A man in his 60s with multiple vascular comorbidities presented with sudden, painless vision loss in one eye. Although he had a high risk for atherosclerotic events, initial evaluation for stroke was negative for acute ischemia, but found to have markedly elevated inflammatory markers. Accordingly, giant cell arteritis was investigated and Ophthalmologic findings and fulfillment of the 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria supported the diagnosis of giant cell arteritis, despite a negative temporal artery biopsy. Management included high-dose glucocorticoids and delayed tocilizumab initiation due to the need for multiple vascular surgeries. Vision loss was irreversible, but systemic symptoms resolved and vascular interventions were successful. This case highlights the diagnostic and management complexities of biopsy-negative giant cell arteritis in patients with severe atherosclerotic vascular disease, emphasizing the importance of clinical judgment and established classification criteria when imaging and biopsy results are inconclusive.

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.

Introduction: Pain is considered as the fifth vital sign that should be considered in assessing patients. For clinicians to evaluate and determine the right pain interventions, there should be parameters such as pain scale. Our objective in this study is to determine factors affecting pain scale preferences in suburban and rural populations. The pain scales used in this study are FPS-R (Faces Pain Scale-Revised), VRS (Verbal Rating Scale), VAS (Visual Analogue Scale), and NRS (Numering Rating Scale). Method: This study uses observational design with an interview approach and a cross-sectional study. Areas covered are within Indonesia, which are marginal areas of Tangerang district border, and two rural areas in Serukam, West Kalimantan, and Soe, East Nusa Tenggara. Data collected will be analyzed using SPSS 25 software. Result: Populations within the suburban areas prefer NRS (52.08%) as their pain scale, and populations in rural areas prefer FPS-R 76.92%). Factors affecting pain scale preferences are location areas, as well as last education, with statistical significance of p<0.05. Discussion: Our study showed that the choice of several pain scales is not appropriate for specific demographics due to the complexity of these scales. Factors that should be considered are the location areas and education level, as some population in remote areas have better understanding of simpler pain scales. Conclusion: Complexity or simpler components may be an underlying reason for the preference of score selection to assess pain scales in some population. Therefore, the selection of pain scales should be adjusted to specific demographics so that clinicians can provide appropriate management with appropriate pain scales.

Background: Nursing students are exposed to significant stress due to academic and clinical demands, which can adversely affect their mental health, academic performance, and future clinical competence. Despite the global acknowledgment of this issue, limited research has been conducted to explore the prevalence and determinants of stress among nursing students in Bangladesh. Methods: This cross-sectional study was conducted from December 2023 to February 2024 among 372 nursing students enrolled in selected nursing colleges in Bangladesh. A purposive sampling technique was used, and data was collected using a semi-structured questionnaire. The questionnaire assessed socio-demographic characteristics, academic challenges, and psychological symptoms, with mental health stress measured using a Likert scale. Descriptive statistics and Chi-square tests were used to analyze the data, with a 95% confidence interval applied to all analyses. Results: The findings revealed that 31.7% of nursing students experienced severe stress, 23.9% reported moderate stress, and 16.7% had mild stress. Age, academic semester, and course load difficulties were significantly associated with stress levels (p < 0.05). Psychological symptoms such as anxiety, difficulty concentrating, and loss of interest in activities were also significantly linked to higher stress levels. Notably, students in their first semester and those reporting harder course loads were more likely to experience stress. However, gender was not significantly associated with stress levels. Conclusions: This study underscores the high prevalence of stress among nursing students in Bangladesh, driven by academic and clinical challenges and psychological symptoms. The findings highlight the need for targeted interventions, such as stress management training, enhanced mental health support, and policies to alleviate academic pressures. Future research should explore longitudinal trends in stress and evaluate the effectiveness of interventions to support a resilient nursing workforce.

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: Severe COVID-19 patients who received ventilator management were not very rare even when the omicron variant became dominant, but the clinical characteristics of these patients are still unclear. Methods: The clinical characteristics of severe COVID-19 patients requiring ventilator management were retrospectively investigated from January 2023 to December 2023. Results: Severe COVID-19 patients who received ventilator management accounted for 11 of 275 (4.2%) patients during the omicron variant period. Their mean age was 70.7 (51-85) years, and males were predominant. Ten of eleven (91.7%) patients were managed in the emergency department and had underlying diseases, including chronic lung/heart/kidney diseases and neurological diseases. However, only 4 of 11 (36.4%) had a clear history of vaccination. The patients showed a positive SARS-CoV-2 antigen titer of 3305.7 (12.9-20912). All 11 patients were treated with remdesivir and dexamethasone, and 5 (45.5%) also received sotrovimab. Pathogenic bacteria were isolated from 7 of 11 (63.6%) patients, and all 11 patients were treated with antibiotics. Only 3 of 11 (27.3%) patients were managed using extracorporeal membrane oxygenation (ECMO), but 9 of 11(81.8%) patients survived. Conclusions: These data suggest that severe COVID-19 patients who required ventilator management were less-vaccinated, elderly patients with underlying disease. These patients were treated successfully using antiviral agents, steroids, neutralizing antibodies, and antibiotics, with a few also treated using ECMO in the omicron era.

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.

The Gambia uses the Primary Health Care model with no trauma response system. Trauma patients are transferred through multiple levels of health care facilities before definitive care hospitals. This study was conducted to identify predictors of injury factors associated with transfer. In this study, we examined characteristics of transferred patients compared to those directly admitted in definitive care hospitals. The study was conducted in two major trauma hospitals in The Gambia. 251 road traffic injury (RTI) patients were either transferred (84%) from lower-level health centers or directly admitted (16%) to one of the study hospitals. Transferred patients were more likely to have been pedestrian/bicyclists (aOR = 1.81; 95% CI = 0.86 – 3.80). Administration of antibiotics was significantly associated with direct admit than transferred patients (aOR = 6.84; 95% CI = 2.38 – 19.68). Transferred patients were more likely to receive intravenous fluid compared to direct admits (aOR = 0.03; 95% CI = 0.01 – 0.08). The study results have implications for policies and planning in the healthcare setting in The Gambia and other LMICs with similar settings. Based on the findings of this study, it is essential that hospital management teams adapt to increasing reliance of RTI patients on lower-level healthcare facilities. The study results suggest increased burden on lower-level health care facilities. Efforts and resources should focus more on supporting lower-level facilities.

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.