Molecular hydrogen: prospective treatment strategy of kidney damage after cardiac surgery
Bibliographic record
Abstract
Cardiac surgery-associated acute kidney injury is a common post-operative complication, mostly due to increasing oxidative stress. Recently, molecular hydrogen (H2 gas) has also been applied to cardiac surgery due to its ability to reduce oxidative stress. We evaluated the potential effect of H2 application on the kidney in an in vivo model of simulated heart transplantation. Pigs underwent cardiac surgery within 3 h while connected to extracorporeal circulation (ECC) and subsequent 60 min of spontaneous reperfusion of the heart. We used two experimental groups: T—pigs after transplantation and TH—pigs after transplantation treated with 4% H2 mixed with air during inhalation of anesthesia and throughout oxygenation of blood in ECC. The levels of creatinine, urea and phosphorus were measured in plasma. Renal tissue samples were analyzed by Western blot method for protein levels of nuclear factor erythroid 2–related factor 2 (Nrf2), Kelch-like ECH-associated protein 1 (Keap-1), and superoxide dismutase (SOD1). After cardiac surgery, selected plasma biomarkers were elevated. However, H2 therapy was followed by the normalization of all these parameters. Our results suggest activation of Nrf2/Keap1 pathway as well as increased SOD1 protein expression in the group treated with H2. The administration of H2 had a protective effect on the kidneys of pigs after cardiac surgery, especially in terms of normalization of plasma biomarkers to control levels.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".