Maharashtra Institute of Medical Science and Research

BACKGROUNDDengue fever remains a major cause of acute febrile illness in India, particularly in rural areas where diagnostic facilities are limited. Early detection using point-of-care tests helps guide timely management and reduce unnecessary antibiotic use.AIMTo determine the seroprevalence and clinical profile of dengue infection among acute febrile patients attending a rural tertiary care hospital in Maharashtra.METHODSA prospective cross-sectional study was conducted at Ashwini Rural Medical College Hospital and Research Centre, Solapur from May to October 2025. A total of 320 consecutive patients presenting with acute fever of ≤ 7 days duration prior to hospital presentation were enrolled. Patients of all age groups were included. Serum samples were tested for dengue NS1 antigen and anti-dengue IgM and IgG antibodies using a commercial rapid test (Manufacturer: Meril diagnostics; Meriscreen dengue NS1 antigen and IgM + IgG antibodies). A 10% subset (n = 32) was tested by enzyme-linked immunosorbent assay (ELISA) for quality assurance. Patients were classified as recent dengue (NS1+ and/or IgM+), probable secondary infection (IgM+ IgG+), past exposure (IgG+ IgM-), or negative (NS1- IgM-). Clinical data and outcomes were recorded. Statistical analysis included χ2, Mann-Whitney U, and logistic regression (α = 0.05), adjusting for age, sex, platelet count, total leukocyte count, and presence of rash.RESULTSOverall, 28% (90/320) had evidence of recent dengue, while 35% (112/320) showed IgG positivity, and 10% (32/320) had probable secondary infection. Median platelet count was significantly lower in the recent dengue group [78000/mm³; interquartile range (IQR): 55000-110000] compared with non-dengue patients (210000/mm³; IQR: 160000-240000; P < 0.001). The most common symptoms were headache (68%), myalgia (61%), and retro-orbital pain (44%). Antibiotic use prior to testing was 60%; among those diagnosed with dengue, a relative 25% reduction in antibiotic prescriptions was observed following laboratory confirmation. Quality assurance analysis showed rapid test sensitivity of 92%, specificity of 95%, and Cohen’s kappa of 0.85 vs ELISA. Logistic regression identified thrombocytopenia (< 100000/mm³) [adjust odds ratio (aOR) 5.2; 95%CI: 2.4-10.9] and rash (aOR 3.6; 95%CI: 1.8-7.0) as independent predictors of dengue positivity.CONCLUSIONA substantial proportion of acute febrile cases in this rural setting were due to dengue, highlighting the utility of NS1/IgM rapid tests for early diagnosis where molecular facilities are unavailable. Integrating rapid dengue testing into routine fever evaluation can improve case detection and rationalize antibiotic use.

Controlled and sustained antibiotic delivery is critical for combating antimicrobial resistance while minimizing side effects. Herein, a novel biodegradable hydrogel system, synthesized via gamma irradiation, incorporating fluorescent carbon dots (CDs) as multifunctional nano-crosslinkers, has been reported. The CDs, prepared from sustainable bio-precursors, reinforced the polymer network and enhanced the mechanical stability and swelling behavior, while simultaneously serving as intrinsic fluorescent probes for potential real-time monitoring of degradation and drug release. Thorough characterization revealed consistent morphology, adjustable biodegradability, and enhanced rheological characteristics. Drug release investigations demonstrated a diffusion-controlled mechanism, wherein the integration of CD diminished the cumulative antibiotic release from approximately 70% to approximately 40%, thereby facilitating precise regulation of release kinetics. The single-step gamma irradiation method facilitates concurrent crosslinking and sterilization, providing an efficient and scalable production strategy. This study presents a multifunctional hydrogel platform that integrates sustainable nanomaterials, regulated drug administration, and real-time monitoring, thereby facilitating the development of advanced theragnostic systems.

