Effects of physical exercise on adiponectin and BDNF levels in the blood and primary visual cortex: a pilot study
Bibliographic record
Abstract
Previous studies have demonstrated that physical exercise can elevate levels of adiponectin and brain-derived neurotrophic factor (BDNF), contributing to enhanced neuroplasticity in the brain, particularly in the hippocampus. However, it remains unclear whether exercise similarly increases these neurotrophic factors in the visual cortex, and whether such changes are detectable in the blood. This pilot study aimed to address this knowledge gap by evaluating whether short-duration exercise protocols can increase adiponectin and BDNF levels in the visual cortex, and whether corresponding changes are reflected in blood. The findings may help identify accessible biomarkers to inform and optimize exercise-based rehabilitation strategies for enhancing visual function. Adolescent wild-type mice were assigned to one of three groups: control (no exercise), intermittent treadmill running (5 min running, 5 min rest, 3 repeats), or continuous running (15 min running) for five consecutive days. Primary visual cortex and serum samples were collected for measurement of adiponectin and BDNF levels using ELISA kits. Continuous exercise significantly increased serum adiponectin levels compared to both the control and intermittent groups, but did not affect adiponectin levels in the primary visual cortex. No significant changes in BDNF levels were observed in either the primary visual cortex or serum across all exercise groups. These preliminary results indicate that continuous exercise enhances serum adiponectin but does not significantly influence BDNF levels in serum or primary visual cortex under these specific conditions.
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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) | 0.002 | 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".