Max Healthcare Institute Limited is a hospital chain based in New Delhi, India. Max Healthcare owns and operates healthcare facilities across the National Capital Region (NCR) of Delhi, North India, and the western port city of Mumbai.
Obesity is a global health crisis affecting developing nations, including India. The management of obesity continues to evolve with newer drugs, metabolic and bariatric surgery and endoscopic interventions, requiring family physicians and specialists to adapt their clinical practice accordingly. There is an urgent need for a standardized algorithm to diagnose, stage, and treat obesity. The Endocrine Society of India (ESI) and the Obesity Surgeons Society of India (OSSI) appointed a steering committee to develop an evidence-based algorithm for managing patients with obesity in India. This was put to vote by 80 specialists (38 from OSSI and 42 from ESI) in a physical meeting. A proposed stage-wise algorithm based on Edmonton Obesity Staging System, Asian definition of obesity, and resources in India, received 100
Predictive multiscale cellular modeling is emerging as a consequential direction in precision medicine, converging hypothesis grammars, digital twins, and integrative genomics to interrogate tumor-immune dynamics, therapeutic resistance, and cellular plasticity. This perspective synthesizes recent progress across these domains and critically maps their translational potential alongside their current limitations. Hypothesis grammars translate mechanistic theories into executable agent-based models (ABMs) and hybrid ODE-PDE systems, enabling rapid in silico hypothesis testing while lowering the authoring barrier for domain scientists. Patient-specific digital twins, driven by multi-omics data, employ stochastic ensemble methods to simulate clonal evolution and microenvironmental interactions, though prospective clinical validation of these capabilities remains at an early stage. Integrative genomics, leveraging algorithms such as SCODE and SimiC, infers causal gene regulatory networks (GRNs) using Bayesian variational autoencoders, embedding dynamic intracellular logic into tissue-scale simulations. Emerging applications include in silico oncology trials for optimizing checkpoint blockade and combination therapies. Large language models are being explored to enhance rule induction, while FAIR-compliant digital cell repositories aim to ensure reproducibility and reuse. Verification, validation, and uncertainty quantification (VVUQ) via Sobol sensitivity analysis and Kennedy-O’Hagan calibration are identified as essential components for addressing non-identifiability and supporting regulatory credibility. Federated learning is discussed as a means of mitigating privacy and bias concerns in multi-institutional settings. Together, these converging approaches outline a plausible pathway toward virtual clinical trials and adaptive theranostics, contingent on the prospective validation, data infrastructure, and governance frameworks that clinical deployment will require.
Large samples have well-known merits in empirical research where multiple factors are at work, some beyond control. Large samples also provide a precise estimate of the effect size, although they produce results for groups and not for individuals. Nonetheless, there are situations where intensive, in-depth, study of a few may be rewarding, particularly in medical research that focuses on basic mechanism rather than treatment effect. With extreme care, logical structure, advanced tools, and rigorous reasoning, many sources of variability can be minimized or eliminated. Thus, small samples tend to foster brilliance by focusing on improved decision making. Reduced cost, faster completion, and clinically relevant effect sizes are among other advantages. Many successful studies have been done on small samples. These merits of small sample studies in certain types of medical research deserve serious consideration.
To assess the efficacy and safety of a fixed-dose combination (FDC) of dapagliflozin and pioglitazone versus a loose combination (LC) as an add-on therapy to metformin in Indian adults with inadequately controlled type 2 diabetes mellitus (T2DM). This 12-week PRO-1 study was a randomized, open-label, multicenter phase 3 trial that enrolled 180 Indian adults with T2DM Glycated Hemoglobin (HbA1c) > 7.5 to 10
