Background: Diabetes mellitus is one of the most common endocrinopathies, estimated to affect about 5.4% of the world's population around 2025. It is thought that nearly a third of diabetic patients have some type of skin condition. Frequently, the cutaneous effects of this heterogeneous syndrome are accurately detected late in the progressive form of the disease. Methods: Skin fragments were harvested from adult white Wistar rats of both sexes, with the weight of 200 g, 12 weeks after streptozotocin diabetes was induced. In parallel with histopathological diagnosis by using hematoxylin and eosine staining, samples were processed by transmission electron microscopy technique and examined with a Philips CM100 microscope. Results: In the apparently macroscopically unchanged tegument, photon microscopy revealed both progressive thinning of the epidermis in the early stages of diabetes and a significant process of fibrosis and collagen hyalinization in the dermis. In addition, in the early stages, the electron microscopic study provided ultrastructural details characteristic of a senescent phenotype with reduced cell proliferation. Conclusions: The morphological changes in the skin may be the first signs of disruption of carbohydrate metabolism and in the case of established diabetes may reflect its progression and the efficacy of therapy.

Background: Transient Hypogammaglobulinemia of Infancy (THI) is a primary immunodeficiency caused by a temporary decline of serum levels of immunoglobulin G (IgG) greater than 2 standard deviations below the mean age-specific reference values in an infant between 5 and 24 months of age. Preterm infants are particularly susceptible to THI, as, in the third trimester of pregnancy, IgG is transferred across the placenta from mother to infant.Objective: To systematically review the diagnostic criteria of Transient Hypogammaglobulinemia of infants.Design & Methods: Systematic Review. Manual searching of 3 electronic databases (PubMed, Medline, & Google Scholar) from September 2021 – April 2022. Abstracts were screened to assess fit to the inclusion criteria. Data was extracted from the selected studies by using an adapted extraction tool from Cochrane.org. Studies were then assessed for bias by using an assessment tool also adapted from Cochrane.org.Results: Of the 215 articles identified, 16 were eligible for examining the diagnostic criteria of THI. These studies were also assessed for bias in 6 domains. 5 studies (31%) had a low risk of bias, while 4 studies (25%) had a high risk of bias, & 7 studies (44%) were unclear for bias.Conclusion: We can conclude that THI is only definitively diagnosed after the abnormal IgG levels have normalized, hence THI is mostly a benign condition, but must be monitored for subsequent recurrent infections. The diagnostic criterion also includes vaccine & isohaemagglutinin responses to differentiate against other immunological disorders in infants.

Male breast cancers are uncommon, as men account for less than 1 percent of all breast carcinomas. Among the predisposing risk factors for male breast cancer, the following appear to be significant: a) breast/chest radiation exposure, b) estrogen use, diseases associated with hyper-estrogenism, such as cirrhosis or Klinefelter syndrome, and c) family health history. Furthermore, there are clear familial tendencies, with a higher incidence among men who have a large number of female relatives with breast cancer and d) major inheritance susceptibility. Moreover, in families with BRCA mutations, there is an increased risk of male breast cancer, although the risk appears to be greater with inherited BRCA2 mutations than with inherited BRCA1 mutations. Due to diagnostic delays, male breast cancer is more likely to present at an advanced stage. A core biopsy or a fine needle aspiration must be performed to confirm suspicious findings. Infiltrating ductal cancer is the most prevalent form of male breast cancer, while invasive lobular carcinoma is extremely uncommon. Male breast cancer is almost always positive for hormone receptors. A worse prognosis is associated with a more advanced stage at diagnosis for men with breast cancer. And randomized controlled trials which recruit both female and male patients should be developed in order to gain more consistent data on the optimal clinical approach.

Prion diseases are progressive and irreversible neurodegenerative disorders with a low incidence (1.5–2 cases per million per year). Genetic (10-15%), acquired (anecdotal) and sporadic (85%) forms of the disease have been described. The clinical spectrum of prion diseases is very varied, although the most common symptoms are rapidly progressive dementia, cerebellar ataxia and myoclonus. Mean life expectancy from the onset of symptoms is 6 months. There are currently diagnostic criteria based on clinical phenotype as well as neuroimaging biomarkers (magnetic resonance imaging), neurophysiological tests (electroencephalogram and polysomnogram) and cerebrospinal fluid biomarkers (14-3-3 protein and real-time quaking induced conversion (RT-QuIC)). The sensitivity and specificity of some of these tests (electroencephalogram and 14-3-3 protein) is under debate and the applicability of other tests such as RT-QuIC is not universal. However, the usefulness of these biomarkers beyond the most frequent prion disease, sporadic Creutzfeldt-Jakob disease, remains unclear. Therefore, research is being carried out on new, more efficient cerebrospinal fluid biomarkers (total tau, ratio total tau/phosphorylated tau and neurofilament light chain) and potential blood biomarkers (neurofilament light chain among others) to try to universalize access to early diagnosis in the case of prion diseases.

Background: Esophageal cancer was hardly diagnosed in early stage, and more potential biomarkers should be found. Methods: 252 patients and normal controls which recruited in Renmin Hospital of Wuhan University, were divided into esophageal carcinoma group (105 cases), disease control group (75 cases) and the control group of healthy people (72 cases). Moreover, TISIDB and GEPIA databases were used to investigate the different expression of EC and normal tissues, and explore the roles of C1q in tumor-immune system interactions in EC. Results: The concentration of serum C1q in EC group is 196.8(180~219.4) mg/L, which is higher than the level of DC [178.10(153.70~200.85) mg/L]and HC [183.00(167.75~201.00) mg/L] (P<0.05). A higher expression level of C1q was observed in Ⅲ and Ⅳ grades [214(192~237.3) mg/ml] than grades Ⅰ and Ⅱ [180.95(172.03~193.85) mg/L] (P<0.05). C1q was positively correlated with eosinophils, active CD8 T cells, myeloid derived suppressor cells, natural killer cells, monocytes and macrophages (r = 0.373; r = 0.659; r = 0.846; r = 0.760; r = 0.499; r = 0.757; P<0.05). Conclusion: The concentrations of C1q increased in EC and related to the severity of EC, which had potential value of diagnosis of EC. There were correlations in C1q and tumor-infiltrating lymphocytes.

Background and Objectives: Overuse and inappropriate use of testing and hospital admission are common in syncope evaluation and management. Though guidelines are available to optimize syncope care, study suggested that the current clinical guidelines have not significantly impacted resource utilization surrounding emergency department (ED) evaluation of syncope. Matching implementation strategies to barriers and facilitators and tailoring strategies to local context hold significant promise for a successful implementation of clinical practice guideline (CPG). Our team applied implementation science principles to develop a stakeholder-based implementation strategy. Methods and Materials: We partnered with patients, family caregivers, frontline clinicians and staff, and health system administrators at four health systems to conduct quantitative surveys and qualitative interviews for context assessment. The identification of implementation strategies was done by applying the CFIR-ERIC Implementation Strategy Matching Tool and soliciting stakeholders’ inputs. We then co-designed with patients and frontline teams, developed and tested specific strategies. Results: 114 clinicians completed surveys and 32 clinicians and stake-holders participated in interviews. Results from the surveys and interview indicated low awareness of syncope guidelines, and communication challenges with patients, lack of CPG protocol integration into ED workflows, and organizational process to change were recognized as major barriers. Thirty-one patients and their family caregivers participated in interviews and ex-pressed their expectations: clarity regarding their diagnosis, context surrounding care plan and diagnostic testing, and a desire to feel cared about. After identifying change methods to address those barriers, the multilevel, multicomponent implementation strategy, MISSION, included pa-tient educational materials, mentored implementation, academic detailing, Syncope Optimal Care Pathway and corresponding Mobile App, and Lean quality improvement methods. The pilot of MISSION demonstrated feasibility, acceptability and initial success on appropriate testing. Con-clusions: Effect multifaceted implementation strategies that target individuals, teams, and healthcare systems can be employed to plan successful implementation and promote adherence to syncope CPGs.

A broad spectrum of lesions, including hyperplastic, metaplastic, inflammatory, infectious, and reactive, may mimic cancer all along the urinary tract. This narrative collects most of them from a clinical and pathologic perspective offering urologists and general pathologists their most salient definitory features. Together with classical, well-known, entities such as urothelial papillomas (conventional and inverted), nephrogenic adenoma, polypoid cystitis, fibroepithelial polyp, prostatic-type polyp, verumontanum cyst, xanthogranulomatous inflammation, reactive changes secondary to BCG instillations, schistosomiasis, keratinizing desquamative squamous metaplasia, post-radiation changes, vaginal-type metaplasia, endocervicosis/endometriosis (müllerianosis), malakoplakia, florid von Brunn nest proliferation, cystitis/ureteritis cystica and glandularis, among others, still other cellular proliferations with concerning histological features and poorly understood etiopathogenesis like IgG4-related disease, PEComa, and pseudosarcomatous myofibroblastic proliferations (post-operative spindle cell nodule, inflammatory myofibroblastic tumor), are reviewed. Some of these diagnoses are problematic for urologists, other for pathologists, and still others for both. Interestingly, the right identification of their definitory features will allow their correct diagnoses thus avoiding overtreatment. The literature selected for this review also focuses on the immunohistochemical and/or molecular data useful to delineate prognosis.

Pattern analysis of salivary metabolic profile has been proven accurate to discriminate generalized periodontitis (GP) patients from healthy individuals (HI) as disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data in the literature, the aim of this study was to determine whether non-surgical periodontal therapy (NST) could change salivary metabolomic profile in GP to one more similar to HI. Unstimulated whole saliva of 11 HI and 12 GP patients were obtained prior to and 3 months after NST. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis. In GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in discriminating the metabolomic profile of each GP patient before and after NST. OPLS was able to discriminate the 3 groups of subjects with an accuracy of 85.6%. However the post-NST metabolic profile of GP patients could not be completely assimilated to that of HS. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

An outbreak of novel coronavirus(2019-nCoV) that began in Wuhan, China, is rapidly spreading to all over China, and gradually to multiple countries. Regarding to put the epidemic prevention and spread under the control, early identification and diagnosis play a critical role. Patients with initially no or mild symptoms of Novel Coronavirus Pneumonia (2019-nCoV), namely “stealth” infection, often lack of typical clinical evidence to establish the diagnosis. Based on the clinical analysis of 4 cases in stealth infection, the results of this study highlight that early diagnosis requires a combination of epidemiological history, clinical manifestations, early chest CT examination, and 2019-nCoV RNA test (nucleic acid test), with particular emphasis on definite epidemiological history and early chest CT findings when positive nucleic acid tests lag behind.

Since chronic wasting disease (CWD) was first identified nearly 50 years ago in a captive mule deer herd in the Rocky Mountains of the United States, it has slowly spread across North America through the natural and anthropogenic movement of cervids and their carcasses. As the endemic areas have expanded, so has the need for rapid, sensitive, and cost effective diagnostic tests – especially those which take advantage of samples collected antemortem. Over the past two decades, strategies have evolved from the recognition of microscopic spongiform pathology and associated immunohistochemical staining of the misfolded prion protein to enzyme-linked immunoassays capable of detecting the abnormal prion conformer in postmortem samples. In a history that parallels the diagnosis of more conventional infectious agents, both qualitative and real-time amplification assays have recently been developed to detect minute quantities of misfolded prions in a range of biological and environmental samples. With these more sensitive and semi-quantitative approaches has come a greater understanding of the pathogenesis and epidemiology of this disease in the native host. Because the molecular pathogenesis of prion protein misfolding is broadly analogous to the misfolding of other pathogenic proteins, including Aβ and α-synuclein, efforts are currently underway to apply these in vitro amplification techniques towards the diagnosis of Alzheimer’s disease, Parkinson’s disease, and other proteinopathies. Chronic wasting disease – once a rare disease of Colorado mule deer – now represents one of the few naturally occurring protein misfolding disorders which might allow continued development and implementation of novel diagnostic strategies in an animal model.

