Wernicke’s encephalopathy (WE) is a neurological emergency related to a severe thiamine (vitamin B1) deficiency, an essential cofactor in cerebral energy metabolism. Although historically associated with chronic alcoholism, this condition can occur in any context of malnutrition, prolonged vomiting, or hypercatabolism. We conducted a retrospective descriptive study on seven patients admitted to our neurology department between 2015 and 2020, in order to de-scribe the clinical, radiological, and outcome characteristics of this pathology. The diagnosis was made in the presence of suggestive signs (at least two among confusion, ataxia, oculomotor disorders), a risk context of deficiency or malnutrition, typical MRI abnormalities and/or rapid improvement after thiamine ad-ministration. Our series included two male patients with chronic alcohol consumption, and five pregnant women with severe hyperemesis gravidarum, with an average age of 32.4 years. Mental confusion was the most frequent sign, followed by gait disturbances and oculomotor abnormalities. The most characteristic MRI lesions involved the thalamus, the periaqueductal region, and the mammillary bodies. All patients received high-dose intravenous thiamine supplementation (500 mg every eight hours for three days), followed by oral maintenance therapy. The outcome was favorable in five cases, while two patients had persistent memory disorders. These observations confirm that WE is not limited to alcoholic forms and must be considered in any situation with nutritional risk. Early diagnosis and rapid administration of intravenous thiamine remain essential to prevent irreversible neurological sequelae and improve functional prognosis.

Background: Hepatocellular carcinoma (HCC) faces significant challenges in early diagnosis and prognostic assessment, necessitating novel molecular biomarkers. The role of GCN1 in tumorigenesis remains unclear, warranting systematic investigation of its clinical value. Methods: Utilizing multi-omics data from 164 HCC patients in the TCGA database, we comprehensively evaluated the diagnostic and prognostic value of GCN1 through differential expression analysis, Cox proportional hazards regression, and gene set enrichment analysis (GSEA). Results: GCN1 expression was significantly upregulated in tumor tissues (P<0.001), with ROC analysis demonstrating an AUC of 0.921 (95% CI: 0.893-0.950) for discriminating tumor from normal tissue. Clinical correlation analysis revealed that high GCN1 expression significantly associated with advanced T stage (OR=1.941, P=0.002) and AFP levels >400 ng/ml (OR=3.697, P<0.001). Multivariate survival analysis confirmed its independent prognostic value (HR=1.454, P=0.038). Functional analysis indicated GCN1 promotes tumor progression by regulating cell cycle (NES=2.385) and axon guidance (NES=2.307) pathways. Conclusion: This study first elucidates the dual clinical value of GCN1 in HCC, providing a theoretical foundation for developing novel diagnostic biomarkers and prognostic evaluation systems. Future research should validate its molecular mechanisms and explore potential targeted therapies.

Delirium is a major complication most commonly observed in patients with advanced cancer. However, despite its prevalence, the early diagnosis, management, and prevention of this condition have not seen significant progress. Aim of this research is to provide insights into the prevalence of delirium, the optimization of interventions for managing delirium symptoms, their effectiveness and the impact of underlying factors on the reversibility of delirium in advanced cancer patients receiving palliative care. The review involved systematic searches of relevant databases including MEDLINE, CINAHL, ProQuest Nursing and Allied Health, and PsychInfo using refined search terms. Eight publications out of 614 studies originally searched were selected and critically reviewed. Their quality was assessed using Joanna Briggs Institute's Critical Appraisal Tool for Case Series. Data abstraction and content analysis were performed to synthesize the findings. Delirium is prevalent among advanced cancer patients in palliative care, with rates ranging from 10.3% to 24.1%. Pharmacotherapy and non-pharmacological interventions showed effectiveness in reducing delirium symptoms. Delirium was found to be reversible through palliative care interventions, antipsychotic medications, and exercise therapy. Effective delirium management is crucial in improving the quality of life of cancer patients. This review emphasizes the importance of subtype-specific treatments, standardized guidelines, and long-term follow-up studies. Implementing evidence-based individualized approaches to delirium management can optimize treatment efficacy and clinical outcomes in patients as well as improve the quality of care. Tailored interventions, standardized protocols, and further research are hereby recommended.

