The Impact of Different Plasma Glucose Levels on Heart Rate in Experimental Rats With Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to evaluate the impact of different plasma glucose levels on heart rate (HR) in experimental rats with acute myocardial infarction (AMI). METHODS: One hundred and twenty-one male Wistar rats were randomly divided into AMI group (n = 70) and sham-operation group (n = 51). Both groups had low, normal and high glucose levels, respectively. In the former group, hypertonic glucose was injected into the rats to make their blood glucose levels above 16 mmol/L and insulin below 3.3 mmol/L; then, the left anterior descending artery was ligated. In the later group, the models of different blood glucose levels were the same as the former ones, but false operations, thread without ligating, were given to the rats. Electrocardiogram and troponin I (TnI) confirmed that the models were prepared successfully. Electrocardiogram expression of AMI was the formation of Q-wave in over three adjacent leads and abnormal elevation of TnI. RESULTS: The HR of the rats in the hypoglycemic group is higher than that of the hyperglycemic group and normal blood glucose group before AMI (P < 0.05). The HR of the hyperglycemic rats is higher than that of the hypoglycemic group and normal blood glucose group after AMI (P < 0.05). In the hypoglycemic group, the HR of the rats who suffered from AMI was lower than that of the rats of the sham group (P < 0.05). CONCLUSION: Hypoglycemia allows faster HR and the HR in the rats with hyperglycemia is higher than that in the rats with hypoglycemia among the AMI rats.
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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".