The variability of solar and wind energy due to weather fluctuations poses significant problems to the stability of the energy system and the utilization of renewable energy sources. Artificial Intelligence (AI) methodologies provide promising algorithms for the prediction and optimization of solar and wind energy. This article examines the use of advanced AI methodologies- specifically Random Forests, Long Short-Term Memory (LSTM) networks, and Transformer models- to forecast the performance of solar and wind farms and enhance their operational efficiency. Utilizing historical weather and energy production data from diverse geographical locations, we developed and evaluated models based on MAPE and RMSE metrics. The LSTM and Transformer-based models consistently outperformed conventional approaches, achieving a MAPE as low as 5.2% for solar predictions and 5.5% for wind forecasts. The strategy involves data pre-processing, feature creation (solar energy density, temperature, wind speed, and wind direction), and hyper parameter optimization using the Bayesian method. Research indicates that AI-driven forecasting facilitates improved integration of renewable energy into the grid, resulting in a proportional decrease in carbon-based resource use and enhanced system resilience. Major Findings: The study found that Transformer and LSTM models significantly outperformed traditional forecasting and optimization methods, achieving MAPE as low as 5.2% for solar and 5.5% for wind predictions. These results demonstrate that AI-driven forecasting enhances grid integration of renewable, reduces reliance on carbon-based energy, and improves overall system stability.

Obesity, a significant comorbidity for various cardiovascular and respiratory diseases, demands individualized and effective management strategies. Without appropriate intervention, obesity can severely compromise an individual’s health. Achieving weight management is feasible through the application of evidence-based knowledge and sustained commitment. Incorporating diverse forms of physical exercise—such as High-Intensity Interval Training (HIIT) and Moderate-Intensity Continuous Training (MICT)—in combination with tailored dietary habits can optimize outcomes for affected individuals. In fact, dietary regulation remains the cornerstone of any weight management program, especially among adolescents and adults facing modern lifestyle challenges. When paired with an exercise regimen aimed at reducing adipose tissue, this combined approach effectively facilitates weight control. Moreover, obesity is characterized by chronic low-grade systemic inflammation and is closely linked to numerous metabolic disorders, including the highly prevalent Diabetes Mellitus. This connection is largely attributable to obesity’s profound impact on hormonal regulation, particularly in the development of insulin resistance. For individuals who are unable to manage their weight through conventional means, bariatric surgery may be considered in advanced cases. However, post-surgical care—comprising proper dietary management and regular physical exercise—is essential for achieving and maintaining favorable outcomes. In this literature review, recent articles from the past 5 years examining the benefits of physical exercise on obesity were analyzed. The findings reveal that different modalities of physical exercise influence weight loss, adipose tissue reduction, body composition, metabolism, physical capacity, cardiorespiratory function, insulin regulation, inflammation, psychological adaptations, hormonal balance, gut microbiota, as well as factors related to pregnancy and aging. Notably, the benefits derived from physical exercise vary according to the specific type of activity performed. Consequently, when prescribing an exercise regimen, it is crucial to align the program with the individual’s specific therapeutic objectives.

