Krishna R. Kalari

This study shows butyrate reduces Aβ42 and TNF-α-induced VCAM-1 expression in BBB endothelial cells by modulating the STAT3/GATA6 axis.

The paper constructs a comprehensive single-nucleus RNA-seq atlas of the human neurovascular unit in Alzheimer's disease, revealing sex-specific vascular dysfunctions and disrupted intercellular communication.

This study shows that Aβ40 exposure disrupts insulin signaling in BBB endothelial cells by reducing Akt phosphorylation and altering AMPK activity.

This study shows TNF-alpha increases A{beta}42 accumulation in BBB endothelial cells by altering endocytosis-related proteins, linking inflammation to A{beta} pathology in Alzheimer's disease.

The paper uses digital spatial profiling to analyze transcriptomic changes in the neurovascular unit of Alzheimer's transgenic mice, identifying dysregulated signaling and potential therapeutic targets.

This study shows gut microbiota affects drug absorption and metabolism by altering gene expression and intestinal permeability in mice.

This paper develops chemotherapy-specific predictors for triple-negative breast cancer using patient-derived xenografts and multi-omic data, achieving high AUROC scores for response prediction.

OmicsFootPrint integrates and interprets multi-omics data using circular images and deep neural networks, achieving high AUC scores in cancer subtype classification and drug response prediction.

The paper identifies transcriptional network disruptions in drug-resistant prostate cancer using TraRe, uncovering key regulatory modules and transcription factors associated with resistance.

The paper analyzes insulin-responsive gene expression dynamics in BBB cells to identify molecular mechanisms linking insulin resistance to AD pathology.

The paper identifies pathologically relevant signaling pathways in cancers by analyzing microRNA response element frequencies using a novel bioinformatics method.

This paper introduces GeneSet MAPR, a supervised learning algorithm for gene set analysis that leverages network patterns to improve identification of genes related to a given gene set, outperforming existing methods.

PANOPLY prioritizes cancer drugs for individual patients using multi-omics data and machine learning, showing effectiveness in a chemotherapy-resistant tumor model.

The paper introduces ProGENI, a gene prioritization method that improves drug sensitivity prediction by integrating gene expression and prior biological knowledge, identifying new genes involved in chemoresistance.