Background: Oral health plays an important role in maintaining life functions and quality of life. Periodontal disease can vary with respect to bacterial etiology, host response, and clinical disease progression. A key role of genetic effects has suggested distribution of lesions and severity of destruction in each individual. Many diseases, particularly digestive disorders, cancer, and infection, show preferences among the ABO blood types. Knowledge of blood groups and their association with oral diseases is very important, as it may help in early diagnosis and treatment strategies‏. Objectives: The study aimed to investigate the distribution of patients with oral and dental health problems according to age, regions, Socio-demographic Variables, and blood groups in the Tripoli region. Material and Methods: The present study was conducted on 200 patients with oral and dental health problems attending six medical centers in Tripoli region from the 01^st March 2022 to the 01^st June 2022. Also, 100 healthy individuals without any oral and dental health problems or any other diseases were recruited as a control group. This study was approved by the Research and Ethical Committee of the medical centers and Libyan Academy of graduate studies. One ml of venous blood was withdrawn from each participant in the study for determination of blood groups. The data were compared using Chi-Square using SPSS Statistics for Windows, Version 25. Results: The results showed that the mean age of the patients was 43.03±13.82 years. The higher distribution of patients was 58 patients (29%) in the age group (26-35) years while the lower distribution was 10 patients (5%) in the age group (66-75) years. The distribution of patients according to the region were 81.5%, 13%, 4%, and 1.5% in Tripoli, South Tripoli, West Tripoli, and East Tripoli, respectively. The Distribution of patients according to occupation were 4.5% Students, 30% Housewives, 10.5% Employers, 12.5% Teachers, 14% Nurses, 7.5% Doctors, 12.5% Laboratory Technicians and 8.5% Freelance workers. The distribution of patients according to levels of education were 23.5% Pre-Secondary, 19% Secondary, 46% Bachelor's or equivalent, and 11.5% Master's or equivalent. The distribution of patients according to marital status were 26% single and 74% married. The distribution of patients according to oral and dental problems were 5% with bridge, 8% with missing teeth, 86% with dental caries, 63.5% with bleeding of gum, and 25.5% with swelling of gum. The degrees of gingival erythema among patients were 36.5% mild, 38% moderate, and 25.5% severe. The degrees of gingival inflammation among patients were 36% mild, 38.5% moderate, and 25.5% severe. The distribution of A, B, AB, and O blood groups showed a significant (P=0.000) difference between healthy individuals and oral and dental health problems among patients that, were 54%, 12%, 4%& 30%, and 21.5%, 9%, 3.5% & 66%, respectively. Also, the distribution of A⁺, A^-, B⁺, B^-, AB⁺, O⁺, and O^- blood groups showed a significant (P=0.000) difference between healthy individuals and patients with oral and dental health problems that, were 49%, 5%, 10%, 2%, 4%, 25%, & 5%, and 18%, 3.5%, 8%, 1%, 3.5%, 60%& 6%, respectively. But, the distribution of Rh⁺ and Rh^- blood groups showed a non-significant (P=0.695) difference between healthy individuals and patients that, were 88% & 12%, and 89.5%& 10.5%, respectively. Conclusion: It can be concluded that the mean age of the patients with oral and dental health problems was 43.03 years and the higher distribution of patients was in the age group (26-35) years. The higher distribution of ABO blood groups was O blood group among patients especially O⁺ blood groups. The distribution of Rh+ and Rh- blood groups were showed a non-significant difference between healthy individuals and patients with oral and dental health problems. Further studies are needed to confirm these results.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that includes a wide range of functional impairments, such as social and communication deficiencies, as well as limited and selective interest and behavioral patterns that are repetitive. Children with ASD often show developmental delay, which is noticeable at an early age, and show a wide range of symptoms that interfere with daily functioning, so early diagnosis includes early interventions. A complex set of genetic and environmental factors is associated with the development of ASD, which makes ASD a complex disorder, so there is a clear distinction between neurodivergent and neurotypical individuals. Since ASD is caused by a combination of certain genetic mutations and the prenatal/postnatal environment, we focused on the etiology of ASD in viral infections, i.e., Cytomegalovirus (CMV) as a possible cause of ASD. CMV is a neurotropic herpesvirus, which can be transmitted from mother to child during pregnancy. Cytomegalovirus (CMV) infection, which is often asymptomatic and can remain latent throughout life, can pose a danger to immune insufficiency individuals during pregnancy. CMV is the most common pathogen that causes intrauterine infections, is the most common cause of nongenetic sensorineural hearing loss in children, and the main cause of neurodevelopmental delay, so research suggests an association between congenital CMV infection with ASD and maternal seropositivity for CMV in pregnancy. spectrum in children. In the research, we used various online databases as sources for our study. The result of our research and processing of the given information indicates that maternal CMV infection in pregnancy is related to the development of autism spectrum disorders in children.