Although instructional feedback plays an essential role in regulating learning and improving performance, few studies have systematically investigated the needs of teachers and students for instructional feedback system or developed designs and experiments, especially at the high school level. To address this research need, the present study investigated the needs of selected students and teachers in a high school in Hubei Province, China, and designed and developed a diagnostic visual feedback system for an experimental study with 125 students from a 10th-grade biology class in the same high school. The results showed that this diagnostic visual feedback report improved student performance (ES=0.37) and that functions such as misconception location, knowledge diagnosis, and knowledge alert were well received by students. These findings have multiple implications for facilitating the design and development of diagnostic visual feedback system.

Primary care pediatricians encounter a wide range of complex conditions in their daily clinical practice. It is not uncommon for their patients to get exposed to medical errors. Diagnostic errors, defined as missed, delayed, or wrong diagnosis, are of great importance as they may cause significant harm to the pediatric patient with severe disease. These errors often occur when the symptoms and signs of a disease are atypical or missing. Data on the most frequent misdiagnosed situations in pediatrics is scarce. One of these groups of conditions is that regarding cardiac diseases. The pediatric cardiology field has met rapid development over the past few decades. Primary care providers play a key role in diagnosis, management, and referral of children with heart disease, both congenital and acquired, because many common cardiac complaints are first addressed in the primary care setting. The scope of this review is to investigate the cardiac entities that are often missed by the general pediatricians. In particular, it aims to spot the most common of them, to describe their clinical presentation, their diagnostic options, red flags, and tips, and to provide the ways to avoid erroneous, delayed, or missed diagnosis.

(1) Background: Motor neuron diseases (MNDs) are fatal neurodegenerative diseases. Biomarkers could help in defining patients’ prognosis and stratification. Beyond neurofilaments, chitinases are a promising family of possible biomarker, which correlate with neuroinflammatory status. We evaluated plasmatic levels of CHI3L1 in MNDs, MND mimics and healthy controls (HCs); (2) Methods: We used Sandwich ELISA to quantify CHI3L1 in plasma samples from 44 MNDs, 7 HSP, 9 MND mimics and 19 HCs. We collect also ALSFRSr scale, MRC scale, spirometry, mutational status, progression rate (PR), blood sampling, neuropsychological evaluation; (3) Results: Plasma levels of CHI3L1 resulted to be different among groups (p= 0.005). Particularly, MND mimics showed higher CHI3L1 levels compared to MNDs and HCs. CHI3LI levels did not differ among PMA, PLS and ALS and we do not find correlation among CHI3L1 levels and clinical scores, spirometry parameters, PR, and neuropsychological features. Of note, red blood cells count and haemoglobin correlated with CHI3L1 levels (respectively, p&lt;0.001, r=0.63; p= 0.022, r=0.52); (4) Conclusions: CHI3L1 plasma levels resulted to be increased in the MND mimics cohort when compared to MNDs. The increase of CHI3L1 in neuroinflammatory processes could explain our findings. We confirmed that CHI3L1 plasma levels did not differentiate between ALS and HCs and nor correlate with neuropsychological impairment.

Pheochromocytoma and sympathetic paraganglioma (PPGL) are rare neuroendocrine tumors characterized by catecholamine production in the adrenal medulla and extra-adrenal paraganglia. PPGL with metastasis was termed malignant PPGL. However, the distinction between “benign” and “malignant” PPGLs has been debated. Currently, all PPGLs are believed to have some metastatic potential and are assigned malignant tumors (ICD-O/3) by the WHO Classification of Endocrine Organs (2017, 4th edition). Therefore, the previous categories benign and malignant PPGL have been eliminated in favor of a risk stratification approach. The Grading of Adrenal Pheochromocytoma and Paraganglioma (GAPP) is a tool for risk stratification for predicting metastasis and the prognosis of patients. At least 30% of PPGLs are hereditary, with 20 genes identified and genotype-phenotype correlations clarified. Of these, VHL, RET, and NF1 have been well investigated and are the primary cause of bilateral PCC. In addition, succinate dehydrogenase gene subunits SDHB and SDHD are strongly correlated with extra-adrenal location, younger age, multiple tumors, metastasis, and poor prognosis. Disease stratification by catecholamine phenotype and molecular profiling correlates with histological grading by GAPP. PPGLs should be understood comprehensively based on clinical, biochemical, molecular, and pathological data for patient care. A flow chart for pathological diagnosis is included.

Immunostaining is an essential histochemical technique for analyzing pathogenesis and making a histopathological diagnosis. The needs are prompted by technical development and refinement, commercial availability of a variety of antibodies, deepened knowledge of immunohistochemical markers, accelerated analysis of morphofunctional correlations, progress in molecular target therapy, and the expectation of advanced histopathological diagnosis. However, immunostaining does have various pitfalls and caveats. We should learn from mistakes and failures, as well as from false positivity and false negativity. The present review article describes various devices, technical hints and trouble-shooting guides to keep in mind in performing immunostaining.

Pinus patula is one of the most planted wood conifer species worldwide; however, no foliar nutrient standards exist for this species up to date. The objective of the present study was to generate and verify two sets of foliar nutrient standards for nearly ten-year-old P. patula trees: critical nutrient concentrations and DRIS norms. Nutrients studied were N, P, K, Ca, Mg, Fe, Cu, Zn, Mn, and B. The reference standards were verified experimentally by installing two fertilization trials; one of them located in Huayacocotla, state of Veracruz and the other one in Aquixtla, state of Puebla, Mexico. Nutrient status of each fertilization trial was correctly predicted by critical nutrient values and DRIS as well. Both standards were able to detect the secondary growth-limiting nutrient deficiency in the Huayacocotla trial, where the primary limitation for growth was scarcity of solar radiation within tree crowns. The limiting nutrient in both experimental trials was K.

Mobile health devices are emerging applications that could help deliver point-of-care (POC) diagnosis, particularly in settings with limited laboratory infrastructure, such as sub-Saharan Africa (SSA). The advent of coronavirus has resulted in an increased deployment and use of mHealth-linked POC diagnostics in SSA. We performed a systematic review and meta-analysis to evaluate the accuracy of mobile-linked point-of-care diagnostics in SSA. Our systematic review and meta-analysis were guided by the Preferred Reporting Items requirements for Systematic Reviews and Meta-Analysis (PRISMA). We exhaustively searched PubMed, Science Direct, Google Scholar, MEDLINE, and CINAHL with full-text via EBSCOhost databases from mHealth inception to March 2021. The statistical analyses were conducted using OpenMeta-Analyst software. All 11 included studies were considered for the meta-analysis. The included studies focused on malaria infections, Schistosoma haematobium, Schistosoma mansoni, soil-transmitted helminths, and trichuris trichiura. The pooled summary of sensitivity and specificity estimates were moderate compared to the gold reference standard. The overall pooled estimates of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio of mobile-linked POC diagnostic devices were as follows: 0.499 (95% CI: 0.458-0.541); 0.535 (95% CI: 0.401-0.663); 0.952 (95% CI: 0.60-1.324); 1.381 (95% CI: 0.391-4.879); and 0.944 (95% CI: 0.579-1.538), respectively. Evidence shows that mobile-linked POC diagnostics' diagnostic accuracy is presently moderate in detecting infections in sub-Saharan Africa. Future research is recommended to evaluate mHealth devices' diagnostics with excellent sensitivities and specificities in diagnosing diseases in this setting.

Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subject. MM manifestations are usually known with the abbreviation CRAB (hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions). In particular, the extent of the bone disease is negatively related to a decreased patients’ quality of life and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially CT and MRI, is becoming crucial from the clinical viewpoint to separate asymptomatic from symptomatic MM patients and the detection of focal lytic lesion in these imaging data is becoming relevant even when no clinical symptoms are present. Therefore, radiology is pivotal in the staging and accurate management of patients with MM even in early phases of the disease. In this review we describe the opportunities offered by quantitative imaging and radiomics in multiple myeloma. At present time there is still high variability in the choice between various imaging methods to study MM patients and high variability in image interpretation with suboptimal agreement among readers even in tertiary centres. Therefore, the potential of medical imaging for patients affected by MM is still to be completely unveiled. In the next years, new insights to study MM with medical imaging will derive from artificial intelligence (AI) and radiomics usage in different bone lesions and from the wide implementations of quantitative methods to report CT and MRI. Eventually, medical imaging data can be integrated with the patient's outcomes with the purpose to find radiological biomarkers for predicting the disease prognostic flow and its therapeutic response.

Alzheimer’s disease is the most common neurodegenerative disease and affects persons of all races, ethnic groups, and sexes. The disease is characterized by neuronal loss leading to cognitive decline and memory loss. There is no cure and the effectiveness of existing treatments is limited and depends on the time of diagnosis. The long prodromal period, during which patients’ ability to live a normal life is not affected despite neuronal loss, often leads to a delayed diagnosis because it can be mistaken for normal aging of the brain. In order to make a substantial impact on AD patients, early diagnosis may provide a greater therapeutic window for future therapies to slow AD-associated neurodegeneration. Current gold standards for disease detection include magnetic resonance imaging and positron emission tomography scans, which visualize amyloid β and phosphorylated tau depositions and aggregates. Liquid biopsies, already an active field of research in precision oncology, are hypothesized to provide early disease detection through minimally or non-invasive sample collection techniques. Liquid biopsies in Alzheimer’s disease have been studied in cerebrospinal fluid, blood, ocular, oral, and olfactory fluids. However, most of the focus has been on blood and cerebrospinal fluid due to biomarker specificity and sensitivity attributed to the effects of the blood-brain barrier and inter-laboratory variation during sample collection. Many studies have identified amyloid β and phosphorylated tau levels as putative biomarkers, however, advances in next-generation sequencing-based liquid biopsy methods have led to significant interest in identifying nucleic acids species associated with Alzheimer’s disease from liquid tissues. Differences in cell-free RNAs and DNAs have been described as potential biomarkers for AD and hold the potential to affect disease diagnosis, treatment, and future research avenues.

Early diagnosis of malaria and treatment seeking behavior play key role in controlling and preventing further complication related to malaria disease. Aim of this study was to determine the responses on early malaria diagnosis and treatment seeking behavior among outpatient clients attending at Sekou toure regional referral hospital in Mwanza, Tanzania. Methods: A cross-sectional study was conducted among outpatient client at Sekou-Touré regional referral hospital, convenient simple random sampling used and self-administered questionnaire were used to collect data and data was entered into Microsoft excel and then exported to SPSS version 25.0 for further analysis and presented on the percentages and table. The analysis of strength of relationships between categorical variables was conducted using the Chi-square test. A p-value of ≤ 0.05 was considered to be statistically significant. Results: A total of 192 respondents completed the study with a response rate of 97.6%. The study revealed that Most of the respondents about 90.6% stated they would seek treatment from health facility when symptoms appear. However, only 6.3% seek treatment within 24 hours of onset of illness (p= 0.017). Half of respondents (50.5%) experienced malaria symptoms in the past six months and only 30% seek for treatment at health facility. Preference of health facility, (51%) respondents were going direct to pharmacy to buy medicine for self-treatment. Overall, cost of service, time consumed and distance of health facility especially health center shows significant with such delay. Conclusion: A low proportion of malaria-suspected patients sought treatment within 24 h of fever onset compared to the national target. Distance from the health facility, cost of service and time consumed were found to be predictors of early treatment-seeking behavior for malaria. Strengthening strategies tailored to increasing awareness for communities about malaria, importance of going hospital and early treatment-seeking behavior is essential.

