AZ had high diagnostic performance to differentiate cognitively normal individuals from those with AD (AUC values >0.88). Conclusion: These findings support the use of U-p53^AZ as a prognostic blood-based biomarker accurately predicting the progression to AD dementia during the preclinical and prodromal stages at least 6 years before receiving the clinical diagnosis of AD dementia.

A simple in vitro biosensor for the detection of β-amyloid 42 in phosphate-buffer saline (PBS) and undiluted human serum was fabricated and tested based on our platform sensor technology. The bio-recognition mechanism of this biosensor was based on the effect of the interaction between antibody and antigen of β-amyloid 42 to the redox couple probe of K₄Fe (CN) ₆ and K₃Fe (CN) ₆. Differential pulse voltammetry (DPV) served as the transduction mechanism measuring the current output derived from the redox coupling reaction. The biosensor was a three-electrode electrochemical system, and the working and counter electrodes were 50 nm thin gold film deposited by sputtering technique. The reference electrode was a thick-film printed Ag/AgCl electrode. Laser ablation technique was used to define the size and structure of the biosensor. Cost-effective roll-to-roll manufacturing process was employed in the fabrication of the biosensor making it simple and relatively inexpensive. Self-assembled monolayers (SAM) of 3-Mercaptopropionic acid (MPA) was employed to covalently immobilize the thiol group on the gold working electrode. A carbodiimide conjugation approach using N-(3-dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride (EDC) and N–hydroxysuccinimide (NHS) was undertaken for cross-linking antibody of β-amyloid 42 to the carboxylic groups on one end of the MPA. The antibody concentration of β-amyloid 42 used was 18.75µg/mL. The concentration range of β-amyloid 42 in this study was from 0.0675µg/mL to 0.5µg/mL for both PBS and undiluted human serum. DPV measurements showed excellent response in this antigen concentration range. Interference study of this biosensor was carried out in the presence of Tau protein antigen. Excellent specificity of this β-amyloid 42 biosensor was demonstrated without interference by other species such as T-tau protein.

In 2011, it was reviewed that there is a strong co-occurrence between major depression and chronic fatigue syndrome (CFS), with fatigue and physio-somatic symptoms being key symptoms of depression, and depressive symptoms appearing during the course of CFS. Moreover, the comorbidity between both conditions may in part be explained by activated immune-inflammatory pathways, including increased translocation of Gram-negative bacteria and increased levels of pro-inflammatory cytokines, such as interleukin (IL)-1. Nevertheless, the possible involvement of activated microglia in this comorbidity has remained unclear. This paper aims to review microglial disturbances in major depression, CFS and their comorbidity. A comprehensive literature search was conducted using the PubMed / MEDLINE database to identify studies that are relevant to this current review. Depressed patients present neuroinflammatory alterations, probably related to microglial activation, while animal models show that a microglial response to immune challenges including lipopolysaccharides is accompanied by depressive-like behaviors. Recent evidence from preclinical studies indicate that activated microglia have a key role in the onset of fatigue. In chronic inflammatory conditions, such as infections and senescence, microglia orchestrate an inflammatory microenvironment thereby causing fatigue. In conclusion, based on our review we may posit that shared immune-inflammatory pathways and activated microglia underpin comorbid depression and CFS and that activated microglia are the main orchestrators of this comorbidity. As such, microglial activation and neuro-inflammation may be promising targets to treat the overlapping manifestations of both depression and CFS.

As the name implies, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus and a member of the corona virus family, primarily affecting the upper respiratory system and the lungs. Like many other respiratory viruses, SARS-CoV-2 can spread to other organ systems. Apart from causing diarrhea, another most common but debilitating complication caused by the SARS-CoV-2 is neurological symptoms and cognitive difficulties, which occur in up to two thirds of hospitalized covid patients and ranging from shortness of concentration, overall declined cognitive speed to executive or memory function impairment. Neuro-cognitive dysfunction and “brain fog” are frequently present in COVID-19 cases, which can last several months after the infection, leading to disruption of daily life. Cumulative evidence suggests that SARS-CoV-2 affects vasculature in the extra pulmonary systems directly or indirectly, leading to impairment of endothelial function and even multi-organ damage. The post COVID-19 long-lasting neurocognitive impairments have not been studied fully; and the underlying mechanism remains elusive. In this review, we summarize the current understanding of the effects of COVID-19 on vascular dysfunction and how vascular dysfunction leads to cognitive impairment in patients.

Pervasive and wide-ranging cognitive deficits are a core feature of schizophrenia and an important determinant of long-term functional outcome. The lack of sufficiently effective treatments for cognitive impairment associated with schizophrenia (CIAS) represents a major unmet need and a central roadblock towards recovery. This is partly due to the current therapeutic focus on clinical symptoms, and the relative neglect of cognitive impairments despite their functionally disabling effects. Furthermore, effective treatment is impeded by our limited knowledge of the complex pathophysiology, which gives rise to perturbed information processing. Here, we review mechanisms and effectiveness of available pharmacological and non-pharmacological treatments for CIAS. Current evidence indicates, that while techniques which broadly enhance neural plasticity show the greatest therapeutic potential, effect sizes are at best moderate. Among other reasons, this is due to a considerable heterogeneity of responses to individual interventions. Furthermore, we discuss how recent conceptual advances in operationalizing cognitive impairments based on cognitive neuroscience have the potential to address these issues and facilitate the development of novel treatment strategies for CIAS. This includes more clearly elucidating pathophysiological mechanisms in both humans and animal models, identifying new treatment targets as well as establishing biomarkers for a better prediction of treatment responses.

Beta-thalassemia major (β-TM) patients are treated with repeated blood transfusions, which may cause iron overload (IO), which in turn may induce immune aberrations. Patients with β-TM have an increased risk of major depressive disorder (MDD). The aims of the present study are to examine whether repeated blood transfusions, IO and immune-inflammatory responses are associated with MDD in children (6-12 years) with β-TM. The Children’s Depression Inventory (CDI), iron status (serum iron, ferritin, transferrin, TS%) and serum levels of CCL11, IL-1β, IL-10, and TNF-α were measured in β-TM with (n=54) and without (n=57) MDD and in healthy children (n=55). The results show that MDD in β-TM is associated with a greater number of blood transfusions, increased IO and IL-1β levels. Partial Least Squares path analysis shows that 68.8% of the variance in the CDI score is explained by the number of blood transfusions, IO, and increased levels of IL-1β and TNF-α. The latter two cytokines partly mediate the effects of IO on the CDI score, while the effects of blood transfusions on the CDI score are partly mediated by IO and the path from IO to immune activation. IO is also associated with increased IL-10 and lower CCL11 levels but these alterations are not significantly associated with MDD. In conclusion, blood transfusions may be causally related to MDD in β-TM children and their effects are in part mediated by increased IO and the consequent immune-inflammatory response. The results suggest that not only IO and its consequences including inflammation and ferroptosis, but also other factors related to the number of transfusions may cause MDD including psychosocial stressors. Current treatment modalities with folic acid and vitamin C are insufficient to attenuate IO and immune-inflammatory responses and to prevent MDD is children with β-TM undergoing blood transfusions.

Psychotherapy is a comprehensive biological treatment modifying complex underlying cognitive, emotional, behavioral, and regulatory responses in the brain, leading patients with mental illness to a new interpretation of the sense of self and others. Psychotherapy is an art of science integrated with psychology and/or philosophy. Neurological science studies the neurological basis of cognition, memory, and behavior as well as the impact of neurological damage and disease on the functions, and their treatment. Both psychotherapy and neurological science deal with the brain; nevertheless, they continue to stay polarized far. Existential phenomenological psychotherapy (EPP) has been in the forefront of meaning-centered counseling for almost a century. The phenomenological approach in psychotherapy originated in the works of Martin Heidegger, Ludwig Binswanger, Medard Boss and Viktor Frankl, and it has been committed to account for the existential possibilities and limitations of one’s life. EPP provides philosophically rich interpretations and empowers counseling techniques to assist mentally suffering individuals by finding meaning and purpose of life. The approach has proven to be effective in treating mood and anxiety disorders. This narrative review article demonstrates the development of EPP, the therapeutic methodology, evidence-based accounts of its curative techniques, current understanding of mood and anxiety disorders in neurological science, and a possible converging path to translate and integrate meaning-centered psychotherapy and neuroscience, concluding that the existential phenomenological psychotherapy potently plays a synergistic role with the currently prevailing medication-based approaches for the treatment of mood and anxiety disorders.

The pig is an important source of meat production and provides a valuable model for certain human diseases. MicroRNA (miRNA), which is non-coding RNA and regulates gene expression at the posttranscriptional level, plays a critical role in various biological processes. Studies on identification and function of mature miRNAs in multiple pig tissues are increasing, yet the literature is limited. Therefore, we reviewed current research to determine the miRNAs expressed in specific pig tissues that are involved in carcass values (including muscle and adipocytes), reproduction (including pituitary, testis, and ovary), and development of some solid organs (e.g., brain, lung, kidney, and liver). We also discuss the possible regulating mechanisms of miRNA. Finally, as pig organs are suitable candidates for xenotransplantation, biomarkers of their miRNA in xenotransplantation were evaluated.

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

Background: Primary deficit schizophrenia (DS) is characterized by enduring negative symptoms and represents a qualitatively different disease entity with respect to non-deficit schizophrenia (NDS). No studies investigated the association between the enzyme paraoxonase 1 (PON1) and DS and its phenomenology. Methods: In this case-control study, Thai women and men, aged 18-65 years, were divided in DS (n=40) and NDS (n=40) and were compared to controls (n=40). PON1 activities against 4-(chloromethyl)phenyl acetate (CMPA) and phenylacetate were determined. Moreover, subjects were genotyped for their PON1 Q192R polymorphism and IgA levels responses directed to Gram-negative bacteria were measured. Results: DS is significantly associated with the QQ genotype and the Q allele as compared with NDS and controls. PON1 activities are significantly and inversely associated with negative symptoms, formal thought disorders, psychomotor retardation, excitation and DS. The presence of the Q allele is associated with increased IgA responses to Pseudomonas aeruginosa, Morganella morganii, and Pseudomonas putida as compared with RR carriers. Conclusions: The PON1 Q allele and lower PON1 activities especially against CMPA are associated with DS, indicating lowered quorum quenching abilities as well as lowered defenses against lipoperoxidation and immune activation. It is suggested that lowered PON1 activity in DS constitutes an impairment in the innate immune system which together with lowered natural IgM may cause lower immune regulation thereby predisposing towards greater neurotoxic effects of immune-inflammatory, oxidative and nitrosative pathways and Gram-negative microbiota.

Melanoma is considered the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in prognosticating this type of cancer. With the emergence of new therapeutic strategies for metastatic melanoma that have shown improvement in patient survival, we developed a transfer learning-based biomarker discovery model that could help in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results reveal that the genes we found show consistency with other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set, and our methods found novel biomarker genes as well. Our ensemble model achieved Area Under the Receiver Operating Characteristic (AUC) of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB). We also assessed the potential sources of bias for our model and confirmed some of them by the model's performance.

Myeloperoxidase (MPO) belong to the family of heme containing peroxidases, produced mostly from polymorphonuclear neutrophils. The active enzyme (150 kD) is the product of MPO gene located on long arm of chromosome 17. The primary gene product undergoes several modifications like removal of introns and signal peptide and leads to the formation of enzymatically inactive glycosylated apoproMPO which complexes with chaperons, producing active proMPO by the insertion of heme moiety. The active enzyme is a homodimer of heavy and light chain protomers. This enzyme is released into extracellular fluid after oxidative stress and different inflammatory responses. MPO is the only type of peroxidase using H2O2 to oxidize several halides and pseudohalides to form different hypohalous acids. So the antibacterial activities of MPO involve the production of reactive oxygen and reactive nitrogen species. Controlled MPO release at the site of infection is of prime importance for its efficient activities. Any uncontrolled degranulation exaggerates the inflammation that can also lead to tissue damage even in absence of inflammation. Several types of tissue injuries and pathogenesis of several other major chronic diseases like rheumatoid arthritis, cardiovascular diseases, liver diseases, diabetes and cancer have been reported to be linked with myeloperoxidase derived oxidants. So the enhanced level of MPO activity is one of the best diagnostic tool of inflammatory and oxidative stress biomarkers among these commonly occurring diseases.

