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.

Silver nanoparticles (Ag⁺NPs) contamination in the environment is a serious concern. This study investigated selected heavy metal (Ag⁺, Cd^2+, Cr²⁺ and Pb²⁺) concentrations at different sampling points to assess the risk to human health (infants, children, and adults). To do this, an enclosed area (laboratory) of 12.6 m X 8.5 m (107.1 m²) was clearly marked at different coded distances of S1, S2, S3, and S4 representing 2, 4, 6, and 8 m, while unpolluted atmosphere at 50 m away without Ag⁺NPs served as the control (S5). The silver fireworks were allowed to burn for an approximate 00h03m30s at each sampling points using a high-volume air sampler mounted at the Environmental Engineering Departmental Laboratory, Rivers State University, with windows and doors closed to simulate indoor conditions. Samples were digested using a mixture of analytical-grade nitric acid, analytical-grade hydrochloric acid and analyzed to evaluate the levels of heavy metals by atomic absorption spectrophotometry. The Ag⁺ result at S1 shows 30,000 µg/cm³, S2 was 29,000 µg/cm³, while S3 was 28000 µg/cm³ and then S4 was 13,000 µg/cm³. These results exceeded the permissible values of the United States National Ambient Air Concentration for rural, urban and industrial areas (0.0005, 0.004 and 0.6 µg/cm³, respectively). The result for the control (S5) (0.037 µg/cm³) was within the maximum allowable value. Results from other heavy metals such as Cd were 1000, 743, 401, 153, 0.001 µg/cm³, Cr was 5000, 4000, 3729, 2960, 0.002 µg/cm³, Pb was 0.048, 0.041, 0.035, 0.034 and 0.01, µg/cm³, respectively. However, higher values of Ag⁺, Cd, and Cr indicated a higher propensity for the metals to be toxic (bioavailable). In addition, the assessment of the potential health risk posed by these metals proved contaminated and harmful. Visitors recorded high values in exposure concentration (EC) and low values in average daily dose (ADD).

Considering the current climate conjuncture, it is a consensus that green spaces in large contemporary urban areas should be increasingly more numerous and simultaneously more sustainable, being adapted to the edaphoclimatic conditions of the site, and with reduced maintenance costs. In the case of Algarve, where this research is focused, the current and future water availability, assumes a preponderant role in the design of green spaces, where the demands mentioned above can only be achieved if we deviate from conventional landscape practices and develop holistic strategies of management and design of green spaces that integrate different areas of knowledge and not merely aesthetic issues. In this context, this work aims to develop more adapted and resilient landscaping practices to the current and future climatic conditions of the Algarve, thus reinventing the concept of landscaping in the south of Portugal. Thus, it will be of paramount importance to develop more sustainable, resilient and tolerant projects to worsening ecological conditions, particularly limitations associated with water availability. The xeromorphic succulents are a group of plants with mechanisms of tolerance to water stress and with very specific characteristics, being succulence one of the most relevant. Studies on these mechanisms are increasingly frequent, which may prove to be very advantageous in our adaptation to future climatic challenges. In addition, their ornamental potential is enormous, since their bold forms and colours are a veritable sensory explosion, which, combined with their morphological and physiological characteristics, make them the species of choice in the reconversion or creation of xerophytic gardens.

Respiratory virus co-infections have been suggested to happen frequently and exacerbate patients’ conditions, but little is known about the detailed rates and the combinations of viruses during the COVID-19 pandemic period. A total of 255 symptomatic patients who underwent multiplex PCR tests were analyzed, and it was found that 6 (6/255=2.4%) patients were infected with multiple viruses. The patients ranged in age from 1 to 38 years, and one female patient was pregnant. Of the 6 patients, 4 had fever, and 5 had human rhinovirus/enterovirus and another virus. These data suggested that the rate of respiratory virus co-infection was low, and the combination of SAS-CoV-2 and other viruses was rare even during the COVID-19 pandemic.

Intestinal malrotation is an embryological abnormality modifying the classic anatomy of the small and large bowels, particularly the topographical one. Inflammatory bowel disease (IBD) is characterised by anatomical lesions with preferential intestinal tropism. These two conditions are rarely associated. They represent a real diagnostic and therapeutic challenge due to the overlap of non-specific symptoms and the complexity of the clinical management. We report the case of a young woman presenting with a flare-up of her IBD and a partial common mesentery syndrome. Clinicians should be aware of the potential pitfalls of the two entities to propose the adequate therapeutic strategy regarding the full understanding of the anatomy, notably when surgery is needed.

Major depressive disorder (MDD) is a significant psychiatric condition, with many affected individuals not gaining remission from conventional treatments, leading to classification as treatment-resistant depression (TRD). This study aimed to investigate the potential of intravenous (IV) ketamine, particularly the S-enantiomer esketamine in nasal spray form, for treating patients with TRD and associated comorbidities. We report three cases of patients with diverse psychiatric and medical backgrounds whom all reported significant symptomatic relief from depressive episodes and suicidal ideation (SI) following esketamine administration. Additionally, esketamine seemed to proffer benefits beyond the primary depressive symptoms, positively impacting other comorbid conditions, such as agitation, self-injurious behavior (SIB), and chronic pain. The goal of this paper is to highlight that while esketamine's primary utility is in addressing TRD, its therapeutic potential may extend to a variety of associated conditions. However, it is crucial to underscore the heterogeneity of MDD, emphasizing the necessity for individualized therapeutic approaches and further research into esketamine's broader applications.