Chandan Singh

Introduction: Ventilator-associated pneumonia (VAP) is a microbial infection that develops in the lungs when a patient has been intubated for mechanical ventilation for more than 48 h. Diagnosis of VAP remains a challenge despite clinical advances. NMR-based metabolomics might play a pivotal role in overcoming the diagnostic complexity of VAP by investigating biomarkers. Materials and Methods: This case–control study was conducted in 2024–2025 at Sir Sunder Lal Hospital, Varanasi. The participated patients had an intubation period of more than 48 h; a total of 44 patients were recruited, of which 20 patients developed VAP and 24 were non-VAP patients. NMR spectra of serum were recorded, and statistical analysis was done. Results: The 2D scatter plot of PC1 versus PC2 scores clearly shows a distinction between the groups, with the principal component analysis explaining 18.9% of the variance. Partial least-squares discriminant analysis demonstrated R ² and Q ² values of 0.328 and 0.0552, respectively, indicating a probability to model fit. A total of 27 distinct metabolites were identified. Univariate receiver operating characteristic curve analysis identified phenylalanine (area under the curve [AUC] = 0.720), acetoacetate (AUC = 0.706), and glutamate (AUC = 0.690) as significant metabolites. With AUC values within the fair accuracy range, these three metabolites are considered acceptable biomarkers for the diagnosis of VAP. Conclusion: The elevated metabolites indicate lung injury and perturbed inflammatory pathways, which may result from invasive mechanical ventilation. We conclude that the levels of phenylalanine, acetoacetate, and glutamate may serve as biomarkers and aid in the diagnosis of VAP, disease monitoring, and therapeutic stratification.

Enzyme-based therapies for multi-drug resistant infections.

ALS is a severe neurodegenerative disorder characterized by motor neuron degeneration, gut dysbiosis, immune dysregulation, and metabolic disturbances. In this study, shotgun metagenomics and ¹H nuclear magnetic resonance (NMR)-based metabolomics were employed to investigate the altered gut microbiome and metabolite profiles in individuals with ALS, household controls (HCs), and nonhousehold controls (NHCs). The principal component analysis (PCA) explained 33% of the variance, and the partial least-squares discriminant analysis (PLS-DA) model demonstrate R² and Q² values of 0.97 and 0.84, respectively, indicating an adequate model fit. The relative bacterial abundance was 99.34% in the ALS group and 98.94% in the HC group. Among the ten identified genera, Bifidobacterium, Lactobacillus, and Enterococcus were more prevalent in ALS individuals, while Lactiplantibacillus and Klebsiella were more abundant in the HC group. We identified 70 metabolites, including short-chain fatty acids (SCFAs), branched-chain amino acids (BCAAs), carbohydrates, and aromatic compounds, using NMR. Orthogonal partial least-squares discriminant analysis (O-PLS-DA) explained 15.8% of the variance, with a clear separation between the ALS and HC groups. Univariate receiver operating characteristic (ROC) analysis identified three fecal metabolites with AUC values above 0.70, including butyrate (0.798), propionate (0.727), and citrate (0.719). These metabolites may serve as potential biomarkers for ALS. The statistical model for metabolic pathway analysis revealed interconnected pathways, highlighting the complexity of metabolic dysregulation, as well as potential microbial and metabolic biomarkers in ALS. These results highlight the role of gut microbiome alterations in ALS and suggest potential avenues for therapeutic intervention.

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder characterized by motor neuron loss, leading to severe physical impairment and mortality. Despite available treatments like Riluzole and Edaravone, their limited efficacy highlights the need for improved understanding of ALS pathology. This study has explored metabolic alterations in North Indian ALS patients using ¹H Nuclear Magnetic Resonance (NMR)-based metabolomics. A case–control study, involving 45 ALS patients and 30 healthy controls (HCs) was performed. Serum samples were analyzed using 600-MHz NMR spectrometer, revealing significant metabolic differences between ALS and HC groups. Multivariate analyses identified nine dysregulated metabolites—pyruvate, glutamine, histidine, isoleucine, leucine, imidazole, arginine, creatinine, and choline—with ROC analysis showing isoleucine as a promising biomarker (AUC 83%). Pathway enrichment analysis highlighted disruptions in key metabolic pathways, including the Glucose-Alanine Cycle, Urea Cycle, Ammonia Recycling, and the Warburg Effect, suggesting potential links to neuroinflammatory and mitochondrial dysfunction in ALS pathogenesis. This pilot study provides insight into ALS-specific metabolic alterations in Indian cohort and demonstrates the potential of these metabolites as diagnostic biomarkers. Our findings identify potential biomarkers that require validation in larger, multi-centric cohorts to support diagnosis, prognosis, and improved management of ALS. Graphical abstract

Oral squamous cell carcinoma (OSCC) is frequently the outcome of oral submucous fibrosis (OSMF), a common possibly premalignant disease. In our study, a cohort of 50 patients with OSCC and OSMF, along with 50 healthy controls, was analyzed to identify significant metabolic differences between the patient and control groups through multivariate statistical analysis using NMR-based metabolomics in saliva samples. The 2D scatter plot of PC1 versus PC2 scores clearly show a distinction between the groups, with the principal component analysis (PCA) explaining 24.6% of the variance. Partial least-squares discriminant analysis (PLS-DA) demonstrated R² and Q² values of 0.94 and 0.90, respectively, indicating a robust model fit. A total of 20 distinct metabolites were identified, including 5 that were up-regulated and 5 that were down-regulated. Univariate ROC curve analysis identified nine salivary metabolites with AUC values exceeding 0.70, including acetone, tryptophan, 5-aminopentanoic acid, betaine, aspartic acid, ethanol, acetoacetate, adipic acid, and citrate. Notably, the distinct presence of three metabolites—acetone, tryptophan, and 5-aminopentanoic acid—yielded AUC values of 0.98123, 0.95358, and 0.91506, respectively. The refined statistical model was subjected to metabolic pathway analysis, revealing interconnected pathways. We were also able to predict the severity of the disease, specifically distinguishing between stage I and stage II OSCC. This differentiation was highlighted by the PCA score plot, which explained 28.6% of the variance. These results were further confirmed by PLS-DA. These insights pave the way for early diagnosis and predicting severity in patients with oral cancer, which will enable better management of the disease.