From 2008 to 2024, the Newborn Screening Translational Research Network (NBSTRN), part of the National Institute of Child Health and Human Development (NICHD) Hunter Kelly Newborn Screening Program, served as a robust infrastructure to facilitate groundbreaking research in newborn screening (NBS), rare disease, and genomics. Over fifteen years, NBSTRN developed into a significant international network, supporting innovative research on novel technologies to screen, diagnose, treat, manage, and understand the natural history of more than 280 rare diseases. A key component of these efforts has been the development of resources and expertise for the newborn screening community in ethical, legal, and social issues (ELSI). This paper presents findings from a survey conducted by the NBSTRN team with input from one of theNBSTRN's expert workgroups, the Bioethics and Legal Workgroup. With responses from 88 members of the NBS community including researchers and state NBS programs, the survey found that individuals rely on internal and collegial ELSI resources and highlighted a substantial demand for structured, accessible training and resources. The survey’s insights emphasize the importance of privacy and informed consent in NBS research, and suggested a need for improved collaborative tools and educational programs focused on ELSI for the NBS community. The survey results may inform future efforts in ELSI and NBS research including the development of expanded ELSI tools that leverage digital platforms and consultative services.

Congenital heart disease (CHD) is a prevalent congenital disorder diagnosed with high frequency, affecting approximately 0.8% to 1.2% of all live births. Research has demonstrated that the use of pulse oximetry and clinical examination as a screening tool for CHD diagnosis has proven extremely valuable. We conducted a retrospective longitudinal study over a 3-year period that involved screening 1188 newborns admitted to the NICU during the first 72 hours of life within this timeframe. 201 newborns were diagnosed with CHD. Almost all (n=198) had a positive screening test during the 72-hour period, while 3 had a negative screening test, but were later diagnosed using echocardiography. The prevalence rate was 16.67%. A confusion matrix was constructed with 198 true positives (TP), 3 false negatives (FN), no false positives (FP), and 987 true negatives (TN). The sensitivity of the screening test was 98.51%, while the specificity 100%, with no false positives. In our study, a total of 9 deaths occurred. Using the Kaplan-Meier method, the survival probability appeared to be slightly above 0.9 (95.5%), indicating that almost all patients survived past 72 hours after being diagnosed with CHD. Pulse oximetry is an effective, low cost and easy-to-use method and a useful tool to assess and lead to the diagnosis of CHD.

Ulcerative colitis (UC) is one of the two disorders known as inflammatory bowel diseases (IBD) along with Crohn’s disease (CD), with complex pathogenesis, requiring costly invasive investigations. Objective: to examine the most recent biomarkers proposed for UC diagnosis; to establish the strategy used to make the differential diagnosis between UC and CD relying on these biomarkers, also adding the benefit of finding new non-invasive tools in managing this condition. The search was performed in a single database (Web of Science) using the specific keywords „ulcerative colitis”, „biomarkers” and „diagnosis” for the last five years. Study eligibility criteria: clinical trials on adults and pediatric patients with ulcerative colitis compared with Crohn’s disease. Results: We selected 57 studies, randomized controlled trials (RCTs) and clinical case series (CCS), summarizing the latest most specific biomarkers in diagnosis of UC. Limitations: we considered RCTs and CCS from one database, limited to the search topics. Our findings indicate a important number of potential biomarkers with diagnostic value, which bring the advantage of a non-invasive method to approach this challenging disorder.

(1) Background: Sudden Infant Death Syndrome (SIDS) represents unexplained and sudden deaths during the sleep of infants under one year of age, despite thorough investigation. Screening for prolonged QTc or Long QT Syndrome (LQTS) should be conducted on all newborns to reduce the incidence of SIDS. Neonatal electrocardiogram (ECG) could identify congenital heart defects (CHD) early, especially those not detected at birth. Infants with prolonged QT intervals typically undergo genetic analysis for Long QT Syndrome; (2) Methods: The study involved infants aged 20-40 days, born clinically healthy, with initial ECG screening. Infants with prenatal diagnoses or signs/symptoms of CHD identified immediately after birth, as well as those who underwent ECG or echocardiogram for other clinical or historical indications, were excluded from the study. The data were analyzed using SPSS version 22.0; (3) Results: Of the 42,200 infants involved, 2,245 were enrolled, with 39.9% being males. Subsequently, 164 children (37.8% males) with prolonged QT intervals underwent further diagnostic investigations. Genetic mutations were identified in 11 out of 18 tested children. The most common mutations were LQT1 (54.5%), LQT2 (36.4%), and LQT3 (1 patient). Medications used included propranolol (39.8%), nadolol (22.2%), inderal (11.1%), metoprolol (11.1%), and no treatment (16.7%). The most common abnormalities were focal right bundle branch block (54.5%), left axis deviation (9.2%), and nonspecific ventricular repolarization abnormalities (7.1%). Multiple anomalies were found in 0.47% of children with focal right bundle branch block. Structural abnormalities were associated with specific features in 267 patients (11.9%), primarily isolated patent foramen ovale (PFO) at 61.4%; (4) Conclusions: The screening has proven effective in early diagnosing Long QT Syndrome and other cardiac rhythm anomalies, with additional benefits in detecting mutations and/or QTc prolongations in family members. Identifying other ECG abnormalities and congenital heart malformations further enhances the benefits of the screening.

Since first described by Dr. Tomisaku Kawasaki in 1967, the clinical diagnosis of Kawasaki disease (KD) has remained relatively unchanged. However, studies since then have suggested that diagnoses using only that original clinical criteria may lead to underdiagnosis. In this review, updated KD diagnostic methods, diagnostic methods for “atypical” KD, diagnostic pitfalls, and other differential diagnoses are discussed. Peripheral blood eosinophil showed important in distinguishing KD from febrile children. We review herein the current literature regarding new biomarkers, scoring systems, and microRNA panels that may aid in the diagnosis of KD, the importance of eosinophil and treatment updates.

In the last two decades, the development of high-throughput diagnostic methods and availabil-ity of effective treatments has increased interest in newborn screening for lysosomal storage disorders. However, long-term follow-up experience is needed to clearly identify risks, benefits and challenges. We report our 8-year experience of screening and follow up on about 250,000 neonates screened for four lysosomal storage diseases (Pompe disease, mucopolysaccharidosis type I, Fabry dis-ease, Gaucher disease), using enzyme activity assay by tandem mass spectrometry, and bi-omarker quantification as second tier test. Among the 126 positive newborns (0.051%), 51 infants were confirmed as affected (positive pre-dictive value 40%), with an overall incidence of 1:4,874. Of these, 3 infantile-onset Pompe dis-ease, 2 neonatal-onset Gaucher disease and 4 mucopolysaccharidosis type I patients were imme-diately treated. Furthermore, another four Gaucher disease patients needed treatment in the first years of life. Our study demonstrated the feasibility and effectiveness of newborn screening for lysosomal storage diseases. Early diagnosis and treatment allow the achievement of better patient out-comes. Challenges such as false positive rates, the diagnosis of variants of uncertain significance or late-onset forms, and the lack of treatment for neuronopathic forms, should be addressed.

Still’s disease, also known as systemic juvenile idiopathic arthritis, is an autoimmune disorder with a triad of fever, salmon pink rash, and inflammatory polyarthritis. Its diagnosis is quite challenging due to the lack of specific symptoms and overlap with many other rheumatological and autoimmune disorders. However, recent studies have shown that high serum ferritin levels can act as a biomarker for this disease. Ferritin is an intracellular iron storage protein that acts as a pro-inflammatory reactant. There are various explanations indicating the presence of higher ferritin levels in Still's disease. High ferritin tells us about disease severity, and prognosis and can also predict life-threatening complications that can be easily prevented and thus it helps in reducing mortality associated with Still's disease. However, there are certain limitations to the use of ferritin as the only diagnostic marker for Still's disease. Overcoming those limitations can take the diagnosis of Still's disease to a higher level.

Alzheimer's disease (AD) is the first cause of dementia worldwide and represents a public health challenge. Current diagnostic methods still rely on extended interviews and paper tests. We aim to create a novel, quick cognitive-screening tool on a numerical tablet. This program, built and edited with Unity®, runs on Android® for the Samsung Galaxy Tab S7 FE®. Composed of seven tasks inspired by the Mini-Mental Status Examination and the Montréal Cognitive Assessment, it browses several cognitive functions. The architectural design of this tablet application is distinguished by its multifaceted capabilities, encompassing not only seamless offline functionality but also a mechanism to ensure the singularity of data amalgamated from diverse sites. Additionally, a paramount emphasis is placed on safeguarding the confidentiality of patient information in the healthcare domain. Furthermore, the application empowers individual site managers by allowing them to access and peruse specific datasets, thereby enhancing their operational efficacy and decision-making processes. We performed a usability assessment among 24 healthy patients with a final F-SUS score of "excellent". Participants perceived the tool as simple to use and achieved the test in a mean time of 142 seconds, confirming that a short assessment on a numerical tablet is possible.

Since the UK commenced newborn screening for isovaleric acidemia in 2015, changes in prescribing have increased the incidence of false positive (FP) results due to pivaloylcarnitine. A review of screening results between 2015-22 identified 24 true positive (TP) and 84 FP cases, with pivalate interference confirmed in 76/84. Initial C5 carnitine (C5C) did not discriminate between FP and TP with median (range) C5C of 2.9 (2.0 - 9.6) and 4.0 (1.8 - >70) µmol/L respectively and neither did Precision Newborn Screening via Collaborative Laboratory Integrated Reports (CLIR), which identified only 1/47 FP cases. However, among the TP cases, disease severity showed a correlation with initial C5C, in ‘asymptomatic’ individuals (n=17), demonstrating a median (range) C5C of 3.0 (1.8 – 7.1) whilst ‘clinically affected’ patients (n=7), showed a median (range) C5C of 13.9 (7.7 - > 70) µmol/L. These findings allowed the introduction of dual cut-off values into the screening algorithm to reduce the incidence of FPs, with initial C5C results ≥ 5 µmol/L triggering urgent referral, and those > 2.0 and < 5.0 µmol/L prompting 2nd tier C5-isobar testing. This will avoid delayed referral in babies at particular risk whilst reducing the FP rate for the remainder.

When the skin is afflicted by disease such as cancer, these essential functions are disrupted, putting the entire person at risk. While the American Cancer Society lists 6 major cancer types, the National Institute of Arthritis and Musculoskeletal and Skin Diseases identifies 13 significant benign skin disorders, reflecting the diversity of skin conditions in dermatology. This topical review aims to provide an overview of the pathophysiology of these major skin cancers and disorders and summarize conventional diagnostic methods and current treatment approaches.

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis by selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), the decision tree of C5.0, support vector machine (SVM), the decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.