Background: Ongoing molecular profiling studies enabled by advances in biomedical technologies are producing vast amounts of ‘omic’ data for early detection, monitoring, and prognosis of diverse diseases. A major common limitation is the scarcity of biological samples, necessitating integrative modeling frameworks that can make optimal use of available data for disease classification tasks. Related data sets are often available from different studies, but may have been generated using different technology platforms. Thus, there is a critical need for flexible modeling methods that can handle data from diverse sources to facilitate the discovery of robust biomarkers that underlie disease regulatory processes. Results: In this paper, we introduce a novel framework called Knowledge Augmented Rule Learning (KARL), which incorporates two sources of knowledge, domain, and data, for pattern discovery from small and high-dimensional datasets, such as transcriptomic data. We propose KARL as a transfer rule learning framework in which knowledge of the domain is transferred to the learning process on data in order to 1) improve the reliability of the discovered patterns, and 2) study the knowledge of the domain when used along with data for modeling. In this work, we generated KARL models on gene expression datasets for five types of cancer, including brain, breast, colon, lung, and prostate. As our knowledge of the domain, we used the Ingenuity Knowledge Base (IKB) to extract genes related to hallmarks of cancer and annotated these prior relationships before learning classifiers from these datasets. Conclusions: Our results show that KARL produces, on average, rule models that are more robust classifiers than the baseline without such background knowledge, for our tasks of cancer prediction using 25 publicly available gene expression datasets. Moreover, KARL helped us learn insights about previously known relationships in these gene expression datasets, along with new relationships not input as known, to enable informed biomarker discovery for cancer prediction tasks. KARL can be applied to modeling similar data from any other domain and classification task. Future work would involve extensions to KARL to handle hierarchical knowledge to derive more general hypotheses to drive biomedicine.

Deficit schizophrenia is characterized by leaky tight and adherens junctions and bacterial translocation. Here we examine whether (deficit) schizophrenia is accompanied by leaky paracellular, transcellular and vascular barriers in the gut and blood brain barriers. We measured IgA responses to occludin, claudin-5, E-cadherin and β-catenin (paracellular pathway, PARA), talin, actin, vinculin and epithelial intermediate filament (transcellular pathway, TRANS) and plasmalemma vesicle-associated protein (PLVAP, vascular pathway) in 78 schizophrenia patients and 40 controls. IgA responses to claudin-5, E-cadherin and β-catenin, the sum of the four PARA proteins and the ratio PARA/TRANS were significantly higher in deficit schizophrenia than in non-deficit schizophrenia and controls. A large part of the variance in PHEMN (psychosis, hostility, excitation, mannerism and negative) symptoms, psychomotor retardation, formal thought disorders, verbal fluency, word list memory, word list recall and executive functions was explained by the PARA/TRANS ratio coupled with plasma IgA responses to Gram-negative bacteria, IgM to malondialdehyde, CCL-11 (eotaxin), IgA levels of the ratio of noxious to more protective tryptophan catabolites (NOX/PRO TRYCATs) and a plasma immune activation index. Moreover, IgA levels to Gram-negative bacteria were significantly associated with IgA to E-cadherin, β-catenin and PLVAP, while IgA levels to claudin-5 were significantly predicted by IgA to E-cadherin, NOX/PRO TRYCAT ratio, Gram-negative bacteria and CCL11. The phenomenology of the deficit syndrome is to a large extent explained by the cumulative effects of lowered natural IgM, breakdown of the paracellular and vascular pathways, increased bacterial translocation, peripheral immune-inflammatory responses and indices of BBB breakdown.

Aberration in microRNA (miRNA) expression or DNA methylation is a causal factor for polycystic ovarian syndrome (PCOS), a common endocrine disorder and leading cause of infertility. However, the epigenetic interactions between miRNA and DNA methylation remain unexplored in PCOS. In this study, we conducted an integrated analysis of RNA-seq, miRNA-seq and MBD-seq on ovarian granulosa cells of PCOS and control groups to reveal the epigenetic interactions involved in the pathogenesis of PCOS. Firstly, we identified 830 genes and 30 miRNAs that were expressed differently in PCOS, and seven miRNAs were found to negatively regulate targeted mRNA expression. Next, in total, 130 miRNAs were found to be significantly differently methylated in promoter regions, while 13 were found to be associated with miRNA expression. Furthermore, the promoter hypermethylation of miR-429, miR-141-3p, and miR-126-3p was proven to suppress miRNA expression and therefore upregulate their corresponding genes, including XIAP, BRD3, MAPK14 and SLC7A5. Our results demonstrate that DNA methylation regulates miRNA expression and therefore controls its corresponding gene expression. The reactivation of the transcription of epigenetically silenced genes may be one of the key elements in PCOS pathogenesis. Meanwhile, the epigenetic mechanisms underlying the regulation of miRNA expression can provide a potential therapeutic target for PCOS in the future.

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

There is evidence that schizophrenia is characterized by activation of the immune-inflammatory response (IRS) and compensatory immune-regulatory (CIRS) systems and lowered neuroprotection. Studies performed on antipsychotic-naïve first episode psychosis (AF-FEP) and schizophrenia (FES) patients are important as they may disclose the pathogenesis of the disease. However, the interactome of FEP/FES is not well delineated. The aim of the current study was to delineate the characteristics of the protein-protein interaction (PPI) network of AN-FEP and its transition to FES and the biological functions, pathways, and molecular patterns, which are over-represented in FEP/FES. PPI network analysis shows that FEP and FEP/FES are strongly associated with a response to a bacterium, TNF, NFκB, RELA, SP1, JAK-STAT, death receptor and TLR4 signaling, and tyrosine phosphorylation of STAT proteins. Specific molecular complexes of the peripheral immune response are associated with microglial activation, neuroinflammation and gliogenesis. FEP/FES is accompanied by lowered protection against inflammation in part attributable to dysfunctional miRNA maturation, deficits in neurotrophin/Trk, RTK and Wnt/catenin signaling and adherens junction organization. Lowered neuroprotection due to reduced neurotrophin/Trk and Wnt/catenin signaling, and DISC1 expression and multiple interactions between lowered BDNF, CDH1, CTNNB, and DISC1 expression, increase the vulnerability to the neurotoxic effects of immune products including cytokines and complement factors. All pathways or molecular patterns enriched in the interactome of FEP/FES are directly or indirectly affected by LPS. In summary: FEP appears to be triggered by a biotic stimulus (e.g. Gram-negative bacteria) which may induce neuro-immune toxicity cascades especially when anti-inflammatory and neurotrophic protections are deficient.

Complete pathologic response (pCR) predicts the long-term outcome of neoadjuvant treated (NAC) breast cancer (BC) but is reached in <10% of hormone-receptor-positive patients. Biomarkers able to guide adjustment or interruption of an ineffective therapy are desired. Here, we evaluated whether shifts in the serum concentration of thymidine kinase 1 (sTK1) during NAC could be utilized as a biomarker. In the PROMIX trial, women with localized HER2- BC received neoadjuvant epirubicin/docetaxel in six cycles. sTK1 was measured with an ELISA in 54 patients at cycles 1-4 and in a total of 131 patients before and 48h after cycle 1. The prognostic significance of the results was evaluated by log-rank tests of Kaplan–Meier estimates. Treatment resulted in a 2-fold increase of sTK1 before and 3-fold increase 48h after the cycles, except for the first cycle, where half of patients reacted with a decrease (post/pre sTK1- ratio <1.12) and the other half reacted with an increase (ratio >1.12). OS rates in ER+ patients with ratios of >1.12 and <1.12 were 97.7% and 78% (p=0.005), respectively, and DFS rates were 90.7% and 68% (p=0.006), respectively. Thus, response of sTK1 at the first cycle of chemotherapy could be used both as an early biomarker for guidance of chemotherapy and for the study of inherent tumor chemo-sensitivity, which could predict long-term outcome prior to therapy.

Background: COVID-19 is associated with neuropsychiatric symptoms including increased depressive, anxiety and chronic fatigue-syndrome (CFS)-like physiosomatic (previously known as psychosomatic) symptoms.Aims: To delineate the associations between affective and CFS-like symptoms in COVID-19 and chest CT-scan anomalies (CCTAs), oxygen saturation (SpO₂), interleukin (IL)-6, IL-10, C-Reactive Protein (CRP), albumin, calcium, magnesium, soluble angiotensin converting enzyme (ACE2) and soluble advanced glycation products (sRAGEs).Method: The above biomarkers were assessed in 60 COVID-19 patients and 30 heathy controls who had measurements of the Hamilton Depression (HDRS) and Anxiety (HAM-A) and the Fibromyalgia and Chronic Fatigue (FF) Rating Scales. Results: Partial Least Squares-SEM analysis showed that reliable latent vectors could be extracted from a) key depressive and anxiety and physiosomatic symptoms (the physio-affective or PA-core), b) IL-6, IL-10, CRP, albumin, calcium, and sRAGEs (the immune response core); and c) different CCTAs (including ground glass opacities, consolidation, and crazy paving) and lowered SpO2% (lung lesions). PLS showed that 70.0% of the variance in the PA-core was explained by the regression on the immune response and lung lesions latent vectors. Moreover, one common “infection-immune-inflammatory (III) core” underpins pneumonia-associated CCTAs, lowered SpO2 and immune activation, and this III core explains 70% of the variance in the PA core, and a relevant part of the variance in melancholia, insomnia, and neurocognitive symptoms.Discussion: Acute SARS-CoV-2 infection is accompanied by lung lesions and lowered SpO2 which both may cause activated immune-inflammatory pathways, which mediate the effects of the former on the PA-core and other neuropsychiatric symptoms due to SARS-CoV-2 infection.

This research aimed to figure out the applications of HbA1c protein and HbA1 gene as the prognostic biomarkers for assessing the risks of different cancers among male T2D patients in Bangladesh considering their serological and oncoinformatic parameters. Depending on the concentrations of HbA1c (%) of the T2D patients (n=300), their individual FBS (mmol/L); THABF (mmol/L); creatinine (mg/dl); SC (mg/dl); STGs (mg/dl); HDLC (mg/dl); and LDLC (mg/dl) were estimated. The values of the patients were compared with the control (n=60) group as the serological analysis. Besides, HbA1 gene (encoding hBA1c protein) overexpression and promotor methylation responsible for BLCA, BRCA, CHOL, COAD, LUAD, LUSC, PAAD, and PRAD cancers in the male T2D patients were profiled as the oncoinformatic parameters based on the sample types; caner stages; racial footprints; gender; age; nodal metastasis; p53 methylations; pancreatitis; diabetes status; smoking behaviors; and overall/disease-free survivability. Finally, the ‘HbA1 gene strings’ responsible for genetic coexpression; endophytic vesicle regulation; antioxidant regulation; oxygen species metabolic regulation; and gene-mediated response to the reactive oxygen molecules were studied comprehensively. A strong correlation between BMI and FBS was observed in both the patients and the control (P<0.0001). Similarly, the values of FBS, THABF, and creatinine resulted in equal significance (P<0.0001) as compared to the HbA1c concentrations of all the T2D and control individuals. The SC, STGs, HDLC, and LDLC concentrations regulated ardently in both the control (P<0.0001), and patients group (P<0.0001), while HbA1c ranged from 3.8-5.8%, and 5.11-15.8% respectively. HbA1 gene is found downregulating with cancer progressed in most of the oncoinformatic parameters. According to the DA, CS, EI, CE, PC, NC, GF, H, and AT profiles; the HbA1 gene interacts with 8 other genes responsible for creating a protein cluster comprising- AHSP, HBA1, HBA2, HBB, HBD, HBE1, HBG2, RPS12, and RPS19 proteins for cancer formation. To recapitulate, HbA1c protein and HbA1 gene can be used as the prognostic serological and molecular biomarkers respectively for determining the risks of cancers among male chronic T2D patients.

