I-FABP as Biomarker for the Early Diagnosis of Acute Mesenteric Ischemia and Resultant Lung Injury
Bibliographic record
Abstract
Acute mesenteric ischemia (AMI) is a life-threatening condition that can result in multiple organ injury and death. A timely diagnosis and treatment would have a significant impact on the morbidity and mortality in high-risk patient population. The purpose of this study was to investigate if intestinal fatty acid binding protein (I-FABP) and α-defensins can be used as biomarkers for early AMI and resultant lung injury. C57BL/6 mice were subjected to intestinal ischemia by occlusion of the superior mesenteric artery. A time course of intestinal ischemia from 0.5 to 3 h was performed and followed by reperfusion for 2 h. Additional mice were treated with N-acetyl-cysteine (NAC) at 300 mg/kg given intraperitoneally prior to reperfusion. AMI resulted in severe intestinal injury characterized by neutrophil infiltrate, myeloperoxidase (MPO) levels, cytokine/chemokine levels, and tissue histopathology. Pathologic signs of ischemia were evident at 1 h, and by 3 h of ischemia, the full thickness of the intestine mucosa had areas of coagulative necrosis. It was noted that the levels of α-defensins in intestinal tissue peaked at 1 h and I-FABP in plasma peaked at 3 h after AMI. Intestinal ischemia also resulted in lung injury in a time-dependent manner. Pretreatment with NAC decreased the levels of intestinal α-defensins and plasma I-FABP, as well as lung MPO and cytokines. In summary, the concentrations of intestinal α-defensins and plasma I-FABP predicted intestinal ischemia prior to pathological evidence of ischemia and I-FABP directly correlated with resultant lung injury. The antioxidant NAC reduced intestinal and lung injury induced by AMI, suggesting a role for oxidants in the mechanism for distant organ injury. I-FABP and α-defensins are promising biomarkers, and may guide the treatment with antioxidant in early intestinal and distal organ injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".