Postprandial hyperlipidemia showing postprandial increase in serum triglyceride (TG) is atherogenic and associated with the development of coronary artery disease (CAD). To diagnose postprandial hyperlipidemia, the oral fat loading test (OFLT) should be performed, however, this test is very time-consuming and is difficult to perform. Ele-vated serum TG levels reflect an increase in TG-rich lipoproteins (TRL) such chylomi-crons (CM), very low-density lipoproteins (VLDL) and their remnants (CM remnant [CMR] and VLDL remnant [VLDLR]). Understanding of elevation in CMR and/or VLDLR can lead us to understand the existence of postprandial hyperlipidemia. The measurements of apo B48 which is a constituent of CM and CMR, non-fasting TG which includes TG content in all lipoproteins including CM and CMR, non-high-density lipoprotein-cholesterol (non-HDL-C) which includes TRL and low-density lipoprotein, and remnant cholesterol, are useful to suspect the existence of postprandial hyperlipidemia. Postprandial hyperlipidemia is observed in patients with familial type III hyperlipoproteinemia, familial combined hyperlipidemia, chronic kidney disease, metabolic syndrome and type 2 diabetes. Postprandial hyperlipidemia is closely related to postprandial hyperglycemia, and insulin resistance may be an in-ducing and enhancing factor for both postprandial hyperlipidemia and postprandial hyperglycemia. Remnant lipoproteins and metabolic disorders associated with post-prandial hyperlipidemia have various atherogenic properties such as induction of in-flammation and endothelial dysfunction. A healthy diet, calorie restriction, weight loss and exercise, positively impact postprandial hyperlipidemia. Anti-hyperlipidemic drugs such pemafibrate, bezafibrate, ezetimibe and eicosapentaenoic acid have shown to improve postprandial hyperlipidemia. Anti-diabetic drugs including metformin, alpha-glucosidase inhibitors, pioglitazone, dipeptidyl-peptidase-4 inhibitors and glu-cagon like peptide 1 analogues, have shown to ameliorate postprandial hyperlipidemia. Although sodium glucose cotransporter-2 inhibitors have not been proven to reduce postprandial hyperlipidemia, they reduced fasting apo B48 and remnant lipoprotein cholesterol. In conclusion, it is important to appropriately understand the existence of postprandial hyperlipidemia and to connect it to optimal treatments.

Lyme carditis (LC) is a manifestation of the early disseminated stage of Lyme disease and often presents as high-degree atrioventricular (AV) block. High-degree AV block in LC can be treated with antibiotics, usually resolving with highly favourable prognosis, thus preventing the unnecessary implantation of permanent pacemakers. We present a systematic approach to the diagnosis and management of LC that implements the Suspicious Index in Lyme Carditis (SILC) risk stratification score.

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.

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.

The pervasiveness of cardiovascular disease and physician misdiagnosis creates the urgent need for artificial intelligence models to improve diagnosis accuracy. The first objective of this study was to train machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompted the creation of an open-source, automated cardiovascular disease diagnosis tool, powered by the MLP. The second objective of this study was to employ a meta-learning methodology called Local Interpretable Model-Agnostic Explanations (LIME) to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering was employed to segment the data into ten clusters, after which each data example was passed through LIME. The resulting histograms depict the complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows the nuances in the MLP's understanding of patients from different clusters. The results of meta-learning analysis reveal that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and slope of the ST segment on an electrocardiogram. Future experiments should replicate the novel methodology introduced in this study on data sets containing more specialized medical features in order to gain practical medical insights about different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models not only for cardiovascular disease, but adaptable to other diseases as well.

Expert system approach, although quite old, is still quite effective in scientific areas where experts are required to make diagnoses and predictions. One of those areas is fish disease diagnosis. It is an application domain that currently employs complicated processes, which require high level skills for making accurate diagnoses. On the other hand, complete datasets for full diagnosis to be able to use machine learning techniques are not available. Therefore, in aquaculture, now more than ever, fish farmers do not have the required expertise or equipment to accurately diagnose a fish disease. For that reason, expert systems that can help in the diagnosis, prevention and treatment of diseases have been developed. In this paper, we attempt to give an overview of the expert system approaches for fish disease diagnosis developed in the last two decades. Based on the analysis of their technical and non-technical characteristics, we propose an expert system architecture and a fish disease diagnosis process aiming at improving the deficiencies of existing such systems. The proposed system can handle all kinds of fish diseases based on image and non-image data as well as on molecular tests results and can provide explanations. The diagnosis process goes through four consecutive levels, where each next level considers an additional category of parameters and provides diagnoses with higher certainty.

It has been well established that hypertension is considered as a predominant risk factor for the development of cardiovascular diseases. Despite the link between hypertension and cardiovascular diseases, hypertension may directly affect the cardiac function leading in heart failure, mostly with preserved ejection fraction (HFpEF). There are echocardiographic findings indicating hypertensive heart disease (HHD) defined as altered cardiac morphology (left ventricular concentric hypertrophy, left atrium dilatation) and function (systolic or diastolic dysfunction) in patients with persistent hypertension irrespective of cardiac pathologies to which it contributes, such as coronary artery disease and kidney function impairment. In addition to classical echocardiographic parameters, novel indices, like speckle tracking of the left ventricle and left atrium, 3D volume evaluation, myocardial work in echocardiography may provide more accurate and reproducible diagnostic and prognostic data in patients with hypertension. However, their use is still underappreciated. An early detection and prompt therapy of HHD will greatly improve the prognosis. Hence, in the present review, we shed light on the role of echocardiography in the contemporary diagnostic and prognostic approach of HHD.

Background: Kawasaki disease (KD) is a form of vasculitis that primarily affects children under the age of 5 years old. Patients may be missed diagnosis when initial clinical symptoms do not fulfill the traditional criteria. We aimed to analyze factors that clinicians could use to differentiate febrile children suspected of KD. Method: We retrospectively enrolled a total of 83 febrile children who were initially suspected of KD, but they did not meet the American Heart Association (AHA) criteria for a diagnosis. However, some of these patients were diagnosed with KD during their second visit. We analyzed patients' characteristics, clinical symptoms, and laboratory data. Results: In total, 50 patients were enrolled in the study. Of those, ten patients were diagnosed with KD on their second visit (group 1), while the other 40 patients still did not fit a KD diagnosis (group 2). A patient with a neutrophil to lymphocyte ratio greater than 1.33 combined with a C-reactive protein more than 33 mg/L was more likely to have KD. Conclusion: Among patients suspected of KD that did not initially meet the criteria, clinicians should pay special attention to elevated neutrophil-to-lymphocyte ratios and CRP levels and closely follow up such patients.

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.

Therapies targeting amyloid, such as monoclonal antibodies like Lecanemab, Aducanumab, and Donanemab, have garnered FDA approval but remain controversial due to limited clinical benefits and significant side effects against Alzheimer. The present theorical work utilized Virtual Screening and Docking techniques to explore potential small natural molecules' interactions with the KIBRA protein, a promising target for drug development. Kibra protein is considered a biomarker for synaptic dysfunction and cognitive decline. Employing Autodock Vina and Pyrx software, on several natural compounds and found Hypericin to exhibit the lowest binding energy of approximately -9 kcal/mol. Previous studies have highlighted Hypericin's strong affinity for Alzheimer's Disease-associated targets like ABAD and BACE1. These findings suggest Hypericin could be as a potential therapeutic agent for Alzheimer's Disease. Further research is warranted to validate its efficacy and safety for clinical use.

Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters’ variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan.

Neurodegenerative diseases (NDs) are a set of progressive, chronic, and incurable diseases characterized by the gradual loss of neurons, culminating in the decline of cognitive and/or motor functions. Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common NDs and represent an enormous burden both in terms of human suffering and economic cost. The available therapies for AD and PD only provide symptomatic and palliative relief for a limited period and are unable to modify the diseases’ progression. Over the last decades, research efforts have been focused on developing new pharmacological treatments for these NDs. However, to date, no breakthrough treatment has been discovered. Hence, the development of disease-modifying drugs able to halt or reverse the progression of NDs remains an unmet clinical need. This review summarizes the major hallmarks of AD and PD and the drugs available for pharmacological treatment. It also sheds light on potential directions that can be pursued to develop of new, disease-modifying drugs to treat AD and PD, thereby decreasing the social and economic burdens linked to these NDs.

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.

Chronic Kidney Disease is a medical condition that causes the decrease in the kidney function and can eventually derive in a total cessation of the work of the organ. It currently affects >10% of the global population, and the number is expected to grow within the next few years, since the correlated diabetes condition is escalating too. Diagnosing the disease on its early stages is crucial to improve life quality, and to increase the chances of survival. The traditional diagnostic methods, which include a biopsy, are invasive, expensive and dangerous. With the use of machine learning algorithms like neural networks, random forest and genetic algorithms, this paper seeks to discover an algorithm with high accuracy, whose selected attributes will be the most optimal combination aiming to reduce the cost, level of invasiveness, and easiness to obtain.

Background: Acute kidney disease (AKD) is a known risk factor for increased mortality and evolution towards chronic kidney disease (CKD) in adults. The data regarding children are scarce. The purpose of our study is to explore the risk factor for AKD and impact of AKD following an acute kidney injury (AKI) episode in children on mortality and new-onset CKD. Methods: We retrospectively evaluated 736 children with ages between 2 and 18 years old with AKI admitted in a tertiary care hospital from the west Romania over a 9 years period. Results: AKD incidence following AKI was 17%. Patients that developed AKD were older, with higher baseline serum creatinine values, urea, C reactive protein and lower proteins, haemoglobin and sodium. They were more often exposed to sepsis, critical illness and nephrotoxic medications and presented certain susceptibilities like CKD, neoplasia, anemia, heart failure, arterial hypertension, stem cell transplant and female gender. AKI causes that increased the risk of AKD were intrinsic ones. AKD increased the overall mortality by three times. The factors that independently increased the risk of evolution towards CKD were AKD, acute tubular necrosis and higher baseline serum creatinine values. Conclusions: AKD is an independent risk factor for mortality and new-onset CKD in children.

Real-time quaking-induced conversion (RT-QuIC) assays have become common in the detection of chronic wasting disease (CWD) and are very sensitive provided the assay duration is sufficient. However, a prolonged assay duration may lead to non-specific signal amplification. The wide range of pre-defined assay durations in current RT-QuIC applications presents a need for optimization of the RT-QuIC assay duration. In this study, receiver operating characteristic (ROC) analysis was applied to optimize assay duration for detection of CWD in obex and retropharyngeal lymph node (RLN) tissue specimens. Two different fluorescence thresholds were used: a fixed threshold based on background fluorescence (Tstdev) and a max-point ratio (maximum/background fluorescence) threshold (TMPR) to determine CWD positivity. The optimal assay duration was 27 h for both obex and RLN based on Tstdev, and 27 and 28 h for obex and RLN, respectively, based on TMPR. The optimized assay durations were then evaluated for screening CWD in white-tailed deer from an affected farm. Results by RT-QuIC using optimized duration based on Tstdev and TMPR were in 100% and 92.3 % or higher agreement with those by the widely used screening assay, ELISA. In comparison, when using a 40 h assay duration, the agreement between RT-QuIC and ELISA reduced to 89.2% or higher, and the RT-QuIC results were significantly (p < 0.05) different from those using optimum durations. These findings demonstrated that the application of ROC analysis for the optimization of assay duration could improve the RT-QuIC assay for screening CWD in white-tailed deer.

