Background: This study investigates hairline patterns among the Etche people in Rivers State, Nigeria, with a focus on the distribution and variation in hairline shapes, lengths, and widths. Materials and Methods: Using a descriptive research design, data were collected from 227 participants through anthropometric measurements using a digital vernier caliper and measuring tape, assessing patterns by gender, age, and marital status. Results and Discussion: The results revealed that the most common hairline type was straight-lined (34.4%), followed by bell-shaped (30.8%) and widow’s peak (30%). The least common hairline patterns were low and high hairlines, each observed in only 0.4% of participants. The average hairline length and width were 61.98 mm and 293.89 mm, respectively, with males displaying longer and wider hairlines compared to females. Singles also showed greater hairline dimensions compared to married individuals. Conclusion: No significant variation was found in hairline characteristics by religious affiliation. These findings contribute valuable anthropometric data on the Etche population and highlight the role of genetic and environmental factors in shaping craniofacial features. The study fills a gap in anthropometric literature and provides a foundation for further genetic, medical, and cultural studies on Nigerian ethnic groups.

The breeding behaviour of Grey Jungle Fowl (Gallas sonneretti)) was studied in village Bari of district Doda of Jammu and Kashmir near agricultural fields during the month of June of 2024. During the course of observation it was observed that female lays 8 eggs in a nest on the ground near agricultural fields. The incubuton period is 21 days. But surprisingly on 15th day of incubation all the eggs were found to be disappeared from nest.

Background: Social media has become an integral part of daily life for adolescents but it is influencing their mental well being significantly. This study investigates social media usage patterns among students of a Government Senior Secondary School in Abuja and their effects on mental health. Methods: A cross-sectional study was conducted with a sample of 265 students, selected through systematic random sampling. Data were collected on age, gender, family structure, social media use, experiences of cyberbullying, and mental health indicators via a structured self-administered questionnaire. Analysis utilized IBM-SPSS version 27, employing descriptive statistics for quantitative variables and Chi-square tests for categorical variables (p ≤ 0.05). Results: Findings revealed that 56.9% of respondents were aged 16-18 years, with 60% female. Most (58.5%) used social media for 1-3 hours daily, while 6.9% exceeded 10 hours. Motivations included social connection and entertainment, with 17.3% reporting experiences of cyberbullying. Mental health assessments indicated that 56.5% displayed poor mental health, though 42.3% retained optimism. Notably, family structure significantly affected digital engagement (p = 0.001). Conclusion: This study underscores the prevalent use of social media among adolescents and its substantial impact on mental health, highlighting factors like cyberbullying. The alarming rate of poor mental health emphasizes the need for targeted interventions, including digital literacy education, enhanced mental health support, and community programs to promote responsible social media use.

Introduction: Hypertension is a leading global health issue, especially in low-income countries, and is strongly associated with dyslipidemia, a key risk factor for cardiovascular disease. Despite known associations, the specific relationship between lipid profiles and hypertension remains inadequately explored in local populations. This study aimed to assess the association between lipid profile components and hypertension among adults. Methods: A cross-sectional study was conducted on 100 participants (50 hypertensive and 50 normotensive individuals) at Sirat e Mustaqeem health care center, Karachi, from October to December 2024. Patients aged 30–60 years, with no prior antihypertensive treatment or chronic comorbidities, were enrolled using purposive sampling. Blood pressure was measured using standard procedures. Fasting venous blood samples were collected for lipid profile analysis, including total cholesterol (TC), triglycerides (TG), LDL-C, and HDL-C. Statistical analysis was performed using SPSS v21, with significance set at p < 0.05. Results: The study population included 65% males and 35% females. Most hypertensive patients exhibited elevated levels of TC, LDL-C, and TG, while HDL-C levels were comparable between groups. ANOVA analysis showed a significant association of cholesterol with BMI (F = 25, p < 0.05) and LDL levels (F = 20, p < 0.05). However, no within-group variability was observed, suggesting potential data homogeneity or recording issues. Conclusion: The findings suggest a significant association between dyslipidemia—particularly elevated TC, LDL-C, and TG—and hypertension. Routine monitoring of lipid profiles in hypertensive patients is recommended to reduce the risk of cardiovascular complications.

Introduction: Autoimmune hepatitis (AIH) is a chronic inflammatory liver disease of unknown etiology that can lead to cirrhosis and liver failure if left untreated. Environmental triggers, particularly viral infections, have been implicated in disease onset. Case Presentation: We report the case of a 24-year-old woman who presented with abdominal pain, nausea, and cholestatic jaundice. Initial serological workup revealed acute hepatitis A (HAV) infection. Despite conservative management, liver function continued to deteriorate. Autoimmune screening showed high-titer antinuclear antibodies (ANA), and liver biopsy revealed interface hepatitis with portal lymphoplasmacytic infiltrates and fibrosis (A3F1), consistent with AIH. Conclusion: This case highlights the potential role of HAV infection as a trigger for autoimmune hepatitis. In cases of persistent liver dysfunction after acute viral hepatitis, clinicians should maintain a high index of suspicion for evolving autoimmune liver disease.

Early prediction of nurses’ turnover and turnover intention is essential to enhancing staff retention, ensuring job satisfaction, and maintaining the quality of patient care. This systematic review evaluated studies that used machine learning techniques to predict either actual nurse turnover or turnover intention, with the goal of identifying key predictive variables and assessing model performance. A comprehensive search was conducted across PubMed, CINAHL, Cochrane Library, PsycINFO, and Google Scholar, following PRISMA guidelines. Out of 596 records screened, eight studies met the inclusion criteria. These studies were appraised using the CASP Clinical Prediction Rule Checklist. The most frequently reported predictors were salary and age. While several models, such as Decision Tree and Random Forest, demonstrated high internal predictive accuracy, external validation was lacking across all studies, limiting generalizability. Future research should focus on validating models in diverse populations and healthcare settings and on improving standardization in outcome measures and reporting practices to enhance the applicability of predictive models in nursing workforce planning.

This thesis aims to explore the intricate interactions, reactions, and counteractions of vitamins within the human body. Vitamins are essential organic compounds required in small quantities for the proper functioning of various physiological processes. While each vitamin plays a unique role, their interdependencies and potential for interactions are crucial to understand. This thesis examines the mechanisms behind vitamin interactions, including absorption, metabolism, and potential antagonistic or synergistic effects. By delving into these complexities, this research seeks to contribute to a comprehensive understanding of how vitamins interact, react, and counteract with each other, and their implications for human health