An unhealthy dietary habit leads to excess calorie consumption (overnutrition) or inadequate supply of one or more essential micronutrients (undernutrition).This nutritional imbalance is assessed by Anthropometric measurements, Biochemical estimations, Clinical examination and assessment of Dietary intakes. Anthropometry is an inexpensive, rapid and non-invasive method that provides details on different components of body structure and is highly sensitive to the broad spectrum of nutritional status. Hence, it has always been an important tool for screening and early diagnosis of malnutrition. Undernutrition in below 5 year children is life-threatening epidemic contributing to about 45% of under 5 child deaths. Children with Severe Acute Malnutrition (SAM) are nine times more likely to die, compared to their healthy counterparts. Therefore, early and accurate diagnosis of children with SAM is crucial for its management and prevention of morbidity and mortality from the same. SAM is defined as weight-for-height Z scores (WHZ) below -3SD of the median or a mid upper arm circumference (MUAC) of <115mm in children of 6-59 months age. The cut-offs for MUAC and WHZ are scientifically approximated to each other and both are used to diagnose children with SAM (Severe Wasting). However, the research findings from various countries revealed that the agreement between WHZ and MUAC is poor as both indices classify the children with SAM differently, with a small overlap, which varies greatly among countries. These discrepancies have an implication when using either one alone for measuring the prevalence of acute malnutrition. Therefore, it is pertinent to adopt both WHZ and MUAC indices to assess the burden of severe acute malnutrition (SAM) in the community.

Cell division that is not controlled leads to cancer, an incurable condition. An early diagnosis has the potential to lower death rates from breast cancer, the most frequent disease in women worldwide. Imaging studies of the breast may help doctors find the disease and diagnose it. This study explores an effectiveness of DL and ML models in a classification of mammography images for breast cancer detection, utilizing the publicly available CBIS-DDSM dataset, which comprises 5,000 images evenly divided between benign and malignant cases. To improve diagnostic accuracy, models such as Gaussian Naïve Bayes (GNB), CNNs, KNN, and MobileNetV2 were assessed employing performance measures including F1-score, recall, accuracy, and precision. The methodology involved data preprocessing techniques, including transfer learning and feature extraction, followed by data splitting for robust model training and evaluation. Findings indicate that MobileNetV2 achieved a highest accuracy99.4%, significantly outperforming GNB (87.2%), CNN (96.7%), and KNN (91.2%). The outstanding capacity of MobileNetV2 to identify between benign and malignant instances was shown by the investigation, which also made use of confusion matrices and ROC curves to evaluate model performance.

Neural networks are bringing a transformation in wearable healthcare technology analytics. These networks are able to analyze a vast amount of data to help in making decisions concerning patient care. Advancements in deep learning have brought neural networks to the forefront, making data analytics a straightforward process. This study will help in unveiling the use of ICT and AI in medical healthcare technology, crawling through some industry giants. Wearable Healthcare Technologies are becoming more popular every day. These technologies facilitate collecting, monitoring, and sharing every vital aspect of the human body necessary for diagnosing and treating an ailment. At the advent of global digitization, health data storage and systematic analysis are taking shape to ensure better diagnostics, preventive, and predictive healthcare. Healthcare analytics powered by neural networks can significantly improve health outcomes, maximizing individuals' potential and quality of life. The breadth and possibilities of connected devices are getting wider. From personal activity monitoring to quantifying every bit of health statistics, connected devices are making an impact in measurement, management, and manipulation. In healthcare, early diagnosis could be a lifesaver. Data analytics can help in a big way to make moves and predictions to save lives. We are in another phase of the digitization era, "Neural Network and Wearable Healthcare Technology Analytics." A neural network could be conceived as an adaptive system made up of a large number of neurons connected in multiple layers. A neural network processes data in a similar way as the human brain does. Using a collection of algorithms, for many neural networks, objects are composed of 'input' and 'output' layers along with the layers of the neural network.

Predictive modeling, supported by machine learning technology, aims to analyze data in order to guide decision-making towards actions generating desired values in the future. It encompasses the set of techniques used to build models that estimate the value of a certain variable predicting a forthcoming event from the past or current values of relevant attributes. In predictive healthcare modeling, the built models represent the relationship among the data concerning customer, provider, production, and other aspects of the healthcare environment in order to assist the decision processes in the prevention of diseases and in the planning of preventive actions by detection of high-risk patients. Contrary to trend analysis, whose goal is to describe past events, predictive models aim to provide useful indications regarding future events and changes. Predictive healthcare modeling supports actions that try to prevent the manifestation of diseases in healthy individuals or try to diagnose as early as possible the incidence of a disease in patients at risk. A sound predictive analysis encompasses not only the model-training task, but also the aspects of data quality, preprocessing, and fusion during its entire implementation lifecycle to ensure appropriate input data preparation. The robustness of the predictive model and its results depends highly on data quality. Due to the variety of data sources in healthcare environments, it becomes essential to use preprocessing in order to remove noise and inconsistencies. The increasing number of endorsable data exchange standards makes each data exchange achievable, but it demands the implementation of a data-governance program. In addition, the influence of the hospital-database architect on the architecture of an early-diagnosis model is important to guarantee appropriate input-formatting modularity.