Coronavirus Disease 2019 (Covid-19) was first described in December 2019 in Wuhan, Hubei Province, China; and produced by a novel coronavirus designed as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Covid-19 has become a pandemic reaching over 1.3 million confirmed cases and 73,000 deaths. Several efforts have been done to identify pharmacological agents that can be used to treat patients and protect healthcare professionals. The sequencing of the virus genome not only has offered the possibility to develop a vaccine, but also to identified and characterize the virus proteins. Among these proteins, main protease (M^pro) has been identified as a potential therapeutic target, since it is essential for the processing other viral proteins. Crystal structures of SARS-CoV-2 M^pro and inhibitors has been described during the last months. To describe additional compounds that can inhibit SARS-CoV-2 M^pro, in this study we performed a molecular docking-based virtual screening against a library of experimental and approved drugs. Top 10 hits included Pictilisib, Nimorazole, Ergoloid mesylates, Lumacaftor, Cefuroxime, Cepharanhine, and Nilotinib. These compounds were predicted to have higher binding affinity for SARS-CoV-2 M^pro than previously reported inhibitors for this protein, suggesting a higher potential to inhibit virus replication. Since the identified drugs have both pre-clinical and clinical information, we consider that these results may contribute to the identification of treatment alternative for Covid-19. Nevertheless, in vitro and in vivo confirmation should be performed before these compounds could be translated to the clinic.

Background: Nonalcoholic fatty liver disease (NAFLD) is regarded as a component of metabolic syndrome, which has insulin resistance (IR) as the primary physiopathological event. The aim of study was to establish the association between IR, assessed using triglyceride and glucose index (TyG), and histopathological features of NAFLD lesions. Methods: The study included patients with metabolic syndrome. Fasting plasma glucose (FPG), fasting lipid profiles and liver enzymes were measured. IR was assessed by TyG index. Liver biopsy was performed for assessment steatosis and fibrosis. Results: TyG index had a mean value of 8.93 ± 1.45, with a higher value in the patients with overweight (p=0.002) and obesity (p=0.004) than in the patients with normal weight. TyG index mean value of 8.78 ± 0.65 in subjects without NASH, 8.91 ± 0.57 in patients with borderline NASH and 9.13 ± 0.55 in patients with definite NASH. Significant difference was found between subjects without NASH and the ones with definite NASH (p=0.004). The analysis of the area under the ROC curve proved that TyG index is a predictor for NASH (p=0.043). Conclusion: TyG index is a facile tool used to identify individuals at risk for NAFLD, but not for the progression of liver lesions.

Human pluripotent stem cells (hPSCs) have become a powerful tool to generate various kinds of cell types comprising the human body. Recently, organoid technology emerged as a platform to build a physiologically relevant tissue-like structure from the PSCs, which provides a more relevant three-dimensional microenvironment to the actual human body than the conventional monolayer culture system for transplantation, disease modeling, and drug development. Although it holds so many advantages, the organoid culture system still has various problems related to culture methods, which became a challenge to get similar physiological properties to their original tissue counterparts. Here, we discuss the current development of organoid culture methods, including the problem that may arise from the currently available culture systems as well as the possible approach to overcoming the current limitation and improving their optimum utilization for translational application purposes.

Neurodegenerative diseases that include Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Huntington's disease (HD), and multiple sclerosis (MS), arise due to numerous causes like protein accumulation and autoimmunity, characterized by neurologic depletion which lead to incapacity in normal physiological function such as thinking and movement in these patients. Glial cells perform a critical role in protective neuronal function; in the case of neuroinflammation, glial cell dysfunction can promote the development of neurodegenerative diseases. MiRNA participates in gene regulation and plays a vital role in many biological processes in the body; in the central nervous system (CNS), it can play an essential part in neural maturation and differentiation. In neurodegenerative diseases, miRNA dysregulation occurs, enhancing the development of these diseases. In this review, we discuss neurodegenerative diseases (Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS)) and how miRNA is preserved as a diagnostic biomarker or therapeutic agent in these disorders. Finally, we highlighted miRNA as therapy.

Background: Patients with Inflammatory Bowel Disease (IBD) believe that diet is a key factor in the pathogenesis of their disease and in worsening their symptoms; they often follow restrictive diets that can lead to malnutrition, anxiety, and stress. Recent studies have found an association between eating disorders and IBD, particularly anorexia nervosa and ARFID (Avoidant Restrictive Food Intake Disorder). None of these studies report the association with orthorexia nervosa, that is the obsession with healthy and natural foods. Aim: to assess the risk of orthorexia nervosa in patients with IBD. Methods: 158 consecutive subjects were recruited, including 113 patients diagnosed with IBD and 45 controls. The Donini standardized questionnaire ORTO-15 was administered to assess the risk of orthorexia; clinical and demographic data were also collected. Results: patients with IBD have a prevalence of orthorexia of 77%, significantly higher than the 47% observed in the control group. In patients with IBD, the risk of orthorexia is associated with lower BMI and history of previous surgery. Conclusion: Many patients with IBD develop orthorexia nervosa that impacts the patient's psychological and social sphere, exposes them to a high risk of nutritional deficiencies, and affects quality of life. Further high quality studies are warranted to evaluate the clinical impact of orthorexia and its correlation with clinical features and frank eating disorders.

Multiple myeloma (MM) is an incurable, diverse cancer in which abnormal plasma cells produce and release monoclonal immunoglobulin (Ig), also known as monoclonal protein or M protein. MM is always preceded by monoclonal gammaglobulinemia of unknown significance (MGUS) and smoldering multiple myeloma (SMM). Diagnosis of MM is made from a bone marrow sample, which involves analysis of the appearance of plasma cells. Quantification of CD138+ plasma cells in core biopsies is performed using techniques such as immunohistochemistry, flow cytometry, fluorescence in situ hybridization (FISH) and conventional cytogenetics. Urinalysis includes total protein by immunofixation and 24-hour urine by serum protein electrophoresis. FISH probes should detect the presence of at least del 13, del 17p, t(4;14), t(11;14), t(14;16), and 1q21 amplifications. Indeed, next-generation sequencing (NGS) has become an increasingly important tool in the diagnosis and treatment of MM. It can be used to identify genetic mutations, copy number changes, and clonal structures to help determine disease prognosis and treatment strategies. NGS can identify subclonal populations of cancer cells that are resistant to therapy and may lead to disease relapse, and can be used to identify potential targets for personalized therapy. For example, NGS can identify mutations in genes involved in drug resistance, enabling the selection of drugs that may be more effective in treating a patient's cancer. This systematic review analyzes 182 papers to determine the position and role of NGS in the diagnosis of MM. We first analyze the platforms and sample types used in the selected studies, and then start discussing the results.

Gallbladder diseases are very common and their diagnosis is based on clinical-laboratory evaluation and imaging techniques. Considering the different imaging diagnostic tools, ultrasound (US) has the advantage of high accuracy combined with easy availability. Therefore, when a gallbladder disease is suspected, US can readily assist the clinician in the medical office or in the emergency department. The high performance of US in the diagnosis of gallbladder diseases is mainly related to its anatomic location. The most frequent gallbladder pathological condition is gallstones disease, easily diagnosed by US examination. Acute cholecystitis (AC), a possible complication of gallstone disease, can be readily recognized, due to its specific sonographic features. Additionally, a number of benign, borderline or malignant gallbladder lesions may be detected by US evaluation. The combined use of standard B-mode US and additional sonographic techniques, such as contrast-enhanced ultrasonography (CEUS), may provide a more detailed study of gallbladder lesions. Multiparametric US (combination of multiple sonographic tools) can improve the diagnostic yield during gallbladder examination.

Preeclampsia (PE) is associated with an increased risk of cardiovascular disease (CVD) in later life. Postpartum cardiovascular risk screening could identify patients who would benefit most from lifestyle interventions. However, there are no readily available methods to identify these high-risk women. We propose that placental lesions may be useful in this regard. Here, we sought to determine the association between placental lesions and lifetime CVD risk. Placentas from 85 PE women were evaluated for histopathological lesions. At 6 months postpartum, a lifetime cardiovascular risk score was calculated. Placental lesions were compared between CVD risk groups and the association was assessed using odds ratios. Multivariable logistic regression was used to develop prediction models for CVD risk with placental pathology. Placentas from high-risk women had more severe lesions of maternal vascular malperfusion (MVM) and resulted in a 3-fold increased risk of screening high-risk for CVD (OR 3.10[1.20-7.92]) compared to women without these lesions. MVM lesion severity was moderately predictive of high-risk screening (AUC 0.63[0.51,0.75]; sensitivity 71.8%[54.6,84.4]; specificity 54.7%[41.5,67.3]. When clinical parameters were added, the model’s predictive performance improved (AUC 0.73[0.62,0.84]; sensitivity 78.4%[65.4,87.5]; specificity 51.6%[34.8,68.0]. The results suggest that placenta pathology may provide a unique modality to identify women for cardiovascular screening.

(1) Background: Newer generation ophthalmologists practicing in the developed world are not very familiar with some tropical ocular diseases due to the absence of reports in the ophthalmic literature over the past thirty years. Because of world globalization or due to influx of immigrants from Sub Saharan Africa, exotic retinal diseases are being encountered more often in ophthalmology clinics; (2) Methods: Case series of chorioretinitis or optic neuritis with obscure etiology; (3) Results: 4 cases qualified with the diagnosis of ocular onchocerciasis based on their residence near fast rivers in endemic areas, multimodal imaging, long term follow-up showing progressive disease and negative workup for other diseases. Characteristic findings include peripapillary choroiditis with optic neuritis or atrophy, subretinal tracts of the microfilaria, progressive RPE atrophy around heavily pigmented multifocal chorioretinal lesions of varying shapes and subretinal white or crystalline dots. Typical skin findings are often absent in eyes with chorioretinitis rendering the diagnosis more challenging; (4) Conclusions: Familiarity with the myriad ocular findings of onchocerciasis, and a high degree of suspicion in subjects residing in endemic areas can help in the correct diagnosis and implementation of appropriate chemotherapy. Onchocercid chorioretinitis is a slow insiduous progressive and prolonged polymorphous disease.

The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at a university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% Confidence Interval (95% CI): 0.629 - 0.989) for the validation sample and 0.920 (95% CI: 0.891 - 0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.

Neurological disorders are the leading cause of cognitive and physical disability worldwide, affecting 15% of the global population. Due to the demographics of aging, the prevalence of neurological disorders, including neurodegenerative diseases, will double over the next two decades. Unfortunately, while available therapies provide symptomatic relief for cognitive and motor impairment, there is an urgent unmet need to develop disease-modifying therapies that slow the rate of pathological progression. In that context, biomarkers could identify at-risk and prodromal patients, monitor disease progression, track response to therapy, and parse causality of molecular events to identify novel targets for further clinical investigation. Thus, identifying biomarkers that discriminate between diseases and reflect specific stages of pathology would catalyze the discovery and development of therapeutic targets. This review will describe the prevalence, known mechanisms, ongoing or recently concluded therapeutic clinical trials, and biomarkers of three of the most prevalent neurodegenerative diseases, including Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and Parkinson’s disease (PD).

Objective: Several authors have analyzed the impact of the pandemic on the incidence rates of different conditions. Our aim was to analyze the impact of the COVID-19 pandemic on primary care new diagnoses of respiratory diseases. Methods: This was an observational retrospective study performed to describe the impact of COVID-19 pandemic on primary care new diagnosis of respiratory diseases other than lung cancer. Incidence rate ratio between pre-pandemic and pandemic period was calculated. Results: We found an decrease in the incidence of respiratory conditions (IRR 0.65) during the pandemic period. When we compared the different groups of diseases according to ICD-10, we found a significant decrease in the number of new cases during the pandemic period, except in the case of pulmonary tuberculosis, abscesses or necrosis of the lungs and other respiratory complications. Instead, we found increases in new diagnoses of flu and pneumonia (IRR 2.17) and respiratory interstitial diseases (IRR 1.41). Conclusion: There has been a decrease in new diagnosis of most respiratory diseases during the COVID-19 pandemic. The real clinical impact of this situation is still unknown. Large-scale real-life studies will make it possible to evaluate the long-term true impact of COVID-19 pandemic on the respiratory diseases management.

