K J Somaiya Medical College

Central nervous system tuberculosis (CNS-TB) commonly presents with tuberculous meningitis and may be complicated by hydrocephalus, usually early in the disease course. Late-onset hydrocephalus occurring after prolonged anti-tubercular therapy (ATT) is rare. We report an 11-year-old girl with extensively drug-resistant (XDR) CNS-TB with multiple tuberculomas who developed asymptomatic obstructive hydrocephalus after 18 months of ATT. She initially presented with miliary TB and subsequently developed TB meningitis with multiple intracranial tuberculomas. Despite prolonged second-line ATT, corticosteroids, and thalidomide for paradoxical upgrading reactions (PUR), follow-up MRI revealed moderate triventricular obstructive hydrocephalus due to persistent tuberculomas. She remained neurologically asymptomatic but required ventriculo-peritoneal shunt placement. This case highlights the possibility of late-onset hydrocephalus in drug-resistant CNS-TB, emphasizes the role of long-term neuroimaging surveillance, and underscores the need for vigilance even in clinically stable patients on prolonged ATT.

We perform a late-time cosmological study, we compare the performance of two Dirac-Born-Infeld (DBI) type k-essence scalar field extensions of the model to the standard framework and a scenario using the Chevallier-Polarski-Linder (CPL) equation of state parametrization. We solve background dynamics numerically as functions of redshift and incorporate them into a Bayesian inference pipeline accelerated by machine learning. We use a Flax-based surrogate emulator to replace repeated direct integrations of the ODE system, reducing computational cost. A hybrid scheme that combines stochastic variational inference (SVI) with No-U-Turn Hamiltonian Monte Carlo constrains cosmological parameters using the Pantheon+SH0ES Type Ia supernova sample, DESI BAO (DR2) data, and cosmic chronometer measurements without CMB-based priors. In both DBI k-essence formulations, present-day dark energy equations of state are consistent with cosmic acceleration, indicating a -like regime with a modest redshift dependence. The model is marginally favored by conventional model selection measures such as , AIC, BIC, and DIC, which are based on goodness of fit and penalized. However, Bayesian predictive measures like WAIC and PSIS-LOO show no significant differences between , , and DBI k-essence scenarios. All have similar model weights and out-of-sample predictive performance for the datasets. Thus, DBI k-essence models mimic the success of the classic paradigm while allowing controlled, redshift-dependent deviations from a strict cosmological constant that are consistent with present late-time observations.

This paper traces the evolution of corporate involvement in child education in Maharashtra from India’s independence in 1947 to the advent of the National Education Policy (NEP) 2020. It argues that this involvement has transitioned through three distinct phases:a post-independence era of charitable philanthropy (1947-1990), a period of liberalisation-driven strategic community investment (1991-2012), and the contemporary phase of mandated, structured Corporate Social Responsibility (CSR) as partnership (2013-2020). Through historical analysis, the paper examines the shifting motivations, scales, modalities, and impacts of corporate interventions. It highlights how the locus of activity shifted from isolated charity by industrial houses to compliance-driven projects, and further towards aligning with state priorities, culminating in the NEP 2020’s explicit call for collaborative partnerships. The study concludes that while the scale and professionalism of corporate engagement have increased, challenges in equity, sustainability, and genuine integration with public systems persist. The paper suggests that the post-NEP 2020 future requires moving beyond project-based CSR to systemic, outcome-focused co-creation with government and communities.

Sodium–glucose cotransporter 2 inhibitors (SGLT2i) were originally developed as glucose-lowering therapies for type 2 diabetes mellitus. However, robust clinical evidence has demonstrated substantial cardiovascular and renal protective effects that extend beyond glycemic control. Emerging data highlight their systemic influence across the cardiovascular–renal–metabolic (CRM) continuum, a conceptual framework describing the shared pathophysiological links between metabolic dysfunction, heart failure (HF), and chronic kidney disease (CKD). Despite the rapid expansion of clinical and mechanistic evidence, the integration of these insights into coordinated therapeutic implementation across cardiology, nephrology, and endocrinology remains incompletely synthesized. This structured narrative review synthesized evidence from PubMed/MEDLINE, Embase, and Google Scholar to identify relevant studies published between January 2016 and December 2025. Emphasis was placed on randomized controlled trials, meta-analyses, large observational cohorts, guideline documents, and translational mechanistic investigations evaluating pharmacologic mechanisms, clinical efficacy, and multidisciplinary applications of SGLT2i across CRM conditions. Cardiovascular and renal outcome trials consistently show that SGLT2i reduce hospitalization for heart failure, delay CKD progression, and improve major cardiovascular outcomes in both diabetic and non-diabetic populations. Mechanistically, these agents restore tubuloglomerular feedback, enhance cardiac energy efficiency through increased ketone utilization, attenuate inflammatory and profibrotic signaling pathways, and improve mitochondrial bioenergetics. These multisystem effects contribute to therapeutic benefits across HF phenotypes and CKD stages while also improving metabolic parameters such as adiposity, blood pressure, and hepatic steatosis. Collectively, current evidence supports SGLT2i as foundational disease-modifying therapies across the CRM spectrum. Future investigations should prioritize precision-based treatment approaches, biomarker-guided patient selection, and rational combination pharmacotherapy to further optimize outcomes across interconnected cardiovascular, renal, and metabolic diseases.