International audience

Abstract Disclosure: R. Elangovan: None. K. Elangovan: None. J.R. Sethuraj: None. E. Krishnan: None. G. Palaniswamy: None. S. Nanduri: None. K. Sakalabaktula: None. U. Qureshi: None. F. Rahman: None. Z. Baloch: None. Z. Ahmed: None. S. Biswas: None. R. Muralidhar: None. C. Robert: None. K.J. Patel: None. G. Rajamani: None. N. Vora: None. S. Malapati: None. K. Noor: None. M. Kaur: None. A.S. Agrawal: None. A. Franklin Johnson: None. P. Uvaraj: None. T. Dontulwar: None. F. Madhumithaa Jagannathan: None. P.C. Sirait: None. M. Amro Alrouh: None. S. Tamma: None. S.S. Bhurchandi: None. S.K. Bhurchandi: None. Introduction: Endocrine cancers present critical challenges due to their systemic hormonal effects and intricate diagnostic profiles. With nearly 10 million cancer-related deaths annually, innovative solutions are imperative. Artificial intelligence (AI) is revolutionizing endocrinology by achieving unparalleled diagnostic precision in endocrine malignancies, offering scalable tools to reduce diagnostic errors and improve outcomes. AIM: This study aimed to develop and validate cutting-edge AI models for endocrine tumor evaluation, using multimodal data, ensuring their global generalizability, and deployment feasibility through a privacy-preserving, universally accessible application to promote equitable cancer care. Methods: Advanced deep learning architectures (EfficientNets and ResNets) were rigorously tested on anonymized multimodal datasets (CT, MRI, ultrasonography, cytopathology, and histopathology) from endocrine tumors in the thyroid, pancreas, pituitary gland, adrenal glands, and ovaries. Internal evaluations ensured model reliability, while external testing across diverse datasets from six continents validated generalizability. An online platform was developed, enabling users to test the models with their own data locally. Real-world applicability was confirmed through independent assessments by healthcare professionals worldwide. Results:EfficientNets and ResNets achieved exceptional diagnostic performance across multiple endocrine tumors (AUROC &amp;gt;99%) and robust generalizability. EfficientNetB0 excelled in treatment response monitoring (&amp;gt;97%). When deployed in an online application, EfficientNetB0 exhibited superior computational efficiency, while maintaining rapid processing speeds (&amp;lt;1 second/image) and diagnostic accuracy. Multi-national healthcare professionals validated the online platform’s reliability and usability, underscoring its global applicability. Conclusion: This study demonstrates the transformative impact of AI in endocrine oncology. Our findings illustrate a paradigm shift in biomedical AI, where minimal computational resources yield maximal diagnostic performance. This highlights the potential of AI-driven approaches to optimize diagnostic workflows, even with minimal computational resources, revolutionizing biomedical AI applications in cancer care. APPLICATION: The AI-powered application provides a privacy-preserving, scalable solution for endocrine tumor detection, classification, treatment monitoring, and advanced clinical decision-making. Validated multi-nationally by healthcare professionals, it ensures robust real-world applicability, fostering equitable cancer care globally. Its adaptability across platforms positions it as a universal tool for cancer care worldwide. Presentation: Sunday, July 13, 2025

In real-world decision-making, uncertainty plays a crucial role, especially when dealing with complex, multi-objective optimization problems. Traditional linear programming (LP) models often have the assumption that the data is precise and deterministic. However, this ‎assumption is often not realistic for many applications, as imprecision will always be present. This paper presents a fuzzy solution to ‎Z-Z-number-based multi-objective linear programming (ZMOLP) models. Z-numbers can combine fuzzy logic and Z-numbers to manage ‎uncertainty in (multi-objective) decision dilemmas. A Z-number consists of two parts: a fuzzy number for the uncertainty in the ‎data and a reliability score that indicates the degree of confidence in the data. The two aspects of Z-numbers make them ef‎fective for modeling imprecise data in multi-objective positioning. The effectiveness and overall computational efficiency of different ‎fuzzy membership functions (triangular, trapezoidal, Gaussian, etc.) were also explored to understand their impact on optimal solu‎tions. Z-numbers, through fuzzy numbers, provide a flexible and adaptable decision-making solution far superior to traditional methods ‎for managing imprecision that leads to a better representation of uncertainty related to objectives and constraints. To demonstrate the viability of Z-number-based approaches, practical applications were employed to illustrate their usage in healthcare decision-making and ‎engineering optimization‎.