Familial Alzheimer’s disease (FAD) can be caused by mutations in PSEN1 that encodes presenilin-1, a component of the gamma-secretase complex that cleaves amyloid precursor protein. Alterations in calcium (Ca2+) homeostasis and glutamate signaling are implicated in the pathogenesis of FAD, however it has been difficult to assess in humans whether or not these phenotypes are the result of amyloid or tau pathology. This study aimed to assess early calcium and glutamate phenotypes of FAD by measuring the Ca2+ response of induced pluripotent stem cell (iPSC)-derived neurons bearing PSEN1 mutations to glutamate and the ionotropic glutamate receptor agonists NMDA, AMPA and kainate compared to isogenic control and healthy lines. The data shows that in early neurons, even in the absence of amyloid and tau phenotypes, FAD neurons exhibit increased Ca2+ responses to glutamate and AMPA, but not NMDA or kainate. Together this suggests that PSEN1 mutations alter Ca2+ and glutamate signaling as an early phenotype of FAD.

Neuronal cells are believed to rely on autophagy to maintain cellular homeostasis. Nevertheless, autophagy may be a therapeutic target for neurodegenerative diseases because of the role it plays in neuronal cell death. Despite Beclin 1 association with autophagy and apoptosis, the molecular mechanisms that underlie Tau-induced vesicular pathology and Tau protein aggregates in humans remain poorly understood. Here, we set out to examine the role of three genes-ATG14L, Beclin 1 and VPS34- in autophagy nucleation failure-associated Tau disorders in fruit fly models. We found that either reduced ATG14L levels improve neuronal outcomes, or transgenic expression of ATG6/Beclin 1 greatly reduced age-dependent neurodegeneration and Tau pathology; whereas, the loss of Beclin 1 exacerbated these effects. Prior to p-Tau accumulation, the neuronal Beclin 1 network is critical for reducing p-Tau accumulation, indicating that the loss of Beclin 1 causes Tau pathology and neurodegeneration. As neurodegeneration progressed, glial cells showed an upregulation of vesicular Beclin 1 immunoreactivities. The reduced VPS34 also resulted in p-Tau accumulation, suggesting that neurons require both Beclin 1 and VPS34 to reduce p-Tau accumulation. Our results provide mechanistic insight into the molecular pathways that cause age-dependent Tau pathology and neurodegeneration due to an excess of autophagy nucleation processes and autophagy activity, and we derive novel inferences on the therapeutic possibilities of rebalanced Beclin 1 network by reducing autophagic specific nucleation component of ATG14L.

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4^th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

Background: The annual mortality of patients with untreated chronic thromboembolism pulmonary hypertension (CTEPH) is approximately 50% unless a timely diagnosis is followed by adequate treatment. In pulmonary embolism (PE) survivors with functional limitation the diagnostic work-up starts with echocardiography. It is followed by lung scintigraphy and right heart catheterization. However, noninvasive test providing diagnostic clues to CTEPH, or ascertain this diagnosis as very unlikely, would be extremely useful since the majority of post PE functional limitations is caused by deconditioning. Methods: Patients after acute PE underwent a structured clinical evaluation with electrocardiogram, routine laboratory tests including NT-proBNP and echocardiography. The aim of study was to verify whether the parameters from echocardiographic or perhaps electrocardiographic examination and NT-proBNP concentration best determine the risk of CTEPH. Results: A total (n = 261, male n = 123) patients after PE were included into the study, in group of 155 patients (59.4%) with reported functional impairment 13 patients (8.4%) had CTEPH and 7 PE survivors had chronic thromboembolic pulmonary disease (CTEPD) (4,5%). Echo parameters differed significantly between CTEPH/CTEPD cases and other symptomatic PE survivors. Patients with CTEPH/CTEPD had also higher level of NT-proBNP (p = 0.022) but concentration of NT-proBNP above 125 pg/ml did not differentiate patients with CTEPH/CTEPD (p>0.05). Additionally, proportion of patients with right bundle brunch block registered in ECG was higher in group with CTEPH/CTED (23.5% vs. 5.8%, p = 0.034) but there were no differences between other ECG characteristics of right ventricle overload. Conclusion: Screening for CTEPH/CTEPD should be performed in patients with reduced exercise tolerance compared to pre PE period, It is not effective in asymptomatic PE survivors. Patients with CTEPH/CTED had predominantly abnormalities indicatingchronic thromboembolism in the echocardiographic assessment. NT-proBNP and electrocardiographic characteristics of right ventricle overload proved to be insufficient in predicting CTEPH/CTEPD development.

Abstract: Cancer survivors are at an increased risk for cardiovascular disease (CVD). However, the association between particulate matter (PM) and CVD risk among cancer survivors (alive >5 years since diagnosis) is unclear. We investigated the risk of CVD among 40,899 cancer survivors within the Korean National Health Insurance Service database. Exposure to PM was determined by assessing yearly average PM levels obtained from the Air Korea database from 2008 to 2011. PMs with sizes <2.5 (PM2.5), <10 (PM10), or 2.5-10 (PM2.5-10) μm in diameter were compared, with each PM level exposure further divided into quintiles. Patients were followed up from January 2012 to date of CVD event, death, or December 2017, whichever came earliest. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for CVD were calculated using Cox proportional hazards regression by PM exposure levels. Compared with cancer survivors in the lowest quintile of PM2.5 exposure, those within the highest quintile had a greater risk for CVD (aHR 1.31, 95% CI 1.07-1.59). Conversely, increasing PM10 and PM2.5-10 levels were not associated with increased CVD risk (p for trend 0.078 and 0.361, respectively). Cancer survivors who reduce PM2.5 exposure may reduce their risk of developing CVD.

In this study, we introduce a neural-learning based approach for the early detection of Parkinson Disease using DaT SPECT imaging, a promising technique in neurodegenerative disease diagnosis. Leveraging the advanced capabilities of deep learning, we developed a convolutional neural network model based on the Inception V3 architecture, which has been enhanced by modulating custom layers to fine-tune the model specifically for the analysis of DaT SPECT images. The imaging dataset was obtained through the Parkinson's Progression Markers Initiative (PPMI), and the model was trained to classify whether a patient had Parkinson Disease through a binary classification of their DaT SPECT Scan using the fine-tuned InceptionV3 model. This specialized adaptation allows for more accurate and nuanced interpretation of the scans, a crucial factor in early and reliable diagnosis of Parkinsonian syndromes. Our model underwent rigorous hyperparameter tuning, focusing on learning rates, the number of neurons in the dense layer, and dropout rates, to ensure optimal performance. Remarkably, the proposed model demonstrated a significant improvement in accuracy, achieving 74.65%, compared to a standard Sequential Model, which only reached an accuracy of 51.41%. Diagnosis of Parkinson Disease through DaT SPECT Scans is a promising approach not only as a more effective diagnostic tool but also as a beacon for future research in the application of deep learning to medical imaging for neurodegenerative diseases. By building layers on the InceptionV3 Architecture, we achieve a higher accuracy on DaT SPECT Scan diagnosis and pave the way for automated diagnosis of patients with Parkinson Disease, allowing for easier medical accessibility and paving the way for advancements in early diagnosis and personalized medicine strategies for Parkinson's disease and related disorders.

Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), defines as an idiopathic, chronic, relapsing, inflammatory disease affecting the gastrointestinal tract and leading to chronic damage. Endoscopy with biopsies is considered the gold standard for IBD diagnosis, whereas magnetic resonance (MRI) for CD extension and complication assess-ment. However, the latter are invasive procedures; thus, the need for a reliable, accessible and noninvasive way to perform IBD diagnosis and monitoring in the tight control era, like intestinal ultrasound (IUS) is. Compared to endoscopy and MRI, IUS has shown reliable diagnostic accuracy in assessing CD and UC severity and extension. Performing IUS in a point-of-care setting (POCIUS) can guide the clinician in driving the diagnostic and therapeutic pathway, thus accel-erating clinical decisions. POCIUS performed with pocket devices can represent the future of physical examination in outpatient or inpatient examination. Accordingly, we performed a liter-ature review about the application of IUS in CD and UC diagnosis. Furthermore, we explored the technical aspects of this imaging technique and its application in a point-of-care setting.

Alzheimer's Disease (AD) represents the most frequent type of dementia in elderly people. There are two major forms of the disease: sporadic (SAD) - whose causes are not completely understood - and familial (FAD) - with clear autosomal dominant inheritance. The two main hallmarks of AD are extracellular deposits of amyloid-beta (Ab) peptide and intracellular deposits of the hyperphosphorylated form of the tau protein (P-tau). An ever-growing body of research supports the infectious hypothesis of sporadic forms of AD. Indeed, it has been documented that some pathogens, such as herpesviruses and certain bacterial species, are commonly present in AD patients, prompting recent clinical research to focus on the characterization of Antimicrobial Peptides (AMPs) in this pathology. Literature also demonstrates that Ab can be considered itself as an AMP thus representing a type of innate immune defense peptide that protect the host against a variety of pathogens. Beyond Ab, other proteins with antimicrobial activity, such as lactoferrin, defensins, cystatins, thymosin β4, LL37, histatin 1 and statherin have been shown to be involved in AD. Here we have summarized and discussed these findings and explored the diagnostic and therapeutic potential of AMPs in AD.

Tick-borne diseases are a growing concern worldwide, affecting both human and animal populations. Ticks are known to harbor a wide range of pathogens and are considered one of the most important vectors of disease. Lyme disease, caused by Borrelia burgdorferi sensu lato, is the most common tick-borne disease in the US and Europe. However, accurate diagnosis of Lyme disease can be challenging due to the complex immune evasion strategies employed by Borrelia species and the limitations of existing diagnostic tests. To address this issue, researchers are exploring novel approaches, including the use of bacteriophages as diagnostic tools. Bacteriophages are highly specific and offer advantages over traditional methods for detecting bacteria, including Borrelia. In particular, the use of multicopy bacteriophages as molecular markers for Borrelia detection is a promising approach that may provide greater sensitivity than targeting single-copy bacterial genes. Nonetheless, the task of identifying trace amounts of bacteriophages in blood samples necessitates attention, and scientists are devising innovative techniques to surmount this hurdle. In summary, employing bacteriophages as a diagnostic tool for tick-borne diseases, such as Lyme disease, by specifically targeting free circulating bacteriophages in blood samples, offers significant potential for enhancing patient outcomes and public health.

Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

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

Lyme disease, the leading vector-borne ailment in the U.S., annually affects an estimated 476,000 individuals, predominantly in the Northeast and Upper Midwest. Despite its increasing incidence, the evaluation of risk within U.S. cities, including natural public lands, remains inadequate. This study focuses on blacklegged tick occurrences and Borrelia burgdorferi infection prevalence in 24 Staten Island parks, aiming to assess Lyme disease exposure risk. Monthly acarological risk index (ARI) calculations from 2019 to 2022 revealed elevated values (0.16-0.53) in specific parks, notably Wolfe’s Pond Park, High Rock Park, Clay Pit Pond Park, Clove Lake Park, and Fair View Park. June (0.36) and November (0.21) consistently exhibited heightened ARIs, aligning with peak tick collection months. Despite stable yearly infection rates at 28.97%, tick densities varied significantly between parks and years. Identifying a high transmission risk in specific parks in Staten Island, a highly urbanized part of New York City, emphasizes the continuous necessity for Lyme disease risk management, even within the greenspaces of large cities.

