Boruta Feature Selection and Deep Learning for Alzheimer’s Disease Classification

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deterioration. The early and accurate classification of AD is crucial for timely intervention and management. This study utilizes the Boruta feature selection method to identify the most relevant features for AD classification, selecting the top 15 features based on importance ranking. Three machine learning models—Deep Neural Networks (DNN), Long Short-Term Memory Networks (LSTM), and Support Vector Machines (SVM)—were evaluated using accuracy, precision, recall, and F1-score as performance metrics. The LSTM model demonstrated the highest accuracy (89.30%), outperforming DNN (88.14%) and SVM (84.19%), owing to its capability of capturing temporal dependencies in inpatient data. Results indicate that deep learning models offer superior performance compared to traditional machine learning approaches in AD classification. The study emphasizes the importance of cognitive, lifestyle, and metabolic features in AD diagnosis while acknowledging limitations such as dataset constraints and model interpretability. Future research should improve explainability, incorporate multi-modal data, and leverage real-time monitoring techniques for enhanced AD detection

Utilization of Social Media Networks for Teaching Effectiveness in Tertiary Institutions of Cross River State, Nigeria: Implications for Learning and Practice in an Environment of Students with Intellectual Disabilities

Aim: This study examines the use of social media networks for teaching effectiveness in public tertiary institutions of Cross River State, Nigeria: Implications for learning and practice in an environment of students with intellectual disabilities. Four study objectives were stated to guide the research. Four research questions were formulated, and one hypothesis statement was made. A literature review was carried out based on the variables under study, as research gaps were also stated. Method: The study utilize7d the descriptive survey research design. The study population comprised 2,800 academic staff of public tertiary institutions of Cross River State. The sampling techniques adopted for this study were the stratified random sampling technique and the accidental random sampling technique. A total sample of 560 respondents was selected from 2,800 academic staff of public tertiary institutions in Cross River State using 20% of the entire population. A validated 25-item four-point modified Likert scale questionnaire was the instrument used for data collection. The face and content validity of the instrument was established by experts in Test and Measurement from the University of Calabar, Calabar-Nigeria. The reliability estimates of 0.89 for the instruments were established using the Cronbach Alpha method. A descriptive analysis of frequency count, percentages, mean, and standard deviation was used to test the research questions. Results: The results obtained from the data analysis revealed that there was a statistically significant joint relationship between the predictor variables (Twitter, Facebook, WhatsApp) and teachers' teaching effectiveness in tertiary institutions in Cross River State, Nigeria. Conclusion: From the findings of this study, one can conclude that there was a statistically significant joint relationship between the predictor variables (Twitter, Facebook, WhatsApp) and teachers teaching effectiveness in tertiary institutions in Cross River State, Nigeria. Key statistical measures, including mean scores, standard deviation, and inferential tests such as Multiple Linear regression, indicate a positive correlation between social media utilization and improved instructional delivery. The findings suggest the need for inclusive digital strategies to maximize learning outcomes, emphasizing the importance of accessible and adaptive teaching approaches. These insights have critical implications for policy formulation, curriculum design, and pedagogical practices in higher education. Recommendation: Based on the result of the study, it was recommended that since the utilization of social sites by teachers improves teaching effectiveness, learning institutions should enact regulations that will govern the proper and positive use of the various types of social media sites among teachers in institutions to promote teachers' teaching effectiveness

Synergistic Effect of Zinc Oxide and Magnesium Oxide Co-Cure Activators on Polybutadiene Rubber Vulcanization: Mechanical Properties and Thermal Characteristics

Zinc oxide (ZnO) is widely recognized as an effective cure activator in the sulphur vulcanization of polybutadiene rubber (PBR). However, its high toxicity to aquatic organisms has raised environmental concerns, prompting the search for non-toxic alternatives. Despite this, no industrially viable substitute has been identified. This study explores the potential of using a combination of ZnO and magnesium oxide (MgO) to reduce ZnO levels while enhancing vulcanization performance. The crosslinking density and thermal stability of the vulcanized PBR were assessed to evaluate the efficacy of MgO. The results demonstrate that the inclusion of MgO as a co-activator significantly accelerates the vulcanization rate. Specifically, formulations with 60% MgO exhibited a tensile strength of 1.1 MPa, elongation at break of 111%, and hardness of 46 Shore A. When using MgO exclusively, the material achieved a tensile strength of 1.4 MPa, elongation at break of 212%, and hardness of 43 Shore A, with an abrasion loss of 64.82 mm³. Swelling studies revealed that crosslink density was highest in the PBR formulation with 3 phr MgO and 2 phr ZnO, exhibiting the lowest swelling index (3.10). As MgO content increased, the swelling index also rose, indicating reduced crosslink density. The highest swelling index (4.24) was observed in the formulation with 5 phr MgO, confirming weaker crosslink formation. These results highlight that MgO alone lacks the ability to form an effective sulfurating complex, but when combined with ZnO, it enhances crosslinking efficiency and vulcanization performance. The use of MgO, either alone or in combination with ZnO, presents a viable approach for developing environmentally friendly PBR compounds with potential applications in high-performance elastomers such as tires

