Effects of Therapeutic Hypercapnia on Mesenteric Ischemia–Reperfusion Injury
Bibliographic record
Abstract
Hypercapnic acidosis protects against direct lung injury in in vivo and ex vivo models, however, lung injury/acute respiratory distress syndrome commonly occurs after a nonpulmonary etiology. We investigated whether therapeutic hypercapnia (TH)-deliberate elevation of carbon dioxide (CO2) tension-would protect against lung injury after splanchnic ischemia-reperfusion injury in an in vivo model. TH was associated with preservation of lung mechanics, attenuation of protein leakage, and improved oxygenation compared with control conditions. Lung protection was therapeutic as well as prophylactic. Protection was dose-dependent, but inspired CO2 concentrations above 5.0% were associated with little additional lung protection. Before lung injury, increasing FICO2 resulted in a dose-dependent increase in PaO2. Lung protection with hypercapnia occurred despite pulmonary artery pressures that were greater than observed with normocapnia. Reperfusion increased lipid peroxidation (tissue 8-isoprostane concentration) in the bowel, liver, and lung, and caused histologically apparent bowel injury; however, none of these effects was altered by TH. Therefore, TH-induced by adding CO2 to inspired gas-provides consistent protection against lung injury in terms of lung permeability, oxygenation, and lung mechanics after mesenteric ischemia-reperfusion. These data further support the emerging evidence for ongoing physiologic study of TH at the bedside.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".