Publications

Cardiac rehabilitation (CR) is a critical component of secondary prevention in cardiovascular disease (CVD) management. In India, where CVD prevalence is rising rapidly, CR remains severely underutilized due to multiple systemic barriers. These include limited infrastructure, insufficient funding, low awareness, and inequitable access across urban and rural regions. This review assesses the current CR landscape in India, contrasts it with global benchmarks, and highlights key implementation gaps. It further explores scalable solutions such as telerehabilitation, community-based programs, and integrated multidisciplinary models. The paper emphasizes the need for robust policy frameworks, sustainable funding, infrastructure strengthening, and comprehensive workforce development. Achieving universal access to CR in India demands a multisectoral, collaborative approach involving government agencies, healthcare providers, academic institutions, nongovernmental organizations (NGOs), and private stakeholders. Enhancing CR services is not only a clinical necessity but also a national public health priority.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease (NAFLD), has emerged as a significant public health concern, affecting approximately 25% of the global population with its prevalence rising from 22% in 1991 to 37% in 2019.1 While the hepatic consequences of MASLD, such as steatohepatitis, fibrosis, and cirrhosis, are well documented, its systemic implications are increasingly coming to light. While traditionally viewed as a hepatic disorder, growing evidence highlights MASLD as a multisystem disease with profound implications on cardiovascular health. Atherosclerotic cardiovascular disease (ASCVD) has now been recognized as the leading cause of mortality in patients with MASLD, surpassing liver-related complications. MASLD is present in up to 75% of patients with type 2 diabetes mellitus (T2DM). Notably, MASLD is linked to a higher risk of cardiovascular diseases (CVD), including arrhythmia, atherosclerotic heart disease, heart failure, and CVD-related mortality.2 The association between MASLD and ASCVD is particularly alarming, positioning MASLD as a critical gateway for cardiovascular morbidity and mortality.

Accurate prediction of patient outcomes in intensive care units (ICUs) is crucial for enhancing clinical decision-making, patient care, and resource allocation. Traditional scoring systems like Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), and Sequential Organ Failure Assessment (SOFA), while valuable, fall short of fully capturing the complexities of critically ill patients. Advances in machine learning (ML) enable the analysis of high-dimensional data, including electronic health records (EHRs), physiological parameters, and genomic information, providing a more comprehensive approach to outcome prediction. This review aims to assess the impact of ML techniques, including deep learning (DL), ensemble machine learning (EML), and reinforcement learning (RL), in improving ICU outcome predictions, particularly in identifying high-risk patients and enabling proactive interventions. Machine learning models have shown superiority over traditional systems, enabling more accurate identification of critical patients. However, implementing ML in ICU settings comes with challenges, including data quality, model interpretability, ethical concerns, and workflow integration. Collaborative efforts between clinicians, data scientists, and multidisciplinary teams, supported by shared databases like Medical Information Mart for Intensive Care (MIMIC), are essential for developing generalizable ML models that work across diverse healthcare environments. Future research should focus on improving real-time prediction using wearable technology and personalized risk assessments to further individualize ICU care. Ethical considerations, particularly data privacy and model transparency, must be addressed as ML becomes more integrated into critical care.

