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Contemporary breast cancer management includes surgical resection combined with a multimodal approach, including chemotherapy, radiotherapy, endocrine therapy, and targeted therapies. Breast cancer treatment is now personalised in accordance with disease and host factors, which has translated to enhanced outcomes for the vast majority of patients. Unfortunately, the treatment of the disease involves patients developing treatment-induced toxicities, with cardiovascular and metabolic side effects having negative implications for long-term quality-of-life metrics. MicroRNAs (miRNAs) are a class of small non-coding ribonucleic acids that are 17 to 25 nucleotides in length, which have utility in modifying genetic expression by working at a post-transcriptional cellular level. miRNAs have involvement in modulating breast cancer development, which is well described, with these biomarkers acting as important regulators of disease, as well as potential diagnostic and therapeutic biomarkers. This review focuses on highlighting the role of miRNAs as regulators and biomarkers of disease, particularly in breast cancer management, with a specific mention of the potential value of miRNAs in predicting treatment-related cardiovascular toxicity.

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL’s applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion. Additionally, it covers issues related to data heterogeneity, scalability, and system interoperability. Alongside these, the review highlights emerging privacy-preserving solutions, such as differential privacy and secure multiparty computation, as critical to overcoming FL’s limitations. Successfully addressing these hurdles is essential for enhancing FL’s efficiency, accuracy, and broader adoption in healthcare. Ultimately, FL offers transformative potential for secure, data-driven healthcare systems, promising improved patient outcomes, operational efficiency, and data sovereignty across the healthcare ecosystem.

Background: Non-alcoholic fatty liver disease (NAFLD) is a common liver condition affecting 25%–40% of the worldwide population. NAFLD is traditionally related to obesity and metabolic disorders. NAFLD can also affect non-obese individuals, termed “lean NAFLD” (LN), who exhibit a paradoxical combination of physical leanness and metabolic obesity. Factors contributing to LN remain unclear, necessitating further research. This analysis aims to understand LN’s prevalence and metabolic characteristics compared to obese NAFLD (ON) populations. Methods: This meta-analysis searched various databases until August 1, 2023. Inclusion criteria involved observational studies comparing LN with overweight/obese NAFLD. Data extraction included baseline characteristics, disease occurrence, metabolic profile, and clinical parameters—statistical analysis employed calculating risk ratios (RR) and standard mean differences. Results: Twenty-five studies were analyzed. LN is associated with lower prevalence in both NAFLD (RR 0.27, 95% confidence interval (CI) 0.14–0.52, p = <0.0001) and total (RR 0.27, 95% CI 0.15–0.51, p < 0.0001) population. LN had lower diabetes mellitus (RR 0.78, 95% CI 0.71–0.87, p < 0.00001), dyslipidemia (RR 0.87, 95% CI 0.79–0.95, p = 0.002), hypertension (RR 0.80, 95% CI 0.74–0.87, p < 0.00001), and metabolic syndrome (RR 0.45, 95% CI 0.31–0.64, p < 0.00001) compared to those with ON. The LN group’s lipid profile, blood pressure, and other clinical parameters were favorable compared to ON. Conclusion: The prevalence of NAFLD among lean and non-lean individuals varies by region. Our analysis revealed that LN is associated with lower metabolic diseases, fasting blood sugar, blood pressure, and a more favorable lipid profile compared to ON. Plain language summary NAFLD prevalence and its characteristics among obese vs lean population Non-alcoholic fatty Liver Disease (NAFLD) is a prevalent liver condition affecting a substantial portion of the global population, commonly linked to obesity and metabolic disorders. However, a subset of individuals with NAFLD, termed “lean NAFLD” (LN), challenges the conventional association by presenting with physical leanness despite metabolic obesity. The factors contributing to this condition are not well understood, prompting this meta-analysis to explore the prevalence and metabolic characteristics of LN compared to obese NAFLD (ON) populations. The study, conducted through August 1st, 2023, analyzed 25 studies meeting inclusion criteria, which involved observational studies comparing LN with Overweight/Obese NAFLD. Data extraction included baseline characteristics, disease occurrence, metabolic profiles, and clinical parameters. Statistical analysis utilized risk ratios (RR) and standard mean differences. The results indicated that LN is associated with a significantly lower prevalence in both the NAFLD and general populations. LN demonstrated lower occurrences of diabetes (DM), dyslipidemia, hypertension, and metabolic syndrome compared to ON. Additionally, the LN group exhibited a more favorable lipid profile, blood pressure, and other clinical parameters in comparison to the ON group. In conclusion, the prevalence of NAFLD varies among lean and non-lean individuals across different regions. The meta-analysis revealed that LN is linked to a lower occurrence of metabolic diseases, lower fasting blood sugar levels, lower blood pressure, and a more favorable lipid profile compared to those with ON. These findings contribute valuable insights into the distinct metabolic characteristics of LN, shedding light on potential avenues for further research and clinical considerations in the understanding and management of NAFLD.