Tobacco is a shadow that stretches across nations, economies, and generations. While often discussed in the context of smoking, a more insidious and varied threat has taken deep root, particularly in developing nations like India: smokeless tobacco. This book, *Beyond Smoke: Youth, Slums and Shadows of Tobacco*, is an attempt to sound an alarm and dismantle a public health crisis unfolding in plain sight—the targeting of adolescents in urban slums with addictive, carcinogenic products. The journey of this research began with a simple, disturbing observation: the sight of younger and younger children with small, colourful sachets of gutkha and pan masala, products marketed like candy and priced like it, too. Despite legislative bans, such as the landmark five-year prohibition in Maharashtra in 2002, these products continued to flow freely into the hands of children, facilitated by pocket money, peer pressure, and a startling lack of awareness about their devastating health consequences. This book is the culmination of a year-long, intensive cross-sectional study conducted among 1800 adolescents in an urban slum of a major Indian metropolis. We sought not only to quantify the prevalence of smokeless tobacco use but to understand the intricate web of factors that enable it—socio-demographic backgrounds, family dynamics, knowledge levels, and the powerful influence of marketing and peer networks. Our findings are a clarion call. A prevalence rate of 13.9% among adolescents is not just a number; it represents hundreds of young lives embarking on a path toward addiction, oral lesions, and potentially, cancer. The data reveals that initiation begins as early as 13-14 years, often motivated by a quest for enjoyment or a need for social acceptance. Perhaps one of our most counterintuitive findings challenges traditional assumptions: users often hail from better socio-economic backgrounds, have educated parents, and receive unsupervised pocket money—primarily from their mothers—which is then used to purchase their daily dose of poison. Perhaps most disheartening is the discovery that while most adolescents know tobacco is harmful, a significant knowledge gap exists regarding the specific dangers of its smokeless forms. This critical disconnect between knowledge and practice, combined with easy availability and social acceptability, creates a perfect storm for addiction to thrive. This work would not have been possible without the participation of the 1800 adolescents who shared their stories and experiences. I am deeply grateful for their honesty and trust. I also extend my sincere thanks to the community health workers, the institutional ethics committee, and my professional colleagues for their unwavering support and guidance throughout this endeavour. On a personal note, this work stands on the foundation of my family's unwavering support. My deepest gratitude flows first to my wife, Dr. Vibha, not only for her patience but for her invaluable insights as a psychiatrist, which helped me weave the crucial psychological perspective into this research. I am eternally grateful to my father-in-law, Mr. Anil, whose wisdom as a scholar and academician provided much-needed guidance through the most challenging phases of this journey. My elder brother, Dr. Rahul Gajbhiye, deserves special credit for being the catalyst who first ignited my passion for rigorous inquiry and shaped me into a researcher. To all of them, and to my entire family for their boundless encouragement and faith, I owe a debt I can never fully repay. It is my earnest hope that this book will serve as a valuable resource for public health policymakers, medical practitioners, educators, and non-governmental organizations. By understanding the patterns laid bare in this book, we can begin to craft interventions that are not just policies on paper, but lifelines extended into the heart of these communities. The future of a generation depends on our collective will to act now and shield them from the long, dark shadow of tobacco.

Avian influenza viruses (AIVs) represent an ongoing threat to global health due to their capacity for genetic evolution, zoonotic transmission, and pandemic emergence. This review highlights recent progress in understanding the molecular mechanisms underlying AIV infection, including viral immune evasion strategies and host-virus interactions. It discusses recent outbreaks involving reassortant strains such as H5N1 and H9N2, and examines their public health consequences. Advancements in antiviral therapy, including resistance patterns, and the development of next-generation vaccines such as messenger RNA and universal influenza vaccines are reviewed. The integration of genomic surveillance, artificial intelligence-driven prediction, and One Health approaches is emphasized as essential for pandemic preparedness. This comprehensive overview aims to provide researchers and policymakers with actionable insights for controlling the evolving threat of avian influenza.

The gut microbiome plays a crucial role in regulating immune responses, influencing susceptibility to viral infections, shaping disease progression, and its outcomes. Emerging research highlights the intricate relationship between gut microbial communities and viral pathogenesis, demonstrating that dysbiosis can compromise antiviral defenses while a balanced microbiome enhances immune resilience. This review explores key microbial mechanisms, including microbiome-mediated immune modulation, interactions with viral replication, and the impact of microbiome on systemic inflammation, highlighting how dietary interventions, such as probiotics, prebiotics, and bioactive compounds, offer potential strategies to modulate gut microbiota and mitigate viral infections. Special emphasis is placed on viruses affecting the gastrointestinal and respiratory systems, including severe acute respiratory syndrome coronavirus 2, norovirus, and influenza. Furthermore, we explore how nutrition-driven microbiome interventions may serve as adjunct therapeutic strategies, improving vaccine efficacy and post-viral recovery. Understanding the role of gut microbiome in viral infections can pave the way for microbiome-driven strategies to combat viral diseases and improve overall health outcomes.