Congenital heart disease (CHD) is among the 4 more common causes of infant mortality in Latin America. Pulse oximetry screening (POS) is useful for early diagnosis and improved outcomes of critical CHD. We describe POS implementation efforts in Latin American countries guided and/or coordinated by the Ibero American Society of Neonatology (SIBEN) as well as the unique challenges that are faced for universal implementation. SIBEN collaborates to improve neonatal quality of care and outcomes. A few years ago, a Clinical Consensus on POS was finalized. Since then, we participated in 12 Latin American countries to educate neonatal nurses and neonatologists on POS and to help with its implementation. The findings reveal that despite wide disparities in care that exist between and within countries, and the difficulties and challenges for implementing POS, significant progress was made. We conclude that universal POS is not easy to implement in Latin America but, when executed, not only it has been of significant value for babies with CHD but also for many with other hypoxemic conditions. The successful and universal implementation of POS in the future is essential to reduce the mortality associated with CHD and other hypoxemic conditions and will ultimately lead to the survival of many more Latin American babies. POS saves newborns’ lives in Latin America.

Darier disease (DD) is an autosomal dominant disorder due to mutation of ATP2A2 gene causing an isolated skin manifestation based on keratinocyte disconnection and apoptosis. Systemic manifestations of DD have not been demonstrated so far although a high incidence of neuropsychiatric syndromes suggest an involvement of central nervous system. A Novel ATP2A2 Gene Mutation c.118G>A, causing keratinocyte and cardiomyocyte disconnection in Darier disease, is reported. Their common pathologic pathway, still unreported, was documented by both skin and left ventricular endomyocardial biopsy because of cardiac dilatation and dysfunction appeared several decades after skin manifestations.Keratinocyte disconnection was paralleled by cardiomyocyte separation at lateral junction. Cardiomyocyte separation was associated to cell disarray, sarcoplasmic reticulum dilatation and increased myocyte apoptosis. Clinically, hyperkeratotic skin papules were associated to chest pain, severe muscle exhaustion and ventricular arrhythmias that improved following administration of aminophylline, a phosphodiesterase inhibitor enhancing SERCA2 protein phosphorylation.Specific mutations of ATP2A2 gene may cause cardiac involvement in patients with DD. Cardiac pathologic changes are similar to those documented in the skin including cardiomyocyte disconnection that promotes precordial pain and cardiac arrhythmias. Phosphodiesterase inhibitors, enhancing SERCA2 protein phosphorylation may substantially attenuate the symptoms.

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.

With the rising prevalence of obesity, Non-Alcoholic Fatty Liver Disease (NAFLD) now affects 20-25% of the global population. NAFLD, a progressive condition associated with oxidative stress, can result in cirrhosis and liver cancer in 10% and 3% of patients suffering NAFLD, re-spectively. Therapeutic options are currently limited, emphasizing the need for novel treatments. In this study, we examined the potential of activating the transcription factor NRF2, a crucial player in combating oxidative stress, as an innovative approach to treating NAFLD. Utilizing a CRISPR-engineered human cell line, we were able to monitor the expression of heme oxygenase-1 (HMOX1), an NRF2 target, using a Nanoluc luciferase tag. Our model was validated using a known NRF2 activator, after which we screened 1200 FDA-approved drugs, unearthing six compounds (Disulfiram, Thiostrepton, Auranofin, Thimerosal, Halofantrine, and Vorinostat) that enhanced NRF2 activity and antioxidant response. These compounds demonstrated protec-tive effects against oxidative stress and lipid accumulation in vitro. Our study underscores the utility of CRISPR tagging with Nanoluc luciferase in identifying potential NRF2 activators, pav-ing the way for potential NAFLD therapeutics.

Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in pre-venting adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an Ensemble Heuristic-Metaheuristic Feature Fusion Learning (EHMFFL) algorithm for heart disease diagnosis. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic-metaheuristic approach that integrates the heuristic knowledge of Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning, aimed at constructing a robust prediction model. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art machine learning, ensemble learning, and feature selection techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.

Background: Both hypertension and carotid atherosclerosis are independent risk factors for cardiovascular disease. Understanding their combined impact could enhance risk assessment and management strategies. Hypothesis: We hypothesized that the synergistic effects of hypertension and carotid plaques would increase the risk of both cardiovascular events and all-cause mortality. Methods: A follow-up study was conducted at the Preventive Cardiology department of Vilnius University Hospital Santaros klinikos between 2012 and 2021. The study recruited participants aged 40-60 who did not have overt CVD and were part of the Lithuanian High Cardiovascular Risk primary preventive program. The study collected demographic and clinical data, including an ultrasound assessment of carotid plaque. The participants were monitored for 4-10 years for CVD events and all-cause mortality. Results: Among 6,138 participants, 954 (16%) experienced cardiovascular disease (CVD) events. The presence of carotid plaque on both sides was significantly associated with CVD events, myocardial infarction, and all-cause mortality. However, the combination of hypertension and carotid plaque did not significantly increase the risk for CVD events, stroke, or all-cause mortality. Conclusion: The combination of hypertension and carotid plaque did not significantly increase the risk of CVD events or all-cause mortality. CV events depend on the spread of the atherosclerosis process in the carotid vessels.

A method for classification is introduced in this article, and it is tested on ADNI database to diagnose alzheimer’s disease (AD). It is obvious that tunning the performance of a classification to get better results is a complicated problem, and when we want model’s accuracy or other peformance measurments higher than 90%, the problem will be more complicated. In this study, we tried and succeeded to discover a method to solve this problem. The final feature set can be used clustering too, because outgrowth feature set of the proposed method is invigorated. In the recent years, a lot of activities is done to develop computer aided systems (CAD) for alzheimer’s disease diagnosis. Most of these recently developed systems concenterated on extracting and combining features from MRI, PET, CSF, and …; in this article, we made attempt to do so and utilized one more technique to increase classification performance. Finding and producing the best features to solve three binary classification problems of AD vs. Normal Control (NC), Mild Cognitive Impairment (MCI) vs. NC, and MCI vs. AD are the purposes of this article. Experiments indicate performance and effectiveness rates of the proposed method, which are accuracies of 98.81%, 81.61%, and 81.40% for AD vs. NC, MCI vs. NC, and AD vs. MCI classification problems, respectively. As can be seen, using this method increased the performance of the three binary problems incredibly.

Annually, more than 1.5 million preventable new hepatitis B (HBV) infections continue to occur, with an estimated global burden of 296 million individuals living with chronic hepatitis B infection. This substantial health challenge results in over 820,000 annual deaths attributed to complications such as liver cirrhosis and hepatocellular carcinoma (HCC). HBV vaccination remains the cornerstone of public health policy to prevent chronic hepatitis B and its related complications. It serves as a crucial element in the global effort to eliminate HBV, as established by the World Health Organization (WHO), with an ambitious 90% vaccination target by 2030. However, reports on global birth dose coverage reveal substantial variability, with an overall coverage rate of only 46%. This comprehensive review thoroughly examines global trends in HBV vaccination coverage, investigating the profound impact of vaccination on HBV prevalence and its consequences across diverse populations, including both high-risk and general demographics. Additionally, the review addresses the formidable challenges and facilitating factors essential for achieving WHO's HBV vaccination coverage objectives and elimination strategies in the coming decade and beyond.

Keywords: Oral health, diabetes, periodontal disease, caries, periodontitis.

The pharmacological effects of the fruiting body of Ophiocordyceps sinensis (Os) exerted antioxidant, anti-virus, and immunomodulatory, whereas the anti-inflammatory effects and active components of Os mycelial submerged culture (OMSC) are not known. This study aimed to investigate the active compounds in the fermented liquid (FLOS), hot water (WEOS), and 50-95% (EEOS-50, EEOS-95) ethanol extracts of OMSC and their anti-inflammatory effects and potential mechanisms in LPS-stimulated microglial BV2 cells. The results demonstrated that all of the OMSC extracts could inhibit NO production in BV2 cells. EEOS-95 exhibited the strongest inhibitory effects (41 % inhibitory ability at 500 µg/mL), and its ergosterol, GABA, total phenolic, and total flavonoid contents were significantly higher than those of the other extracts (18.60, 18.60, 2.28, and 2.14 mg/g, p &lt; 0.05, respectively). EEOS-95 also has a strong inhibitory ability against IL-6, IL-1β and TNF-α with IC50 of 617, 277 and 507 µg/mL, respectively, which is higher than that of 1 mM melatonin. The anti-inflammatory mechanism of EEOS-95 seems to be associated with up-regulation of PPAR-γ/Nrf-2/HO-1 antioxidant-related expression and down-regulation of NF-κB/COX-2/iNOS pro-inflammatory expression signaling. Therefore, we hypothesized that EEOS-95 can prevent neurodegenerative diseases, but the effect on the application in vivo should be further investigated.

Background: The purpose of this article is to update the diagnostic assessment, therapeutic approach, and 12-18 month follow up of patients added in the Italian Lombardy ADHD Register. Methods: Data on patients evaluated by the 18 Regional ADHD Reference Centres in the ten year period from 2011 to April 2021 were analysed. Results: 4091 of 5934 added patients received a diagnosis of ADHD. In 20.3% of cases, there was a family history of ADHD. 2879 children (70.4%) had at least one comorbidity disorder, the most common of which was learning disorder (39%). Nearly all (95.9%) received at least one psychological prescription, 17.9% of them almost one pharmacological treatment, and 15.6% a combination of both. Values of ≥5 of the Clinical Global Impressions- Severity (CGI-S) are more commonly presented by patients with a pharmacological prescription than with a psychological treatment (p < .0001). A significant improvement was reported in half of the patients followed after 1 year, with Clinical Global Impressions- Improvement (CGI-I ≤ 3). In all, 233 of 4091 are 18 year old patients. Conclusion: A ten year systematic monitoring of models of care was a fruitful shared and collaborative initiative in order to promote significant improvement in clinical practice, providing effective and continuous quality of care. The unique experience here reported should spread.

The coronavirus disease 2019 (COVID-19) is a novel global pandemic infectious disease with higher potential for outbreaks than the other epidemic disease such as severe acute respiratory syndrome (SARS), influenza A (H1N1), and the Middle East respiratory syndrome (MERS), which identified in China on December 31, 2019. This disease is caused by a new generation of betacoronavirus termed as the 2019 novel coronavirus (2019-nCoV) or SARS-CoV-2. Although, the first report of this disease was in recent months, now, the COVID-19 is known as a global pandemics. Hence, the aim of this article is the quick review of the recent studies on the novel coronavirus disease 2019 including researches on the epidemiological parameters, mechanism of action, diagnosis, and treatment of the novel coronavirus disease, as well as clinical features of patients infected with COVID-19. Moreover, the novel COVID-19 has comprised of SARS, H1N1, and MERS.