In 2001, the first author of this paper reported that schizophrenia is associated with an increased frequency of the haptoglobin (Hp)-2 gene. The precursor of Hp-2 is zonulin, a molecule that affects intercellular tight junction integrity. Recently, we reported increased plasma IgA/IgM responses to Gram-negative bacteria in deficit schizophrenia indicating leaky gut and gut dysbiosis. The current study was performed to examine the integrity of the paracellular (tight and adherens junctions) and transcellular (cytoskeletal proteins) pathways in deficit versus non-deficit schizophrenia. We measured IgM responses to zonulin, occludin, E-cadherin, talin, actin and vinculin in association with IgA responses to Gram-negative bacteria, CCL-11, IgA responses to tryptophan catabolites (TRYCATs), immune activation and IgM to malondialdehyde (MDA) and NO-cysteinyl in 78 schizophrenia patients and 40 controls. We found that the ratio of IgM to zonulin + occudin / talin + actin + viculin (PARA/TRANS) was significantly greater in deficit than in non-deficit schizophrenia and higher in schizophrenia than controls and was significantly associated with increased IgA responses to Gram-negative bacteria. IgM responses to zonulin were positively associated with schizophrenia (versus controls), while IgM to occludin was significantly associated with deficit schizophrenia (versus non-deficit schizophrenia and controls). A large part of the variance (90.8%) in negative and PHEM (psychosis, hostility, excitation and mannerism) symptoms was explained by PARA/TRANS ratio, IgA to Gram-negative bacteria, IgM to E-cadherin and malondialdehyde (MDA) and memory dysfunctions, while 53.3% of the variance in the latter was explained by PARA/TRANS ratio, IgA to Gram-negative bacteria, CCL-11, TRYCATs and immune activation. The results show an upregulated paracellular pathway with breakdown of the tight and ahherens junctions and increased bacterial translocation in deficit schizophrenia. These dysfunctions in the intestinal paracellular route together with lowered natural IgM, immune activation and production of CCL-11 and TRYCATs contribute to the phenomenology of deficit schizophrenia.

One third of a person’s life is spent on sleep, therefore the quality and habit of sleep affects health. A single case study indicated that perspiration could serve as a prognostic marker. Diagnosing nocturnal perspiration is common clinical practice, since this serves as a major symptom in many pathologies. Till this day no specific evidence-based approach for diagnosing nocturnal perspiration exists. By introducing the Q-strip, a device which quantitatively measures nocturnal perspiration, this could be acquired. The Q-strip could serve a purpose in diagnosing nocturnal perspiration more efficient without being intrusive. In addition to its health sensing potentials, the Q-strip makes it possible to visualise perspiration patterns. This introduces the possibility to examine the quality of sleep. Future research is recommended to investigate this.

Background: Chronic Myeloid Leukemia (CML) is initiated in bone marrow due to chromosomal translocation t(22;9) leading to fusion oncogene BCR-ABL. Targeting BCR-ABL by tyrosine kinase inhibitors (TKI) have changed fatal CML into an almost curable disease. Despite that, TKIs lose their effectiveness due to disease progression. Unfortunately, mechanism of CML progression is poorly understood and common biomarkers for CML progression are unavailable. This study was conducted to find out novel biomarkers of CML progression by employing whole exome sequencing (WES).Materials and Methods: WES of accelerated phase (AP-) and blast crisis (BC-) CML patients was carried out, with chronic phase CML (CP-CML) patients as control. After DNA library preparation and exome enrichment, clustering and sequencing was carried out using Illumina platforms. Statistical analysis was carried out using [SAS/STAT] software version 9.4 and R package employed to find mutations shared exclusively by all AP-/BC-CML. Confirmation of mutations was carried out using Sanger sequencing and protein structure modelling using I-Tasser followed by mutant generation and visualization using PyMOL. Results: Three novel genes (ANKRD36, ANKRD36B and PRSS3) were mutated exclusively in all AP-/BC-CML patients. Only ANKRD36 gene mutations (c.1183_1184 delGC and c.1187_1185 dupTT) were confirmed by Sanger sequencing. Protein modelling studies showed that mutations induce structural changes in ANKRD36 protein. Conclusions: Our studies show that ANKRD36 is a potential common biomarker and drug target of early CML progression. ANKRD36 is yet uncharacterized in human. It has the highest expression in bone marrow, specifically myeloid cells. We recommend carrying out further studies to explore the role of ANKRD36 in biology and progression of CML.

Ursolic and oleanolic acids are secondary plant metabolites that are known to be involved in the plant defence system against water loss and pathogens. Nowadays these triterpenoids are also regarded as potential pharmaceutical compounds and there is mounting experimental data that either purified compounds or triterpenoid-enriched plant extracts exert various beneficial effects, including anti-oxidative, anti-inflammatory and anticancer, on model systems of both human or animal origin. Some of those effects have been linked to the ability of ursolic and oleanolic acids to modulate intracellular antioxidant systems and also inflammation- and cell death-related pathways. Therefore, our aim was to review the current knowledge about the distribution of ursolic and oleanolic acids in plants, bioavailability and pharmacokinetic properties of these triterpenoids and their derivatives, and to discuss their neuromodulatory effects in vitro and in vivo.

Objective: Major depression (MDD) and a lifetime history of MDD are characterized by increased nitrosylation, while bipolar disorder type 1 (BP1), but not BP2, is accompanied by highly increased levels of oxidative stress and nitric oxide (NO) production. Nevertheless, it is unknown whether nitrosylation is involved in BP and whether there are differences in nitrosylation between BP1 and BP2.Methods: Serum IgM antibodies directed against nitroso (NO)-adducts were examined in MDD, BP1, BP2 and healthy controls, namely IgM responses to NO-cysteine, NO-tryptophan (NOW), NO-arginine and NO-albumin (SBA) in association with IgA/IgM responses to Gram-negative bacteria, IgG responses to oxidized low-density lipoprotein (oxLDL) and serum peroxides.Results: Serum IgM levels against NO adducts were significantly higher in BP1 and MDD as compared with healthy controls, whereas BP2 patients occupied an intermediate position. IgM responses to NO-albumin were significantly higher in BP1 and MDD than in BP2 patients. There were highly significant associations between the IgM responses to NO-adducts and IgG responses to oxLDL and IgA/IgM responses to Gram-negative bacteria.Conclusions: BP1 and MDD are characterized by an upregulation of the nitrosylome (the proteome of nitrosylated proteins), and increased IgM responses to nitrosylated conjugates. Increased nitrosylation may be driven by increased bacterial translocation and is associated with lipid peroxidation processes. Innate like (B1 and marginal zone) B cells and increased nitrosylation may play a key role in the major affective disorders through activation of immune-inflammatory and oxidative pathways, cardiovascular comorbidity and impairments in antioxidant defenses, neuro-glial interactions, synaptic plasticity, neuroprotection, neurogenesis, etc.

There is now evidence that major depression is accompanied by lowered serum zinc, an immune-inflammatory biomarker. However, the effect of anti-inflammatory drugs as adjuvant to antidepressants on serum zinc and copper in relation to pro- and anti-inflammatory cytokines are not studied. The aim of the present work is to examine the effects of treatment with sertraline with and without ketoprofen on serum levels of zinc and copper in association with immune-inflammatory biomarkers in drug-naïve major depressed patients. We measured serum zinc and copper, interleukin (IL)-1β, IL-4, IL-6, IL-18, interferon (IFN)-γ, and transforming growth factor (TGF)-β1 in 40 controls and 133 depressed patients. The clinical efficacy of the treatment was measured using the Beck Depression Inventory-II (BDI-II) at baseline and 8 weeks later. In drug-naïve major depressed patients we found significantly reduced baseline levels of serum zinc and copper in association with upregulation of all cytokines, indicating activation of the immune-inflammatory responses system (IRS) as well as the compensatory immune regulatory system (CIRS). Treatment with sertraline significantly increased zinc and decreased copper levels, while ketoprofen did not have a significant add-on effect on zinc but attenuated the suppressant effects of sertraline on copper levels. During treatment, there was a significant inverse association between serum zinc and activation of the IRS/CIRS. The improvement in the BDI-II during treatment was significantly associated with increments in serum zinc coupled with attenuation of the IRS/CIRS. In conclusion, lower serum zinc is a hallmark of depression, while increments in serum zinc and attenuation of the immune-inflammatory response during treatment appear to play a role in the clinical efficacy of sertraline. Intertwined changes in zinc levels and the immune response play a role in the pathophysiology of major depression and participate in the mechanisms underpinning the clinical efficacy of antidepressants.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.

Major depressive disorder (MDD) is associated with changes in the levels of the cations calcium (Ca) and magnesium (Mg) as well as circulating pro- and anti-inflammatory cytokines. The immune-inflammatory nature of MDD has encouraged researchers to use anti-inflammatory drugs as an adjuvant treatment for MDD. However, the effect of this treatment on cation levels has not been studied. The present study examined a) differences in both cations between drug-naïve MDD patients and controls, and b) the effects of a combination of sertraline and ketoprofen, an anti-inflammatory drug, on Ca and Mg (both total and ionized). In the same patients we also examined the associations between both cations and IL-1β, IL-4, IL-6, IL-18, IFN-γ, TGF-β1, zinc and indoleamine 2,3-dioxygenase (IDO). Clinical improvement was estimated using the Beck Depression Inventory-II (BDI-II) at baseline and after follow up for two months. Serum Ca and Mg (total and ionized) were significantly lower in MDD patients as compared with controls, while treatment significantly increased calcium but decreased magnesium levels. There were significant and inverse correlations between the BDI-II scores from baseline to endpoint and Ca (both total and ionized), but not Mg, levels. The effects of calcium on the BDI-II score remained significant after considering the effects of zinc, IDO and an immune activation z unit weighted composite score based on the sum of all cytokines. There was a significant and inverse association between this immune activation index and calcium levels from baseline to endpoint. In conclusion, reduced levels of both cations play a role in the pathophysiology of major depression. Increased calcium levels are coupled to the clinical efficacy of antidepressants and attenuation of immune activation. The suppressant effect of antidepressants on Mg levels may be a side effect of those drugs. New antidepressant treatments should be developed that increase the levels both Ca and Mg.

Background: CCL11 (eotaxin) is a chemokine with an important role in allergic conditions. Recent evidence indicates that CCL11 plays a role in brain disorders as well. Aims: This paper reviews the associations between CCL11 and aging, neurodegenerative, neuroinflammatory and neuropsychiatric disorders.Methods: Electronic databases were searched for original articles examining CCL11 in neuropsychiatric disorders.Results: CCL11 is rapidly transported from the blood to the brain through the brain-blood barrier. Age-related increases in CCL11 are associated with cognitive impairments in executive functions, episodic and semantic memory and, therefore, this chemokine was described as an “endogenous cognition deteriorating chemokine” (ECDC) or “accelerated brain-aging chemokine” (ABAC). In schizophrenia, increased CCL11 is not only associated with impairments in cognitive functions, but also with key symptoms including formal thought disorders. Some patients with mood disorders and premenstrual syndrome show increased plasma CCL11 levels. In diseases of old age, CCL11 is associated with lowered neurogenesis and neurodegenerative processes and, as a consequence, increased CCL11 increases risk towards Alzheimer's Disease. Polymorphisms in the CCL11 gene are associated with stroke. Increased CCL11 also plays a role in neuroinflammatory disease including multiple sclerosis. In animal models, neutralization of CCL11 may protect against nigrostriatal neurodegeneration. Increased production of CCL11 may be attenuated by glucocorticoids, minocycline, resveratrol and anti-CCL11 antibodies.Conclusion: Increased CCL11 production during inflammatory conditions may play a role in human disease including age-related cognitive decline, schizophrenia, mood disorders and neurodegenerative disorders. Increased CCL11 production is a new drug target in the treatment and prevention of those disorders.

Esophageal adenocarcinoma (EAC) detection relies on endoscopy-biopsy diagnosis, with routine endoscopic surveillance recommended for Barrett’s esophagus (BE) patients. Here, we examine the utility of blood biomarkers in patient risk stratification by translating the EAC blood biomarker Jacalin lectin binding complement C9 (JAC-C9) into a novel microfluidic immunoassay, the EndoScreen Chip. Cohort evaluation (n=46) showed elevated serum total C9 and JAC-C9 in EAC. Logistic regression modeling demonstrated that addition of C9 and JAC-C9 to patient risk factors (age, body mass index and heartburn/reflux history) improved EAC prediction from AUROC of 0.838 to 0.931. Serum JAC-C9 strongly predicted EAC (vs BE OR= 4.6, 95% CI: 1.6-15.6, p = 0.014; vs Healthy OR=4.1, 95% CI:1.2-13.7, p = 0.024) while total C9 was moderately predictive for BE (vs EAC OR=1.4; 95% CI: 1.0-1.8, p = 0.032; vs Healthy OR=0.8; 95% CI: 0.6-1.0, p = 0.039). This translational study demonstrates the potential utility of blood biomarkers in improving triaging for diagnostic endoscopy.