Introduction: Coronaviruses are zoonotic viruses that include human epidemic pathogens such as the Middle East Respiratory Syndrome virus (MERS-CoV), and the Severe Acute Respiratory Syndrome virus (SARS-CoV), among others (e.g., COVID-19, the recently emerging coronavirus disease). The role of animals as potential reservoirs for such pathogens remains an unanswered question. No systematic reviews have been published on this topic to date. Methods: We performed a systematic literature review with meta-analysis, using three databases to assess MERS-CoV and SARS-CoV infection in animals and its diagnosis by serological and molecular tests. We performed a random-effects model meta-analysis to calculate the pooled prevalence and 95% confidence interval (95%CI). Results: 6,493articles were retrieved (1960-2019). After screening by abstract/title, 50 articles were selected for full-text assessment. Of them, 42 were finally included for qualitative and quantitative analyses. From a total of 34 studies (n=20,896 animals), the pool prevalence by RT-PCR for MERS-CoV was 7.2% (95%CI 5.6-8.7%), with 97.3% occurring in camels, in which pool prevalence was 10.3% (95%CI 8.3-12.3). Qatar was the country with the highest MERS-CoV RT-PCR pool prevalence, 32.6% (95%CI 4.8-60.4%). From 5 studies and 2,618 animals, for SARS-CoV, the RT-PCR pool prevalence was 2.3% (95%CI 1.3-3.3). Of those, 38.35% were reported on bats, in which the pool prevalence was 14.1% (95%CI0.0-44.6%). Discussion: A considerable proportion of infected animals tested positive, particularly by nucleic acid amplification tests (NAAT). This essential condition highlights the relevance of individual animals as reservoirs of MERS-CoV and SARS-CoV. In this meta-analysis, camels and bats were found to be positive by RT-PCR in over 10% of the cases for both; thus, suggesting their relevance in the maintenance of wild zoonotic transmission.

The co-occurrence of substance use disorders (SUDs) and psychiatric conditions, often referred to as comorbidity or concurrent disorders, presents intricate challenges in both diagnosis and treatment. This comprehensive narrative review aims to synthesize and critically evaluate the existing evi-dence surrounding the management of individuals with comorbid SUDs and psychiatric disorders. Comorbidity in these domains carries profound implications for clinical practice, research, and policymaking, emphasizing the need for a holistic understanding of the intricate dynamics that arise when these conditions coexist. This review explores recent research findings, evidence-based guidelines, and emerging trends within the field, offering valuable insights for clinicians, re-searchers, and policymakers seeking to navigate the complex terrain of comorbidity in substance use and psychiatric disorders.

A key component for the proper functioning, availability, and reliability of power grids is the power trans- former. Although these are very reliable assets, the early detection of incipient degradation mechanisms are very important to prevent failures that may shorten their lifetime. In this work a review and comparative analysis, of classical Machine Learning algorithms (such as single and ensemble classification algorithms) and two automatic machine learning classifiers, are presented for the fault diagnosis of power transformers. The goal is to determine whether fully automated ML approaches are worse or better than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from oil-dissolved gases data. The methodology uses a DB compiled from published and proprietary transformer fault samples. Faults data is obtained from the literature, the Duval pentagon method, and user-expert knowledge. The parameters from, either single or ensemble classifiers, were optimized through standard machine learning procedures. The results showed that the best performing algorithm is a robust automatic machine learning classifiers model followed by classical algorithms such as neural networks and stacking ensembles. These results highlight the ability of a robust automatic machine learning model for, handling imbalanced power transformers faults datasets with high accuracy, using the minimum tuning effort by the electrical experts. By providing the most probable transformer fault condition will reduce the time required to find and solve the fault.

Currently, large cell neuroendocrine carcinoma of the lung (LCNEC) is classified as a rare lung cancer subtype, but given the high incidence of lung cancer, the overall number of cases is considerable. The pathologic diagnosis is based on the microscopic appearance of the tumor cells, the amount of intra-tumoral necrosis, the mitotic rate, and the presence of positive neuroendocrine markers identified by immunohistochemistry. Recently, a subdivision into two main categories was proposed based on mutation signatures involving the RB1, TP53, KRAS, and STK11/LKB1 genes, into SCLC-like (small cell lung cancer-like) and NSCLC-like (non-small cell lung cancer-like) LCNEC. In terms of treatment, surgery remains the best option for resectable, stage I–IIIA cases. Chemotherapy and radiotherapy have conflicting evidence. Etoposide/platinum remains the standard chemotherapy regimen. However, based on the newly proposed LCNEC subtypes, some retrospective series report better outcomes using a pathology-driven chemotherapy approach. Encouraging outcomes have also been reported for immunotherapy and targeted therapy, but the real impact of these strategies is still being determined in the absence of adequate prospective clinical trials. The current paper scrutinizes the epidemiology, reviews the reliability of pathologic diagnosis, discusses the need for molecular subtyping, and reviews the heterogeneity of treatment algorithms in LCNEC.

Safe, high-quality and economical electric energy transportation is the basic requirements of modern power system operation. Transformer, as one of the core equipment of power system and national grid system, has the characteristics of diverse types, variable models and wide deployment. It is the basic equipment for power system to realize voltage change and electric energy distribution. Because the power system transformer needs to operate with load for a long time, the probability of failure is usually higher than that of other power equipment in general. This paper starts from the transformer fault and fault diagnosis. Firstly, the transformer fault types, traditional transformer fault diagnosis techniques and the advantages of machine learning in transformer fault diagnosis are reviewed. Secondly, the application of information system theory and information entropy in transformer fault diagnosis is introduced. Then it introduces machine learning technology, feature selection and extraction technology in transformer fault diagnosis, and machine learning algorithms such as support vector machine and extreme learning machine for transformer fault diagnosis. Finally, the potential development trend of information system theory, information entropy and machine learning in transformer fault diagnosis is forecasted. The combination of transformer fault prediction and machine learning algorithm is helpful for power system maintenance personnel to accurately predict the running state of power equipment, and also provides a new method and technology for the safe and reliable operation and regular maintenance of power system.

Coronavirus that is also known as COVID-19 disease is produced by SARSCoV-2. This causative agent is highly contagious and can cause potentially fatal pneumonia worldwide with serious public health concerns. In the beginning among infected individuals, most of them were those who were mainly shown to the wet animal market in a big city of China known as Wuhan. So, it was suggested that this was almost certainly the zoonotic source of COVID-19illness. The transitional source of origin and their mode of transmission to humans were not known obviously. Conversely, from human to human rapidly transformation have been confirmed generally. Currently, there is no availability of FDA approved clinically antiviral drugs and/or vaccines to be used against the COVID-19. Afterward, SARS-CoV and MERS-CoV, the occurrence of SARS-CoV-2 has been manifested as the third sketch of an enormously pathogenic coronavirus into human population globally. In this review, we provide a brief overview of the history of COVID-19 in Pakistan up-to 18^th weeks after beginning, current situation, epidemiology, and its impact on the human population. Moreover, we focused on physiological variation during the incubation period, genome analysis of SARS-CoV2, supportive treatment approaches, and safety measures in the Pakistani population, which may be supportive for combating the risk of COVID-19 epidemic. We also reviewed the future approaches for the development of therapeutic interventions and vaccines to cope with the COVID-19 epidemic.

The purpose of feature selection is to find important features from the original high-dimensional space. As atypical feature selection algorithm, Locally linear embedding(LLE)-based feature selection algorithm, which applies the idea of LLE to the graph-preserving feature selection framework, has been received wide attention. However, LLE-based feature selection framework is sensitive to noise and K-nearest neighbors. To address these problems, an improved LLE-based feature selection algorithm, robust LLE (RLLE) vote, is proposed. In this algorithm, $l_1$ and $l_2$ regularization are introduced into the high-dimensional reconstruction model of LLE. Furthermore, RLLE vote also proposes a criterion to measure the difference between the reconstruction features and the original features, and then the importance features can be selected by this criteria. Extensive experiments are carried out on a benchmark fault data set and the bearing data set collected from our own laboratory, and the experimental results demonstrate that RLLE vote achieves the most significant performance compared existing state-of-art methods.

Modern gastroenterology, which occupies the pages of scientific journals without alternative, began as an initiative of manufacturers of diagnostic equipment. For widespread advertising, they selected practical doctors who were not familiar with the methodology of science and superficial knowledge of physiology. This is how a group led by DeMeester arose, which in 1976 published the DeMeester score, i.e., the normal limit for esophageal pH monitoring. pH <4, less than 4% of the time per day was considered normal, i.e., it was unreasonably claimed that such reflux is physiological. This article proves that this study was carried out with numerous methodological errors, which led to an erroneous conclusion. For a long time, pH monitoring was considered the gold standard and with its help hypotheses were proposed that form the basis of modern gastroenterology. The article proves the fallacy of 13 hypotheses, which are presented as theories. Numerous articles using pH monitoring have publicized this method. Practitioners became "great scientists", equipment manufacturers became successful businessmen. Over time, they found other groups of practitioners to promote devices for impedance ph monitoring, high-resolution manometry, and many others. At the same time, a network of reviewers who believe in the infallibility of the luminaries do not allow studies that contradict their faith to be published. Thus, fundamental science was destroyed, which alone could assess the harm of false research both for science and for patients. Conclusion This study provides a list of hypotheses, that were previously considered theories, but since scientific evidence has proven these assumptions to be false, they should be excluded from subsequent use and citation.

More than one million curable sexually transmitted infections occur every day. Trichomonas vaginalis is one of the main responsible for these epidemiological data, however, the diagnosis of this protozoan is still mainly based on microscopic and culture identification. The commerciali-zation of immunological tests and the development of molecular techniques have improved the sensitivity of classical methods. Nevertheless, the fact that trichomoniasis is a neglected parasitic infection, hinders the development of novel techniques and their implementation in routine di-agnosis. This review article shows the different methods developed to identify T. vaginalis in population and the difficulties to diagnose male and asymptomatic patients. The importance of including this parasite in routine gynecological screening, especially in pregnant women, and the importance of considering T. vaginalis as an indicator of high-risk sexual behavior is also discussed.

Background Reliable and field-applicable diagnosis of schistosome infections in non-human animals is important for surveillance, control, and verification of interruption of human schistosomiasis transmission. This study aimed to summarize uses of available diagnostic techniques through a systematic review and meta-analysis. Methods and principal findings We systematically searched the literature and reports comparing two or more diagnostic tests in non-human animals for schistosome infection. Out of 4,909 articles and reports screened, 18 met our inclusion criteria, four of which were considered in the meta-analysis. A total of 14 techniques (parasitologic, immunologic, and molecular) and nine types of non-human animals were involved in the studies. Notably, four studies compared parasitologic tests (miracidium hatching test (MHT), Kato-Katz (KK), the Danish Bilharziasis Laboratory technique (DBL), and formalin-ethyl acetate sedimentation-digestion (FED-SD)) with quantitative polymerase chain reaction (qPCR), and sensitivity estimates (using qPCR as the reference) were extracted and included in the meta-analyses, showing significant heterogeneity across studies and animals hosts. The pooled estimate of sensitivity was 0.21 (95% confidence interval (CI): 0.03 – 0.48) with FED-SD showing highest sensitivity (0.89, 95% CI: 0.65 – 1.00). Conclusions and significance Our findings suggest that the parasitologic technique FEA-SD and the molecular technique, qPCR, are the most promising field-applicable techniques for schistosome diagnosis in non-human animal hosts. Future studies are needed for validation and standardization of the techniques for real-world field applications.

MicroRNAs are used as biomarkers for classification of cancer subtypes since certain miRNAs are differentially expressed in normal and patient samples. Moreover, miRNAs target mRNAs and can heavily influence Gene Expressions. Thus, deregulation of miRNAs is linked to various disorders. Thus, miRNAs can be used for prognosis and developing personalized health solutions for patients. Given the importance of miRNAs, there has been substantial work done in the field. In this paper, recent works in the field of using miRNAs expressions of patients were considered. A total of 20 papers were surveyed which utilized feature selection ensembles, fuzzy logic as well as deep learning. 10 papers have been reported which offer insight into how miRNAs can be utilized for subtype-specific or generalized cancer diagnosis.

