Physical exercise upregulates Irisin in extracellular vesicles
Bibliographic record
Abstract
BACKGROUND: Physical exercise improves overall brain health, cognition, and stimulates the release of extracellular vesicles (EVs) in humans. Exercise upregulates irisin, a myokine derived from fibronectin type III domain-containing protein 5 (FNDC5) previously shown to mediate the beneficial actions of exercise on memory in mouse models of Alzheimer's disease (AD). Here, we investigated if physical exercise upregulates EVs. We further studied if EV's cargo includes irisin and tested whether physical exercise protocols modulate its content in EVs. METHOD: Individuals underwent a high intensity interval training exercise program. HIIT sessions were performed 3 times a week, over 6 weeks. Serum was collected before and the day after the last exercise session. Mice were submitted to a daily swimming exercise protocol, 5 times per week, over 5 weeks. Mice were terminated 1 hour after the last bout of exercise and plasma was collected. EVs concentration and number in humans and mice. EVs obtained from the cerebrospinal fluid (CSF) from cynomolgus and Rhesus monkeys at rest obtained by lumbar punctures were also studied. We further investigated if EV's cargo includes irisin and tested whether physical exercise protocols modulate irisin content in EVs un mice and humans. FNDC5/Irisin in EVs was detected by Western blotting, Mass spectometry and ELISA. RESULT: Our results indicate that irisin is associated with serum, plasma and CSF EVs from humans, mice and monkeys. Exercise upregulated EV-associated FNDC5/Irisin in mice and humans, but did not alter circulating EV concentration. EV-associated FNDC5/Irisin correlated positively with BDNF levels in serum obtained from humans post-exercise. CONCLUSION: These findings indicate a potential physiological role of exercise-induced upregulation of EV-associated irisin.
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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 | 0.000 |
| 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".