Assessment of renal function is critical to diagnose and manage renal age-related decline, disease (KD), and failure, which are prevalent in the elderly population. Glomerular filtration rate (GFR) is widely used as an indicator of kidney function, but its direct measurement is challenging, as are its age and gender caveats. This makes difficult the differential diagnosis, between age-related physiological decline and KD and/or failure. Currently, the inflammation-based modified Glasgow prognostic score (mGPS) is emerging as promising biomarker of several inflammatory acute/chronic diseases. In this study, the large variability of eGFR with age and gender was evaluated, as the association of eGFR values with mGPS levels. A population of 57449 adult participants (age ≥18 years) was enrolled. Appropriate circulating biomarkers were measured to detect eGFR and mGPS values. The data obtained demonstrated a significant decrease of eGFR in men vs. women across the four selected age classes (18-40, 40-60, 60-80, 80-100 years), eGFR classes were significantly associated with mGPS (p<0.001), as age-classes and gender with mGPS categories. Accordingly, the percentage of people having a mGPS score= 2 significantly increased across the eGFR classes: with a 11% in the G1/eGFR class to achieve the 44% in G5/eGFR. Thus, the combination of mGPS with eGFR could represent the best benchmark risk model for differentiating age-related renal decline from a very KD, and for a better identification of different degrees of KD severity.

Apple replant disease (ARD) is a complex issue caused by various fungal, oomycetes pathogens and parasites which can differ in their abundance between sites within a geographic region. Knowledge of the specific etiology of ARD at a particular site is required in order to develop commercially viable soil management strategies to combat specific/individual components of the disease. In this study we analyzed the soil from six ARD sites for the presence of fungal, bacterial and oomycetes communities and plant parasitic nematodes. Five fungal, and 17 bacterial classes were differentially represented in the microbiomes from different locations. Mortierellomycetes was the most abundant fungal taxa represented followed by Sordariomycetes. Mortierella exigua a fungal endophyte was the most abundant fungal amplicon sequence variant (ASV) in the core microbiome. Proteobacteria was the most prevalent phylum identified in orchard soils. Several potential phytopathogenic fungi involved in ARD and endophytes including Fusarium oxysporum, F. solani, Nectria ramulariae, Ilyonectria robusta and Nectriaceae were identfied in the orchard soil. Pythium attrantheridium (Globisporangium attrantheridium), P. monospermum and P. ultimum (Globisporangium ultimum) were the most abundant oomycete taxa identified in these soil samples. Six different groups of plant-parasitic nematodes were found across the six orchards soil. Root-lesion nematodes, Pratylenchus spp., which are commonly associated with ARD, were identified in all orchards soil at population densities ranging from 12 to 33/100 cm3 soil. This research contributes valuable understanding of the ARD complex as the apple industry needs alternative approaches to combat the disease.

Background: Amyloid beta peptides (Aβ) have been identified as the main pathogenic agents in Alzheimer’s disease (AD). Soluble Aβ oligomers, rather than monomer or insoluble amyloid fibrils, show membrane-binding capacity to red blood cells (RBCs) and trigger several morphological and functional alterations in RBCs that can result in impaired oxygen transport and delivery. Since bioactive lipids have been recently proposed as potent protective agents against Aβ toxicity, we investigated the role of sphingosine-1-phosphate (S1P) in signaling pathways involved in the mechanism underlying ATP release in Aβ-treated RBCs. Methods: In RBCs following different treatments, ATP, 2,3 DPG, cAMP levels, and caspase 3 activity were determined by spectrophotometric and immunoassay. Results: S1P rescued the inhibition of ATP release from RBCs triggered by Aβ through a mechanism involving caspase-3 and restoring 2,3 DPG and cAMP levels within the cell. Conclusions: These findings reveal the molecular basis of S1P protection against Aβ in RBCs and suggest new therapeutic avenues in AD.

Interstitial lung disease (ILD) is one of the most serious extra-articular complications of rheu-matoid arthritis (RA), which increases the mortality of RA. Because the pathogenesis of RA-ILD remains poorly understood, appropriate therapeutic strategies and biomarkers have not yet been identified. Thus, the goal of this review was to summarize and analyze the reported data on the etiology and pathogenesis of RA-ILD. The incidence of RA-ILD increases with age, and is also generally higher in men than in women and in patients with specific genetic variations and eth-nicity. Lifestyle factors associated with an increased risk of RA-ILD include smoking and exposure to pollutants. The presence of an anti-cyclic citrullinated peptide antibody, high RA disease activity, and rheumatoid factor positivity also increase the risk of RA-ILD. We also explored the roles of biological processes (e.g., fibroblast–myofibroblast transition, epithelial–mesenchymal transition, and immunological processes), signaling pathways (e.g., JAK/STAT and PI3K/Akt), and the his-topathology of RA involved in RA-ILD pathogenesis based on published preclinical and clinical models of RA-ILD in animal and human studies.

The main objective of this work is the assessment of the annual number of hospital admissions for respiratory disease (HARD) due to the exposure to in-healable particulate matter (PM10), within the greater Athens area (GAA), Greece. Towards this aim, the time series of the particulate matter with aerodynamic diameter less than 10μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001-2013, is used. Initially, a descriptive statistical treatment of PM10 concentrations took place. Furthermore, the AirQ2.2.3 software developed by the WHO was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, during the examined period PM10 concentrations present a significant decreasing trend. Also, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban location) and 40 (city centre location). Approximately 70% of the annual HARD cases are due to city centre residents. In all examined locations, a declining trend in the annual number of HARD cases is appeared. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found.

The association between normal-range triglycerides and diabetes mortality remains unclear. This cohort study aimed to elucidate this relationship by examining 19,010 US adult participants with fasting serum triglycerides below 150 mg/dL. Cox proportional hazards models were employed to estimate mortality hazard ratios (HRs) and 95% confidence intervals (CIs). Participants were followed up for a mean of 15.3 years, during which 342 diabetes deaths were recorded. A 1-natural-log-unit increase in triglycerides was associated with a 57% higher risk of diabetes mortality (adjusted HR, 1.57; 95% CI, 1.04–2.38). Comparable results were obtained when triglycerides were analyzed in quartiles. Receiver operating characteristic curve analysis identified an optimal triglyceride cutoff of 94.5 mg/dL for diabetes mortality; individuals with triglyceride levels above this threshold faced a greater risk of diabetes mortality (adjusted HR, 1.43; 95% CI, 1.12–1.83). Further investigation revealed a positive association between normal triglyceride levels and all-cause mortality, though no association was observed between normal triglycerides and mortality from hypertension or cardiovascular disease. In conclusion, elevated triglycerides within the normal range were associated with an increased risk of diabetes mortality. Individuals with triglyceride levels of 95 mg/dL or higher may require vigilant monitoring for diabetes and its associated complications.

Background and Objectives: High job strain is worsening the outcome in coronary artery disease, CAD. Severe dissatisfaction on workplace, imbalance between high demands , and low rewards, are affecting the quality of life, and diminishing the duration of life, especially in CAD patients. Our study included two samples : CAD patients (experimental group), and without CAD patients, but with traditional risk factors (control group). The main objective of the study was the role played by high sensitivity C reactive protein, hs CRP, and microalbuminuria, as mediators of job strain and CAD relationship. Secondary objectives were the following: identification of the most vulnerable patients( with significant job strain) and intervention towards this category, for cardiovascular events prevention (primary or secondary). Materials and Methods: The utilized methods were the following : clinical exam; electrocardiogram; echocardiogram; non contrast enhanced computed chest tomography; laboratory findings ( lipid profile, glycemia, hs CRP, microalbuminuria); type D scale, DS 14; depression questionnaire, PHQ-9; and satisfaction with work scale, SWWS. Results: The highest values for hs CRP and microalbuminuria were noticed in patients with the lowest Likert score, for both samples CAD, and without CAD patients. The highest job strain was noticed in females. More than half of CAD patients ,with low Likert scores, revealed myocardial infarction. Conclusions: Microalbuminuria and hs CRP were statistical significant in the study as intercessors, in job strain and CAD relationship. The most vulnerable patients for vascular events were females, in both groups : CAD and “CAD waiting desk”. In CAD sample, the vascular vulnerability was also increased by high job strain.

Background and Objectives: Wilson disease (WD) is a disorder of copper metabolism caused by genetic muta-tions in the ATP7B gene which lead to the accumulation of copper in the body. The aim of the present study is to investigate the severity of WD in patients in Romania. Materials and Methods: This study was conducted us-ing an online questionnaire consisting of 32 questions in a group of patients suffering from Wilson disease. Results: The study included 67 people aged 7 to 56 with WD. The most common symptoms of the participants were hepatic and neurological in 22 cases (32.8%). The most common neurological symptom in the assessed cases was muscle stiffness (49.5%), followed by tremor (45%), and speech disorders (40.5%). The Kay-ser-Fleischer ring was present in 50.7% of the participants. The most commonly used drug treatment in the participants was D-penicillamine (77.61%). Conclusions: The severity of WD is based on many aspects, the most important being early diagnosis and access to treatment throughout the year. The prognosis is favorable with early diagnosis and treatment, but it is crucial to diagnose individuals before they develop major symp-toms. Therefore, advances in WD screening can lead to an earlier diagnosis and better results.

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, neglected tropical diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context.Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives.Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics.In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa.Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies.Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

In Brazil, Brazilian borreliosis (BB), characterized molecularly, exhibits symptoms akin to Lyme disease (LD) but presents unique epidemiological, clinical, and morphological features. This study explores the incidence, diagnostic challenges, and awareness initiatives regarding this disease in Brazil. Employing a narrative review methodology, data were sourced from Pubmed, Ebsco, Google Scholar, and Web of Science without temporal constraints. Results reveal diagnostic complexities in Brazil due to the low sensitivity of conventional tests and the use of North American B. burgdorferi antigens. Despite challenges, epidemiological studies indicate an upswing in cases in humans and animals. Therefore, there is an urgent need to define new biomarkers, molecular strategies, or new antigenic targets to advance the development of vaccines and diagnostics. Early and accurate pathogen identification is crucial for active surveillance to comprehend and manage this zoonosis.

Chronic kidney disease patients treated by hemodialysis present a high cardio-vascular morbidity and mortality. There is an imperative need for novel biomarkers in identifying and to offer possible therapeutically intervention in these patients. We performed a prospective observational cohort study on 77 patients in the period October 2021 – October 2023. We measured serum plasma levels of Interleukin 1-Beta, Galectin 3, human suppression of tumorigenicity factor 2, bone morphogenetic protein 2 and fibroblastic growth factor 23 at the inclusion. We evaluated the correlations of these biomarkers with cardiac function and structure evaluated by echocardiography. Mean age was 61.02 (±11.81) years, 45 (56.2%) males and with a dialysis vintage of 4.95 (2.4-7.8) years. Median ejection fraction was 51 (43-54 %) and more than two thirds of the patients presented valvular calcifications. Overall mortality was 22%. Interleukin 1-Beta correlated positively with ejection fraction and global longitudinal strain and negatively with left atrium diameter and left ventricle tele systolic diameter. Galectin 3 values negatively correlated with aortic valve fibrosis and mitral valve calcifications and human suppression tumorigenity factor 2 negatively correlated with mitral valve calcifications. Some of these novel biomarkers could be used to better asses the cardio-vascular disease in patients on maintenance hemodialysis.

