Activation of<i>N</i>-methyl-d-aspartate receptors reduces heart rate variability and facilitates atrial fibrillation in rats
Bibliographic record
Abstract
AIMS: The goal of this study was to assess the effects of N-methyl-d-aspartate (NMDA) receptors activation on heart rate variability (HRV) and susceptibility to atrial fibrillation (AF). METHODS AND RESULTS: Rats were randomized for treatment with saline, NMDA (agonist of NMDA receptors), or NMDA plus MK-801 (antagonist of NMDA receptors) for 2 weeks. Heart rate variability was evaluated by using implantable electrocardiogram telemeters. Atrial fibrillation susceptibility was assessed with programmed stimulation in isolated hearts. Compared with the controls, the NMDA-treated rats displayed a decrease in the standard deviation of normal RR intervals, the standard deviation of the average RR intervals, the mean of the 5-min standard deviations of RR intervals, the root mean square of successive differences, and high frequency (HF); and an increase in low frequency (LF) and LF/HF (all P< 0.01). Additionally, the NMDA-treated rats showed prolonged activation latency and reduced effective refractory period (all P< 0.01). Importantly, AF was induced in all NMDA-treated rats. While atrial fibrosis developed, connexin40 downgraded and metalloproteinase 9 upgraded in the NMDA-treated rats (all P< 0.01). Most of the above alterations were mitigated by co-administering with MK-801. CONCLUSION: These results indicate that NMDA receptors activation reduces HRV and enhances AF inducibility, with cardiac autonomic imbalance, atrial fibrosis, and degradation of gap junction protein identified as potential mechanistic contributors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".