Management of Antipsychotic Therapy in Patients with Schizophrenia

Antipsychotic therapy is the main approach in the treatment of schizophrenia, but there is often irrational use due to inappropriate drug selection, inappropriate dosage, and long-term use without evaluation. Factors that support therapeutic rationality include adherence to clinical guidelines, selection of safer antipsychotics, and optimal management of side effects. Therefore, it is important to evaluate the factors that contribute to rational and irrational therapy in the use of antipsychotics in patients with schizophrenia. This study aims at antipsychotic medication management and factors that cause irrational therapy, as well as evaluating factors that support therapeutic rationality in the use of antipsychotics in schizophrenic patients. This study used a cross-sectional study design involving schizophrenia patients undergoing antipsychotic therapy in a psychiatric hospital. Data were collected through patient medical records and interviews with health workers. Quantitative data were analyzed using descriptive statistics and inferential tests, including chi-square and regression analysis, to determine the association between patient characteristics and antipsychotic selection as well as therapy rationality. The results showed that 26.7% of patients received irrational therapy, with the main causes being inappropriate drug selection (45%), inappropriate dosage (30%), and long-term use without evaluation (25%). Meanwhile, 73.3% of patients received rational therapy, with the main contributing factors being adherence to clinical guidelines (50%), selection of safer antipsychotics (30%), and good side effect management (20%). Irrational antipsychotic therapy remains a significant problem in the management of schizophrenia. Adherence to clinical guidelines and appropriate therapy selection can improve treatment effectiveness and reduce the risk of side effects. Regular evaluation and a multidisciplinary approach are needed to improve the rationality of antipsychotic therapy

Effectiveness of Art-Therapy-Based Intervention Programmes for Improving Social Communication in Children with Rett Syndrome

The research into effective art-therapy-based interventions for improving the social communication skills of children with Rett syndrome is important for the adaptation of this group of children. This study aims to evaluate the effect of a 6-month art-therapy-based intervention program based on art therapy on improving social communication in children with Rett syndrome. The research employed a quasi-experimental method, direct (unstructured) observation, a standardized Social Responsiveness Scale, and mathematical and statistical data processing methods (Levene test, paired sample t-test). The results showed a significant improvement in social communication in the experimental group (EG) after the intervention, as evidenced by paired and independent sample t-tests. This indicates statistically significant differences between pre-and post-test scores in the EG (mean difference 14.525 with a standard deviation of 22.592). The standard error for this group was 3.572, and the 95% confidence interval for the mean difference ranged from 7.300 to 21.750. The Student's t-test reached 4.066 with 39 degrees of freedom, resulting in a two-tailed p-value of less than 0.001. It has been found that art therapy can significantly improve social communication and emotional regulation subscales in children with Rett syndrome. The obtained data indicate the need to include therapeutic strategies based on art therapy in intervention programs for children with Rett syndrome. Prospects for further research are based on studying the impact of art therapy and other interventions not only on social communication but also on the cognitive development of children with Rett syndrome

Serum 25-Hydroxyvitamin D Concentration Status and Neonatal Immune Function: New Perspectives in Anticipating Late Onset Sepsis among Preterm Neonates at Tertiary Care Centres (A Prospective Study)

Objective: This study aims to investigate the impact of low vitamin D levels in cord blood on the incidence of neonatal sepsis in preterm infants. Patients and Methods: This prospective study was conducted at Al-Azhar and Helwan University Hospitals from September 2024 to January 2025. 150 neonate premature infants with a gestational age of <37 weeks were enrolled. In the present study, vitamin D deficiency (group 1, n=75) was defined as a 25-hydroxyvitamin D (25(OH)D) concentration <15 ng/mL; and vitamin D sufficiency (group 2, n =75), 25(OH)D concentration ≥15 ng/mL. Results: All markers were higher in Group 1 compared to the other groups (P < 0.05). Interestingly, the mean Del PCT was lower in group 2 compared to different groups. The cut-off of the umbilical cord CRP was 10.5 mg/L, the sensitivity, specificity, PPV and NPV were 41, 88.0, 29 and 28%, respectively. At a PCT cut-off of 1.18 ng/mL, the sensitivity, specificity, PPV and NPV were 79, 91, 51 and 61%, respectively Conclusion: Our study is one of the few that examines the relationship between neonatal sepsis in preterm newborns and the level of vitamin D in cord blood. Based on the findings of our investigation, we concluded that neonatal sepsis in preterm newborns is not related to vitamin D levels in the cord blood. To investigate these findings further, a larger patient sample or randomized controlled trials are required