Extracellular vesicles (EV) released by tumor cells are a major aspect of the resistance-associated secretory phenotype (RASP), by which immune evasion can be established. Heat shock proteins (HSPs) are an evolutionarily conserved family of molecular chaperones, which stabilize proteins, minimize protein misfolding and aggregation within the cell, besides facilitating protein translocation, refolding and degradation. (i) Releases of extracellular HSPs (ex-HSP) and EV-associated HSPs (EV-HSP) are essential in RASP, by which molecular cotransfer of HSPs with oncogenic factors into recipient cells can promote cancer progression and resistance against stress such as hypoxia, radiation, chemicals, and immune system. (ii) RASP of tumor cells can eject anticancer drugs, molecularly targeted therapeutics, and immune checkpoint inhibitors with EVs. (iii) Cytotoxic lipids can be also released from tumor cells as RASP. Nevertheless, ex-HSP and EV-HSP can play immunostimulatory and immunosuppressive roles by binding to receptors such as LRP1/CD91/A2MR, scavenger receptors, and toll-like receptors expressed on recipient cells. Liquid biopsy of HSPs in body fluids may be useful in diagnosis, prognosis, and treatment in cancer. Regarding HSP90-targeted therapeutics, we summarize the pros, cons, and problem solutions in this review. Although production of HSPs are canonically induced by heat shock factor 1 (HSF1) and hypoxia-inducible factor 1 (HIF-1), recent studies discovered that production of HSPs is also regulated by matrix metalloproteinase 3 (MMP3) and heterochromatin protein 1 (HP1) and production of cochaperone CDC37 is reciprocally regulated by myeloid zinc finger 1 (MZF1) and SCAN-D1.

There is evidence that chronic fatigue spectrum disorders (CFAS-D) including Myalgic Encephalomyelitis (ME), chronic fatigue syndrome (CFS) and chronic fatigue with physiosomatic symptoms including when due to comorbid medical disease are characterized by neuroimmune and neuro-oxidative biomarkers. The present study was performed to delineate the protein-protein interaction (PPI) network of CFAS-D and to discover the pathways, molecular patterns and domains enriched in their PPI network. We performed network, enrichment and annotation analysis using differentially expressed proteins and metabolics, which we established in CFAS-D patients. PPI network analysis revealed that the backbone of the highly connective CFAS-D network comprises NFKB1, CTNNB1, ALB, peroxides, NOS2, TNF, and IL6, and that the network comprises interconnected immune-oxidative-nitrosative and Wnt/catenin subnetworks. MultiOmics enrichment analysis shows that the CFAS-D network is highly significantly associated with cellular (antioxidant) detoxification, hydrogen peroxide metabolic process, peroxidase and oxidoreductase activity, IL10 anti-inflammatory signaling, and neurodegenerative, canonical Wnt, the catenin complex, cadherin domains, cell-cell junctions and TLR2/4 pathways; and the transcription factors NF-κB and RELA. The top-10 DOID annotations of the CFAS-D network include four intestinal, three immune system disorders, cancer and infectious disease. Custom GO term annotation analysis revealed that the CFAS-D network is associated with a response to a toxic substance, lipopolysaccharides, bacterium or virus. In conclusion, CFAS-D may be triggered by a variety of stimuli and their effects are mediated by aberrations in the cross-talks between redox, NF-κB, and Wnt/catenin signaling pathways leading to dysfunctions in multicellular organismal homeostatic processes.

Objective: The aim of study is to find key genes and enriched pathways associated with lung cancer. Participants and Methods: Differentially expressed genes (DEGs) data of 54674 genes based on stage, tumor and status of lung cancer was taken from 66 patients of African American (AAs) origin. 2392 DEGs were found based on stage, 13502 DEGs were found based on tumor, 2927 DEGs were found based on status having p value (p<0.05). Results: Total 33 common DEGs were found from stage, tumor and status of lung cancer. Gene ontology (GO) and KEGG pathway enrichment analysis was performed and 49 significant pathways were obtained, out of which 10 pathways were found to be exclusively involved in lung cancer development. Protein-protein interaction (PPI) network analysis found 69 nodes and 324 edges and identified 10 hub genes based on their highest degrees. Module analysis of PPI found that ‘Viral carcinogenesis’, ‘pathways in cancer’, ‘notch signaling pathway’, ‘AMPK signaling pathways’ had a close association with lung cancer. Conclusion: These identified DEGs regulate other genes which play important role in growth of lung cancer. The key genes and enriched pathways identified can thus help in better identification and prediction of lung cancer.

Chronic fatigue and fibromyalgia symptoms frequently occur in major depressive disorder (MDD). The pathophysiology of these symptoms may in part, be ascribed to activated immune pathways, although it is unclear whether muscular factors play a role in their onset. The aim of the present study is to examine the role of muscle proteins in major depression in association with symptoms of chronic fatigue and fibromyalgia. We measured serum levels of agrin, talin-2, titin, and creatine phosphokinase (CPK) as well as the FibroFatigue (FF), the Hamilton Depression Rating Scale (HAM-D) and the Beck Depression Inventory (BDI-II) in 60 MDD patients and 30 healthy controls. The results show a significant increase in agrin and talin-2 in MDD patients as compared with controls. There were highly significant correlations between agrin and HAM-D, BDI-II and FF scores. Agrin, but not talin or titin, was significantly and positively associated with all 12 items of the FF scale. We found that a large part of the variance in HAM-D (47.4%), BDI-II (43.4%) and FF (43.5%) scores was explained by the regression on agrin, smoking, female sex (positively associated) and education (inversely associated). CPK was significantly and inversely associated with the total FF score and with muscle and gastro-intestinal symptoms, fatigue, a flu-like malaise, headache and memory, autonomic and sleep disturbances. These results suggest that aberrations in neuromuscular (NMJs) and myotendinous junctions may play a role in MDD and that the aberrations in NMJs coupled with lowered CPK may play a role in symptoms of chronic fatigue and fibromyalgia in MDD. Moreover, the increase of agrin in MDD probably functions as part of the compensatory immune-regulatory system (CIRS).

The standard of care measures for kidney function, proteinuria, and serum creatinine (SCr) are poor predictors of early stage kidney disease. Measures that can detect chronic kidney disease in its earlier stages are needed to enable therapeutic intervention and reduce adverse outcomes of chronic kidney disease. We have developed the Kidney Injury Test (KIT) and a novel KIT Score based on the composite measurement and validation of multiple biomarkers across a unique set of 397 urine samples. The test is performed on urine samples that require no processing at the site of collection and without target sequencing or amplification. We sought to verify that the pre-defined KIT test, KIT Score, and clinical thresholds correlate with established chronic kidney disease (CKD) and may provide predictive information of early kidney injury status above and beyond proteinuria and renal function measurements alone. Statistical analyses across six DNA, protein, and metabolite markers were performed on a subset of residual spot urine samples with CKD that met assay performance quality controls from patients attending the clinical labs at the University of California, San Francisco (UCSF) as part of an ongoing IRB approved prospective study. Inclusion criteria included selection of patients with confirmed CKD and normal healthy controls; exclusion criteria included incomplete or missing information for sample classification, logistical delays in transport/processing of urine samples or low sample volume, and acute kidney injury. Multivariate logistic regression of kidney injury status and likelihood ratio statistics were used to assess the contribution of the KIT Score for prediction of kidney injury status and stage of CKD as well as assess the potential contribution of the KIT Score for detection of early stage CKD above and beyond traditional measures of renal function. Urine samples were processed by a proprietary immunoprobe for measuring cfDNA, methylated cfDNA, clusterin, CXCL10, total protein, and creatinine. The KIT Score and stratified KIT Score Risk Group (High versus Low) had a sensitivity and specificity for detection of kidney injury status (healthy or CKD) of 97.3% (95% CI: 94.6%–99.3%) and 94.1% (95% CI: 82.3%–100%). In addition, in patients with normal renal function [eGFR ≥ 90], the KIT Score clearly identifies those with predisposing risk factors for CKD, which could not be picked up by eGFR or proteinuria (p < 0.001). The KIT Score uncovers a burden of kidney injury that may yet be incompletely recognized, opening the door for earlier detection, intervention and preservation of renal function.

Glioblastoma (GBM) is the most common primary brain tumor in adults, with few available therapies and a 5-year survival rate of 7.2%. Hence, strategies for improving GBM prognosis are urgently needed. The translocator protein 18kDa (TSPO) plays crucial roles in essential mito-chondria-based physiological processes and is a validated biomarker of neuroinflammation, which is implicated in GBM progression. The TSPO gene has a germline single nucleotide pol-ymorphism, rs6971, which is the most common SNP in the Caucasian population. High TSPO gene expression is associated with reduced survival in GBM patients; however, the relation between the most frequent TSPO genetic variant and GBM pathogenesis is not known. The present study ret-rospectively analyzed the correlation of the TSPO polymorphic variant rs6971 with overall and progression-free survival in GBM patients using three independent cohorts. TSPO rs6971 poly-morphism was significantly associated with shorter overall survival and progression-free survival in male GBM patients but not in females in one large cohort of 441 patients. We observed similar trends in two other independent cohorts. These observations suggest that the TSPO rs6971 pol-ymorphism could be a significant predictor of poor prognosis in GBM, with a potential for use as a prognosis biomarker in GBM patients. These results reveal for the first time a biological sex-specific relation between rs6971 TSPO polymorphism and GBM.

Virtual reality (VR) describes a family of technologies which immerse users in sensorily-stimulating virtual environments. Such technologies have increasingly found applications in the treatment of neurological and mental health disorders. Depression, anxiety, and other mood abnormalities are of concern in the growing elderly population – especially those who reside in long-term care facilities (LTCFs). The transition from the familiar home environment to the foreign LTCF introduces a number of stressors that can precipitate depression. However, recent studies reveal that VR therapy (VRT) can promote positive emotionality and improve cognitive abilities in the elderly, both at home and in LTCFs. VR thus holds potential in allowing elderly individuals to gradually adapt to their new environments – thereby mitigating the detrimental effects of place attachment and social exclusion. Nevertheless, while the current psychological literature is promising, the implementation of VR in LTCFs faces many challenges. LTCF residents must gain trust in VR technologies, care providers require training to maximize the positive effects of VRT, and decision makers must evaluate both the opportunities and obstacles in adopting VR. Here, we concisely review the implications of depression related to place attachment in LTCFs, and explore the potential therapeutic applications of VR.

Background: Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed because of a lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated comorbidities. Methodology: The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites of diabetes (DB) and diabetes-related comorbidity (DC) along with the healthy control (HC) subjects. A combined and pairwise analysis was performed, between the serum samples of HC (n=50), and DB (n=38), and DC (n=35) individuals to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites. Results: The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline and threonine). The metabolites were further used to identify the perturbed metabolic pathway and correlation of metabolites in DC patients. Conclusion: The 1H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its comorbidities on their onset or progression by determining the altered levels of the metabolites in serum.

Rheumatoid arthritis (RA) is a chronic autoimmune disease, causing inflammation of joints, cartilage destruction and bone erosion. Biomarkers and new drug targets are actively sought and progressed to improve available options for patient treatment. The Collagen Triple Helix Repeat Containing 1 protein (CTHRC1) may have an important role as a biomarker for rheumatoid arthritis, as CTHRC1 protein concentration is significantly elevated in the peripheral blood of rheumatoid arthritis patients, compared to osteoarthritis (OA) patients and healthy individuals. CTHRC1 is a secreted glycoprotein that promotes cell migration and has been implicated in arterial tissue-repair processes. Furthermore, high CTHRC1 expression is observed in many types of cancer and this is associated with cancer metastasis to the bone and poor prognosis. However, the function of CTHRC1 in RA is still largely undefined. The aim of this review is to summarize recent findings on the role of CTHRC1 as a potential biomarker and pathogenic driver of RA progression that may be linked to the pathogenic behavior of fibroblast-like synoviocytes, cartilage destruction, and bone erosion.

