Metabolomic profiling to characterize acute intestinal ischemia/reperfusion injury
Bibliographic record
Abstract
Sepsis and septic shock are the leading causes of death in critically ill patients. Acute intestinal ischemia/reperfusion (AII/R) is an adaptive response to shock. The high mortality rate from AII/R is due to the severity of the disease and, more importantly, the failure of timely diagnosis. The objective of this investigation is to use nuclear magnetic resonance (NMR) analysis to characterize urine metabolomic profile of AII/R injury in a mouse model. Animals were exposed to sham, early (30 min) or late (60 min) acute intestinal ischemia by complete occlusion of the superior mesenteric artery, followed by 2 hrs of reperfusion. Urine was collected and analyzed by NMR spectroscopy. Urinary metabolite concentrations demonstrated that different profiles could be delineated based on the duration of the intestinal ischemia. Metabolites such as allantoin, creatinine, proline, and methylamine could be predictive of AII/R injury. Lactate, currently used for clinical diagnosis, was found not to significantly contribute to the classification model for either early or late ischemia. This study demonstrates that patterns of changes in urinary metabolites are effective at distinguishing AII/R progression in an animal model. This is a proof-of-concept study to further support examination of metabolites in the clinical diagnosis of intestinal ischemia reperfusion injury in patients. The discovery of a fingerprint metabolite profile of AII/R will be a major advancement in the diagnosis, treatment, and prevention of systemic injury in critically ill patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".