Rishikesan Kamaleswaran

This study evaluates a simplified PDMS sampling method for skin VOC collection, finding it captures individual VOC profiles with lower within-donor variability than between-donor variability.

BIOCURRENT infers donor-specific pseudotime geometry in transcriptomic data by modeling gene expression with microenvironmental context and latent pseudotime, revealing condition- and donor-specific distortions in progression intervals.

The paper identifies 54 differentially expressed genes in pediatric septic shock patients and links them to immune response and chemokine pathways, distinguishing survivors from non-survivors.

This study develops machine learning models to predict sepsis onset using real-time physiological data and white blood cell counts, achieving early detection up to 3.5 hours before symptoms.