Impact of resistance training program configuration on the circulating brain-derived neurotrophic factor response
Bibliographic record
Abstract
This study examined the acute and resting changes of brain-derived neurotrophic factor (BDNF) and inteleukin-6 (IL-6) and if changes in these biomarkers were correlated during resistance training (RT). Fifteen men with ≥2 years of RT experience (age: 23 ± 3 years, body mass: 84.4 ± 12.3 kg) participated. Subjects performed RT 3×/week for 6 weeks in either a high-repetition (HR; n = 8) or low-repetition (LR; n = 7) group. Protocols during week 1 were HR – Monday: 4 (sets) × 12 (repetitions) at 60% of 1-repetition maximum, Wednesday: 4 × 10 at 65%, Friday: 5 × 8 at 70%; LR – Monday: 8 × 6 at 75%, Wednesday 9 × 4 at 80%, Friday: 10 × 2 at 85%. Total volume was equated for the 6 weeks but not for individual sessions. Greater volume and intensity were performed in LR versus HR (p < 0.01) on Mondays. Plasma was collected immediately before and after exercise of the Monday session. There were no significant interactions or main effects for BDNF (p > 0.05). There was a moderate between-group effect size (0.57) in favor of LR in week 6, suggesting a potentially greater acute increase in BDNF in LR versus HR. For IL-6, a statistically significant main effect was observed for training (p < 0.0001), showing an acute increase in IL-6 in both weeks (p < 0.01); however, no other 3-way or 2-way interactions existed (p > 0.05). A minimum volume threshold of RT may be needed to induce acute elevations in BDNF. Novelty A minimum RT volume threshold may be needed to elicit BDNF. A close proximity to failure may be needed to elicit BDNF. BDNF and IL-6 did not correlate.
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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) | 0.002 | 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".