Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in the diagnosis of various diseases, including tropical fevers such as dengue and malaria. However, there is a lack of standard guidelines to develop the AI models, the gap between clinical and engineering expertise and clinical validation of the models, and hence there is a critical need for the development of an integrated diagnostic tool which uses demographical, laboratory variables and epidemiological parameters of patient and provides early prediction.

The present study aimed to develop and evaluate a machine-learning (ML) prediction tool for differential diagnosis of tropical fevers for adult patients (>18 years) using a three-phase approach in a tertiary care centre in South India by January 2026. Phase involves identification of the prevalent tropical fevers and associated clinical parameters to develop the AI model through a retrospective audit and qualitative interview. Phase Ⅱ involves retrospective data collection from hospital medical records for finalised diseases (1000 cases per disease) and clinical parameters, with data being used for model development using the Python language. Support vector machine, logistic regression, K-Nearest Neighbors, Naïve Bayes and ensemble models such as decision tree and Random Forest will be employed along with explainable AI techniques. They are used as they are easy to understand and interpret, well established, most effective for structured data, enhancing the transparency and interpretability of the predictive machine learning models, and their use has been widely supported in previous studies across various contexts. Suitable statistical parameters like specificity, sensitivity and area under receiver operating characteristic (AUROC) will be applied to evaluate model performance. In phase , the developed model will be implemented prospectively to assess the feasibility of model implementation. Model performance such as specificity, sensitivity and AUROC will be calculated, and the finally developed model will be implemented in a single tertiary care hospital to evaluate its overall performance.

Ethical approval for the study has been obtained from the institutional ethics committee of the Kasturba Medical College and Kasturba Hospital, Manipal (IEC number: 6/2024). Informed consent will be taken for obtaining the data of the patient for the evaluation of the model in the third phase of the study, and data will be kept confidential. The study results will be disseminated by publishing them in a peer-reviewed journal.

The protocol has been registered with the Clinical Trial Registry of India (CTRI) (CTRI/2024/04/065866) and approved on 16 April 2024.

Diabetes is a widespread chronic disease, with steadily rising prevalence in most countries. In 2019, the global prevalence of diabetes was estimated at 9.3%, affecting 463 million people. This figure is projected to rise to 10.2% by 2030 and 10.9% by 2045.2 All people with type 1 diabetes and many people...

Haematuria contributes significantly to emergency urology admissions with over 4 per 1000 annual UK emergency admissions and 10% readmitted within 30 days. However, there is limited focus on optimising inpatient pathways internationally. Existing studies highlight a substantial underlying malignancy rate (32%) in patients presenting with visible haematuria, yet many receive inconsistent care, leading to prolonged hospital stays and increased resource use. A systematic review performed by our research group found no large-scale prospective studies have been performed in this area, and little is known about current practice. This study aims to address these gaps by investigating current management practices and their impact on outcomes, with the goal of informing evidence-based guidelines and improving patient care.

The Ward AdmiSsion of Haematuria: an Observational mUlticentre sTudy is an international, multicentre prospective observational study designed to describe the management of patients with unplanned admission to hospital with haematuria under the care of the urology team. The study will use a collaborative methodology using the British Urology Researchers in Surgical Training model. This model delivers international multicentre studies by empowering trainees to lead all aspects of multi-centre clinical studies, building research skills cost-effectively while shaping the future urological consultant workforce. Data on demographics, comorbidities, management practices and outcomes will be collected using a standardised case report form and analysed using multilevel linear regression modelling. Primary outcomes include length of stay, while secondary outcomes cover hospitalisation free survival, mortality, readmission rates at 90 days and resource use. The study was launched in January 2024 and will continue follow-up data collection through December 2025. Patient and public involvement (PPI) has been integral to the study design, ensuring that outcomes reflect patient priorities and that the research addresses key areas of concern.

Ethical and regulatory approvals will be obtained as required in each participating region. In the UK, the study is classified as a service evaluation and does not require individual patient consent. Participating sites must obtain local audit department approval. Data will be collected and stored securely, ensuring patient confidentiality. Results will be disseminated through scientific conferences, peer-reviewed publications and patient advocacy groups.

To examine the association between individual social capital and depression in older adults in Iran and to test the hypothesis that higher levels of social capital are inversely associated with depressive symptoms.

Cross-sectional study using baseline data from a longitudinal cohort.

Community-based study conducted in primary care settings across urban and rural areas of Birjand County, Eastern Iran.

A total of 1348 community-dwelling individuals aged 60 years and older were recruited through multistage stratified cluster random sampling. Participants who were bedridden or had end-stage disease (life expectancy

The primary outcome was depression status, measured using the Patient Health Questionnaire 9 items, with a score≥10 indicating depression. The main explanatory variable was social capital, assessed using a validated 69-item questionnaire capturing domains such as collective activity, social trust and network structure. Univariable and multivariable logistic regression analyses were conducted to estimate adjusted ORs and 95% CIs for associations between depression and social capital dimensions. Statistical analyses were performed using Stata V.12.0

Of the total participants, 268 (19.94%) were identified as having depressive symptoms, with a significantly higher prevalence among women (27.44%) compared with men (11.88%). Depression was more prevalent among those in the lowest wealth quintile (32.09%) and individuals with low literacy levels (28.10%). Participation in collective activities was inversely associated with depression in the second (OR=0.62, 95% CI (0.42 to 0.93)), third (OR=0.45, 95% CI (0.29 to 0.71)), fourth (OR=0.59, 95% CI (0.37 to 0.93)) and fifth (OR=0.37, 95% CI (0.22 to 0.61)) quintiles. Social trust was also associated with lower odds of depression in the third (OR=0.62, 95% CI (0.39 to 0.99)) and fourth (OR=0.64, 95% CI (0.42 to 0.97)) quintiles. Furthermore, the second (OR=0.63, 95% CI (0.40 to 0.99)) and fifth (OR=0.38, 95% CI (0.23 to 0.63)) quintiles of social network structure were inversely related to depression. These findings suggest that higher levels of social capital, particularly in terms of collective participation, trust and social networks, are associated with a reduced likelihood of depressive symptoms in older adults.

Higher levels of social capital, particularly collective engagement, interpersonal trust and diverse social networks, are associated with lower odds of depression in older adults. These findings support the need for community-based interventions to strengthen social capital as a strategy for mental health promotion among the elderly in low-income and middle-income settings.