Low Frequency Electromagnetic Field Conditioning Protects against I/R Injury and Contractile Dysfunction in the Isolated Rat Heart
Bibliographic record
Abstract
Low frequency electromagnetic field (LF-EMF) decreases the formation of reactive oxygen species, which are key mediators of ischemia/reperfusion (I/R) injury. Therefore, we hypothesized that the LF-EMF protects contractility of hearts subjected to I/R injury. Isolated rat hearts were subjected to 20 min of global no-flow ischemia, followed by 30 min reperfusion, in the presence or absence of LF-EMF. Coronary flow, heart rate, left ventricular developed pressure (LVDP), and rate pressure product (RPP) were determined for evaluation of heart mechanical function. The activity of cardiac matrix metalloproteinase-2 (MMP-2) and the contents of coronary effluent troponin I (TnI) and interleukin-6 (IL-6) were measured as markers of heart injury. LF-EMF prevented decreased RPP in I/R hearts, while having no effect on coronary flow. In addition, hearts subjected to I/R exhibited significantly increased LVDP when subjected to LF-EMF. Although TnI and IL-6 levels were increased in I/R hearts, their levels returned to baseline aerobic levels in I/R hearts subjected to LF-EMF. The reduced activity of MMP-2 in I/R hearts was reversed in hearts subjected to LF-EMF. The data presented here indicate that acute exposure to LF-EMF protects mechanical function of I/R hearts and reduces I/R injury.
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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) | 0.002 | 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".