Application of machine learning tools and integrated OMICS for screening and diagnosis of inborn errors of metabolism.

Introduction: Tandem mass spectrometry (TMS) has emerged an important screening tool for various metabolic disorders in newborns. However, there is inherent risk of false positive outcomes. Objective To establish analyte-specific cutoffs in TMS by integrating metabolomics and genomics data to avoid false positivity and false negativity and improve its clinical utility.

Application of machine learning algorithms for the differential diagnosis of peroxisomal disorders.

We have established diagnostic thresholds of very long-chain fatty acids (VLCFA) for the differential diagnosis of peroxisomal disorders using the machine learning tools. The plasma samples of 131 controls and 90 cases were tested for VLCFA using gas chromatography-mass spectrometry following stable isotope dilution. These data were used to construct association rules and for recursive partitioning.

In vitro studies on liposomal amphotericin B obtained by supercritical carbon dioxide-mediated process.

Nanotechnology in drug delivery is a rapidly expanding field. Nanosized liposomal preparations are already in use for efficient drug delivery with better therapeutic indices. Existing methods of liposome preparation are limited by problems of scale-up, difficulty in controlling size, and intercalation efficiency.