Early diagnosis and appropriate treatments are crucial to reducing mortality risk in septic patients. Low SOFA scores and current biomarkers may not discern adequately patients that could develop severe organ dysfunction or have an elevated mortality risk. The aim of this prospective observational study is to evaluate the predictive value of the biomarkers midregional proadrenomedullin (MR-proADM), procalcitonin (PCT), C-Reactive Protein (CRP), and lactate for 28-day mortality in patients with sepsis and a SOFA score ≤ 6. 284 patients were included, with a 28-day all-cause mortality of 8.45 % (N=24). Non-survivors were older (p=0.003), required mechanical ventilation (p=0.04) and were ventilated for longer (p=0.02), had a higher APACHE II (p=0.015) and SOFA (p=0.027) scores. Lactate showed the highest predictive ability for all-cause 28-day mortality with an area under the receiver-operating characteristic curve (AUROC) of 0.67 (0.55–0.79). The AUROC for all-cause 28-day mortality in patients with community-acquired infection was 0.69 (0.57–0.84) for SOFA, and 0.70 (0.58–0.82) for MR-proADM. A 2.1 nmol/L cut-off point for this biomarker in this subgroup of patients discerned with 100% sensibility survivors from non-survivors at 28 days. In patients with community acquired sepsis and initial SOFA score ≤ 6, MR-proADM could help identify patients at risk of 28-day mortality.

The stator winding insulation system is the most critical and weak part of the EMU's (electric multiple unit) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent the fault from expanding into phase-to-phase or ground short-circuits. The TCU(traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is stationary. Three ITSC fault diagnostic conditions are based on different IGBTs control logic. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on random forest. The proposed method was validated using a simulation experimental platform for ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with the increase of ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location both in train and test samples are 100%.

Early identification of fetal abnormalities is a huge challenge for modern obstetrics. Quantitative fluorescent polymerase chain reaction (QF-PCR) has quickly become an effective means of chromosome anomaly detection due to its advantages in terms of timing, manpower and accuracy. The QF-PCR results also make a significant change in clinicians’ attitude to some extent, assisting them providing parents with professional and valuable advice on pregnancy management. In this review, the advantages and drawbacks of QF-PCR will be explored. By reviewing studies published in Vietnamese Medical Journals, we conclude QF-PCR can become a potential screening test in the field of prenatal diagnosis.

Post-extubation dysphagia (PED) can lead to serious health problems in critically ill patients. Contrasting its high incidence rate of 12.4% reported in a recent observational study, many ICUs lack routine bedside screening, likely due to limited awareness. This study aimed to establish baseline data on the current approaches and the status of perceived best practices in PED screening and treatment, as well as to assess awareness of PED. A nationwide cross-sectional, online survey was conducted in all fourteen adult ICUs in the Republic of Cyprus in June 2018, with a 100% response rate. Over 85% of ICUs lacked a standard screening protocol for PED. The most commonly reported assessment methods were cough reflex testing and the water swallow test. Treatment approaches included muscle strengthening exercises without swallowing and swallowing exercises. Only 28.6% of ICUs acknowledged PED as a common issue. The study identified significant gaps in awareness and knowledge regarding PED screening and treatment in Greek-Cypriot ICUs. Urgent implementation of comprehensive dysphagia education programs within the units is necessary, and interdisciplinary collaboration among nurses, intensivists, and speech and language therapists is crucial to improve the quality of care provided.

Background: Malaria remains a serious public health concern worldwide, particularly in tropical and subtropical regions, and the leading causes of mortality and morbidity in children especially among those aged under five in developing countries. Malaria can be fatal if not promptly and accurately diagnosed, especially in children.This study was carried out to determine the level of malaria infection and its associated risk factors among febrile children.Methods: Blood samples were collected and analyzed from two- hundred (200) systematically selected febrile children aged 1-10 years old. assay of samples collected were carried out using standard methods.data were collected using a pretested structured questionnaire. Results: Results obtained from study subjects shows that, of the eighty-seven (87) infected subjects, 34 (39.1%) were aged 1-5 years, while 52 (59.8%) were within the range of 6 years and above.severity of infection showed 48 (55.2%) with scanty parasitic infection while 25 (28.7%) showed moderate infection, and 13 (14.9%) showed heavy infection.The association of age and level of parasitemia showed that 34 (58.8% ) subjects below 5 years, recorded scanty malaria compared to 14.7% with severe malaria within the age range.Considering gender, of the 33 infected female subjects, 45.5% had scanty malaria, 36.4% showed moderate malaria, while 18.2% had severe malaria, compared to the males subjects.Conclusion: This study reveals the prevalence of malaria infection with some level of severity among children at our study location.The need for prompt diagnosis and improved access to all malaria interventions becomes relevant,with a special focus on the high risk group.

The vibration signal of heavy gearbox presents non-stationary and nonlinear characteristics, which increases the difficulty to extract the fault feature. When the gear has a subtle fault, it may cause a perceptible change of local fluctuation rather than the large scale fluctuation. Therefore, the feature parameters extracted from local fluctuation can effectively improve the recognition performance of the gear fault. In this paper, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to identify the gear fault of heavy gearbox. Firstly, the raw vibration signal is decomposed several mode components by VMD, which is an adaptive and non-recursive signal decomposition method. Next, the sensitive mode component is selected by a maximal indicator, which is composed of kurtosis and correlation coefficient of relative higher frequency mode components corresponding to local fluctuation of raw vibration signal. Finally, the characteristics of the double-scales feature parameters of selected sensitive mode are extracted by DFA. In addition, the position of turning point of double scales is estimated by sliding windowing algorithm. The proposed method is evaluated through its application to gear fault classification using vibration signal. The results demonstrates that the recognization rate of gear faults condition have marked improvement by proposed method than the DFA of Small Time Scale (STS-DFA) method.

Tick-borne diseases are among the most common vector-borne diseases. These include various infections caused by viruses, bacteria, rickettsiae, spirochetes and protozoa. The aim of our review is to present the relationship between the clinico-epidemiological aspect of some of the emergency tick-borne diseases and their diagnosis, treatment and prevention. We will review three diseases- anaplasmosis, babesiosis and ehrlichiosis. Anaplasmosis and ehrlichiosis are caused by small gram-negative obligate intracellular alphaproteobacteria in the order Rickettsiales, family Anaplasmataceae. The prognosis of disease depends on early diagnosis and proper treatment. The symptoms of anaplasmosis largely overlap with those of ehrlichiosis. Babesiosis is a malaria-like disease that is caused by intraerythrocytic protozoa. Like malaria, the disease proceeds with hemolysis and its severity is determined by the parasitemia. Treatment of babesiosis involves the use of antimalarial agents and antibiotics, most commonly in a combination. Understanding the clinical, epidemiological and laboratory characteristics of tick-borne diseases is crucial for their prevention, diagnosis and treatment.

Our target point within this following steps, is prostate cancer. Phosphatases are implicated in differents cell alterations, including proliferation, differentiation and carcinogenesis. In specific life circumstances, proteins are regulators with implications in intracellular signaling pathways. Alterations in phosphatases functionality represent a key point in malignancy development. We try within our description, to focus on an important process refering to dysregulation of phosphatase activity. More than, we will discuss therapeutic key points refering to compounds that inhibits phosphatase proteins with implications in carcinogenesis. So finally after folowing describtion steps, purposed aim is to conclude on prostate cancer and different key points abordation.

Fibromyalgia is a complex and often misunderstood chronic pain disorder. It is characterized by widespread musculoskeletal pain, fatigue, and heightened sensitivity, and has evolved in diag-nostic criteria and understanding over the years. Initially met with skepticism, fibromyalgia is now recognized as a global health concern affecting millions of people, with a prevalence trans-cending demographic boundaries. The clinical features and diagnosis of fibromyalgia encompass a range of symptoms beyond pain, including sleep disturbances and cognitive difficulties. The study emphasizes the importance of a comprehensive evaluation for accurate diagnosis, considering the shift from tender point reliance to a more holistic approach. Etiology and pathophysiology involve genetic predisposition, neurotransmitter dysregulation, central sensitization, and immune system involvement. Risk factors such as gender, age, family history, and comorbid conditions contribute to susceptibility. The impact on quality of life is profound, affecting physical and social aspects, often accompanied by mood disorders. Management approaches include pharmacological interventions, non-pharmacological therapies, lifestyle modifications, and alternative treatments. The study also delves into emerging research, exploring advances in neurobiological understanding, brain imaging, genetic markers, glutamate modulation, cannabinoids, gut microbiome, and digital health tools for fibromyalgia manage-ment. Overall, this study provides a nuanced and up-to-date overview of the complexities sur-rounding fibromyalgia, aiming to enhance understanding and support for individuals grappling with this challenging condition.

The straight leg raise test (SLR) has been proposed to detect increased nerve mechanosensitivity of the lower limbs in individuals with low back pain or leg-related low back pain. However, its validity in the diagnosis of lumbosacral radiculopathy show very variable results. The aim of this study was to analyse the diagnostic validity of the SLR including well defined diagnostic criteria in a sample of participants in phase III with suspicion of lumbar radiculopathy using the nerve conduction studies (NDS) as the reference standard. A phase III diagnostic accuracy study was designed. 142 individuals with suspected lumbosacral radiculopathy referred for NCS participated in the study. Each participant was tested with NCS and SLR. SLR was considered positive using three diagnostic criteria. The sensitivity of the SLR for the criterion 3 was 89.02% (CI 81.65-96.40), the specificity was 25.00% (CI 13.21-36.79), and the +LR and -LR were 1.19 (CI 1.01-1.40) and 0.44 (0.21-0.94), respectively. SLR showed limited validity in the diagnosis of lumbosacral radiculopathy. The incorporation of more objective diagnostic criteria (asymmetry in range of motion or localization of symptoms) improved diagnostic validity but the imprecision of the confidence intervals limited the interpretation of the results.

The purpose of this article is to highlight the new advancements in molecular and diagnostic genetic testing as well as properly classify all ovarian cancers. In this article we address statistics, histopathological classification, molecular pathways implicated in ovarian cancer, genetic screening panels and details about the genes, and also candidate genes. We hope to bring new information in the medical field as to better prevent and diagnose ovarian cancer.

The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this are modular process plants that consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but available solutions are not applicable. The authors suggest an approach in which model-based symptoms are derived from few measurements and observers that are based on manufacturer knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcases the feasibility and the potential of this approach.

Introduction: This study aimed to identify factors associated with late diagnosis and clinically monitor newly diagnosed HIV/AIDS patients. Method: Retrospective longitudinal study, based on secondary data from a specialized unit at a tertiary hospital. Data collection included sociodemographic, behavioral, clinical, and laboratory data of newly diagnosed HIV patients between 2015-2019. Data analysis adopted inferential statistical tests using the SPSS program, considering α ≤ 0.05. Results: 314 individuals were newly diagnosed with HIV/AIDS. 70.3% (208) had a late diagnosis, and 57.1% (169) were diagnosed very late. There was an association of the very late diagnosis with the variables sex and education and with origin, entry, occurrence of opportunistic diseases, use of ARV therapy, and death, associated with late and very late diagnosis, respectively. The results of the regression model indicate that males had 2.28 [95% CI 1.11 - 4.46] higher chances of having a late diagnosis compared to females. Conclusions: This study evidenced a high prevalence of late and very late diagnoses in newly diagnosed male HIV patients who presented with opportunistic diseases, requiring hospitalization, and having a significant risk of progressing to death.

Leptospirosis is a zoonotic disease that has a cosmopolitan geographical distribution, reported in domestic and wild animals, which act as reservoirs and contribute to the spread of the microorganism in the environment. In Brazil, studies on the occurrence of leptospirosis in buffaloes in the Amazon biome are scarce. The objective of this study was to determine the occurrence of antibodies against Leptospira spp., including serovar Hardjo (Bolivia), isolated from cattle in Brazil and not yet tested in buffaloes. A total of 387 blood serum samples of animals from nine municipalities in the Marajó Island, state of Pará, northern Brazil, were obtained from a biological sample bank and analyzed using the serum agglutination microscopic test (SAM). Serology revealed 91.5% (387/354) of the animals tested positive for anti-Leptospira antibodies. Among the serovars tested, the serovar Hardjo (Bolivia) was the most prevalent, present in 79.3% of the reactive buffaloes.

