Minnie Sarwal

The paper presents a multi-modal atlas of human kidney disease, identifying cellular and spatial mechanisms of injury resolution or failure and linking them to clinical outcomes.

Transplant-Agents uses a multi-agent AI framework to identify reproducible biomarkers and predict transplant rejection risk with high AUROC scores across multiple datasets.

The paper introduces the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets for comparative vaccine response analysis.

The paper constructs a reference tissue atlas of the human kidney using multi-omics data to identify cell types, pathways, and genes across nephron segments and interstitium.

The paper identifies a pre-vaccination transcriptional state linked to pro-inflammatory genes that predicts higher antibody responses to multiple vaccines.

The paper constructs a comprehensive urine proteome database from diverse renal and prostate cancer conditions, identifying potential biomarkers and unique proteins.

This study uses single-cell RNA sequencing to analyze Tocilizumab-treated PBMCs, showing reduced inflammatory gene expression in monocytes, supporting its potential to mitigate cytokine storms in COVID-19.

The paper identifies 100 distinct kidney cell clusters using Mux-Seq and scRNA-seq, revealing novel proximal tubular subsets and cell state transitions in the collecting duct.

The paper develops a molecular and cellular instability score to reclassify histologically stable kidney allografts, identifying those with molecular signatures of acute rejection and predicting clinical outcomes.