Non‐uniform myostatin transcription in the acute response to a myocardial infarction (699.7)
Bibliographic record
Abstract
Myostatin, a negative regulator of skeletal muscle growth, is transcriptionally upregulated in the heart following myocardial infarction (MI). Investigations have largely focused on myostatin production in the left ventricle (LV) during development of LV dysfunction. However, MI causes significant right ventricle (RV) dysfunction along with greater re‐expression of fetal genes as compared to LV. Our objective was to examine relative RV and LV myostatin expression following MI. Methods. Mice were sacrificed following MI and the infarct, peri‐infarct and RV were separated. Myostatin mRNA levels were determined via qPCR. Results. Baseline myostatin expression was statistically similar between the LV and RV. One‐hour post‐MI, a 10 and 2.3 fold increase in myostatin mRNA was observed in the RV and peri‐infarct tissue, respectively. Myostatin mRNA levels peaked 12h post‐MI with a 30 and 7.5 fold increase in RV and peri‐infarct regions, respectively. Out to 1 week, myostatin levels remain significantly higher in the RV than in the peri‐infarct tissue. Conclusion. Myostatin transcription in response to a MI is 4‐5 times greater in the RV than in other regions of the heart. Determining the mechanism behind this region specific increase in myostatin transcription may provide key details in understanding the role of myostatin expression in cardiac pathology. Grant Funding Source : Canadian Institute of Health and Research
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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) | 0.000 | 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) | 0.003 | 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".