Background: Craniofacial injuries are thought to be commonly associated with traumatic brain injury (TBI), but there is conflicting evidence in the literature. The objective of this retrospective cohort study is to evaluate the incidence of TBI in patients with craniofacial trauma. Methods: The study included 2982 consecutive patients with either solitary or concurrent diagnoses of TBI and facial fractures, seen and evaluated at a single level II trauma center between January 1, 2010 and December 31, 2020. Continuous variables were compared against whether the patient had one or both diagnoses.Results: Of the target population, 55.8% had a solitary diagnosis of TBI, 30.28% had a solitary diagnosis of facial fractures, and 13.92% had concurrent diagnoses of both TBI and facial fractures. Patients with concurrent diagnoses had a significantly longer mean length of stay (LOS) compared to those with solitary diagnoses (9.92 ± 16.33 days vs. 6.21 ± 10.96 days, p&lt;0.01), but age (p=0.68) and ICU LOS (p=0.09) did not differ significantly between the two groups. Conclusions: Trauma to the face should be given special attention due to the close relationship between facial bones and the brain. Patients with concurrent diagnoses of TBI and facial fractures had worse hospital outcomes than those with solitary diagnoses of either TBI or facial trauma.

Rapid, accurate, and convenient diagnosis is essential for effective disease management. Various detection methods, such as enzyme-linked immunosorbent assay, have been extensively used, with lateral flow immunoassay (LFIA) recently emerging as a major diagnostic tool. Nanoparticles (NPs) with characteristic optical properties are used as probes for LFIA, and researchers have presented various types of optical NPs with modified optical properties. Herein, we review literature on LFIA with optical NPs for the detection of specific targets in the context of diagnostics.

Background: Endoscopic Ultrasound (EUS) is widely used for the diagnosis of bilio-pancreatic and gastrointestinal (GI) tract diseases, for the evaluation of subepithelial lesions, and for sampling of lymph nodes and solid masses lo-cated next to the GI tract. The role of Artificial Intelligence in healthcare in growing. This review aimed to provide an overview of the current state of AI in EUS from imaging to pathological diagnosis and training. Methods: AI algorithms can assist in lesion detection and characterization in EUS by analysing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning tech-niques, such as convolutional neural networks (CNNs), have shown great potential for tumour identification and subepithelial lesion (SEL) evaluation by extracting important features from EUS images and using them to classify or segment the images. Results: AI models with new features can increase the accuracy of diagnoses, provide faster diagnoses, identify subtle differences in disease presentation that may be missed by human eyes, and provide more information and in-sights into disease pathology. Conclusions: The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accura-cy, leading to better patient outcomes and to a reduction of repeated procedures in case of non-diagnostic biopsies.

Background: Due to the importance of brain death (BD) diagnostic do not mistake, reliable confirmatory exams should be performed to enhance its security. This study aims to evaluate the intracranial pressure (ICP) pulse morphology behavior in brain-dead patients through a noninvasive monitoring system. Methods: A pilot case-control study was conducted in adults that met the BD national protocol criteria. Quantitative data from the ICP waveforms were extracted and analyzed comparing BD patients and health subjects. Results: Fifteen patients were included. ICP waveforms presented substantial differences amidst BD patients when compared to the control group. Moreover, pulse amplitude and time to peak variables values in the case group were also statistically significant. Conclusions: In this exploratory study, noninvasive ICP waveforms have shown potential as a screening method in patients with suspected brain death. Future studies should be carried out in a larger population.

We hypothesized that biofilm production occurs on stainless steel when incubated with tissue specimens in thioglycolate broth media (TBM). In a diabetic foot infection (DFI) cohort, applying the Kirschner wire and conventional methods were more sensitive than applying only the latter (CI 90%; 0.167 versus 0.375).

Pancreatic ductal adenocarcinoma (PDAC) is expected to be the second cause of cancer death by 2022. For nearly 80% of patients, diagnosis occurs at an advanced, non-surgical stage, making such patients incurable. Gemcitabine is still an important component in PDAC treatment and is most often used as a backbone to test new targeted therapies and there is, to date, no routine biomarker to predict its efficacy. Samples from a phase III randomized trial were used to develop trough a large approach based on blood-based liquid biopsy, transcriptome profiling, and machine learning, a 9 gene predictive signature for gemcitabine sensitivity. Patients with a positive test (41.6%) had a significantly longer progression free survival (PFS) (3.8 months vs. 1.9 months p=0.03) and a longer overall survival (OS) (14.5 months vs. 5.1 p<0.0001). In multivariate analyses, this signature was independently associated with PFS (HR=0.5 (0.28-0.9) p=0.025) and OS (HR=0.39 (0.21-0.7) p=0.002).

To date, viral RNA detection is almost the only way to confirm SARS-CoV-2infectionin practice.However, variousreasons can cause low sensitivity for RNA detection, and thisposes aserious challenge to disease control. We tested the performance of detecting total antibody(Ab) and IgM levels in serum by the methods of chemiluminescence, enzyme-linked immunosorbent assay (ELISA), and colloidal golddetection. The datashowed that the sensitivity and specificity for detecting total Ab and IgM levels were high by all three methods, and the sensitivity was higher for detecting total Ab than for detecting IgM. Evidence from studieshas shown thatviral RNA testingcombinedwith serological testing could increase the diagnostic sensitivity while maintaining a high specificity. Specific serology testsfor SARS-CoV-2 havegreat value for clinical practice and public health.

Molecular imaging modalities are used for different type of cancers detection and diagnosis. In recent few years, there has been an increased focus on developing novel nanoparticles as new imaging contrast agents for early detection of cancer. The aim of this review article is to summarize molecular imaging technologies accompanying with using nanoparticles to improve potential imaging for cancer detection and hence valuable therapy in the future. Nanoprobes are rapidly becoming potentially transformative tools on cancer diagnostics for a wide range of imaging modalities such as CT, MRI, SPECT, PET, Ultrasound and Optical imaging. The study results seen in the recent literature are provided and discussed the diagnostic performance of imaging modalities for cancer diagnosis and their future directions. With knowledge of the correlation between the application of nanoparticles and molecular imaging modalities and with the development of targeted contrast agents or nanoprobes, they may provide better cancer diagnosis in the future.

Safety management assessment systems for national level units’ in South Korea focus on responding capacity to cope with impending accident occurrence and danger occurrence. Since the four stage systems for prevention-preparation-response-recovery, which are core elements of national disaster management, assess the capacities by item such as those of individuals, disaster management departments, institutions, and management networks, there is no assessment function for the organic operation states of the entire systems. Therefore, for efficient disaster management, systematic evaluation indices that will enable active pre-checks in departments in organizations should be developed in place of the existing simply checking methods. In this study, an assessment model that will enable active disaster management centered on practice was developed using resilience engineering techniques. This model consists of disaster management items from the viewpoint of proactive responses instead of prevention. A total of 56 items that constitute four capacities; which are prediction (13 items), monitoring (14 items), proactive response (15 items), and safety learning (14 items) capacities were adopted in this model through Delphi analysis. Institutional capacities for infectious disease disaster management were evaluated based on this model and the resultant scores were prediction 4.41, monitoring 4.63, proactive response 4.69, safety learning 4.56 out of the full score of 5.0 points with an overall average of 4.51. This is an excellent capacity management score comparable to the score 4.57 of diagnosis of similar capacities by the WHO\_JEE (The Joint External Evaluation) in 2017. In fact, in 2015, when infectious disease capacity management was poor, in case of MERS (Middle East Respiratory Syndrome) infectious disease spread in South Korea, 36 patients died and 6,729 patients were isolated. However, through capacity reinforcement, in the case of MERS occurrence in South Korea in September 2018, a management capacity that prevented spread was shown as one confirmed case was completely cured in 10 days and 21 contacts were isolated and tested negative. Therefore, this capacity management assessment model is judged to be usable in enhancing disaster response and management capacities.

The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

Designed by a group of ME/CFS researchers and health professionals, the European Network on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (EUROMENE) has received funding from the European Cooperation is Science and Technology (COST) (https://www.cost.eu/cost-actions/what-are-cost-actions/ ) - COST action 15111 - from 2016 to 2020. The main goal of the Cost Action was to assess the existing fragmented knowledge and experience on health care delivery for people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in European countries, and to enhance coordinated research and health care provision in this field. We report on the recommendations for clinical diagnosis, heath services and care for people with ME/CFS in Europe, as prepared by the group of clinicians and researchers from 22 countries and 55 European health professionals and researchers, who have been informed by people with ME/CFS (https://www.cost.eu/actions/CA15111/#tabs|Name:overview).

Volatile compounds, abundant in breath, can be used to accurately diagnose and monitor a range of medical conditions. This offers a non-invasive, low-cost approach with screening applications; however, uptake of this diagnostic approach has been limited by conflicting published outcomes. Most published reports rely on large scale screening of the public, at single time points and without reference to ambient air. Here, we present a novel approach to volatile sampling from cellular headspace and mouse breath that incorporates multi-time point analysis and ambient air subtraction revealing compound flux as an effective proxy of active metabolism. This approach to investigating breath volatiles offers a new avenue for disease biomarker discovery and diagnosis. Using gas chromatography mass spectrometry (GC/MS), we focus on low molecular weight, metabolic substrate/by-product compounds and demonstrate that this non-invasive technique is sensitive (reproducible at ~1 µg cellular protein, or ~500,000 cells) and capable of precisely determining cell type, status and treatment. Isolated cellular models represent components of larger mammalian systems and we show that stress- and pathology-indicative compounds are detectable in mice, supporting further investigation using this methodology as a tool to identify volatile targets in human patients.

Background: Cancer has been one of the most critical health problems during the past decades which require serious consideration, especially in developing countries. Methods: 80 parents of the children with different types of cancers were studied via both questionnaire and person-to-person interview after announcing their informed consent. Results: The highest delay in patients' initial referral was due to the following factors: lack of attention or ignoring the first symptoms, delay in referring to the physician, low economic status and even lack of family support for the patient. In addition, visiting several doctors after the initial diagnosis, uncertainty about the first proposed method, and high cost of treatment can be mentioned as the main causes of delays in the start of treatment. Conclusion: Education plays an important role in identifying the signs of cancer. In addition, proper relationship and cooperation between the health system and physicians as well as provision of adequate information to patients could lead to the long-term cooperation of these patients in continuing their treatment.

In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity to reach 90 % accuracy of the prediction.

Background: Polycystic ovary syndrome (PCOS) is a common endocrinopathy that is highly prevalent in women of Indian ethnicity. Clinical practice guidelines do not adequately consider ethnic-cultural differences in the care of women with PCOS. This study aimed to explore ethnic Indian women’s experiences with PCOS diagnosis and management, their concerns and it’s impact on their quality of life (QoL). Methods: Global online survey of ethnic Indian women of reproductive age living with PCOS. Results: Respondents (n=4409) had a mean age of 26.8 (SD 5.5) years and the majority were diagnosed with one or more co-morbidities (anxiety/depression being the most common). Women waited one year on average before seeking treatment following symptom onset. Irregular periods, cysts on the ovaries and excess unwanted facial hair growth were their three major concerns. Weight and emotional concerns had the greatest impact on QoL. One-third did not receive information on long-term complications, appropriate behavioral advice to improve diet or exercise, or emotional support following diagnosis. Among those who received information at diagnosis, the majority were dissatisfied. Conclusions: Ethnic Indian women wait for a considerable time before seeking medical help for PCOS, have poor QoL relating to weight and mental health and are dissatisfied with the information and support provided at diagnosis. These gaps in care should be addressed in clinical practice and future research.

Abstract: Female pelvic floor dysfunction (FPFD) is a series of conditions caused by the displacement or dysfunction of the patient's pelvic organs due to defects, injuries, and deterioration of the pelvic floor supportive structures, with stress urinary incontinence and pelvic organ prolapse being the most common. The mechanism of pelvic floor dysfunction is complex and not yet fully understood, and is often related to one or more functional abnormalities of muscles, connective tissues, fascia, nerves and blood vessels. Specialized clinicians are often unable to cover the comprehensive medical professional information of various specialties such as gynecology and obstetrics, urology, anus and intestines, imaging, etc., and the systematic and comprehensive diagnosis and treatment of the disease will be limited. The application of artificial intelligence concepts in the medical field not only improves the diagnostic efficiency of physicians, but also provides reference and basis for the development of comprehensive treatment programs. This article follows and draws on the diagnosis and treatment process of pelvic floor dysfunction recommended in various guidelines, incorporates physical therapy techniques into the conventional non-surgical treatment of pelvic floor dysfunction, provides guidance for the diagnosis and treatment of pelvic floor dysfunction, and briefly introduces the clinical application and value of pelvic floor intelligent diagnosis and treatment in pelvic floor dysfunction.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

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.