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

Imaging techniques such as computed tomographies (CT) play a major role in clinical imaging and diagnosis of malignant lesions. In recent years, spectral CT has emerged in the field of computed tomographies, utilizing detailed information from extracted spectral parameters of the specimen. Metal nanoparticle platforms enable effective payload delivery for this technique. Due to the possibility of surface modification, metal nanoparticles are predestined to facilitate molecular tumor targeting. In this work, we demonstrate the feasibility of anti-plasma membrane Heat shock protein 70 functionalized gold nanoparticles (AuNPs) for tumor specific multimodal imaging. Membrane-associated Hsp70 is exclusively presented on the plasma membrane of malignant cells of multiple tumor entities, but not on corresponding normal tissue cells, predestining this target epitope for tumor-selective in vivo targeting. In vitro microscopical analysis revealed the presence of cmHsp70.1-AuNP in the cytosol of tumor cell lines, being internalized via the endosomal-lysosomal pathway. In tumor bearing mice the biodistribution as well as the intratumorally enrichment of AuNP were examined 24h after i.v. application, in vivo. In parallel to spectral CT analysis, histological analysis confirmed the presence of tumor cells. In contrast to control NP, a significant enrichment of cmHsp70.1-AuNPs has been detected selectively in tumors of different preclinical mouse models. Furthermore, the biodistribution of AuNP, following i.v. injection, was analyzed by a machine-learning approach on digitalized slides. In summary, utilizing mHsp70 on tumor cells for guidance of cmHsp70.1 antibody functionalized nanoparticles enables sufficient enrichment and uniform distribution of AuNPs in mHsp70-expressing tumor cells, adequate for various microscopical imaging techniques and spectral-CT-based tumor delineation, in vivo.

Shiga toxigenic E. coli (STEC) are an important cause of foodborne disease globally with many outbreaks linked to the consumption of contaminated foods such as leafy greens. Existing methods for STEC detection and isolation are time-consuming. Rapid methods may assist in preventing contaminated products from reaching consumers. This proof-of-concept study aimed to determine if a metabolomics approach could be used to detect STEC contamination in spinach. Using untargeted metabolic profiling, the bacterial pellets and supernatants arising from bacterial and inoculated spinach enrichments were investigated for the presence of unique metabolites that enabled categorization of three E. coli risk groups. A total of 109 and 471 metabolite features were identified in bacterial and inoculated spinach enrichments, respectively. Supervised OPLS-DA analysis demonstrated clear dis-crimination between bacterial enrichments containing different risk groups. Further analysis of the spinach enrichments determined that pathogen risk groups 1 and 2 could be easily discriminated from the other groups, though some clustering of risk groups 1 and 2 was observed, likely representing their genomic similarity. Biomarker discovery identified metabolites that were significantly associated with risk groups and may be appropriate targets for potential biosensor development. This study has confirmed that metabolomics can be used to identify the presence of pathogenic E. coli likely to be implicated in human disease.

Despite rapid progress in the treatment of many cancers, glioblastoma remains a devastating disease with dismal prognosis. The aim of this study was to identify immune-related biomarkers that more effectively predict outcome of glioblastoma. Since heat shock protein 70 (Hsp70) and IL-2 are known to increase the expression of activatory NK cell receptors, recognizing aggressive human tumor cells that present Hsp70 on their cell surface, extracellular Hsp70 levels were determined in glioma patients together with activatory NK cell receptors. All gliomas are membrane Hsp70-positive (mHsp70+) and high grade gliomas more frequently show an overexpression of Hsp70 in the nucleus and cytosol. Significantly increased extracellular Hsp70 levels are detected predominantly in glioblastomas with large necrotic areas. Overall survival (OS) is more favorable in patients with low Hsp70 serum levels indicating that a high Hsp70 expression is associated with an unfavorable prognosis. Elevated frequencies of NK cells are associated with a more favorable outcome. Of caution, a glucocorticoid therapy reduces the prevalence of NK cells. In summary, elevated frequencies of Hsp70-reactive NK cells at diagnosis and lower Hsp70 levels predict a more favorable prognosis in glioblastoma patients.

The development of biosensors with high sensitivity and low-detection limits provides a new direction for medical and personal care. Graphene and graphene derivatives have been used to prepare various types of biosensors due to their excellent sensing performance (e.g. high specific surface area, extraordinary electronic properties, electron transport capabilities and ultrahigh flexibility). This perspective review focuses on graphene-based biosensors for quantitative detection of cancer related biomarkers such as DNA, miRNA, small molecules and proteins by integrating with different signal outputting approaches including fluorescent, electrochemistry, surface plasmon resonance, surface enhanced Raman scattering etc. The article also discussed their challenges and potential solutions along with future prospects.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Introduction: multiple sclerosis (MS) is the most common immune-mediated chronic neurodegenerative disease of the central nervous system (CNS), affecting young people. Due to the permanent disability, cognitive impairment, and the enormous detrimental impact MS can exert on the patients’ health-related quality of life. It is of great importance to recognise it in time and commence adequate treatment at an early stage. The currently used disease-modifying therapies (DMT) aim to reduce disease activity and thus halt disability development, which in current clinical practice are monitored by clinical and imaging parameters but not by biomarkers found in blood and/or the cerebrospinal fluid (CSF). Both clinical and radiological measures routinely used to monitor disease activity lack information on the fundamental pathophysiological features and mechanisms of MS. Furthermore, they lag behind the disease process itself. By the time a clinical relapse becomes evident or a new lesion appears on the MRI scan, potentially irreversible damage has already occurred in the CNS. In recent years several biomarkers that previously have been linked to other neurological and immunological diseases have received increased attention in MS. Additionally, other novel, potential biomarkers with prognostic and diagnostic properties have been detected in the CSF and blood of MS patients. Areas covered: in this review, we summarise the most up to date knowledge and research conducted on the already known and most promising new biomarker candidates found in the CSF and blood of MS patients. Author commentary: the current diagnostic criteria of MS rely on three pillars; MRI imaging, clinical events and the presence of oligoclonal bands in the CSF (which was reinstated into the diagnostic criteria by the most recent revision). Even though the most recent McDonald criteria made the diagnosis of MS faster than the prior iteration, it is still not an infallible diagnostic toolset, especially at the very early stage of clinically isolated syndrome. Together with the gold standard MRI and clinical measures, ancillary blood and CSF biomarkers may not just improve diagnostic accuracy and speed but very well may become agents to monitor therapeutic efficacy and make even more personalised treatment in MS a reality in the near future. The major disadvantage of these biomarkers in the past has been the need to obtain CSF to measure them. However, the recent advances in extremely sensitive immunoassays made their measurement possible from peripheral blood even when present only in minuscule concentrations. This should mark the beginning of a new biomarker research and utilisation era in MS.

End-stage renal disease (ESRD) is associated with fatigue and physio-somatic symptoms. The aims of this study are to delineate the associations between severity of fatigue and physio-somatic symptoms and glomerular filtration rate, inflammatory biomarkers, and Wnt/catenin-pathway proteins. The Wnt-pathway related proteins β-catenin, Dickkopf-related protein 1 (DKK1), R-spondin-1, and sclerostin were measured by ELISA technique in 60 ESRD patients and 30 controls. The Fibromyalgia and Chronic Fatigue Syndrome (FF) Rating Scale was used to assess severity of FF symptoms. ESRD is characterized by a significant increase in the total FF score, muscle tension, fatigue, sadness, sleep disorders, GI symptoms, and a flu-like malaise. The total FF score was significantly correlated with serum levels of urea, creatinine, phosphate, and copper (positively), and β-catenin, eGFR, hemoglobin, albumin, and zinc (inversely). The total FF score was associated with the number of total dialysis and weekly dialysis sessions, and these dialysis characteristics were more important in predicting FF scores than eGFR measurements. Partial Least Squares analysis showed that the FF score comprised two factors which are differently associated with biomarkers: a) 43.0% of the variance in fatigue, GI symptoms, muscle tension, sadness, and insomnia is explained by hemoglobin, albumin, zinc, β-catenin, and R-spondin-1; and b) 22.3% of the variance in irritability, concentration and memory impairments by increased copper and cations/chloride ratio, and male sex. ESRD patients show high levels of fatigue and physio-somatic symptoms which are associated with hemodialysis and mediated by dialysis-induced changes in inflammatory pathways, the Wnt/catenin pathway, and copper.

Objective: To examine associations between IgA responses to Gram-negative gut-commensal bacteria and peri-menstrual symptoms and sex hormone levels during the menstrual cycle in women with and without premenstrual symptoms. Methods: Forty women aged 18-45 years completed the Daily Record of Severity of Problems (DRSP) during all 28 consecutive days of the menstrual cycle. We assayed, in plasma,, IgA responses to 6 Gram-negative bacteria, i.e. Hafnei alvei, Pseudomonas aeruginosa, Morganella morganii, Klebsiella pneumoniae, Pseudomonas putida and Citrobacter pylori, progesterone and oestradiol at days 7, 14, 21 and 28 of the menstrual cycle. Results: Significant changes in Δ (actual minus one week earlier) IgA to LPS of the 6 Gram-negative bacteria during the menstrual cycle were observed with peak IgA levels at T4 (day 28) and lows at T1 or T2 (day 7 or 14). The Δ IgA changes in H. alvei, M. Morganii, P. putida during the menstrual cycle were significantly and positively associated with changes in the total DRSP score, and severity of physio-somatic, anxiety and breast-craving, but not depressive, symptoms. The changes in IgA responses to LPS were largely predicted by changes in progesterone and steady-state levels of progesterone averaged over the luteal phase. Discussion: Menstrual cycle-associated changes in IgA directed against LPS and by inference bacterial translocation may be driven by effects of progesterone on transcellular, paracellular and vascular pathways thereby contributing to the severity of physio-somatic and anxiety symptoms as well as fatigue, breast swelling and food cravings.

In contrast to normal cells, tumor cells of multiple entities overexpress the Heat Shock Protein 70 (Hsp70) not only in the cytosol, but also present it on their plasma membrane in a tumor-specific manner. Furthermore, membrane-Hsp70 positive tumor cells actively release Hsp70 into lipid microvesicles termed exosomes into the blood. Due to conformational changes of Hsp70 in the lipid environment, most commercially available antibodies fail to detect membrane-bound and exosomal Hsp70. To fill this gap and to assess the role of exosomal Hsp70 in the circulation as a potential tumor biomarker, we established the novel complete Hsp70 (compHsp70) sandwich ELISA using two monoclonal antibodies (mAbs) that are able to recognize both, free and lipid-associated Hsp70 on the cell surface of viable tumor cells and exosomes. The epitopes of the mAbs cmHsp70.1 (aa 451-461) and cmHsp70.2 (aa 614-623) that are conserved among different species reside in the substrate-binding domain of Hsp70, with measured affinities of 0.42 nM and 0.44 nM, respectively. Validation of the compHsp70 ELISA revealed a high intra- and inter-assay precision, linearity in a concentration range of 1.56 to 25 ng/ml, high recovery rates of ‘spiked’ liposomal Hsp70 (>84%), comparable values between human serum and plasma samples, and no interference by food intake or age of the donors. Hsp70 concentrations in the circulation of patients with glioblastoma, squamous cell or adeno non-small cell lung carcinoma (NSCLC) at diagnosis were significantly higher than those of healthy volunteers. Hsp70 concentrations dropped concomitantly with the decrease in viable tumor mass on irradiation of patients with approximately 20 Gy (range 18 – 22.5 Gy) or after completion of radiotherapy (60 - 70 Gy). In summary, the compHsp70 ELISA presented herein provides a highly sensitive and reliable tool for measuring free and exosomal Hsp70 in liquid biopsies of tumor patients, levels of which can be used as a predictive tumor-specific biomarker, risk assessment and for monitoring therapeutic outcome.

