Antidigoxin antiserum prevents endogenous digitalis-like compound-mediated reperfusion injury via modulating sodium pump isoform gene expression
Bibliographic record
Abstract
Endogenous digitalis-like compound (EDLC) is an endogenous ligand of the digitalis receptor and can remarkably inhibit Na+/K+-ATPase activity. Antidigoxin antiserum (ADA), a selective EDLC antagonist, may lessen myocardial reperfusion injury; however, the molecular mechanisms underlying the effect remain unclear. Therefore, this study investigated whether ADA may prevent myocardial reperfusion injury and modulate gene expression of sodium pump alpha isoforms. Cardiac function was examined in isolated rat hearts subjected to ischemia and reperfusion (I/R). The infarct size, EDLC level, Na+/K+-ATPase activity, and the levels of mRNA for sodium pump alpha isoforms were measured in vivo I/R rat hearts in the presence or absence of ADA. It was found that ADA significantly improved the recovery of cardiac function, decreased infarct size, decreased EDLC level, and recovered Na+/K+-ATPase activity in I/R hearts. Further studies showed that sodium pump alpha1, alpha2, and alpha3 isoform mRNA levels were significantly reduced in I/R hearts, and pretreatment with ADA induced a large increase in the mRNA levels. These results indicate that EDLC may participate in depressing Na+/K+-ATPase activity and sodium pump alpha isoform gene expression in I/R heart. It is suggested that treatment with ADA may prevent EDLC-mediated reperfusion injury via modulating sodium pump isoform gene expression.
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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 | 0.000 | 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".