The platelet count, a component of the full blood count, has been identified as a useful diagnostic marker for cancer in primary care. The reference range for the platelet count is 150 to 400 or 450 × 109/l; this range does not account for natural variation in platelet count by age and sex. This study used three primary care cohorts from England Canada, and Australia. Patients aged 40 years and over with a full blood count were included and stratified by age (in 10-year bands), sex, (male/female), and platelet count group. Cancer incidence within one year of test date was estimated from linked registry data. In all three countries there was a clear upwards trend in cancer incidence with increasing platelet count, for both sexes and at all age groups. Lung and colorectal were the most common sites. These results have important implications for the international application of this work; analysis of local health datasets will be crucial to determine appropriate thresholds. Appropriate upper thresholds will depend on local populations, healthcare needs, and priorities. Further research is needed to assess the likely impact of new recommendations on the healthcare system, on cancer outcomes, and patient benefit.

Evidence-based data for endometriosis management are limited. Experiments are excluded without adequate animal models. Data are limited to symptomatic women and occasional observations. Hormonal medical therapy cannot be blinded if recognised by the patient. Randomised controlled trials are not realistic for surgery since a variable disease with low numbers. Each diagnosis and treatment is an experiment with an outcome and experience is Bayesian updating from the past. If the experience of many is similar, this has more value than an opinion. The combined experience of a group of endometriosis surgeons was used to discuss problems in the management of endometriosis. Considering endometriosis as several genetically/epigenetically different diseases is important for medical therapy. Imaging cannot exclude endometriosis and diagnostic accuracy is limited for superficial lesions, deep lesions, and cystic corpora lutea. Surgery should not be avoided for emotional reasons. Shifting infertility treatment to IVF without considering fertility surgery is questionable. The concept of complete excision should be reconsidered. Surgeons should introduce quality control and teaching should move to explain why it is done. The perception of information has a personal bias. These are the major problems in the management of endometriosis identified by the combined experience of endometriosis surgeons.

Background: A new coronavirus (SARS-CoV-2) that emerged from Wuhan, Hubei Province, China, has spread throughout the world and is declared a pandemic by the World Health Organization (WHO). A lot remains to be understood of SARS-CoV-2 and the disease (COVID-19). SARS-CoV-2 has until recently been identified as responsible for both asymptomatic and serious life-threatening infections. The unavailability of specific therapeutic agents is a major hurdle in the treatment and management of COVID-19 patients. The present review attempts to evaluate the immunobiochemical aspects of the pathogenesis, diagnosis, and management of SARS-CoV-2 infection. Main Body: This review is a comprehensive evaluation of the data collected through various sources, including Google Scholar, PubMed, and Scopus. The articles were searched and selected using key words such as “Coronavirus disease (COVID-19)”, “Diagnosis of COVID-19”, Pathogenesis of Covid-19”, “management of COVID-19”, “Immunology of COVID-19”, and “Complications of COVID-19”. The study noted that the novel Coronavirus infection could result in an exaggerated immune response, causing a cytokine storm and damaging several organs of the body. The infected patients develop several complications, including immunological, hematological, and biochemical alterations. Consequently, COVID-19 patients may develop cardiovascular, liver, renal, and neurological complications, among others. Conclusion: An increased understanding of the immunobiochemical aspects of the disease may contribute to better management of SARS-CoV-2-infected persons, as evidenced from the available literature. A holistic approach to the management of COVID-19 patients taking into consideration the effect of COVID-19 infection on various organs of the body assumes increased significance in patient management.

A novel strategy is presented for reliable diagnosis and progression prediction of diseases with special attention to COVID-19 pandemy. A plan is presented for how the model can be implemented worldwide in healthcare and how novel treatments and targets can be detected. The idea is based on poikilosis, pervasive heterogeneity and variation at all levels, systems and mechanisms. Poikilosis in diseases can be taken account in pathogenicity model, which is based on distribution of three independent condition measures – extent, modulation and severity. Pathogenicity model is a population or cohort-based description of disease components. Evidence-based thresholds can be applied to the pathogenicity model and used for diagnosis as well as for early detection of patients in risk of developing the most severe forms of the disease. Analysis of patients with differential course of disease can help in detecting biomarkers of diagnostic and prognostic significance. A practical and feasible plan is presented how the concepts can be implemented in practice. Collaboration of many actors, including the World Health Organization and national health authorities, will be essential for success.

Diesel engines, as power equipment, are widely used in the fields of automobile industry, ship and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in time domain and angular domain, on which the current diagnosis methods based, are easily affected by working conditions or hard to extract accurate enough, as the diesel engine keeps running in transient and non-stationary process. This work arms at diagnosing this fault mainly based on frequency band features which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively，a decomposition technique based on improved variational mode decomposition is investigated in this work. As the connection between the features and the fault is fuzzy, the random forest algorithm is used to analyze the correspondence between features and faults. In addition, the feature dimension is reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition show that the method based on variational mode decomposition and random forest is capable to detect valve clearance fault effectively.

The identification of an emerging pathogen in humans can remain difficult by conventional methods such as enrichment culture assays that remain highly selective, require appropriate medium and cannot avoid misidentifications, or serological tests that use surrogate antigens and are often hampered by the level of detectable antibodies. Although not originally designed for this purpose, the implementation of polymerase-chain-reaction (PCR) has resulted in an increasing number of diagnostic tests for many diseases. However, the design of specific molecular assays relies on the availability and reliability of published genetic sequences for the target pathogen as well as enough knowledge on the genetic diversity of species and/or variants giving rise to the same disease symptoms. Usually designed for clinical isolates, molecular tests are often not suitable for environmental samples in which the target DNA is mixed with a mixture of environmental DNA. A key challenge of such molecular assays is thus to ensure high specificity of the target genetic markers when focusing on clinical and environmental samples in order to follow the dynamics of disease transmission and emergence in humans. Here we focus on Buruli ulcer (BU), a human necrotizing skin disease mainly affecting tropical and subtropical areas, commonly admitted to be caused by Mycobacterium ulcerans worldwide although other mycolactone-producing mycobacteria and even mycobacterium species were found associated with BU or BU-like cases. By revisiting the literature, we show that many studies have used non-specific molecular markers (IS2404, IS2606, KR) to identify M. ulcerans from clinical and environmental samples and propose that all mycolactone-producing mycobacteria should be definitively considered as variants from the same group rather than different species. Importantly, we provide evidence that the diversity of mycolactone-producing mycobacteria variants as well as mycobacterium species potentially involved in BU or BU-like skin ulcerations might have been underestimated. We also suggest that the specific variants/species involved in each BU or BU-like case should be carefully identified during the diagnosis phase, either via the key of genetic identification proposed here or by broader metabarcoding approaches, in order to guide the medical community in the choice for the most appropriate antibiotic therapy.

Background: Scabies is an unfortunately neglected tropical skin disease. Scabies occurs in Australia, however it is drastically more common amongst select socioeconomically disadvantaged groups. Amongst these, Aboriginal and Torres Strait Islander communities are affected particularly severely, likely secondary to rampant overcrowding. Methods: This literature review has comprehensively explored the relevant evidence available since the year 2000. Objectives: The objective of this article is to provide updates on the identification, management, and control of scabies in Aboriginal and Torres Strait Islander communities. The article also aims to reveal issues with these factors that may be contributing to the inflated prevalence of scabies amongst these groups. Conclusions: Diagnosis currently depends upon clinical examination, dermatoscopy, and skin scraping microscopy; all of which are prone to error. In Australia, topical permethrin remains first-line treatment, however resistance is developing. Other management aspects to consider are environmental measures and treating secondary infections. Scabies prevention is far more cost-effective than managing advanced individual cases, typically ensured through mass drug administration of permethrin or ivermectin. Sustained prevention can prove troublesome for some communities. In order to address the status of scabies as a neglected tropical disease, these issues must be managed first.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

Epilepsy is one of the most common disorders of the central nervous system, impacting nearly 50 million people around the world. Heterogeneous in nature, epilepsy presents in children and adults alike. Currently, surgery is the only treatment that can cure epilepsy. However, not all individuals are eligible or have successful outcomes. Difficulty in accessing samples of human brain tissue along with advances in sequencing technology have driven researchers to investigate sampling liquid biopsies in blood, serum, plasma, and cerebrospinal fluid within the context of epilepsy. Liquid biopsies provide minimal or non-invasive sample collection approaches and can be assayed relatively easily across multiple time points, unlike tissue-based sampling. Various efforts have investigated circulating nucleic acids from these samples including microRNAs, cell-free DNA, transfer RNAs, and long non-coding RNAs. Here, we review nucleic acid-based liquid biopsies in epilepsy to improve understanding of etiology, diagnosis, prediction, and therapeutic monitoring.

Admission and discharge diagnoses of in-hospital patients are often in discord. Incorrect admission diagnoses are related to increased cost of care and patient safety. Additionally, due to the seasonality of many conditions, this discord may vary across the year. In this paper, we used medical claims data to develop a methodological framework that examines these differences, for Medicare beneficiaries. We provide examples for pneumonia, a condition with seasonal implications, and aneurysm, where early detection can be life-saving. Following a Bayesian approach, our work quantifies and visualizes with time series plots the degree that any clinical condition is correctly diagnosed upon admission. We examined differences in weekly intervals, during a calendar year. The mean length of stay and hospital charges were furthermore compared between matching and non-matching {admission, discharge Dx} pairs, and 95% confidence intervals of the difference of means were estimated. We applied Statistical Process Control methods and then visualized differences, for the hospital charges and the length of stay, per week, with time series plots. Our methodology and the visualizations underline the importance of a rigorous and non-delayed diagnostic process upon admission since there are significant implications in terms of hospital outcomes and cost of care.

Purpose: Expanded carrier screening (ECS) informs couples of their risk of having offspring affected by certain genetic conditions. Limited data exists assessing the actions and reproductive outcomes of at-risk couples (ARCs). We describe the impact of ECS on planned and actual pregnancy management in the largest sample of ARCs studied to date. Methods: Couples who elected ECS and were found to be at high risk of having a pregnancy affected by at least one of 176 genetic conditions were invited to complete a survey about their actions and pregnancy management. Results: Three hundred ninety-one ARCs completed the survey. Among those screened before becoming pregnant, 77% planned or pursued actions to avoid having affected offspring. Among those screened during pregnancy, 37% elected prenatal diagnostic testing (PNDx) for that pregnancy. In subsequent pregnancies that occurred in both the preconception and prenatal screening groups, PNDx was pursued in 29%. The decision to decline PNDx was most frequently based on the fear of procedure-related miscarriage, as well as the belief that termination would not be pursued in the event of a positive diagnosis. Conclusions: ECS results impacted couples’ reproductive decision-making and led to altered pregnancy management that effectively eliminates the risk of having affected offspring.

Cardiovascular and cerebrovascular disease incidence has risen mainly due to poor control of preventable risk factors and still constitutes a significant financial and health burden worldwide. ChatGPT is an artificial intelligence language-based model developed by OpenAI. Due to the model’s unique cognitive capabilities beyond data processing and the production of high-quality text, there has been a surge of research interest concerning its role in the scientific community and contemporary clinical practice. To fully exploit ChatGPT's potential benefits and reduce its possi-ble misuse, extreme caution must be taken to ensure its implications ethically and equitably. In this article, we explore the language model's possible applications and limitations while empha-sizing its potential value for diagnosing, managing, and prognosis cardiovascular and cerebro-vascular disease.

