MPS 07-03 EFFECT OF EXERCISE TRAINING ON FNDC5/BDNF PATHWAY IN THE HEART POST MYOCARDIAL INFARCTION
Bibliographic record
Abstract
Objective: Exercise training post myocardial infarction (MI) attenuates the progressive left ventricle (LV) remodeling and dysfunction, but the mechanisms by which exercise triggers these beneficial effects are still unclear. Exercise increases brain-derived neurotrophic factor (BDNF) in the hippocampus through up-regulation of a myokine, fibronectin type III domain-containing protein 5 (FNDC5). Activation of cardiac BDNF may play a cardioprotective role post MI by inhibiting cardiomyocyte apoptosis and causing angiogenesis. Whether exercise post MI also activates the FNDC5/BDNF pathway in the heart is unknown. Therefore, the effects of exercise training post MI on cardiac FNDC5 and BDNF were investigated. Design and Method: After sham surgery (n = 7) or ligation of left descending coronary artery, surviving MI rats were divided either sedentary (Sed-MI, n = 10) or exercise group (ExT-MI, n = 9). Exercise training was done for 4 weeks (5 days/week) on a motor-driven treadmill. LV function was then evaluated by echocardiography and Millar catheter. FNDC5 and BDNF protein was assessed in non-, peri- and infarct areas of the LV by western blot. Results: Exercise did not affect MI size, but attenuated the decrease in EF and the increase in LVEDP. In Sed-MI, FNDC5 protein was markedly increased in infarct area, whereas mature BDNF (mBDNF) protein was decreased in infarct area. Exercise had no effect on FNDC5 protein, but increased mBDNF protein in non-and peri-infarct areas. EF correlated with mBDNF protein in non-infarct area (r = 0.76, p < 0.001). Conclusions: Exercise training post MI increased mBDNF protein in non-infarct area of the LV, independent of changes in FNDC5. Increase in cardiac mBDNF may contribute to the improvement of cardiac function by exercise.
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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) | 0.001 | 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) | 0.008 | 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".