Rice, as a staple crop feeding billions, faces constant threats from various diseases jeopardizing global food security. Precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. Integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with better understanding of their functional roles in disease resistance. Improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can give a transformative impact on rice breeding for enhancing disease resistance.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

LC-SPIK is a liver cancer-specific isoform of Serine Protease Inhibitor Kazal and has been proposed as a new biomarker for the detection of HCC given its unique 3-D structure which differs from normal pancreatic SPIK. An ELISA technology based on its unique structure was developed to use LC-SPIK as an effective biomarker for the clinical diagnosis of HCC. AFP, the most widely used biomarker for HCC surveillance currently, suffers from poor clinical performance, especially in the detection of early-stage HCC. In one case-control study, which included 164 HCC patients and 324 controls, LC-SPIK had an AUC of 0.87 compared to only 0.70 for AFP in distinguishing HCC from liver disease controls (cirrhosis, HBV/HCV). LC-SPIK also performed significantly better than AFP for the 81 patients with early-stage HCC (BCLC stage 0 and A), with an AUC of 0.85 compared to only 0.61 for AFP. Cirrhosis is the major risk factor for HCC, about 80% of patients with newly diagnosed HCC have preexisting cirrhosis. LC-SPIK’s clinical performance was also studied in HCC patients with viral and non-viral cirrhosis including cirrhosis caused by nonalcoholic fatty liver disease (NAFLD) and alcoholic liver disease (ALD). In a total of 163 viral cirrhosis patients with 93 HCC patients (50 early-stage), LC-SPIK had an AUC of 0.85, while AFP had an AUC of 0.70. For patients with early-stage HCC, LC-SPIK had a similar AUC of 0.83, while AFP had an AUC of only 0.60. For 120 patients with nonviral cirrhosis, including 62 HCC (23 early-stage) patients, LC-SPIK had an AUC of 0.84 while AFP had an AUC of only 0.72. For the 23 patients with early-stage HCC, LC-SPIK had a similar AUC of 0.83, while the AUC for AFP decreased to 0.65. All these results suggest that LC-SPIK has significantly better performance in the detection of HCC than AFP in all etiologies of liver diseases. In addition, LC-SPIK accurately detected the presence of HCC in 71%-91% of HCC patients with false-negative AFP test results in viral associated HCC and non-viral associated HCC.

Pulmonary hypertension (PH) is a progressive and potentially fatal complication of sickle cell disease (SCD) affecting 6-10% of adult SCD patients. Various mechanisms and theories have been evaluated to explain the pathophysiology of the disease. However, questions remain, particularly regarding the clinical heterogeneity of the disease in terms of symptoms, complications, and survival. Beyond the classical mechanisms that have been thoroughly investigated and include hemolysis, nitric oxide availability, endothelial disorders, thrombosis, and left heart failure, attention is focused nowadays on the potential role of the genes involved in such processes. Potential candidate genes are investigated through next-generation sequencing, with the TGF-β pathway being the initial target. This field of research may also provide novel targets for pharmacologic agents in the future as is already the case with idiopathic PH. The collection and processing of data and samples from multiple centers can yield safe results that will allow a better understanding of SCD-related PH as a part of the disease’s clinical spectrum. The following review attempts to capture the most recent findings of studies on gene polymorphisms that have been associated with PH in SCD patients.

Visual exploration of complex data helps interpretation, especially in the case of omics data analysis in life sciences and clinical research. Generating and analysing omics data requires bioinformatic skills, while they are interpreted by domain experts, for whom parsing large and complex data structures may be challenging. However, outcomes of visual analytics are often difficult to quantify, which emphasises precision and reproducibility, especially for research on human diseases. Here, we propose a workflow combining a reproducible computational environment with a dedicated visualisation functionality for systems biology diagrams. By linking the Galaxy with the MINERVA Platform, we visualise and explore COVID-19 transcriptomic data to demonstrate the utility of our workflow. Visualised data recapitulate findings of the original publication and offer new insights about the COVID-19 pathology. Our results offer a blueprint for quick prototyping of computational workflows that facilitate communication and exploration of complex data in biomedical research.

Alzheimer’s disease (AD) poses a significant public health challenge due to its irreversible and progressive nature while lack of a cure. As a potential strategy to combat AD, its prevention becomes increasingly attractive. Mild Cognitive Impairment due to AD (MCI-AD) represents a critical transitional stage between normal age-related cognitive decline and more severe AD conditions, occurring just before dementia onset. Unfortunately, there is currently no established animal model that accurately recapitulates MCI-AD characteristics. While many laboratories have traditionally used normally aged wild-type animals as experimental models, this approach falls short in representing the inherently worse state of MCI-AD compared to normal aging. To address this gap, we introduce an animal model—a transgenic mouse line with genetic inactivation of G protein-coupled receptor kinase-5 (GRK5), commonly known as the GRK5 knockout (GRK5KO) mouse. These GRK5KO mice exhibit amnesia, cognitive deficits, increased β-amyloid levels, neurofibrillary tangle (NFT) immunopositive axonopathy, and hippocampal neurodegenerative changes. Importantly, these pathological alterations predominantly impact the entorhinal, transentorhinal, and hippocampal cortices, aligning with human MCI-AD criteria and Braak stage II of human AD progression. Notably, female GRK5KO mice show approximately 2.5 times more NFT-positive axonopathy than males, mirroring the higher prevalence of AD cases in women. Collectively, existing data strongly supports the GRK5KO mouse as a qualified animal model for studying MCI-AD.

Alzheimer’s Disease (AD) affects the quality of life of millions of people worldwide and represents one of the biggest challenges for the whole society. The SH-SY5Y neuroblastoma cell line is often used as an in vitro model of neuronal function and is widely applied to study the molecular events leading to AD. In the last few years, basic research on SH-SY5Y cells has provided interesting insights for the discovery of new drugs and biomarkers for improved AD treatment and diagnosis. At the same time, untargeted NMR metabolomics is widely applied on biological fluids for (i) metabolic profile analysis, (ii) screening for differential metabolites, (iii) analysis of metabolic pathways, and (iv) the discovery of new biomarkers. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have proved to be powerful methods for processing NMR data, being useful in signal quantization, even if more sophisticated --- typically non--linear --- techniques are needed to obtain compact yet information--rich embeddings for complex spectra. In this paper, a compression technique based on convolutional autoencoders is proposed, which can perform a high dimensionality reduction of the spectral signal (up to more than 300 times), maintaining informative features (guaranteed by a reconstruction error always smaller than 5%). Moreover, before compression, an ad hoc preprocessing method was devised to remedy the scarcity of available data. The compressed spectral data were then used to train some SVM classifiers to distinguish diseased from healthy cells, achieving an accuracy close to 78%, a significantly better performance with respect to using PCA--compressed data.

Pregnancy is a complex biological process which is associated with significant metabolic and hemodynamic changes of the maternal body which allow a normal fetal growth. The inability to adapt to these changes can affect the cardiovascular health. During pregnancy, women may experience mood swings, anxiety and emotional ambivalence. These symptoms can lead to stress and have a negative impact on the mental well-being of expectant mothers. It is crucial to know the aspects that can influence the development of cardiovascular problems among pregnant women in order to manage these cases properly. The current narrative review explores and summarizes the evidence regarding the impact of stress and anxiety on cardiovascular health in pregnant women. Understanding the physiological adaptations that the body undergoes throughout pregnancy is crucial for educating and supporting individuals as well as healthcare professionals. This enables them to recognize any risk factors or pathological conditions that may exacerbate pregnant women's health. In order to reduce maternal morbidity and mortality from cardiovascular disease, a psychological and cardiovascular risk factor screening that is done either before or during pregnancy may be able to identify situations that call for particular medical and/or psychological therapies.

we describe an atypical case of Whipple disease involving exclusively the spinal cord in an adolescent receiving immunosuppressive therapy for systemic lupus erythematosus. The diagnosis was particularly difficult since lupus and Whipple disease can present similar clinical features and the patient’s prolonged contact with sewage was initially not mentioned. A literature review of the clinical, imaging, diagnostic, and therapeutic challenges of Whipple disease is also performed.

(1) Background: Cardiorespiratory fitness (CRF) is a prognostic factor regarding long time morbidity and mortality. To assess CRF the well-known gold standard is cardiopulmonary exercise testing (CPET), which is a time consuming and expensive test. Therefore, alternative methods for routine evaluation of CRF are needed for the general population as well as for specific patient groups such as children and young adults with congenital heart disease. The Stair Climbing Test (SCT) is a simple and resource saving test reported in the current literature. This test is used in various settings to assess different health associated risk factors including cardiorespiratory fitness, muscular resilience, therapy effectiveness or pre- and postoperative morbidity and mortality.

Chronic kidney disease (CKD) and cardiovascular disease (CVD) are highly prevalent conditions, each significantly contributing to the global burden of morbidity and mortality. CVD and CKD share a great number of common risk factor, such as hypertension, diabetes, obesity, and smoking among others. Their relationship extends beyond these factors, encompassing intricate interplay between the two systems. Within this complex network of pathophysiological processes, vitamin D has emerged as a potential linchpin, exerting influence over diverse physiological pathways implicated in both CKD and CVD. In recent years, scientific exploration has unveiled a close connection between these two prevalent conditions and vitamin D, a crucial hormone traditionally recognized for its role in bone health. This article aims to provide an extensive review of vitamin D's multifaceted and expanding actions concerning its involvement in CKD and CVD.

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

Mediterranean Diet (MD), rich in minimally processed plant foods and in monounsaturated fats, but low in saturated fats, meat and dairy products, represents one of the most studied diets for cardiovascular health. It has been shown, from both observational and randomized controlled trials, that MD reduces body weight, improves cardiovascular disease surrogates such as waist-to-hip ratio, lipids, and inflammation markers, and even prevents the development of fatal and non-fatal cardiovascular disease, diabetes, obesity, and other diseases. However, it is unclear whether it offers cardiovascular benefits from its individual components or as a whole. Furthermore, limitations in the methodology of studies and meta-analyses have raised some concerns on its potential cardiovascular benefits. MD is also associated with characteristic changes in the intestinal microbiota, mediated through its constituents. These include increased growth of species producing short-chain fatty acids, such as Clostridium leptum and Eubacterium rectale, increased growth of Bifidobacteria, Bacteroides and Faecalibacterium prausnitzii species, and reduced growth of Firmicutes and Blautia species. Such changes are known to be favorably associated with inflammation, oxidative status, and overall metabolic health. This review will focus on the effects of MD on cardiovascular health through its action on gut microbioma.

Background: Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. Objectives: To reach a deeper understanding of how patients themselves contribute to the diagnostic process. To describe the genesis of the LD hypothesis in care pathways. Methods: In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were : suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. Results: The diagnosis of Lyme disease was primarily triggered by identification with personal testimonies found on the internet. Most of patients were leading their own diagnostic investigation. The majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. Conclusion: GPs should first systematically explore patients’ etiologic representations in order to improve adherence to the diagnosis especially in the management of medically unexplained symptoms. Long COVID-19 syndrome challenge offers an opportunity to promote active patient involvement in diagnosis.

Abstract: The VSI represents a significant advancement in the assessment of gastrointestinal-specific anxiety among patients with Irritable Bowel Syndrome (IBS) and other Chronic Inflammatory Bowel Diseases (IBD). However, an Italian version of the instrument is not yet available for the reference population. This study utilized a national sample of 500 individuals, including healthy controls and patients with Ulcerative Colitis and Crohn's Disease, to test the validity and reliability of the Italian VSI. Using back-translation methodology to ensure translation fidelity, the research applied a questionnaire and the VSI through an online format to 500 participants. Data analysis revealed that the Italian VSI had excellent psychometric properties, demonstrating high internal consistency (Cronbach’s α = 0.95) and construct validity. The scale proved sensitive in detecting significant differences in visceral sensitivity among groups, highlighting its utility as a clinical and research assessment tool. Specifically, the Italian VSI exhibited a unidimensional factorial structure and maintained a strong correlation with other measures of anxiety and the severity of gastrointestinal symptoms, confirming its role in enhancing the understanding and management of IBS and IBD in Italy.

