The effects of combined probiotic ingestion and circuit training on muscular strength and power and cytokine responses in young males
Bibliographic record
Abstract
To our knowledge, the efficacy of combined probiotic supplementation with circuit training has not been evaluated. Thus, we investigated the effects of probiotic supplementation combined with circuit training on isokinetic muscular strength and power and cytokine responses in young males. Forty-eight healthy sedentary young males were recruited and randomised into 4 separate groups: sedentary placebo control, probiotics (P), circuit training with placebo (CT), and circuit training with probiotics (CTP). Participants in the CT and CTP groups performed circuit training 3 times/week with 2 circuits of exercises from weeks 1–8 followed by 3 circuits of exercises from weeks 9–12. Participants in the P and CTP groups consumed multi-strain probiotics containing 3 × 1010 colony-forming units of Lactobacillus acidophilus, L. lactis, L. casei, Bifidobacterium longum, B. bifidum and B. infantis twice daily for 12 weeks. Measurements of body height and weight, blood pressure, resting heart rate, blood samples, and isokinetic muscular strength and power were carried out at pre- and post-tests. Isokinetic knee strength and power in CT and CTP groups were significantly higher (P < 0.05) at post-test. In addition, interleukin (IL)-10 concentration was significantly increased (P < 0.0001) at post-test in P and CT but a trend toward significant increase in CTP (P = 0.09). Nevertheless, there was no significant difference in IL-6. This study suggests that 12 weeks of circuit training alone and the combination of circuit training and probiotic consumption improved muscular performance while circuit training alone and probiotics alone increased IL-10 concentration.
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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 | 0.000 |
| 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".