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• W2950231844 abstract "Abstract Our molecular understanding of clinical conditions progressing from acute organ injury to irreversible dysfunction is limited. We used renal transplantation as a model to characterize the transcriptional response along the transition from acute kidney injury to allograft fibrosis in humans. The integrated analysis of 163 transcriptomes with machine learning techniques identified shared and divergent transcriptional trajectories determining distinct clinical outcomes in a heterogeneous population. The molecular map of renal responses to injury was validated in a mouse ischemia-reperfusion injury model and highlighted early markers of disease progression. This generally applicable approach opens the way for an unbiased analysis of progressive diseases." @default.
• W2950231844 created "2019-06-27" @default.
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• W2950231844 date "2018-06-08" @default.
• W2950231844 modified "2023-09-23" @default.
• W2950231844 title "Transcriptional trajectories of human kidney disease progression" @default.
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• W2950231844 doi "https://doi.org/10.1101/342972" @default.
• W2950231844 hasPublicationYear "2018" @default.
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