Acute Kidney Injury Research

The paper identifies potential prognostic targets in acute kidney injury using bioinformatics analysis of NGS data from a GEO dataset.

This study maps epigenetic changes during AKI repair in mice and humans, identifying CREB5 as a regulator of both successful and failed tubular repair.

This study uses human kidney organoids to model hypoxic injury and maladaptive repair, identifying molecular and metabolic changes associated with AKI and CKD progression.

This study identifies cytokine biomarkers in urine that can detect AKI early and suggests their role in regulating TP53 and SIRT1 pathways.

This study uses urinary single-cell RNA sequencing to analyze injury and repair processes in acute kidney injury, identifying distinct cellular and molecular dynamics.

This study evaluates urinary exosome NHE3 as a noninvasive biomarker for acute kidney injury (AKI) across multiple animal models and human patients, showing increased NHE3 levels in all tested AKI conditions.

This study identifies sex-dependent gene expression patterns in a pig model of renal ischemia-reperfusion injury, revealing temporal and sexual dimorphism in injury and recovery processes.

This study uses single-nuclei transcriptomics to analyze human kidney injury responses, identifying four injury-associated cell states and showing similar gene expression changes between AKI patients with and without COVID-19.

The paper integrates spatial transcriptomics and single cell sequencing to identify immune and epithelial cell cross-talk patterns in acute kidney injury models.

This study uses single-cell RNA sequencing to identify novel transcriptional signatures and epithelial-stromal interactions in acute kidney injury.

The paper uses single nucleus RNA sequencing to identify a proinflammatory and profibrotic proximal tubule cell state in mouse AKI that fails to repair, suggesting it as a therapeutic target.

This study uses human kidney organoids to model acute kidney injury induced by cisplatin, finding that repeated low-dose treatment reduces toxicity while maintaining AKI responses.

This study uses unsupervised machine learning on EHR data to identify two subphenotypes of sepsis-associated AKI with differing mortality rates, emphasizing the importance of intra-patient variability.

This study develops a machine learning model to predict acute kidney injury using electronic health records, achieving higher accuracy than traditional clinical scores.