Manav Rachna International Institute of Research and Studies

Athletes are chronically exposed to high physiological stress from intense training, prolonged exercise, and inadequate recovery, whichcan trigger systemic inflammation, oxidative stress, and immune dysregulation. These immune challenges highlight the need for dietary strategies that can modulate inflammatory pathways and support athletic performance and recovery. Pearl millet (Pennisetum glaucum), an underutilised ancient grain, has recently gained attention for its dense nutrient profile and bioactive compounds with potential immunomodulatory effects. Thus, this review aim is to explore the potential of pearl millet in modulating the inflammatory cytokines. Pearl millet is highly nutritious crop packed with fibre, polyphenols, flavonoids, peptides, and essential minerals such as iron, magnesium, and zinc, as well as B-vitamins, which enable it to exhibit antioxidant, anti-inflammatory, and gut-microbiota modulating properties that may contribute to improved immune resilience. Insights from cell-based and whole-organism studies indicates that millet consumption can effect inflammatory inducing cytokines (e.g., TNF-α, IL-6), enhance antioxidant enzyme function, improve metabolic health, and support gut integrity, all of which are directly relevant to athletes’ immune function. Emerging research in human populations further suggests that millet-based diets reduce oxidative stress and chronic low-grade inflammation. This review explores current scientific evidence on the immunomodulatory potential of pearl millet, with a focus on its applicability in sports nutrition, and highlights its role as a promising functional grain that can mitigate exercise-induced inflammation and enhance athletic health and performance.

Achieving sustainable health system performance (SHP) is a pressing challenge in emerging economies like India, where disparities in infrastructure, technology, workforce, and policy persist. This study investigates the complex, interdependent drivers of SHP by integrating three analytical methods. Decision-Making Trial and Evaluation Laboratory (DEMATEL), Partial least squares structural equation modeling (PLS-SEM), and artificial neural networks (ANN). Guided by socio-technical systems theory and systems thinking, we explore how health infrastructure (HI), technology adoption (TA), health workforce (HW), policy support (PS), and community engagement (CE) influence SHP. Data were collected from 412 stakeholders across six Indian states using a stratified purposive sampling method. DEMATEL identified causal relationships among constructs; PLS-SEM tested hypothesized paths, including mediation by CE and moderation by PS; and ANN validated predictive strength and variable importance. Results reveal that HI, TA, HW, and PS positively affect SHP. CE significantly mediates the impact of HI, TA, and HW, while PS strengthens the effects of TA and HW. TA and PS emerged as the most influential predictors. These findings underscore the need for integrated, community-centered, and policy-supported strategies to strengthen health systems. This study offers a novel multi-method framework to inform evidence-based health reforms in India and similar contexts.

The growing demand for lightweight, high-strength materials in automotive and aerospace industries has positioned aluminum-based hybrid metal matrix composites (HMMCs) as game-changing alternatives to conventional alloys. However, their enhanced mechanical properties present significant machinability challenges that require advanced processing strategies. This study addresses this critical gap by systematically investigating the electrical discharge machining (EDM) characteristics of a novel Al6063‐10SiC‐5B4C‐Mg hybrid composite fabricated through pressurized stir casting. Employing Response Surface Methodology with Central Composite Rotatable Design (CCRD), we developed robust second-order predictive models for three critical performance indicators: material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR). Comprehensive statistical validation through ANOVA and residual diagnostics confirmed excellent model adequacy at a 95

Cluster headache (CH) is a severe unilateral trigeminal autonomic cephalalgia with limited well tolerated therapies. Calcitonin gene related peptide monoclonal antibodies, including galcanezumab, fremanezumab, and eptinezumab, have established efficacy in migraine and are under evaluation in CH. This study assesses their efficacy and safety using pairwise and network meta analysis. PubMed, Embase, Scopus, and ClinicalTrials.gov were searched through April 2025. Randomized controlled trials enrolling adults with episodic or chronic CH were included. The primary endpoint was change in weekly attack frequency. Secondary outcomes included subtype specific effects and adverse events. Frequentist random effects network meta analysis and pairwise meta analysis were performed in R. Five trials with approximately 1,000 participants were included. No agent demonstrated statistically significant reduction in weekly attack frequency versus placebo, although consistent numerical improvements were observed. Dose specific network estimates showed mean differences of − 0.81 for galcanezumab 300 mg, − 0.17 for eptinezumab 400 mg, 0.71 for fremanezumab 675/225 mg, and − 1.27 for fremanezumab 900/225 mg. Pooled dose estimates were − 0.81 for galcanezumab, − 0.27 for fremanezumab, and − 0.17 for eptinezumab. Pairwise meta analysis yielded a pooled mean difference of − 0.41. Subgroup analysis indicated minimal change in chronic CH and greater numerical reduction in episodic CH. CGRP monoclonal antibodies demonstrate modest directional reductions in attack frequency without statistical significance. Signals appear more pronounced in episodic CH, supporting the need for adequately powered, subtype specific trials.

Food literacy is increasingly recognized as a multidimensional determinant of diet quality and non-communicable disease (NCD) risk, particularly among young adults exposed to rapidly changing food environments. Evidence from India remains limited, especially using validated multidimensional tools linked to population-level diet quality indicators. A cross-sectional study was conducted among 119 Indian adults aged ≥ 18 years. Food literacy was assessed using an adapted 42-item Self-Perceived Food Literacy Scale encompassing planning, management, and selection, preparation, and consumption domains. Diet quality was evaluated using the Diet Quality Questionnaire India (DQQ-India), generating indicators of dietary diversity score, NCD-protective score, NCD-risk score, and global dietary recommendation adherence score. Exploratory factor analysis examined construct validity, and internal consistency was assessed using Cronbach’s alpha. Multivariable linear and exploratory logistic regression analyses were performed to examine associations between food literacy domains, diet quality indicators, BMI, and gender. Participants had a mean age of 26.1 ± 9.6 years, with 46.2