Early stroke diagnosis remains a big challenge in healthcare partly due to the lack of reliable diagnostic blood biomarkers, which in turn leads to increased rates of mortality and disability. Current screening methods are optimised to identify patients with a high risk of cardio-vascular disease, especially among the elderly. However, in young adults and children, these methods suffer low sensitivity and specificity and contribute to further delays in their triage and diagnosis. Accordingly, there is an urgent need to develop reliable blood biomarkers for triaging patients suspected of stroke in all age groups, especially children and young adults. This review explores some of the existing blood biomarkers, as single biomarkers, or biomarker panels, and examine their sensitivity and specificity for predicting stroke. A review was performed on PubMed and Web of Science for journal articles published in English during the period 2001 to 2021 which contained information regarding biomarkers of stroke. In this review article, we provide comparative information on the availability, clinical usefulness, and time-window periods of eight single blood biomarkers and six biomarker panels that have been used for predicting stroke in emergency situations. The outcomes of this review can be used in future research for developing more effective stroke biomarkers.

Development of effective treatments for high-grade glioma (HGG) is hampered by 1) the blood-brain barrier (BBB), 2) an infiltrative growth pattern, 3) rapid development of therapeutic resistance, and, in many cases, 4) dose-limiting toxicity due to systemic exposure. Convec-tion-enhanced delivery (CED) has the potential to significantly limit systemic toxicity and in-crease therapeutic index by directly delivering homogenous drug concentrations to the site of disease. In this review, we present clinical experiences and preclinical developments of CED in the setting of high-grade gliomas.

Myelodysplastic syndromes (MDS) are diseases that occur when blood-producing cells in the bone marrow are damaged; such damage can affect one or more types of blood cells. Common types of MDS are refractory anemia with ring sideroblasts and refractory anemia with excess blasts (MDS-RS and MDS-EB, respectively). This work analyzed the proteomics of the medullary plasma of 10 patients with MDS-RS and MDS-EB compared to healthy control people. Overexpressed proteins that may be potential candidates for biological markers for the evaluation, study, and diagnosis of these diseases have been identified. These samples were subjected to immunodepleting, concentrated, and digested for further analysis by mass spectrometry. The ratios between selected groups and healthy people were calculated. Seven overexpressed proteins in both syndromes were identified as potential biomarker candidates: vitronectin (VTN), (2) fibrinogen (FGA), (3) pregnancy zone protein (PZP), (4) kininogen (KNG1), (5) immunoglobulin lambda chain (IGLL1), (6) complement factor C4b, and (7) hemopexin (HPX). A modified affinity chromatographic column with lectin frutalin (FTL) was used for non-depleted samples. Immunoglobulin M (IgM) was expressed in the samples from both syndromes. Surprisingly, IgM from patients with syndromes was over retained on the frutalin (FTL) column when compared with the control group. We further hypothesized that over retention of this protein by the FTL is due to the presence of α-galactosidic residues in the IgM of MDS-RS and MDS-EB patients. Differential recognition of proteins on non-depleted samples from the use of FTL appears to be a powerful tool for proteomic analysis.

Background: A meaningful part of schizophrenia patients suffer from physiosomatic symptoms (formerly named psychosomatic) which are reminiscent of chronic fatigue syndrome and fibromyalgia (FF) and are associated with signs of immune activation and increased levels of tryptophan catabolites (TRYCATs). Aims: To examine whether FF symptoms in schizophrenia are associated with breakdown of the paracellular pathway, zonulin, lowered natural IgM responses to oxidative specific epitopes (OSEs); and whether FF symptoms belong to the behavioral-cognitive-physical-psychosocial-(BCPS)-worsening index consisting of indices of a general cognitive decline (G-CoDe), symptomatome of schizophrenia, and quality of life (QoL)-phenomenome. Methods: FF symptoms were assessed using the Fibromyalgia and Chronic Fatigue Rating scale in 80 schizophrenia patients and 40 healthy controls and serum cytokines/chemokines, IgA levels to TRYCATs, IgM to OSEs, zonulin and transcellular/paracellular (TRANS/PARA) molecules were assayed using ELISA methods. Results: A large part (42.3%) of the variance in the total FF score was explained by the regression on the PARA/TRANS ratio, pro-inflammatory cytokines, IgM to zonulin, IgA to TRYCATs (all positively) and IgM to OSEs (inversely). There were highly significant correlations between the total FF score and G-CoDe, symtopmatome, QoL phenomenome and BCPS-worsening score. FF symptoms belong to a common core shared by G-CoDe, symtopmatome, and QoL phenomenome. Discussion: The physio-somatic symptoms of schizophrenia are driven by various pathways including increased zonulin, breakdown of the paracellular tight-junctions pathway, immune activation with induction of the TRYCAT pathway, and consequent neurotoxicity. It is concluded that FF symptoms are part of the phenome of schizophrenia and BCPS-worsening as well.

It is widely known that vitamin D receptors have been found in neurons and glial cells and their highest expression is in the hippocampus, hypothalamus, thalamus and subcortical grey nuclei, and substantia nigra. The vitamin D helps the regulation of neurotrophin, neural differentiation and maturation, through the control operation of growing factors synthesis (ie NGF and GDNF), the trafficking of the septo-hyppocampal pathway, and the control of the synthesis process of different neuromodulators (such as Ach, DA, and GABA). Based on these assumptions, we have written this review in order to summarize the potential role of vitamin D in neurological pathologies. The work could be titanic, and might result very fuzzy and even incoherent, if we would not have conjectured to taper our first intentions and devoted our interests towards three mainstreams: demyelinating pathologies, vascular syndromes and neurodegeneration. Due to the lack of effective therapeutic options, a part from the disease modifying strategies, the role of different risk factors should be investigated in neurology, as far as their correction may lead to the improvement of the cerebral conditions. We have explored the relationships between the gene-environmental influence and long term vitamin D deficiency, as a risk factor for the development of different types of neurological disorders, along with the role and the rationale of therapeutic trials with vitamin D implementation.

In schizophrenia, a single latent trait underlies psychosis, hostility, excitation, mannerism, negative (PHEMN) symptoms, formal thought disorders (FTD) and psychomotor retardation (PMR). Schizophrenia is accompanied by a breakdown of gut and blood-brain-barrier (BBB) pathways, increased tryptophan catabolite (TRYCAT) levels, bacterial translocation, and lowered natural IgM and paraoxonase (PON)1 activity. The aim of this study was to examine the factor structure of schizophrenia symptom domains and the biomarker correlates of these factors. We recruited 80 patients with schizophrenia and 40 healthy subjects and assessed the IgA/IgM responses to paracellular/transcellular (PARA/TRANS) ratios, IgA responses to TRYCATs, natural IgM to malondialdehyde and Gram-negative bacteria, and PON1 enzymatic activity.Direct Hierarchical Exploratory Factor Analysis showed a bifactorial oblique model with a) a general factor which loaded highly on all symptom domains, named overall severity of schizophrenia (“OSOS”); and b) a single-group factor (SGF) loading on negative symptoms and PMR. We found that 40% of the variance in the OSOS score was explained by IgA/IgM to PARA/TRANS ratio, male sex and education while 36.9% of the variance in SGF score was explained by IgA to PARA/TRANS, IgM to Gram-negative bacteria, female sex (positively associated) and IgM to MDA, and PON1 activity (negatively associated). Schizophrenia phenomenology comprises two biologically-validated dimensions, namely a general OSOS dimension and a single-group negative symptom dimension, which are associated with a breakdown of gut/BBB barriers, increased bacterial translocation and lowered protection against oxidation, inflammation and bacterial infections through lowered PON1 and natural IgM.

Background: Identification of cancer biomarkers that are differentially expressed (DE) under two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, which does not satisfy the requirements of paired samples where the gene expressions are taken from the same patients before and after treatment. Furthermore, the traditional biomarker identification methods based on either p-values or fold change (FC) values. However, sometimes, p-value based results do not comply with FC based results due to the smaller variance of gene expressions. There are some methods that combine both p-values and FC values to solve this problem. But, these methods also show weak performance for small-sample case in presence of outlying expressions. To overcome this problem, in this paper an attempt is made to develop a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values based on SAM statistic using minimum β-divergence method, which is designed for paired samples. This method introduces a weight function known as β-weight function. This weight function produces larger weights corresponding to usual/normal expressions and smaller weights for unusual/outlying expressions. The β-weight function plays the significant role on the performance of the proposed method. Results: The proposed method uses β-weight function as a measure of outlier detection by setting β=0.2. We unify both classical and robust estimates using β-weight function such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP and FCROS) using both simulated and real gene expression profiles for both small-and large-sample cases. From the simulation and a real spike in data analysis results we observed that the proposed method outperforms other methods for small-sample case in presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP and FCROS) otherwise. From a head-and-neck cancer (HNC) dataset the proposed method identified 2 genes (CYP3A4, NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these 2 genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank databases. Conclusion The identified drugs showed statistical significance and critical role of the proteins indicate that these proteins might be therapeutic target in cancer. Thus, elucidating the associations between the drugs identified in the present study require further investigations.

Although, staging models gained momentum to stage define affective disorders, no attempts were made to construct mathematical staging models using clinical and biomarker data in patients with major depression and bipolar disorder.The aims of this study were to use clinical and biomarker data to construct statistically-derived staging models, which are associated with early lifetime traumata (ELTs), affective phenomenology and biomarkers.In the current study, 172 subjects participated, 105 with affective disorders (both bipolar and unipolar) and 67 controls. Staging scores were computed by extracting latent vectors (LVs) from clinical data including ELTs, recurring flare ups and suicidal behaviors, outcome data such as disabilities and health-related quality of life (HR-QoL), and paraoxonase (PON)1 actvities and nitro-oxidative stress biomarkers.Recurrence of episodes and suicidal behaviors could reliably be combined into a LV with adequate composite reliability (the “recurrence LV”), which was associated with female sex, the combined effects of multiple ELTs, disabilities, HR-QoL and impairments in cognitive tests. All those factors could be combined into a reliable “ELT-staging LV” which was significantly associated with nitro-oxidative stress biomarkers. A reliable LV could be extracted from serum PON1 activities, recurrent flare ups, disabilities and HR-QoL.Our ELT-staging index scores the severity of a relevant affective dimension, shared by both major depression and bipolar disorder, namely the trajectory from ELTs, a relapsing course and suicidal behaviors to progressive disabilities. Patients were classified into three stages, namely an early stage; a relapse-regression stage; and a suicidal-regression stage. Lowered lipid-associated antioxidant defenses may be a drug target to prevent the transition from the early to the later regression stages.

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in the toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (Expectation-Maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. We observed that Word’s HC method with each of Euclidean, Manhattan and Minkowski distance measures produces better clustering/co-clustering results. For an example, in case of glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the PPAR signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. These results are validated by the available literature and functional annotation.

Discovery of novel variants from data derived from local population provides confident targets for developing biomarkers for personalized medicine. Biomarker discovery would be an important tool in advancing and utilizing the concept of precision and personalized medicine in the clinic. We identified the need to generate high quality sequencing data from local population and understand the pattern of occurrence of variants in colorectal cancer patients. In this report, we used archived samples from Saudi Arabia and used Ampliseq Comprehensive Cancer panel to identify novel somatic variants. We report a comprehensive analysis of next generation sequencing results with a coverage of >300X. We identified 466 novel variants which were previously unreported in COSMIC and ICGC databases. We analyzed the genes associated with these variants in terms of their frequency of occurrence, probable pathogenicity and clinicopathological features. Among pathogenic somatic variants, 174 were identified for the first time in large intestine. APC, RET and EGFR genes were most frequently mutated. Higher number of variants were identified in left colon. Occurrence of variants in ERBB2 was significantly correlated with those of EGFR and ATR genes. Network analyses of the identified genes provide functional perspective of the identified genes and suggest affected pathways and probable biomarker candidates. This report lays the ground work for biomarker discovery and identification of driver gene mutations in local population.

