Doxycycline Attenuates Cardiac Injury and Improves Cardiac Function with Inhibition of Myocardial Matrix Metalloproteinase (MMP)‐2 in a Swine Model of Hypoxia‐ Reoxygenation (H‐R)
Bibliographic record
Abstract
Background Cardiac dysfunction is common in asphyxiated neonates. Although small animal studies support attenuation of cardiac injury with the known MMP inhibitor doxycycline in H‐R, this effect has not been tested in a clinically relevant large animal model of H‐R. We hypothesized that doxycycline would improve the recovery of cardiac function in newborn piglets with H‐R. Methods Piglets were instrumented for hemodynamic monitoring and subjected to 2 hr of hypoxia followed by 4 hr of normoxic reoxygenation. Piglets were blindly randomized to receive i.v. saline or doxycycline (3, 10, or 30 mg/kg) 5 min into reoxygenation (n=7/group). Sham piglets (n=5) received no H‐R. Markers of myocardial injury (serum and myocardial tissue troponin; myocardial lactate) and oxidative stress (lipid hydroperoxides) were measured by ELISA and Western blot. Myocardial MMP‐2 activity was quantified by gelatin zymography. Results Doxycycline dose‐dependently improved cardiac (CI) and stroke volume (SVI) index (30mg/kg: 84±3 (SEM)% and 78±5% of baseline vs. 65±3% and 50±5% in controls [p<0.05]). Markers of myocardial injury, oxidative stress and MMP‐2 activity were improved in doxycycline groups vs. control [all p<0.05]. Significant correlations were found between markers of myocardial injury and CI and SVI recovery (r =−0.5, p<0.01), with a negative correlation also observed between myocardial tissue and serum troponins (r =−0.4, p=0.02). Conclusions Doxycycline attenuates cardiac injury and improves functional recovery in newborn pigs with H‐R. Funded by: Canadian Institutes of Health Research and the Women and Children's Health Research Institute
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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 | 0.000 |
| 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".