Advisory Opinions under Protocol No. 16 to the ECHR. A Theoretical and Empirical Analysis of the Legal Nature of the ‘Questions of Principle’

One of the most significant legal arguments against the ratification of Protocol No. 16 to the European Convention on Human Rights (ECHR) is that advisory opinions issued by the European Court of Human Rights (ECtHR) would pose a threat to national sovereignty and judicial discretion. Several counterarguments have already been examined by scholars. The counterargument that will be demonstrated here is that advisory opinions cannot pose a threat to national sovereignty or judicial discretion because they are issued on ‘questions of principle’. In other words, this means that the requesting domestic highest courts or tribunals keep sufficient margin of discretion, when it comes to the concrete case brought before them. Such hypothesis will be demonstrated from a theoretical perspective, reflecting upon the legal concept of ‘principle’; and through an empirical analysis of the advisory opinions issued so far by the ECtHR. Demonstrating the hypothesis would be relevant in order to allow the States to understand that the ratification of Protocol No. 16 would not pose any threat to the discretion of domestic Courts, neither in theory nor in practice

Effect of Nutrients on Cognitive Function during Childhood to Adolescence: A Review

Background: Cognitive functioning and development include making decisions, processing information, and responding properly to the environment. People with healthy brains can identify their skills and modify their cognitive, mental, emotional, and behavioral functions to cope as best they can with various life situations. Methods: Studies from the last 15 years included from various search engines like Google Scholar, Pubmed, Science Direct, Scopus Result: The health of the brain is affected by many situations, including illnesses, injuries, mood disorders, substance addiction, and aging-related changes in the brain. There is evidence of numerous changeable lifestyle factors, even though some cannot be changed: Food and exercise, social interaction and mental activity, as well as alcohol and tobacco use, can all help stabilize or enhance deteriorating cognitive performance. Conclusion: Each macronutrient and micronutrient plays a critical role in supporting cognitive function, and their combined effects may be synergistic due to the interrelated nature of their physiological and biochemical actions

Structural Equation Modeling of Oral Stomatitis and Its Determinants among the Sundanese Ethnic Group: Evidence from IFLS-5

Background: Oral stomatitis is an inflammation of the mucosa in various oral structures such as cheeks, gums, tongue, lips, palate, and floor of the mouth that commonly occurs in communities, including among the Sundanese ethnic group. Risk factors affecting stomatitis incidence in the Sundanese population need to be analyzed for developing more effective prevention programs. Aim: To analyze risk factors for stomatitis among the Sundanese population using panel data from the Indonesian Family Life Survey (IFLS). Method: This was an analytical observational study using secondary data from IFLS-5. The research design employed structural equation modelling (SEM) analysis examining variables including age, gender, education, residential area classification, general health status, and smoking habits. Results: The study revealed that age and general health variables had significant associations with stomatitis occurrence (p<0,001). Ages below 25 years and suboptimal health conditions proved to be significant factors influencing increased stomatitis incidence. Meanwhile, gender, education level, residential area classification, and smoking habits showed no significant correlation. Conclusion: Age and general health status are the main risk factors for stomatitis occurrence among the Sundanese population, which can serve as a reference for prevention program development

Improving Alzheimer’s Disease Detection with Transfer Learning

Accurate and prompt diagnosis of Alzheimer's disease (AD) remains a challenge, with only a small percentage of patients receiving timely confirmation. Manual interpretation of MRI scans, the primary diagnostic tool, is time-consuming, subjective, and prone to error, particularly in differentiating between disease stages. This study aimed to develop a computer-aided diagnosis system (CAD) for AD classification using deep learning models. MobileNetV1 and Xception architectures were employed to classify AD into four stages: mild, normal, moderate, and severe. Transfer learning and layer freezing techniques were applied for feature extraction and classification. Model performance was evaluated using precision, recall score, and accuracy metrics. The Xception model achieved a higher accuracy (79%) compared to MobileNetV1 (73%) in classifying AD stages. Compared to MobileNetV1, this study shows that Xception-based CAD systems have the potential to diagnose AD more accurately, providing a promising path for future research and clinical application