Lung cancer remains the leading cancer killer worldwide. Early diagnosis can effectively increase the patient cure rate but existing diagnostic methods limit early lung cancer diagnosis. Therefore, development of a simple but efficient lung cancer screening method is important to improvement of both the diagnosis rate and the survival rate of lung cancer patients. In this study, ten photosensitive materials with high sensitivity and high specificity were screened accurately to construct a microarray sensor that can rapidly identify six types of lung cancer biomarkers in exhaled breath. Results from hierarchical cluster analysis (HCA), principal component analysis (PCA) and difference maps showed that the classification of the analytes agreed with structure similarity laws. The detection results from parallel experiments and structurally similar analytes, in turn, cluster into a group; the fingerprints of the different analytes have specific response regions. The well-screened sensor chip fabrication workload and cost were both reduced by approximately two thirds, while the microfluidic device sensitivity and stability increased by approximately 1.3 times their corresponding values before optimization. The dual-channel device also offers real-time contrast detection and synchronous parallel detection functions and has potential application prospects for use in extensive screening of high-risk populations for lung cancer.

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

Background: Post-operative delirium in elderly with hip fracture is associated with various adverse clinical outcomes. Nevertheless, the pathophysiological processes underpinning delirium have remained elusive. The aim of this study is to explore the associations between delirium and its features and immune-inflammatory and blood gas biomarkers.Methods: In this prospective study we examined 65 patients who underwent a hip fracture surgery and assessed the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), Richmond Agitation-Sedation Scale (RASS), and Delirium Rating Scale Revised-98 (DRS-R-98) before and during 4 days after the surgery. Complete Blood Count (CBC) and venous blood gas markers were obtained at the same time points.Results: Delirium was observed in 19 patients and was accompanied by significantly increased pO2, number of white blood cells, neutrophil percentage, and neutrophil/lymphocyte ratio, and lower mean platelet volume (MPV) (after adjusting for age, central nervous system (CNS) disease, blood loss during surgery, sleep disorders, and body mass index. The severity of delirium was associated with lowered number of platelets and MPV. Psychomotor disorders were associated with lower bicarbonate levels. The requirement of physical restraint of the patients was predicted by increased percentages of neutrophils and lymphocytes. Prior CNS disease was together with these biomarkers a significant predictor of delirium and severity of delirium. Conclusion: Delirium and psychomotor disorders following hip fracture and surgery may be caused by immune-inflammatory and oxidative stress pathways probably attributable to an aseptic inflammatory process. Oxygen administration may aggravate these pathways.

UPF3B is a constituent of the classical nonsense-mediated mRNA decay (NMD) pathway that degrades both the aberrant transcripts and a set of physiological transcripts. In higher eukaryotes, UPF3B have significant biochemical functions in diverse cellular processes including NMD and translation. UPF3B plays a crucial role in neuronal development and differentiation. Next-generation sequencing technologies identified several loss-of-function mutations in the UPF3B gene that results in neuro-developmental disorders in humans. To uncover the mechanistic role of UPF3B in neuronal functions, we have generated the UPF3B-knockout mammalian cell line model system using CRISPR-Cas9 gene editing method. RNA-Sequencing Analysis of cellular transcriptome from UPF3B-KO cells identified specific genes involved in cell growth and neuronal functions. Altered expression of genes related to the axon guidance pathway delineated the UPF3B function to regulate the neuron-specific genes. Functional enrichment analysis identified the genes involved in the disorders related to mental health and intellectual disability. Our study has the potential to identify the direct players of intellectual disability and will have broader implications.

This study used established biomarkers of death due to ischemic stroke (IS) and performed network, enrichment, and annotation analysis. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell surface interactions, and the proteoglycan-integrin-extra cellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, VEGFA, proteoglycan-integrin-ECM complex, NFKB/RELA and SP1.

A subset of patients with schizophrenia experience physio-somatic symptoms reminiscent of chronic fatigue and fibromyalgia. In schizophrenia, these symptoms contribute to impaired quality of life, and are strongly related to neuro-cognitive deficits, and increased IgA responses to tryptophan catabolites. Negative and PHEM (psychosis, hostility, excitation, mannerism) symptoms, psychomotor retardation (PMR) and formal thought disorders, appear to be manifestations of a single trait reflecting overall severity of schizophrenia (OSOS). In this study, 120 patients with deficit schizophrenia (DEFSCZ) and 54 healthy subjects were assessed with the FibroFatigue (FF) rating scale, and the above-mentioned symptom domains as well as neuro-cognitive tests and biomarkers were measured. In DEFSCZ, there were robust associations between the FF score and all above-mentioned symptom domains, and impairments in semantic and episodic memory and executive functions. Furthermore, the FF score loaded highly on an OSOS latent vector (LV), which showed adequate convergent validity, internal consistency reliability and predictive relevance and fitted a reflective model. Soft Independent Modelling of Class Analogy (SIMCA) showed that the FF items discriminated DEFSCZ from controls with an overall accuracy of 100%. Interleukin IL-1β, IL-1 receptor antagonist (sIL-1RA), tumour necrosis factor (TNF)-α and CCL-11 (eotaxin) explained 66.8% of the variance in the FF score and 59.4% of the variance in OSOS. In conclusion, these data show that physio-somatic symptoms are a core component of the phenomenology of DEFSCZ and are largely mediated by neurotoxic effects of activated immune pathways, including aberrations in CCL-11, IL-1β and TNF-α signalling.

Neurofilament light chain (Nf-L) is a well-known biomarker for axonal damage, however the corresponding circulating Nf-L analyte in CSF is poorly characterized. We, therefore, isolated new monoclonal antibodies against synthetic peptides, and these monoclonals were characterized for their specificity on brain-specific intermediate filament proteins. Two highly specific antibodies, ADx206 and ADx209, were analytically validated for CSF applications according to well-established criteria. Interestingly, using three different sources of purified Nf-L proteins, a significant impact on interpolated concentrations was observed. With a lower limit of analytical sensitivity of 100 pg/ml using bovine Nf-L as the calibrator, we were able to quantify the Nf-L analyte in each sample and these Nf-L concentrations were highly correlated to the Uman diagnostics assay (Spearman rho= 0.97, P<0.001). In the clinical diagnostic groups, the new Nf-L ELISA could discriminate patients with Alzheimer’s disease (AD, n=20) from frontotemporal lobe dementia (FTD, n=20) and control samples with subjective cognitive decline (SCD, n=20). Henceforth, this novel Nf-L ELISA with well-defined specificity and epitopes can be used to enhance our understanding of harmonizing the use of Nf-L as a clinically relevant marker for neurodegeneration in CSF.

Soybean is a highly nutritious legume grown globally as a food and feed crop. An examination of a collection of 10 cultivated and 6 wild Korean soybean varieties showed that there is phenotypic variability notable in different soybeans. Therefore, to develop a list of biomarker candidates useful for growing soybeans of better quality and quantity, the genes of 16 Korean soybean varieties were compared with those of the reference Glycine max var. Williams 82. The comparison was made through gene sequencing to facilitate selection of nsSNPs. The objective of the study was to find out the structural and functional variations caused by nsSNPs and discuss whether the collection of Korean soybean varieties qualifies as biomarkers based on their phenotypic traits. Analysis of the data collected was done using four software: SIFT, Polyphen, PANTHER, and I-mutant 2.0, which are designed to detect the rate of functional and structural variations caused by the nsSNPs in cultivated and wild soybean varieties. Genotypic information obtained in the analysis was used to develop a core collection of biomarkers based on whether nsSNP content was found in more than half of the 16 samples. Therefore, the list of biomarker candidates developed from this study showed that Korean soybean could provide valuable information needed in both future crop genetic research and identification of biomarkers.

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC remains its fatality rate about half of the infected populations globally. The major screening biomarkers and therapeutic target identification have now become a global concern. The present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from the transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4,643 differentially expressed genes. The up-regulatory genes are mainly concentrating on immune-inflammatory response and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment and they were mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC. Furthermore, the survival of the hub genes was also assessed, and among them, finally, CXCR4 has identified as one of the most potential differentially expressed gene that might play a vital role to the survival of CC patients. Thus CXCR4 could be used as a prognostic biomarker and development of a drug target for CC.

Objective: To examine associations between chemokines and menstrual cycle associated symptoms (MCAS). Methods: Forty-one women completed the Daily Record of Severity of Problems (DRSP) rating scale during 28 consecutive days of the menstrual cycle. MCAS is diagnosed when the total daily DRSP score during the menstrual cycle is > 0.666 percentile. We assayed plasma CCL2, CCL5, CCL11, CXCL8, CXCL10, EGF, IGF-1, and PAI-1 at days 7, 14, 21 and 28 of the menstrual cycle. Results: CCL2, CCL5, CCL11 and EGF are significantly higher in women with MCAS than in those without. Increased CCL2, CXCL10, CXCL8, CCL11 and CCL5 levels are significantly associated with DRSP scores while CCL2 is the most significant predictor explaining 39.6% of the variance. The sum of the neurotoxic chemokines CCL2, CCL11 and CCL5 is significantly associated with the DRSP score and depression, physiosomatic, breast-craving and anxiety symptoms. The impact of chemokines on MCAS symptoms may differ between consecutive weeks of the menstrual cycle with CCL2 being the most important predictor of increased DRSP levels during the first two weeks, and CXCL10 or a combination of CCL2, CCL11 and CCL5 being the best predictors during week 3 and 4, respectively. Discussion: The novel case definition “MCAS” is externally validated by increased levels of uterus-associated chemokines and EGF. Those chemokines are involved in MCAS and are regulated by sex hormones and modulate endometrium functions and brain neuro-immune responses, which may underpin MCAS symptoms. As such, uterine-related chemokines may link the uterus with brain functions via a putative uterine-chemokine-brain axis.

Major depression (MDD) is accompanied by higher serum IgM/IgA responses to LPS of Gram-negative bacteria, suggesting increased bacterial translocation and gut dysbiosis. Gut dysbiosis may occur in bipolar disorder (BD) and there are differences between MDD and BD type 1 (BP1) and -2 (BP2) in nitro-oxidative stress biomarkers associated with leaky gut. This study examines serum IgM/IgA responses directed to LPS of 6 Gram-negative bacteria in 29 BP1, 37 BP2, 44 MDD and 30 healthy individuals. MDD plus BD was best discriminated from controls by increased IgM/IgA responses to Pseudomonas aeruginosa. BP1 patients showed higher IgM responses to Morganella morganii as compared with MDD and BP2 patients. Patients with melancholia showed higher IgA responses to Citrobacter koseri as compared to controls and non-melancholic depression. The total score on the Hamilton Depression Rating Scale was significantly associated with IgA responses, especially C. koseri. IgG responses to oxidized low-density lipoprotein were significantly associated with signs of increased bacterial translocation. In conclusion, not only MDD but also BP1 and BP2 are accompanied by an immune response due to the increased load of plasma LPS of gut commensal bacteria while these aberrations in the gut-brain axis are most pronounced in BP1 and patients with melancholic features. Activated oxidative stress pathways and autoimmune responses to oxidative specific epitopes in mood disorders may be driven by a breakdown in gut paracellular, transcellular and/or vascular pathways. If replicated, drugs that protect the integrity of the gut barrier may offer novel therapeutic opportunities for BP1 and MDD.

Rationale: Major depressive disorder (MDD) is characterized by signaling aberrations in interleukin (IL)-6, IL-10, beta-endorphins as well as mu (MOR) and kappa (KOR) opioid receptors. Here we examined whether these biomarkers may aid in the classification of unknown subjects into the target class MDD.Methods: The aforementioned biomarkers were assayed in 60 first-episode, drug-naïve depressed patients and 30 controls. We analyzed the data using joint principal component analysis (PCA) performed on all subjects to check whether subjects cluster by classes; support vector machine (SVM) with 10-fold validation; and linear discriminant analysis (LDA) and SIMCA performed on calibration and validation sets and we computed the figures of merit and learnt from the data. Results: PCA shows that both groups were well separated using the first three PCs, while correlation loadings show that all 5 biomarkers have discriminatory value. SVM and LDA yielded an accuracy of 100% in validation samples. Using SIMCA there was a highly significant discrimination of both groups (model-to-model distance=87.5); all biomarkers showed a significant discrimination and modeling power, while 10% of the patients were identified as outsiders and no aliens could be identified.Discussion: We have delineated that MDD is a distinct class with respect to neuro-immune and opioid biomarkers and that future unknown subjects can be authenticated as having MDD using this SIMCA fingerprint. Precision psychiatry should employ SIMCA a) to authenticate patients as belonging to the claimed target class and identify other subjects as outsiders, members of another class or aliens; and b) to acquire knowledge through learning from the data by constructing a biomarker fingerprint of the target class.