Perovskite solar cells (PSCs) are emerging as leading candidates for sustainable energy generation due to their high power conversion efficiencies and low fabrication costs. However, their performance remains constrained by non-radiative recombination losses primarily at grain boundaries, interfaces, and within the perovskite bulk that are difficult to characterize under realistic operating conditions. Traditional methods such as photoluminescence offer valuable insights but are complex, time-consuming, and often lack scalability. In this study, we present a novel Long Short-Term Memory (LSTM)-based deep learning framework for dynamically predicting dominant recombination losses in PSCs. Trained on light intensity-dependent current–voltage (J–V) characteristics, the proposed model captures temporal behavior in device performance and accurately distinguishes between grain boundary, interfacial, and band-to-band recombination mechanisms. Unlike static ML approaches, our model leverages sequential data to provide deeper diagnostic capability and improved generalization across varying conditions. This enables faster, more accurate identification of efficiency limiting factors, guiding both material selection and device optimization. While silicon technologies have long dominated the photovoltaic landscape, their high-temperature processing and rigidity pose limitations. In contrast, PSCs—especially when combined with intelligent diagnostic tools like our framework—offer enhanced flexibility, tunability, and scalability. By automating recombination analysis and enhancing predictive accuracy, our framework contributes to the accelerated development of high-efficiency PSCs, supporting the global transition to clean, affordable, and sustainable energy solutions.

Burnout, characterized by emotional exhaustion (EX), depersonalization (DP), and a reduced sense of personal efficacy (PF) among medical and nursing students can lead to suicidal ideation, lack of empathy, and dropouts. Previous studies have used over-simplified definitions of burnout that fail to capture its complexity. We describe the prevalence of burnout profiles and its risk factors among medical and nursing students. A cross sectional study was conducted at a tertiary care University Hospital in Pakistan. The Maslach Burnout Inventory (MBI) survey was disseminated via SurveyMonkey over a period of 4 months (November 2019 to February 2020) to 482 Medical and 441 nursing students. The MBI tool measures the dimensions of EX, DP, and PF to describe seven burnout profiles. Multivariable regression was used to identify predictors of burnout. The response rate was 92% in nursing and 87.3% in medical students. The prevalence of burnout in medical and nursing students was 16.9% and 6.7% respectively (p<0.001), with 55.7% (n = 427) suffering from at least one burnout profile. Only 32.5% (n = 250) students felt engaged, (42.3% medical, 22.7% nursing students, p<0.001). The most common profile was ineffective (32.5%, n = 250), characterized by a reduced sense of personal efficacy (35.6% medical, 29.4% nursing students; p = 0.065). Medical students were at higher risk of burnout compared to nursing students (OR = 2.49 [1.42, 4.38]; p<0.001) with highest risk observed in year 4 (OR = 2.47 [1.02, 5.99]; p = 0.046). Other risk factors for burnout included occasional drug use (OR = 1.83 [1.21, 8.49]; p = 0.017) and living in a hostel (OR = 1.64 [1.01,2.67]; p = 0.233). Two-thirds of our participants experienced at least one dimension of burnout with the highest prevalence of a reduced sense of PF. Drivers of burnout unique to a lower-middle-income country need to be understood for effective interventions. Faculty training on principles of student evaluation and feedback may be beneficial.

Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations.

Background & objective Diabetic kidney disease (DKD) is a major global burden, especially in Asia. This study aimed to evaluate the efficacy and safety of finerenone in diabetic kidney disease, comparing outcomes between Asian and non-Asian populations through a systematic review and meta-analysis. Methods A systematic search was conducted across PubMed, Cochrane Library, ClinicalTrials.gov, Google Scholar, and the Undermind AI platform from inception through March 2025. Studies included randomized controlled trials (RCTs) and subgroup analyses that evaluated finerenone in DKD patients. Primary outcomes included a reduction in the urinary albumin-to-creatinine ratio (UACR) and a decline in the estimated glomerular filtration rate (eGFR) of ≥40%. Secondary outcomes included cardiovascular events, mortality due to kidney failure, hyperkalemia (serum potassium >5.0 mmol/L), treatment discontinuation, hospitalization, and adverse event–related mortality. Risk ratios (RRs) and mean differences (MDs) were pooled using a random-effects model, and subgroup analyses were performed by ethnicity. Results Five eligible studies, comprising 8,763 participants, were included in this analysis. Finerenone significantly reduced UACR compared with placebo (MD = −0.38, 95% CI −0.42 to −0.35; p < 0.001), with consistent effects across Asian and non-Asian populations (subgroup p = 0.28). It also significantly reduced the risk of eGFR decline ≥40% (MD = −0.24 [−0.40, −0.09]; p = 0.002), with a greater benefit in the Asian subgroup (subgroup p = 0.03). Cardiovascular event risk was also reduced (RR = 0.85 [0.77–0.95]; p = 0.004), while mortality due to kidney failure showed a non-significant reduction (RR = 0.83 [0.64–1.07]; p = 0.15). Hyperkalemia risk was higher with finerenone (RR = 1.73, 95% CI 1.39–2.14), whereas adverse event–related mortality was lower (RR = 0.65, 95% CI 0.46–0.91). Conclusion Finerenone provides robust renoprotective and cardioprotective effects in DKD, with broadly consistent efficacy across Asian and non-Asian populations. A greater renal benefit was observed in Asians for eGFR decline ≥40%, though this requires cautious interpretation. Hyperkalemia risk was increased but largely manageable. These findings support integration into DKD therapy and highlight the need for ethnically inclusive, long-term, real-world trials. Clinical trial number Not applicable.

Background One of the leading long-term complications of type 2 diabetes mellitus (T2DM) includes renal dysfunction and urinary tract infections (UTI) which are considered to be prevalent in uncontrolled diabetes. Moreover, physiological factors like age, gender, duration of diabetes, other diabetic complications like neuropathy, autonomic neuropathy and glycosuria are also considered as predisposing factors for increased prevalence of UTI in diabetes which can be symptomatic or asymptomatic. Methods This was a cross-sectional, multi-centre study including diabetic patients from 12 clinical sites spread across major cities of Pakistan. The inclusion criteria were adult Pakistani population of age between 18 to 75 years both genders and suffering from T2DM irrespective of duration. A detailed clinical history of the past 3 months was recorded and, biochemical investigations of blood samples were conducted. Urine culture analysis performed identified the type of pathogen present and was done only for asymptomatic patients. Results A total of 745 type 2 diabetic patients were initially screened, out of 545 patients considered for final analysis 501 (91.92%) were negative and the rest 44 (8.08%) had positive urine culture. Female gender had a significantly higher proportion of positive urine culture (77.27%, p -value< 0.001). Body mass index and mean age had insignificant distribution among the two groups of positive and negative urine culture, with age 40–59 years having higher proportion (70.45%) in the positive group. Escherichia coli was detected in most of the positive samples (52.3%). All bacterial samples were found resistant to Ciprofloxacin. Conclusion Diabetic Pakistani muslim female patients are identified to be at high risk of suffering from asymptomatic UTI and age more than 40 years is an important risk factor. Escherichia coli was the most common causative organism among people living in this geographical area.

