This article presents a mathematical model designed to simulate the impact of targeted interventions aimed at preventing HIV transmission among female sex workers (FSWs) and their clients, while also analyzing their effects on the health of the general population. The compartmental model distinguishes between high-risk populations (FSWs and their clients) and low-risk populations (sexually active men and women in the general population), and links prevention efforts in high-risk groups to the evolution of the epidemic in the low-risk population. The fundamental properties of the model, such as the positivity of solutions and the boundedness of the system, have been verified, and the basic reproduction number R₀ has been calculated. Finally, the stability of the model was studied using Varga’s theorem and the Lyapunov method. Simulation results show that targeted prevention among FSWs and their clients reduces HIV incidence in the general population. This framework provides a valuable tool for guiding policymakers in the design of effective strategies to combat the epidemic, especially relevant in the context of suspension of USAID funding.

COVID-19 vaccines have demonstrated efficacy in reducing the prevalence of serious illnesses. The relative risk of hospitalization and mortality for patients who get breakthrough infections after immunization versus those who develop reinfections after a prior spontaneous infection is examined in this correspondence. Based on a study on U.S. Veterans who were not vaccinated and experienced reinfections had a much higher risk of experiencing severe illness outcomes compared to those who had received immunizations and experienced breakthrough infections, even if the rates of reinfection and breakthrough infection were similar. Our findings highlight the value of immunization in reducing severe COVID-19 outcomes, even in the presence of reinfections.

Objective: The knowledge, attitudes, and practices of SUA students about the use of plastics containing endocrine disruptors were investigated in this study. Methodology: A study with 150 participants was conducted to assess individuals' knowledge about endocrine disruptors, attitudes, and plastic use practices. Results: The findings indicate that the participants possessed an average degree of knowledge 50.2 ± 3.85 with the main emphasis of awareness being generic concepts rather than specific substances. Regarding the potential health impacts of endocrine-disrupting chemicals present in plastics, respondents' attitudes ranged from fair to positive, with a mean score of 3.5 ±0.09 indicating a fair attitude overall. Conclusion: It is important to practice polite behavior and increase public awareness of safe plastic disposal methods. Surprising only 38.0% of the participants mentioned that they refrain from heating their food in plastic containers to reduce their exposure to plastics. Students' practices revealed a notable dependence on plastic products despite their awareness of the concerns surrounding endocrine disruptors, as most of them reported using plastic water bottles, plastic cups, and plastic bags almost always. Additionally, only 20.7% of the respondents consistently implemented strategies to prevent exposure to endocrine-disrupting chemicals. Recommendation: The study recommended increasing the use of cleaner plastic substitutes and improving educational programs to convert information into practical actions. Policies that encourage environmentally friendly behavior and raise public awareness of safe plastic disposal techniques should be put into practice.

Background: Type 2 Diabetes Mellitus (T2DM) and Alzheimer’s Disease (AD) are increasingly linked through shared pathophysiological mechanisms, giving rise to the concept of Type 3 Diabetes Mellitus (T3DM). Brain insulin resistance, oxidative stress, and neuroinflammation are central to both conditions, contributing to cognitive decline and AD progression. Aim: This review aims to explore this emerging relationship and its implications for prevention and management. Methods: Using an integrative review, 21 studies were systematically analyzed. The review focused on identifying demographic, genetic, and lifestyle factors contributing to T2DM and AD and examined shared molecular pathways such as insulin dysregulation and amyloid-beta accumulation. Results: The findings reveal that T3DM shares key features with T2DM and AD, including insulin resistance and chronic inflammation. Lifestyle interventions, such as diet and exercise, alongside routine cognitive and metabolic screenings, are critical in mitigating progression. Conclusions: Further research into diagnostic biomarkers and targeted therapies is essential to manage T3DM and its impact on AD. The role of nursing professionals in early detection, education, and holistic management is emphasized as vital in addressing this dual disease burden. This review offers actionable insights into integrated strategies for addressing these interconnected conditions.

Background: Food security and dietary diversity are essential determinants of maternal health, particularly among pregnant women in refugee populations who face heightened vulnerabilities due to displacement and inadequate living conditions. This study examines the impact of food security on dietary diversity and nutritional intake among pregnant Rohingya women residing in the makeshift camps of Ukhiya, Cox’s Bazar. Methods: A descriptive cross-sectional study was conducted among 96 pregnant Rohingya women from June to September 2022. Data were collected using structured questionnaires assessing socio-demographic characteristics, food security, and dietary diversity. Food security was evaluated using the Household Food Insecurity Access Scale (HFIAS), while dietary diversity was assessed through a 24-hour dietary recall and a 7-day food frequency questionnaire. Data were analyzed using SPSS (Version 26) and Stata (Version 13), employing descriptive statistics and chi-square tests to examine associations. Results: Most participants (57.3%) were food secure, and 85.4% demonstrated high dietary diversity, consuming seven or more food groups. However, 21.9% of households experienced severe food insecurity, highlighting ongoing challenges in food access. The highest consumption was observed for starch, flesh foods, dark green leafy vegetables, and vitamin A-rich fruits and vegetables (99.0%), while dairy products (69.8%) and organ meat (34.4%) were consumed less frequently. Despite high dietary diversity, severe food insecurity persists, indicating gaps in food assistance programs. Conclusions: While food support programs appear to contribute to high dietary diversity among pregnant Rohingya women, severe food insecurity remains a significant concern. Strengthening food security interventions, improving access to diverse nutrient-rich foods, and integrating sustainable food assistance models are essential to addressing these challenges. Future research should explore long-term strategies to enhance food security and assess the impact of targeted nutritional interventions on maternal health outcomes in refugee settings.

