Acute organ injury is associated with alterations in the cell-free plasma transcriptome
Bibliographic record
Abstract
BACKGROUND: Despite a genomic revolution in biological sciences, clinical medicine has yet to integrate diagnostics based upon gene expression into practice. While commonly used plasma protein assays rely on organ-specific origins, nearly all nucleic acid in whole blood is derived from white blood cells limiting their utility to diagnose non-immune disorders. The aim of the study was to use cell-free plasma to define circulating messenger RNA sequences diagnostic of acute organ injury, including myocardial infarction (MI) and acute kidney injury (AKI). METHODS: In healthy human subjects (N = 4) and patients with acute MI (N = 4), we characterized the concentration and nature of circulating plasma RNA through spectrophotometry and chromatography. Through reverse transcriptase polymerase chain reaction (RT-PCR) of amplicons up to 939 base pairs, we determined whether this mRNA was intact but of insufficient quantity to sequence. In mice, we induced an acute anterior myocardial infarction through 1 h of ischemia followed by reperfusion of the left anterior descending (LAD) artery. We compared the cell-free plasma transcriptome using cDNA microarray in sham-operated mice compared to ischemia upon reperfusion and at 1 and 4 h. To determine organ specificity, we compared this profile to acute ischemia-reperfusion of the kidney. RESULTS: In humans, there is more plasma RNA in those with acute MI than in healthy controls. In mice, ischemia-reperfusion of the LAD artery resulted in a time-dependent regulation of 589 circulating mRNA transcripts with less than a 5% overlap in sequences from acute ischemia-reperfusion injury of the kidney. CONCLUSIONS: The mRNA derived from cell-free plasma defines organ injury in a time and injury-specific pattern.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".