Graves' disease (GD) is an autoimmune disorder characterized by activation of the TSH receptor by stimulatory autoantibodies (TSH Receptor Antibodies, or TRAbs), leading to unregulated thyroid hormone production. Diagnosis is largely based on the typical clinical picture and laboratory thyroid panel. Establishment of elevated serum levels of TRAbs by competitive binding assay or cell-binding assay has its unique role in diagnosis and management of GD, especially in the differential diagnosis, therapy selection, prognostication, evaluation of thyroid function during pregnancy, peri-conceptional and neonatal thyroid workup, and in certain special situation. Inclusion of TRAbs in GD diagnostic algorithm can improve cost-effectiveness of GD management. The current best practice guidelines were developed to provide evidence-based recommendations in the use of TRABs in GD management for healthcare providers in South Asia. A panel of endocrinologists with minimum 10 years of clinical experience in thyroid disorders reviewed existing literature and their quality, and after deliberation and discussion, developed 21 recommendations surrounding the best practices surrounding the role of TRAbs in GD management.

Background Nivolumab (Nivo) and ipilimumab (Ipi) have revolutionized cancer treatment by targeting different pathways. Their combination shows promising results in various cancers, including melanoma, but not all studies have demonstrated significant benefits. A meta-analysis was performed to assess the effectiveness and safety of Nivo-Ipi compared to Nivo alone in advanced cancer types (excluding melanoma). Methods Following PRISMA guidelines, we conducted a meta-analysis up to September 30, 2023, searching databases for randomized controlled trials (RCTs). We focused on advanced solid malignancies (excluding melanoma) with specific Nivo and Ipi dosing. Primary outcomes were overall survival (OS), progression-free survival (PFS), grades 3-4 adverse events (AEs), and treatment-related discontinuations. Secondary outcomes included specific adverse events. Statistical analysis in Review Manager included hazard ratio (HR) and risk ratio (RR), assessing heterogeneity (Higgins I.sup.2). Results Nine RCTs, involving 2152 patients covering various malignancies, were analyzed. The Nivo plus Ipi group exhibited a median OS of 12.3 months and a median PFS of 3.73 months, compared to monotherapy with 11.67 months and 3.98 months, respectively. OS showed no significant difference between Nivo and Ipi combination and Nivo alone (HR = 0.97, 95% CI: 0.88 to 1.08, p = 0.61). PFS had a slight improvement with combination therapy (HR = 0.91, 95% CI: 0.82 to 1.00, p = 0.04). Treatment-related cumulative grades 3-4 adverse events were higher with Nivo and Ipi (RR = 1.52, 95% CI: 1.30 to 1.78, p < 0.00001), as were treatment-related discontinuations (RR = 1.99, 95% CI: 1.46 to 2.70, p < 0.0001). Hepatotoxicity (RR = 2.42, 95% CI: 1.39 to 4.24, p = 0.002), GI toxicity (RR = 2.84, 95% CI: 1.44 to 5.59, p = 0.002), pneumonitis (RR = 2.29, 95% CI: 1.24 to 2.23, p = 0.008), dermatitis (RR = 2.96, 95% CI: 1.08 to 8.14, p = 0.04), and endocrine dysfunction (RR = 6.22, 95% CI: 2.31 to 16.71, p = 0.0003) were more frequent with Nivo and Ipi. Conclusions Combining nivolumab and ipilimumab did not significantly improve overall survival compared to nivolumab alone in advanced cancers (except melanoma). However, it did show slightly better PFS at the cost of increased toxicity, particularly grades 3-4 adverse events. Specific AEs occurred more frequently in the combination group. Further trials are needed to fully assess this combination in treating advanced cancers.