Analyzing synovial fluid from joints affected by the pathological process of psoriatic arthritis is part of the overall patient examination, since it may have differential diagnostic significance. The purpose of this study was to assess the presence of crystals in the synovial fluid of psoriatic arthritis patients as biomarkers for disease activity. Materials and methods: The synovial fluid of 156 patients with proven PSA diagnosis (patients covered CASPAR criteria) was analyzed over 24 months and compared to 50 patients with activated gonarthrosis. The Leica DM4500P polarization microscope (Leica Microsystems, Germany) was used for crystal detection. Pain and disease activity measures were also evaluated (PSA VAS for pain, DAPSA, PASDAI, mCPDAI, and HAQ-DI). The statistical analysis was carried out using SPSS version 26 with a significance set at p < 0.05. Results: The macroscopic appearance of synovial fluid from patients with psoriatic arthritis was clear in 84.6% of the patients. Synovial fluid crystals were found in 23.71% of patients with psoriatic arthritis - predominantly monosodium urate (67.58%) but also calcium pyrophosphate (21.62%) and lipid drops (5.4%). The presence of monosodium urate crystals significantly correlates with all pain and disease activity measures – VAS for pain, DAPSA, PASDAI, mCPDAI, and HAQ-DI. In 67.56% of patients with established crystals treatment with an anti-TNF blocker was started at the discretion of the treating rheumatologist due to high levels of disease activity. Conclusion: Examining the synovial fluid in PSA patients is a necessary minimally invasive procedure in cases of joint effusion, since the presence of synovial fluid crystals is a significant indicator of disease severity. The current analyses demonstrate that the presence of synovial fluid crystals in PSA patients can be used as a biomarker for disease severity and the necessity to commence biological treatment (most often TNF-a-blocker).

Background: Methamphetamine (MA) is one of the most common drugs of abuse in Thailand. MA use may cause neurotoxicity, immune-inflammatory and oxidative stress responses and, consequently, MA-induced psychosis (MIP).Aims: This study aims to examine the effects of MA use and dependence and MA withdrawal symptoms on the telomere to single copy gene (T/S) ratio and whether shortening of the latter is associated with MIP.Methods: This study included 185 MA-abuse, 118 MA-dependent, and 67 MIP patients, diagnosed using DSM-IV-TR criteria. The Semi-structured Assessment for Drug Dependence and Alcoholism (SSADDA) questionnaire was employed to collect clinical and MA-related data. MIP was confirmed using the Methamphetamine Experience Questionnaire (MEQ). The leukocyte telomere length was measured in all participants using real-time polymerase chain reaction measuring the Telomere/Single gene ratio (T/S ratio). Results: There were no significant associations between the T/S ratio and severity of MA-use, MIP, MA withdrawal symptoms including depression and psychomotor retardation. MIP was significantly predicted by alcohol dependence, antisocial personality disorder, and MA-use severity. There were significant and positive associations between the T/S ratio and previous traumatic events and life-threatening accidents. The T/S ratio was not affected by comorbid alcohol and nicotine dependence. Alcohol and nicotine dependence, antisocial personality, and severity of MA use increased risk of MA withdrawal symptoms. Conclusion: MIP and MA-use severity do not affect leukocyte telomere length, but telomere length may be affected by previous traumatic events and life-threatening accidents.

Depressive disorder in childhood and adolescence is a highly prevalent mood disorder that tends to recur throughout life. Untreated mood disorders can adversely impact a patient's quality of life and cause socioeconomic loss. Thus, an accurate diagnosis and appropriate treatment is crucial. However, until now, diagnoses and treatments are conducted according to clinical symptoms. Objective and biological validation are lacking. This may result in a poor outcome for patients with depressive disorder. Research has been conducted to identify the biomarkers that are related to depressive disorder. Cumulative evidence has revealed that certain immunologic biomarkers in-cluding brain-derived neurotrophic factor (BDNF) and cytokines, gastrointestinal biomarkers, hormones, oxidative stress, and certain hypothalamus-pituitary axis biomarkers are associated with depressive disorder. This article reviews the biomarkers related to the diagnosis and treatment of pediatric depressive disorders. To date, clinical biomarker tests are not yet available for diagnosis or for the prediction of treatment prognosis. However, cytokines such as Interleukin-2, interfer-on-gamma, tumor necrosis factor-alpha, and BDNF have shown significant results in previous studies of pediatric depressive disorder. These biomarkers have the potential to be used for diagnosis, prognostic assessment, and group screening for those at high risk.

Meningioma (MGM) is the most common type of intracranial tumor in adults. The validation of novel prognostic biomarkers to better inform tumor stratification and clinical prognosis is urgently needed. Many molecular and cellular alterations have been described in MGM tumors over the past few years, providing a rational basis for the identification of biomarkers and therapeutic targets. The role of receptor tyrosine kinase (RTKs), including those of the ErbB family of receptors, as oncogenes has been well established in several cancer types. Here, we review histological, molecular, and clinical evidence suggesting that RTKs, including the epidermal growth factor receptor (EGFR, ErbB 1), as well as other members of the ErbB family, may be useful as biomarkers in MGM.

Nowadays, colon cancer prognosis still difficult to predict, especially in the early stages. Recurrences remain elevated, even in the early stages after curative surgery. Carcidiag Biotechnologies has developed an immunohistochemistry (IHC) kit called ColoSTEM Dx, based on a MIX of biotinylated plant lectins that specifically detects colon cancer stem cells (CSCs) through glycan patterns that they specifically (over)express. A retrospective clinical study was carried out on tumor tissues from 208 non-treated and 21 treated patients with colon cancer, that were stained by IHC with the MIX. Clinical performances of the kit were determined, and prognostic and predictive values were evaluated. With 78.3% and 70.6% of diagnostic sensitivity and specificity respectively, our kit shows great clinical performances. Moreover, patient prognosis is significantly poorer when the MIX staining is “High” compared to “Low”, especially at 5-years of overall survival and for early stages. The ColoSTEM Dx kit allows an earlier and a more precise determination of patients’ outcome. Thus, it affords an innovating clinical tool for predicting tumor aggressiveness earlier and determining prognosis value regarding therapeutic response in colon cancer patients.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

Renal cell carcinoma (RCC) represents around 3% of all cancers, within which clear cell RCC (ccRCC) are the most common type (70–75%). The RCC disease regularly progresses asymptomatically and upon presentation is recurrently metastatic, so an early method of detection is necessary. The identification of one or more spe-cific biomarkers measurable in biofluids (i.e urine) by combined approaches could surely be appropriate for this kind of cancer, especially due to easy obtainability by non invasive method. OLR1 is a metabolic gene that encodes for the Lectin-like oxidized low-density lipoprotein re-ceptor-1 (LOX-1), implicated in inflammation, atherosclerosis, ROS and metabolic disor-der-associated carcinogenesis. Specifically, LOX-1 is clearly involved in tumor insurgence and progression of different human cancers. This work reports for the first time the presence of LOX-1 protein in ccRCC urine and its peculiar distribution in tumoral tissues. In parallel, urine samples headspace has been analyzed for the presence of the volatile compounds (VOCs) by SPME-GC/MS and gas sensor array. In particular, it was found by GC/MS analysis that 2-Cyclohexen-1-one,3-methyl-6-(1-methylethyl)- correlates with LOX-1 concentration in urine. Thus, the combined approach of VOCs analysis and protein quantification could led to promis-ing results in terms of diagnostic and prognostic potential for ccRCC tumor.

As new treatment modalities are being explored in neuro-oncology, viruses are emerging as a promising class of therapeutics. Virotherapy consists of introduction of either wild-type or engineered viruses to the site of disease, where they exert anti-tumor effect. These viruses can either be non-lytic, in which case they are used to deliver gene therapy, or lytic, which induce tumor cell lysis and subsequent host immunologic response. Replication-competent viruses can then go on to further infect and lyse neighboring glioma cells. This treatment paradigm is being explored extensively in both preclinical and clinical studies for a variety of indications. Virus-based therapies are advantageous due to the natural susceptibility of glioma cells to viral infection, which improves therapeutic selectivity. Furthermore, lytic viruses expose glioma antigens to the host immune system and subsequently stimulate an immune response that specifically targets tumor cells. This review surveys the current landscape of oncolytic virotherapy clinical trials in high-grade glioma, summarizes preclinical experiences, identifies challenges associated with this modality across multiple trials, and highlights potential to integrate this therapeutic strategy into promising combinatory approaches.

Instead of tissue-based detection, blood-based tumor mutation burden (bTMB) is becoming an alternative promising alternate to predict the response of immune checkpoint inhibitor in cancers, especially non-small-cell lung cancer. Although bTMB is more convenient and less invasive, many evidences identified its limited predictive ability and less accurate discrimination of candidates to receive immunotherapy. Several ways of adjustments have been applied to improve the clinical usefulness of bTMB, such as setting restriction for threshold of allele frequency to exclude some unwanted mutations. But many questions remained to be explored such as the number and the type of mutations that should be incorporated into the bTMB estimation. This viewpoint summarized the current attempts to modify bTMB and provided granular aspects that have implications for further enhancement of bTMB’s predictive capability.

Sarcopenia, the degenerative and systemic loss of skeletal muscle mass, indicates patient frailty and impaired physical function. Sarcopenia can be caused by multiple factors, including advanced age, lack of exercise, poor nutritional status, inflammatory diseases, endocrine diseases, and malignancies. Recently, growing evidence has indicated the importance of sarcopenia in the management of patients with various cancers. Sarcopenia is associated with not only higher rates of treatment-related complications but also worse prognosis in cancer-bearing patients. In this article, we conducted a systematic literature review regarding the significance of sarcopenia as a prognostic biomarker of bladder cancer. We also reviewed recent studies focusing on the prognostic role of changes in skeletal muscle mass during the course of treatment in bladder cancer patients.

Autistic spectrum disease (ASD) is an increasingly common diagnosis nowadays with a prevalence of 1-2% in most countries. Its complex causality – a combination of genetic, immune, metabolic and environmental factors - is translated into pleiomorphic developmental disorders of various severity, which have in common two main aspects: repetitive, restrictive behaviors, and difficulties in social interaction varying from awkward habits and verbalization to complete lack of interest from the ASD child for the outside world. The wide variety of ASD causes also makes it very difficult to find a common denominator – a disease biomarker and medication – and currently there is no commonly used diagnostic and therapeutic strategy besides clinical evaluation and psychotherapy.It is known that inflammation is present in a majority of ASD children, and blood inflammatory markers, together with metabolic, electrophysiological markers and imagistics are useful for ASD diagnostic validation and treatment; however, they tend to leave out a sizeable proportion of ASD kids who do not have such modifications. Genetic testing – whole exome sequencing or targeted profiling on about 500 ASD-linked genes- may also provide good insight into pathophysiology, however many times its results are more restrictive rather than offering additional therapeutic options.Here we describe a new biomarker for ASD - the neuron-specific enolase (NSE) - which was elevated above the normal clinical range (less than 16.3 ng/mL) in the vast majority of ASD kids tested in our study (40 of 41, or 97.5%). This finding opens up a new direction for diagnostic confirmation, dynamic evaluation and therapeutic intervention for ASD kids.

Titin is a giant protein that functions as a molecular spring in sarcomeres. Titin interplays the contraction of actin-containing thin filaments and myosin-containing thick filaments. The breakdown product of titin has been measurable in urine as urinary titin N-fragments. Urinary titin N-fragment was originally reported to be a useful biomarker in the diagnosis of muscle dystrophy. Recently, the urinary titin N-fragment has been increasingly gaining attention as a novel biomarker of muscle atrophy and intensive care unit-acquired weakness in critically ill patients, in whom titin loss is a possible pathophysiology. Furthermore, several studies reported that the urinary titin N-fragment also reflected muscle atrophy and weakness in patients with chronic illnesses. It may be used to predict the risk of post-intensive care syndrome or to monitor patients’ condition after hospital discharge for better nutritional and rehabilitation management. We provide several tips on the use of this promising biomarker in post-intensive care syndrome.