In this paper we introduce the concept of Nano Beta Closure, Nano Beta Interior and study the relationship between them also study some of their properties. Based on these notions we introduced the topological Approach to Decision Making and Its Application in the Medical Field" which describes the use of topological methods in decision-making, specifically through the utilization of nano beta closure and nano beta interior. These mathematical tools are applied in the medical field to improve diagnostic accuracy and aid in treatment planning. The authors demonstrate the effectiveness of this approach through a case study on the diagnosis of Chronic Kidney. The results show that the topological approach using nano beta closure and nano beta interior provides a reliable and accurate method for medical decision-making. Finally, we introduce a medical application for disease groups patients of with chronic kidney. To identify the main causative agent of kidney disease. where using this approach, the medical team can identify the specific areas of the kidney that are affected by the infection, as well as the extent of damage and any potential complications. This information is used to create an individualized treatment strategy that is customized to meet the specific requirements of the patient. and medical history. We write an algorithm that can be a helpful tool for doctors to utilize in diagnosing infection disease Chronic Kidney infections. By utilizing this approach, clinicians can provide accurate diagnoses, develop effective treatment plans, and ultimately improve patient outcomes. finally, we show that fractals hold significant importance in future nano topological generation of the human factor. Their unique properties and self-similar patterns offer insights into the complex organization of biological systems and provide a foundation for designing advanced Nanosystems.

Autoimmune hemolytic anemias (AIHAs) are conditions whereby a person produces an antibody to their own red blood cells (RBCs). These conditions can be primary with unknown cause or secondary, associated with diseases or infections. There are seven different categories of AIHA, warm-antibody AIHA (wAIHA); cold-antibody AIHA that includes cold agglutinin syndrome (CAS) and cold agglutin disease (CAD); mixed AIHA, also called combined cold and warm AIHA; paroxysmal cold hemoglobinuria (PCH) also termed Donath-Lansteiner test-positive AIHA; direct antiglobulin test-negative AIHA (DAT-neg AIHA); drug-induced immune hemolytic anemia (DIIHA); and passenger lymphocyte syndrome. This review will discuss each of these AIHAs and provide information on diagnosis and treatment.

In 2022, the new WHO Classification of Endocrine and Neuroendocrine Tumors, Fifth Edition (beta version) (WHO 5th) was published. The importance of understanding the molecular genetics of thyroid tumors as revealed by large-scale genomic analyses such as The Cancer Genome Atlas (TCGA). Consequently, the WHO 5th was fundamentally revised, resulting in a systematic classification based on tumor cell-of-origin and clinical risk. This paper outlines following critical points of the WHO 5th. 1. Genetic mutations in follicular cell-derived neoplasms (FDN) highlight the role of mutations in the MAP kinase pathway, including RET, RAS, and BRAF, as drivers of carcinogenesis. Differentiated thyroid cancers such as follicular thyroid carcinoma (FTC) and papillary thyroid carcinoma (PTC) have specific genetic alterations that correlate with morphological classifications: RAS-like tumors (RLTs) and BRAF p.V600E-like tumors (BLTs), respectively. 2. The Framework for benign lesions have revised. The WHO 5th introduces new categories such as "developmental abnormalities" and "thyroid follicular nodular disease" in benign FDN. 3. Low-Risk Tumors include NIFTP, TT-UMP, and HTT. 4. PTC histological variants are reclassified as "subtypes" in the WHO 5th. 5. The concept of high-grade carcinomas is introduced, encompassing poorly differentiated thyroid carcinoma (PDTC), differentiated high-grade thyroid carcinoma (DHGTC), and high-grade medullary thyroid carcinoma (MTC). 6. Squamous cell carcinoma is included in anaplastic thyroid carcinoma (ATC) in the WHO 5th due to shared genetic and prognostic features. 7. Hürthle cell adenoma/carcinoma is renamed oncocytic thyroid adenoma/carcinoma. 8. Other miscellaneous tumors are classified into salivary gland-type carcinomas of the thyroid, thyroid tumours of uncertain histogenesis, thymic tumours within the thyroid, and embryonal thyroid neoplasms. The WHO 5th thus emphasizes the importance of classifying tumors based on genetic abnormalities together with histomorphology, aiding in accurate pathological diagnosis and treatment selection, including molecular-targeted therapies.

Borderline Personality Disorder (BPD) is a complex and severe mental disorder. It is associated with greater use of medical services. It is characterized by intense fear of abandonment, recurring suicidal thoughts or self-harm, paranoid ideation or dissociation, identity difficulties, chronic feelings of emptiness, impulsive behavior and unstable moods and relationships. BPD is one of the most prevalent personality disorders with a higher diagnosis in females. For a diagnosis of BPD in patients, they must have a chronic pattern of functional impairment in addition to five of nine listed DSM-5 criteria. This review was a narrative literature search between 2000 to 2023 with a focus on BPD comorbidities. Results: Researchers observed a complex pattern of comorbidities with BPD that connects with other externalizing and internalizing disorders. BPD has a high comorbidity rate with mood disorders including bipolar I and II disorders, major depressive disorder, anxiety disorders, panic disorder alcohol use disorder, nicotine dependence, marijuana dependance, posttraumatic stress disorder, other personality disorders, binge eating disorder and ADHD. Diagnosis and management of BPD in presence of some of these disorders are discussed.

Power grid fault diagnosis technology has been widely used at home and abroad. With the access of distributed power sources and diversified loads, the distribution network has changed from a passive network to an active network, and the current has changed from "one-way" to "two-way". The structure of distribution network, equipment environment and operating conditions are becoming more and more complex, and the fault diagnosis and localization of active distribution network is becoming more and more difficult. With the rapid development of artificial intelligence, power grid fault diagnosis based on intelligence is unprecedented. This paper introduces the research status of distributed photovoltaic grid-connected fault diagnosis technology, and expounds the history of fault diagnosis technology, which mainly includes expert system, artificial neural network, Bayesian network, fuzzy set theory, rough set theory, Petri net analytical model and multi-source information fusion, etc. The applicability and characteristics of these diagnoses are described, and the existing defects and the overall direction of improvement are briefly described Finally, the key technical problems and future directions in the field of power system fault diagnosis are pointed out by combining theory with practice, so as to promote the further development of this field.

Although acute heart failure (AHF) is a common disease associated with significant symptoms, morbidity and mortality, the diagnosis, risk stratification and treatment of patients with hypertensive acute heart failure (H-AHF) still remain a challenge in modern medicine. Despite great progress in diagnostic and therapeutic modalities, this disease is still accompanied by a high rate of both in-hospital (from 3.8% to 11%) and one-year mortality (from 20% to 36%). Considering the high rate of re-hospitalization (22% to 30% in the first three months), the treatment of this disease represents a major financial blow to the health system of each country. This disease is characterized by heterogeneity in precipitating factors, clinical presentation, therapeutic modalities and prognosis. Since heart decompensation usually occurs quickly (within a few hours) in patients with H-AHF, establishing a rapid diagnosis is of vital importance. In addition to establishing the diagnosis of heart failure itself, it is necessary to see the underlying cause that led to it, especially if it is de novo heart failure. Given that hypertension is a precipitating factor of AHF and in up to 11% of AHF patients, strict control of arterial blood pressure is necessary until target values are reached in order to prevent the occurrence of H-AHF, which is still accompanied by a high rate of both early and long-term mortality.

Liver is the sixth most common site of primary malignant tumors, and the third leading cause of cancer death worldwide. Hepatocellular carcinoma is the most common primary liver malignancy and lungs, abdominal lymph nodes and adrenal glands are the most common sites of its metastasis. We present a rare case of an isolated metastasis of a hepatocellular carcinoma in the left mandibular ramus, with regards to its clinical and radiological presentation and with regards to possible routes of the metastatic spread.

A diagnosis of typical chronic lymphocytic leukemia (CLL) requires the presence of ≥5000 clonal B-lymphocytes/μL with the coexistence of CD19, CD20, CD5, and CD23 and the restriction of light chain immunoglobulin, and the lack of expression of antigens CD22 and CD79b. Atypical CLL (aCLL) can be distinguished from typical CLL morphologically and immunophenotypically. Atypical CLL cells are defined as prolymphocytes; morphologically, these present as deeply-clefted lymphocytes which are larger than those in typical CLL, with condensed chromatin and without prominent nucleoli. However, current aCLL diagnostics rely more on immunophenotypic characteristics rather than atypical morphology. Immunophenotypically, atypical CLL differs from classic CLL in the lack of expression of one or fewer surface antigens, most commonly CD5 and CD23, and the patient does not meet the criteria for a diagnosis of any other B-cell lymphoid malignancy. Morphologically atypical CLL has more aggressive clinical behavior and worse prognosis than classic CLL. Patients with aCLL are more likely to display markers associated with poor prognosis, including trisomy 12, unmutated IGVH, and CD38 expression compared with classic CLL. However, no standard or commonly-accepted criteria exist for differentiating aCLL from classic CLL and the clinical significance of aCLL is still under debate. This review summarizes the current state of knowledge on the morphological, immunophenotypic and genetic abnormalities of aCLL.

Recently, a database of human piRNAs has been created, which allows studying the binding of many piRNAs to mRNAs of genes involved in many diseases, including cancer. In the present work, we investigated what piRNAs can interact with candidate esophageal squamous cell carcinoma (ESCC) genes. The binding of 480 thousand piRNAs with mRNAs of 66 candidate ESCC genes was studied. Bioinformatic studies found that piRNAs bind only to the mRNAs of nine candidate genes: AURKA, BMP7, GCOM1, ERCC1, MTHFR, SASH1, SIX4, SULT1A1, and TP53. It has been shown that piRNAs can bind to mRNA by overlapping nucleotide sequences in limited 3'UTR and 5'UTR regions called clusters of binding sites (BSs). The existence of clusters of piRNA BSs significantly reduces the proportion of nucleotide sequences of these sites in the mRNA of target genes. Competition between piRNAs occurs for binding to the mRNA of target genes. Individual piRNAs and groups of piRNAs that have separate BSs and clusters of BSs in the mRNAs of two or more candidate genes have been identified in the mRNAs of these genes. This organization of piRNAs BSs indicates the interdependence of the expression of candidate genes through piRNAs. Significant differences in the ability of genes to interact with piRNAs avoid the side effects of piRNAs on genes with the lack of binding of such piRNAs. Individual piRNAs and sets of piRNAs are proposed and recommended for the diagnosis and therapy of ESCC.

Dry eye disease (DED) is one of the most common eye diseases. There is at least one DED patient in almost every five people. AI-based research methods increasingly become the focus of DED diagnosis research. This study utilizes a systematic review method on DED AI-based diagnosis. 2112 unduplicated records are extracted from Google Scholar, Web of Science (WOS), PubMed, China National Knowledge Infrastructure (CNKI), and Scopus databases. The most contributed countries, institutions, authors, journals, references, and disciplines are recognized. Keyword distribution and hot topics are identified. Popular databases of ophthalmic images, videos, and electronic demographic medical records are discussed. The DED diagnosis, classification, and grading criteria are identified. The major diagnosing methods are clustered, compared, and investigated. Findings show that diagnosing method research could be classified into three categories based on the relationship between AI techniques, which are (1) ground truth and/or comparable standards for AI DED diagnosis (TBUT, S Ⅰ T, TMH, and OSDI), (2) potential methods for AI-based methods have a great advantage(DED detection based on meibometry Images, CASPs, IVCM Images, OCT Images, blink videos and ultrasonic imaging), (3) and the potential direction and supplemented methods for AI-based DED detection (DED detections based on tear osmolarity, proteomic analysis, TCM and demographic information). AI-based approaches based on digital ophthalmologic images play an important role in early screening. Challenges and future perspectives are discussed at the end of this article, academically and practically.

