Inhibition of inducible nitric oxide synthase protects against the deleterious effects of sub-lethal sepsis and ethanol in the cardiorenal system
Bibliographic record
Abstract
We tested the hypothesis that ethanol would aggravate the deleterious effects of sub-lethal cecal ligation and puncture (SL-CLP) sepsis in the cardiorenal system and that inhibition of inducible nitric oxide synthase (iNOS) would prevent such response. Male C57BL/6 mice were treated with ethanol for 12 weeks. One hour before SL-CLP surgery, mice were treated with N6-(1-iminoethyl)-lysine (L-NIL, 5 mg/kg, i.p.), a selective inhibitor of iNOS. A second dose of L-NIL was administered 24 h after SL-CLP surgery. Mice were killed 48 h post surgery and the blood, the renal cortex, and the left ventricle (LV) were collected for biochemical analysis. L-NIL attenuated the increase in serum creatinine levels induced by ethanol, but not by SL-CLP. Ethanol, but not SL-CLP, increased creatine kinase (CK)-MB activity and L-NIL did not prevent this response. In the renal cortex, L-NIL prevented the redox imbalance induced by ethanol and SL-CLP. Inhibition of iNOS also decreased lipoperoxidation induced by ethanol and SL-CLP in the LV. L-NIL prevented the increase of pro-inflammatory cytokines and reactive oxygen species induced by ethanol and (or) SL-CLP in the cardiorenal system, suggesting that iNOS modulated some of the molecular mechanisms that underlie the deleterious effects of both conditions in the cardiorenal system.
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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) | 0.000 | 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 | 0.001 |
| 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".