Cardiovascular disease (CVD) is the leading cause of death worldwide. Cardiac rehabilitation (CR) has proven to be effective in reducing the rate of recurrence and disease as secondary prevention (evidence-based intervention) in patients who have suffered from myocardial infarction (MI). CR is a multidisciplinary path in which the patient is followed pharmacologically, from a psychological, nutritional, nursing and physiotherapy point of view. Post-MI patients present a reduction in the performance of the diaphragm muscle, the main inspiratory muscle, and this condition can become a risk factor for further relapses or for the onset of heart failure. Despite the solidity of the international guidelines for CR, the latter are lacking in specifically indicating an evaluation and training path regarding the inspiratory muscles in post-MI patients who have not undergone cardiac surgery. The article reviews the information on the adaptation of the diaphragm post-MI and highlights the need for clearer indications for a rehabilitation process that gives importance to the diaphragm.

Background: Cardiovascular (CV) morbidity and mortality are many times higher in chronic kidney disease (CKD), as well as IgA nephropathy (IgAN), than in the general population. The occurrence of metabolic syndrome (MetS) and metabolic risk factors are independent risk factors for CV disease and renal progression. The study aimed to determine the prognostic role of metabolic profiles in a homogenous group of CKD patients. Methods: One hundred and forty-five IgA nephropathy patients (92 male, 53 female, age 54.7 ± 13 years) with CKD stage 1-4 were investigated and followed for a median of 190 months. The composite (CV and renal) endpoints included all-cause mortality and any CV event such as stroke, myocardial infarction, revascularization (CV), end-stage renal disease, and renal replacement therapy (renal). Results: Patients with MetS had significantly more endpoint events (16/27 patients vs. 15/98 patients, p = 0.001) compared to the nonMetS group. Of the secondary endpoints (CV or renal separately), the rate of the renal and CV endpoints was significantly higher in the MetS group (p = 0.001 and p = 0.029). The primary endpoint independent predictors of survival were dyslipidemia, eGFR, hemoglobin, urine albuminuria, and diabetes mellitus, as determined by Cox regression analysis. Independent predictors of secondary renal endpoint were dyslipidemia, hemoglobin, urine albumin, and eGFR. Predictors of secondary cardiovascular endpoints were gender, BMI, and diabetes. Kaplan-Meier curves showed significant differences in MetS and non-MetS when examined at the combined endpoints (CV and renal) or at each endpoint separately. The primary endpoint rate increased significantly with the increased number of MetS components (MetS comp. 0 vs. MetS comp. 2+, primary endpoints, p = 0.012). Conclusion: Our results showed that the metabolic profile has a prognostic role not only for renal endpoints but also for CV endpoints in IgAN. BMI, hyperuricemia, hypertension, and diabetes have a predictive value for the prognosis of IgA nephropathy.

Bovine respiratory disease (BRD) is the leading cause of morbidity and mortality in cattle raised in North America. At the feedlot, cattle are subject to metaphylactic treatment with macrolides to prevent BRD, a practice that may promote antimicrobial resistance and has resulted in an urgent need for novel strategies. Mannheimia haemolytica is one of the major bacterial agents of BRD. The inhibitory effects of two amphipathic, α-helical (PRW4, WRL3) and one β-sheet (WK2) antimicrobial peptides were evaluated against multidrug-resistant (MDR) M. haemolytica isolated from Alberta feedlots. WK2 was not cytotoxic against bovine turbinate (BT) cells by the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. All three peptides inhibited M. haemolytica, with WK2, being the most efficacious against multiple isolates. At 8-16 µg/ml, WK2 was bactericidal against Mh 330 in broth and at 32 µg/ml in the presence of BT cells, reduced the population by 3 logs CFU/ml without causing cytotoxic effects. The membrane integrity of Mh 330 was examined using NPN (1-N-phenylnaphthylamine), and ONPG (o-Nitrophenyl β-D-galactopyranoside) with both the inner and outer membranes being compromised. Thus, WK2 may be a viable alternative to the use of macrolides as part of BRD prevention and treatment strategies.

Acute and chronic graft versus host disease (a/cGVHD) are major complications of allogeneic stem cell transplantation and a frequent cause of non relapse-mortality. The treatment of a/cGVHD consists of steroid administration at full dose. In the case of aGVHD, around 60 % of patients with grade III/IV fails to respond (steroid-resistant, SR), requiring a second line therapy. Extracorporeal photopheresis (ECP) represents a good option for second line treatment, leading to multiorgan response in several patients. Here, we have evaluated the performances of a cytofluorimetric quality control of the first ECP procedure in 29 patients with severe SR aGVHD who underwent ECP. The quality control represents a check control of in vitro cell apoptosis/death induction in collected and irradiated CD3+ cells by ECP. Our analysis shows that in more than 70 % quality controls a relevant apoptotosis/death of CD3+ cells was observed after cell irradiation, as compared to non-irradiated controls. Albeit observed in a small patients’ series, the occurrence of relevant in vitro effect after ECP relates to less procedures and/or to a better survival on ECP. Age, disease, gender, aGVHD grade and time from transplant to the first ECP had not impact on ECP efficacy in this patients’ series.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

The domestic ducks (Anas platyrhynchos domesticus) are resistant to most of the highly pathogenic avian influenza virus (HPAIV) infections. In this study, we characterized the lung proteome and phosphoproteome of ducks infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala) at 12 hr, 48 hr and 5 days post infection condition. Total 2082 proteins were differentially expressed and 320 phosphorylation sites mapping to 199 phosphopeptides corresponding to 129 proteins were identified. Functional annotation of proteome data analysis reveals activation of RIG-I-like receptor and Jak-STAT signaling pathways, that lead to induction of interferon stimulated genes (ISGs) expression. The pathway analysis of phosphoproteome datasets also confirms the activation of RIG I, Jak-STAT signaling, NF-kappa B signaling and MAPK signaling pathways in the lung tissues. The induction of ISGs proteins (STAT1, STAT3, STAT5B, STAT6, IFIT5 and PKR) establishes protective antiviral immune response in duck lung tissue. Further, the protein-protein interaction network analysis identified proteins like AKT1, STAT3, JAK2, RAC1, STAT1, PTPN11, RPS27A, NFKB1 and MAPK1 as main hub proteins that might play important roles in disease progression in ducks. Together, functional annotation of proteome and phosphoproteome datasets reveals the molecular basis of the disease progression and disease resistance mechanism in ducks infected with HPAI H5N1 virus.

Middle-aged adults can start to be affected by some arterial diseases, such as abdominal aortic or popliteal artery aneurysms, lower extremity arterial disease, internal carotid or renal artery or subclavian artery stenosis. These vasculopathies are often paucisymptomatic before manifesting themselves with dramatic complications. Therefore, early detection of peripheral artery disease (PAD) and aneurysmal disease is fundamental to reduce the risk of major adverse cardiovascular and limb events. Furthermore, PAD carries a high correlation with silent coronary artery disease (CAD). This study focuses on the most common PADs and aneurysmal diseases, in the attempt to summarize some key points which should selectively drive screen. Since it is evident how we are not in the human and economic possibilities to instrumentally screening wide populations, deep knowledge of semeiotics and careful anamnesis must play a central role in our daily activity as physician. The presence of some risk factors for atherosclerosis, or an already known history of CAD, can raise the clinical suspecion of PAD or aneurysmal disease after a careful clinical history and a deep physical examination. The clinical suspicion must then be confirmed by a first-level ultrasound investigation and, if so, adequate treatments can be adopted to prevent dreadful complications.

Background:Caregiving for individuals with neurological disease involves providing physical, emotional, and social support, which can be a challenging and stressful experience. This study aims to compare the caregivers' burden, including the impact on mental and physical health, social support, and quality of life, in different neurodegenerative disorders. Methods:Fifty-five primary caregivers (F=41;mean age=53.07±12.29) of patients with Parkinson's Disease (PD=26), Multiple System Atrophy (MSA=16), and Amyotrophic Lateral Sclerosis (ALS=13), took part in the study. Standardized screening scales for the quality of life (EQ-5D/EQ-VAS and PQoL), levels of the caregiving burden (CBI and FSQ), and the assessment of the anxiety-depressive axis (HADS) were administered. Results: The mean CBI score was higher for caregivers of MSA patients (28.81±21.90) and ALS (25.15±16.23) than for caregivers of PD patients (16.08±10.34; p=0.036). Critical burden (cut-off>36) was present in the caregivers of MSA (31.3%), ALS (23.1%), and PD (19.2%) patients. Caregivers of patients with ALS had a worse perception of quality of life (EQ-5D total score) than caregivers of patients with MSA and PD (p

This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

Neuron degeneration is the most common old age complications worldwide. Alzheimer's disease (AD) is one of the most common neurodegenerative condition with limited drug options and significant adverse effects. Hence, there is a growing interest in exploring natural products for their potential pharmacological activities against AD. This study investigates the efficacy of Verbesina encelioides flower extract (VFE) in alleviating neurobehavioral damage induced by Scopolamine in rat mimicking AD model. GC-MS analysis showed that V. encelioides flower extract is rich in carotenoids like spirilloxanthin. Various behavioural models were used to assess the rats' learning and memory capabilities. Additionally, biochemical tests were conducted on the AD rat models to evaluate oxidative stress in the brain, including superoxide dismutase (SOD), nitric oxide (NO), reduced glutathione (GSH), and anti-acetylcholinesterase (AChE) enzyme assays. Findings from this study suggest that the VFE significantly improved the rats' cognitive aptitude and alleviated them from oxidative stress by reducing free radical generation. Based on the results obtained from GC-MS analysis, in silico interaction analysis of gingkolide A and spirilloxanthin with acetylcholinesterase was carried out. In summary, this study suggests that V. enceloides flower extract improves learning abilities and mitigates cognitive deficits caused by scopolamine. Thus V. enceloides could be potentially seen as natural therapeutic intervention against neurodegenerative disorders likes Alzheimer’s disease and dementia.

Introduction: Carcinoid heart disease is a rare cause of right-sided valvular dysfunction, an important complication in patients with carcinoid syndrome. Objectives: We aim to present our own experience regarding early myocardial dysfunction in patients with carcinoid syndrome. Methods: 64 patients (35-90 year old, 62,5% female, 37,5% male patients) with carcinoid syndrome were evaluated be-tween October 2017 and June 2021. All patients underwent two-dimensional echocardiography with additional tissue Doppler and speckle-tracking techniques. For all patients there were measured urinary level of 5- hydroxyindolacetic acid, serotonin and NT-proBNP values. Results: The following echocardiographic features: global longitudinal strain of both ventricles, mean systolic velocity of the left ventricle, systolic velocity of the free wall of right ventricle, correlated with high values of specific biomarkers: serotonin, and urinary 5-hydroxyindolacetic acid, and also with the presence of hepatic metastases and poorly differentiated tumors. Discussion: In this study we identified several patients with early myocardial dysfunction due to carcinoid syndrome, representing a predictive model of poor prognosis, requiring further investigations. Conclusion: Echocardiographic assessment based on the measurement of systolic velocities, as well as the global longitudinal strain of both ventricles, together with specific biomarkers may contribute to better recognition of carcinoid heart disease in clinical practice, but this needs validation through further studies enrolling larger number of patients.