The new NICE guideline for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), published in October 2021, makes significant changes in treatment recommendations. It acknowledges the complexity of this chronic medical condition, which always impacts quality of life and can be profoundly disabling, recognising the prejudice and stigma that people with ME/CFS often experience in the absence of any specific diagnostic test. The guideline outlines steps for accurate diagnosis, recognising post-exertional malaise as a core symptom; importantly, ME/CFS can now be diagnosed after just 3 months in a bid to improve long-term health outcomes. It recommends the need for individual, tailored management by a multi-disciplinary team, ensuring that the wellbeing of the individual is paramount. The guideline makes clear that any programme based on fixed incremental increases in physical activity or exercise, for example graded exercise therapy (GET), should not be offered as a treatment for ME/CFS and emphasises that cognitive behavioural therapy (CBT) should only be offered as a supportive intervention. Because of the rigorous methodology required by NICE Committee review and the inclusion of the testimony of people with lived experience as committee members, this guideline will influence the future diagnosis and management of ME/CFS in the UK and beyond.

The aim of the current study was to determine the relation between subclinical mastitis and rumination behavior registered with an online sensor system. According to the results of the general clinical examination of 650 milking cows, 10 cows with subclinical mastitis (SCM) and 10 clinical healthy cows (HG) were selected (without clinical signs of any diseases). Rumination behavior biomarkers were registered with RumiWatch noseband sensor (RWS; ITIN + HOCH GmbH, Fütterungstechnik, Liestal, Switzerland). Sensors were implanted on the first day after calving. Data from the RWS from 13th days before the diagnosis and 13 days after diagnosis of SCM were used to compare with HG data during the same period. The healthy cows were monitored during the same days as the SCM cows.

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.

Background. A previous study demonstrated the performance of the Salivette® (SARSTEDT, Numbrecht, Germany) as a homogeneous saliva collection system to diagnose COVID-19 by RT-qPCR, notably for symptomatic and asymptomatic patients. However, for convalescent patients, the corroboration of molecular detection of SARS-CoV-2 in paired nasopharyngeal swabs (NPS) and saliva samples was unsatisfactory. Objectives. The aim of the present work was to assess the concordance level of SARS-CoV-2 detection between paired sampling of NPSs and saliva collected with Salivette® at two time points, with ten days of interval. Results. A total of 319 paired samples from 145 outpatients (OP) and 51 healthcare workers (HW) were collected. Due to significant waiting rate at hospital, most of the patients ate and/or drank in waiting their turn. Consequently, a mouth washing was systematically proposed prior saliva collection. None of the HW were diagnosed SARS-CoV-2 positive using NPS or saliva specimens at both time points (n=95) by RT-qPCR. The virus was detected in 56.3% (n=126/224) of the NPS samples from OP, but solely 26.8% (n=60/224) of the paired saliva specimens. The detection of the internal cellular control, the human RNase P, in more than 98% of the saliva samples, underlined that the low sensitivity of saliva specimens (45.2%) for SARS-CoV-2 detection was not attributed to an improper saliva sample storing or RNA extraction. Conclusions. Then, the mouth washing decreased viral load of buccal cavity conducting to impairment of SARS-CoV-2 detection. Viral loads in saliva neo-produced appeared insufficient for molecular detection of SARS-CoV-2. At the time that saliva tests could be a rapid, simple and noninvasive strategy to assess on large scale schooled children in France, the determination of the performance of saliva collection become imperative to standardize procedures.

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained as one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered as a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR is heavily dependent on the clinical presentation and non-specific features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose the Myocarditis. The hybrid CNN-KCL method performs the early and accurate diagnosis of Myocarditis. To the best-of-our-knowledge, a Convolutional neural network has never been used before for the diagnosis of Myocarditis. In this study, we used 47 subjects to diagnose myocarditis patients from Tehran's Omid Hospital. The total number of data examined is 10425. Our results demonstrate that CNN-KCL achieves 92.3% in terms of diagnosis myocarditis prediction accuracy which is significantly better than those reported in previous studies.

Most diagnostic tests for tuberculosis (TB) rely on sputum samples, which are difficult to obtain and have low sensitivity in immunocompromised patients, patients with disseminated TB, and children, delaying treatment initiation. The World Health Organization (WHO) calls for the development of a rapid, biomarker-based, non-sputum test capable of detecting all forms of TB at the point-of-care to enable immediate treatment initiation. Lipoarabinomannan (LAM) is the only WHO-endorsed TB biomarker which can be detected in urine, an easily collected sample. This review discusses the characteristics of LAM as a biomarker, describes the performance of first-generation urine LAM tests and reasons for slow uptake, and presents considerations for developing the next-generation of more sensitive and impactful tests. Next-generation urine LAM tests have the potential to reach adult and pediatric patients regardless of HIV status or site of infection and facilitate global TB control. Implementation and scale-up of existing LAM tests and development of next-generation assays should be prioritized.

Background: Malignant pleural mesothelioma (MPM) is an atypical, belligerent tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Pleural mesothelioma is a common type of mesothelioma that accounts for about 75 percent of all mesothelioma diagnosed yearly in the United States. Diagnosis of mesothelioma takes several months and is expensive. Given the difficulty of diagnosing MPM, early identification is crucial for patient survival. Our study implements artificial intelligence and recommends the best fit model for early diagnosis and prognosis of MPM. Method: We retrospectively retrieved patient’s medical reports generated by Dicle University, Turkey and implemented multi-layered perceptron (MLP), voted perceptron (VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic gradient decent SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and primal estimated sub-gradient solver for support vector machine (s-Pegasos). We evaluated the models, compared and tested using paired T-test (corrected) at 0.05 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers characteristic curve (ROC), and precision-recall curve (PRC). Results: In phase-1 SGD, AdaBoost.M1, KLR, MLP, VFDT generates optimal results with the highest possible performance measures. In phase-2, AdaBoost with a classification accuracy of 71.29% outperformed all other algorithms. C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate mesothelioma. Conclusion: This study confirms that data obtained from biopsy and imagining tests are strong predictors of mesothelioma but are associated with high cost, however, can identify mesothelioma with optimal accuracy. Predictive analytics without using biopsy results can diagnose mesothelioma with acceptable accuracy. Implementation of phase-2 followed by phase-1 can address diagnosis expenses and maximize disease prognosis. Additionally, results indicate improved MPM diagnosis using AI methods dependent upon the specific application.

The right heart catheterisation constitutes the gold standard for pulmonary hypertension (PH) diagnosis. However, echocardiography remains a reliable, non-invasive, inexpensive, convenient, and easily reproducible modality not only for the preliminary screening of PH but also for PH prognosis. The aim of this review is to describe a cluster of echocardiographic parameters for the detection and prognosis of PH and analyse the challenges of echocardiography implementation in patients with suspected or established PH. The most important echocardiographic index is the calculation of pulmonary arterial systolic pressure (PASP) through the tricuspid regurgitation (TR). It has shown high correlation with invasive measurement of pulmonary pressure, but several drawbacks have questioned its accuracy. Besides this, the right ventricular outflow track acceleration time (RVOT-AT) has been proposed for PH diagnosis. A plethora of echocardiographic indices: right atrial area, pericardial effusion, the tricuspid annular plane systolic excursion (TAPSE), the TAPSE/PASP ratio, tricuspid annular systolic velocity (s′) and recently, can reflect the severity and prognosis of PH. Recent advances in echocardiography with 3-dimensional right ventricular (RV) ejection fraction, RV free wall strain and right atrial strain may further assist the prognostication of PH.

Cancer diagnosis stands at the cusp of a profound transformation, driven by the burgeoning capabilities of Artificial Intelligence (AI) and Machine Learning (ML). This comprehensive review illuminates the extraordinary impact of AI and ML in the field, unraveling their multifaceted roles in the realm of oncology. In the arena of cancer diagnosis, AI and ML serve as invaluable allies, empowering healthcare professionals with unparalleled tools for precision and efficiency. Notably, AI's prowess in analyzing medical images, including radiological scans and pathology slides, elevates the early detection of malignancies to new heights. Coupled with its ability to dissect genomic data, AI tailors therapeutic strategies to the individual, promising optimized treatment outcomes. However, the incorporation of AI and ML into clinical practice necessitates a careful navigation of ethical considerations, data privacy, and regulatory landscapes. Safeguarding patient data, ensuring transparency, and addressing algorithmic biases emerge as pivotal challenges that require vigilant attention. Yet, the future of AI and ML in cancer diagnosis is brimming with promise. The integration of multi-modal data, real-time monitoring, and Explainable AI (XAI) methods holds the potential to enrich diagnostic capabilities and engender patient trust. Global collaboration and data sharing initiatives are fostering the development of robust AI models. Furthermore, AI's role in cancer prevention, identifying high-risk individuals and enabling targeted preventive strategies, is poised to revolutionize healthcare. AI and ML are forging a path toward an era of unparalleled accuracy, efficiency, and personalization in cancer diagnosis. Challenges notwithstanding, these technologies bear the promise of fundamentally reshaping patient care, elevating outcomes, and advancing the fight against cancer. The ongoing exploration and responsible implementation of AI and ML in oncology will be pivotal in harnessing their full potential and paving the way for a brighter future for cancer patients.

Covid-19 infections are rapidly spreading worldwide with more than 100000 death and thus understanding the molecular mechanism of tropism of human cells is an urgent need for drug design. We have described here a bioinformatics approach to predict the functional aspects of non-structural nsp16 protein of Corona virus. The covid-19 7098 AA large polyprotein was degraded into sixteen proteins and last nsp16 protein was found an RlmE type rRNA methyltransferase. Nsp16 has no similarity to bacterial RlmABCD but has 25 percent similarity to the bacterial RlmE protein which methylates the U2551 2-hydroxy group of Ribose. The nsp16 proteins of different corona viruses like covid-19, bat-coronavirus, SARS and MERS have strong homology. Mrm2 and Dim1 like yeast and mammalian rRNA methyltransferases have 26-33 percent homologies but not with 2-O-capping MTase as reported previously. Rrp8 MTases also has no similarity to nsp16. We postulated that mitochondrial rRNA methylation of bronchial cells were mediated by the nsp16 protein causing inhibition of protein synthesis due to poor assembly of aminoacyl-tRNA or mRNA and peptidyl transferase at the PTC. This is one of the new molecular mechanism of corona virus cellular tropism and different than ACE-2 mediated blockage of cellular signalling to inhibit aldesterone biosynthesis with abnormal Na⁺ ions in cells. We also designed primers based on nsp16 cDNA sequence (nt 20659-21552, accession no MT121215) specific for Covid-19 diagnosis by RT-PCR.

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

The abnormal accumulation of cells in the human brain, if left untreated, may cause brain damage. Management and treatment of these tumours require an early and accurate diagnosis, while their prognostic characterisation can also be beneficial in the choice of care planning for the patient. CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists to deal with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alerting physicians of requirement of change in treatment plans. The aim of this systematic review is to identify various CDSSs used in brain tumour diagnosis and prognosis, that rely on data captured by any imaging modality. Based on the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. This review examines various CDSS tool types, system features, techniques used, accuracy, and outcome, to provide the latest evidence available in the field of neuro-oncology. An overview of different types of CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with highlighting their benefits, challenges, and future perspectives has been provided.

This study was performed to synthesize multimodal radiopharmaceutical designed for the diagnosis and treatment of prostate cancer. To achieve this goal, Super Paramagnetic Iron Oxide (SPIO) nanoparticles were used as a platform for targeting molecule (PSMA-617) and for complexation of two scandium radionuclides, 44Sc for PET imaging and 47Sc for radionuclide therapy. TEM and XPS images showed that the Fe3O4 NPs have a uniform cubic shape and a diameter from 38 to 50 nm. The Fe3O4 core are surrounded by SiO2 and an organic layer. The saturation magnetization of the SPION core was 60 emu/g. However, coating the SPIONs with silica and polyglycerol reduces the magnetization significantly. The obtained bioconjugates were labeled with 44Sc and 47Sc, with a yield higher than 97%. The radiobioconjugate exhibited high affinity and cytotoxicity towards the human prostate cancer LNCaP (PSMA+) cell line, much higher than for PC-3 (PSMA-) cells. High cytotoxicity of the radiobioconjugate was confirmed by radiotoxicity studies on LNCaP 3D-spheroids. In addition, the magnetic properties of the radiobioconjugate should allow for its use in guide drug delivery driven by magnetic field gradient.

Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.