L-NAME improves doxycycline and ML-7 cardioprotection from oxidative stress
Bibliographic record
Abstract
Matrix metalloproteinase-2 (MMP-2) mediated degradation of myosin light chain 1 (MLC1) and troponin I (TnI) contributes to myocardial ischemia/reperfusion (I/R) injury. Modifications of MLC1 triggered by oxidative stress are mediated by myosin light chain kinase (MLCK), nitric oxide synthase (NOS), and MMP-2. Previous studies have shown that inhibiting both MLCK and MMP-2 protects against I/R injury. Here, we hypothesized that the addition of NOS inhibitor (L-NAME) at subprotective concentration to the mixture of subprotective concentrations of ML-7 and doxycycline (Doxy), will increase a synergistic cardioprotection of Doxy and ML-7 during I/R. Isolated rat hearts were subjected to global ischemia without or with administration of the mixture of inhibitors. Markers of I/R injury were measured in hearts and coronary effluents. Addition of L-NAME to the mixture of Doxy and ML-7 led to full recovery of heart contractility in comparison to combination of Doxy and ML-7. Improved heart contractility was associated with reduced degradation of TnI and MLC1. The combined administration of NOS, MMP-2 and MLCK inhibitors provides a novel strategy to protect heart from I/R 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".