Lyme disease (LD) is a multisystem inflammatory zoonosis affecting the skin, heart, nervous system, and joints, transmitted by ticks and caused by infection with species of the Borrelia burgdorferi sensu lato (B. burgdorferi s.l.) complex. It is the most common emerging vector-borne disease in the United States. The Centers for Disease Control and Prevention (CDC) estimated the annual occurrence of 3,29,000 cases of LD in the United States during 2005-2010, and it increased to 4,76,000 during 2010-2018. The incidence of various clinical manifestations of LD differs among countries or regions based on the prevalent genospecies of the B. burgdorferi s.l. complex responsible for infection. Ticks of Ixodes spp. are the main vectors involved in the transmission of LD, which occurs mainly during the spring season. However, in North America and Europe, there is a rise in temperature due to global warming, leading to the extension of tick habitats toward northern areas. These ticks now stay active for an extended period of the year, increasing the chances of transmission to humans, and it is postulated to be one of the reasons responsible for the rising cases of LD. Early diagnosis and treatment with appropriate antibiotics can resolve the early manifestations of LD and prevent subsequent complications, which are known to occur if not treated appropriately. The disease is most common in rural areas and is difficult to differentiate clinically from other tropical infections such as rickettsial infections. The literature on LD in India is limited; however, LD has been reported from at least 12 states of India. A recently concluded study by the Indian Council of Medical Research (ICMR) has documented the seroprevalence of this disease in eight sites situated in areas of North (Himachal Pradesh and Haryana) and Northeast India (Meghalaya, Assam, Mizoram, and Tripura). LD remains grossly underdiagnosed in India. The lack of awareness among clinicians regarding the prevalence of LD and the limited availability of diagnostic investigations may have contributed toward it. LD should no longer be confined to textbooks, but it should find a place in the list of differential diagnoses in clinical practice. This review is an endeavor to sensitize physicians regarding LD and its impending rise worldwide due to global warming.

BACKGROUND AND AIM: Abdominal tuberculosis (ATB) poses significant diagnostic challenges due to its varied clinical manifestations and its ability to mimic other diseases. Histopathology is a promising diagnostic tool to diagnose ATB. This narrative review aims to synthesize evidence on the evolving role of histopathology in diagnosing ATB, highlighting its integration with molecular and microbiological diagnostics, and discussing its limitations and emerging technologies.

METHODOLOGY: A structured search of databases including PubMed, Scopus, Web of Science, and Google Scholar was performed, focusing on literature published from January 2002. The review includes peer-reviewed original articles on the diagnosis of ATB using histopathology and integrated diagnostic modalities.

RESULTS: Histopathology remains crucial for diagnosing ATB, especially in resource-limited settings, due to its ability to visualize granulomatous inflammation and other cellular features. The integration of histopathology with molecular diagnostics like GeneXpert Mycobacterium tuberculosis/rifampicin (MTB/RIF) and tuberculosis polymerase chain reaction (TB-PCR) has improved diagnostic accuracy. However, limitations include diagnostic overlap with other conditions and the impact of prior treatment on tissue samples. Emerging technologies such as digital pathology and artificial intelligence (AI)-driven image analysis are poised to enhance diagnostic precision.

CONCLUSION: The review underscores the importance of a multimodal diagnostic approach, combining histopathology with other techniques to improve sensitivity and specificity. As ATB continues to be a global health concern, advancements in histopathological techniques and interdisciplinary collaboration are essential for timely and accurate diagnosis.

Artificial intelligence (AI) is universally adopted in our day-to-day life, including medical science, and transforming healthcare in various ways, like scientific discovery, collecting and interpreting large data, and gaining insights that might not have been possible by traditional scientific tools. AI also helps learning by geometric understanding, leveraging knowledge, enhanced accuracy and efficiency in diagnostics, imaging, clinical decisions, predictive analysis, drug discovery, virtual assistance, administrative automation, telemedicine, and precision medicine. However, AI lacks emotional consciousness, moral understanding, spiritual insight, and human psychology. AI is a tool to help us and not a human being. Humanity, sociality, spirituality, and emotions are difficult to define. Human emotions are internal, subjective experiences such as happiness, sadness, anger, fear, love, empathy, and sympathy, deeply rooted in our biological systems, memories, and personal experiences, and AI can simulate these emotions but cannot feel or experience them, while spirituality involves meaning, purpose, and belief in something more than oneself (e.g., God or supreme power). AI has no soul or belief and spiritual practices. However, concerns persist, including biases ingrained in AI algorithms, lack of transparency in decision-making, potential compromises of patient data, privacy, and safety of AI implementation in clinical settings. Artificial intelligence has enormous potential in choosing complex regimes, faster calculations, streamlining workflows, and expanding access to healthcare. Nevertheless, AI cannot experience emotions, exercise moral reasoning, or offer genuine spiritual companionship, and successful integration requires AI to function strictly as an assistant to healthcare professionals (HCPs). "AI has vast potential, but it cannot be human, social, emotional, and spiritual."