GUT LUMINAL LACTATE MEASURED BY MICRODIALYSIS MIRRORS PERMEABILITY OF THE INTESTINAL MUCOSA AFTER ISCHEMIA
Bibliographic record
Abstract
The aim of the present study was to investigate the influence of a prolonged initial intestinal ischemic insult on transmucosal permeability after a subsequent ischemic event and whether microdialysis of biomarkers released to the gut lumen is able to reflect changes in intestinal permeability. The superior mesenteric artery was cross-clamped for 60 min followed by 4 h of reperfusion in 16 pigs. Nine pigs had a second cross-clamp of 60 min and 3 h of reperfusion, whereas seven pigs were observed for a further 4 h of reperfusion. Intestinal mucosal integrity was assessed by permeability of C-polyethylene glycol (PEG-4000) over the gut mucosa, luminal microdialysis of lactate, glucose and glycerol, and tonometry. During reperfusion, the PEG-4000 amount in venous blood was two times higher after the first than after the second ischemia (area under the curve, 44,780 [13,441-82,723] vs. 22,298 (12,213-49,698] counts min mL(-1), P=0.026 [mean {range}]). There was less lactate detected in the gut lumen after the second ischemia compared with the first (area under the curve, 797 [412-1,700] vs. 1,151 [880-1,969] mmol min L(-1), P=0.02) and a lower maximum concentration (4.8 [2.7-9.4] vs. 8.5 [5.0-14.9] mM, P=0.01). The same pattern was also seen for luminal glycerol and glucose. During the second ischemia, the intestinal mucosal/arterial CO2 gap was identical to the level during the first ischemic episode. A prolonged ischemic insult of the intestine confers protection, for reduced hyperpermeability against further ischemia. Microdialysis of biomarkers mirrors permeability changes associated with this type of protection. Lactate reflects permeability across the intestinal mucosa more precisely than glycerol.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".