Background: Maternity leave, whether paid or unpaid, is a critical resource that can significantly impact maternal well-being and newborn outcomes. However, its availability and utilization among mothers living in poverty remain understudied. Education is widely recognized as a key factor that increases access to both paid and unpaid leave. However, the theory of Minorities’ Diminished Returns (MDRs) posits that structural racism, segregation, and labor market discrimination limit the benefits of socioeconomic resources, such as education, for Black and Latino individuals. This suggests that the effects of education on maternity leave may not be uniform across racial and ethnic groups. Objective: This study aimed to examine the MDRs of education on access to unpaid and paid maternity leave among Black and Latino mothers compared to White mothers giving birth while living in poverty. Methods: We utilized baseline data from the Baby’s First Years Study (BFY), a longitudinal investigation of the effects of poverty on child development. The sample consisted of 1,050 mothers living in poverty who had recently given birth. Maternity leave (paid and unpaid) was assessed via self-report, and educational attainment was measured in years of schooling. Structural equation modeling (SEM) and interaction terms were employed to analyze racial and ethnic differences in the relationship between education and access to maternity leave. Results: Educational attainment was positively associated with access to unpaid maternity leave for the overall sample of mothers giving birth in poverty, but this association was weaker for Black and Latino mothers compared to non-Latino White mothers. Education did not significantly increase the likelihood of paid maternity leave, and there were no group differences for this association. Conclusion: This study highlights the urgent needs to address structural racism, labor market discrimination, and residential segregation that diminish the impact of education on living conditions for Black and Latino mothers, compared to non-Latino White mothers, even for those living under poverty. Policymakers and practitioners should develop targeted interventions to reduce racial and ethnic disparities in access to paid and unpaid maternity leave and other critical resources, particularly for new mothers living in poverty. Addressing these inequities is essential for improving maternal and newborn health outcomes and promoting social justice.

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Background: The COVID-19 pandemic posed significant psychological challenges to frontline healthcare workers (HCWs), including anxiety, stress, and emotional strain. Aim: This study investigates the psychological impact on HCWs during the pandemic and explores coping strategies employed to manage distress. Methods: An integrative review was conducted using 24 studies published between January and December 2020. These studies were analyzed to identify common psychological outcomes and coping mechanisms among HCWs. Results: Healthcare workers experienced significant psychological distress during the COVID-19 pandemic, including anxiety, stress, insomnia, and depression. Anxiety was the most commonly reported issue, particularly among women, younger healthcare workers, and frontline staff. Stress levels were heightened by high workloads, exposure to COVID-19 patients, and inadequate protective measures. Coping strategies and self-care behaviors, such as seeking social support and utilizing institutional resources, varied in effectiveness across populations. Conclusion: The findings highlight the urgent need for targeted mental health support and resilience programs for HCWs, ensuring they are better equipped to face future health crises.

Background: Diabetes is a chronic global health issue that requires effective management to improve patient outcomes and quality of life. Nursing education plays a critical role in empowering diabetic patients with self-management skills. Aim This systematic review evaluates the impact of diabetes-focused nursing education on patient outcomes and quality of life. Methods: This study uses PRISMA guidelines and a systematic approach to identify and evaluate relevant literature. Results and Discussion: Among the 14 studies reviewed, eight emphasized self-management education, while four incorporated multidisciplinary approaches. Findings consistently demonstrated that structured nursing education programs significantly improved self-management behaviors, glycemic control, and patient knowledge. For instance, nurse-led self-management programs resulted in substantial enhancements in self-care skills and diabetes-related knowledge. Moreover, interventions that combined health education with psychological support were particularly effective, leading to better blood glucose control and increased adherence to treatment. Studies that examined quality of life reported reductions in anxiety, improved lifestyle habits, and better overall self-management. These findings highlight the multifaceted benefits of nursing education, suggesting that structured, supportive programs positively impact both clinical and psychological aspects of diabetes care. Conclusion: The review emphasizes the value of comprehensive nursing education that integrates both clinical guidance and psychological support for holistic diabetes management. Implications: Ongoing professional development and culturally sensitive education programs are recommended to address the diverse needs of diabetic patients. Future research should investigate the long-term effects of nursing education and explore innovative strategies to enhance diabetes management outcomes.