[Electroacupuncture for myocardial ischemia injury in rats via AMPK-HDAC5-HIF-1α signaling].
Bibliographic record
Abstract
OBJECTIVE: To explore the impact of electroacupuncture (EA) on the AMPKα-HDAC5-HIF-1α signaling in the heart of the rats with myocardial ischemia (MI) via detecting the expressions of AMP-activated protein kinase α (AMPKα), histone deacetylase 5 (HDAC5), hypoxia inducible factor (HIF-1α) and vascular endothelial growth factor (VEGF). METHODS: Thirty-six healthy male SD rats were randomized into a sham operation group (6 rats), a sham + EA group (6 rats), a model group (12 rats) and an EA group (12 rats). We ligated the left anterior descending artery (LAD) for MI model, and exposed the heart of rats after opening the chest without ligation for the rats in the sham operation gorup and the sham + EA group. On the 2nd day after LAD ligation, EA was applied at "Neiguan" (PC 6) with 2 Hz/15 Hz and 1.5-2 mA for 30 min in the EA group and sham+EA group, once a day for 4 days. The same fixation was used in the sham operation group and the model group, without EA. Myocardial infarction area was observed by TTC staining and serum cardiac troponin T (cTnT) was detected by radioimmunoassay. The expression of VEGF mRNA was detected by real time PCR. The protein expressions of AMPKα, HDAC5, HIF-1α and VEGF were detected by western blot. RESULTS: <0.01). CONCLUSION: EA could regulate the AMPKα-HDAC5-HIF-1α signaling in myocardial tissue, which may activate VEGF expression for angiogenesis signaling, reduce myocardial infarction area so as to achieve cardioprotective effect.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".