Circuit resistance training in sedentary women: body composition and serum cytokine levels
Bibliographic record
Abstract
Exercise can generate alterations in body composition and modulate the immune system. The objective of this study was to verify whether a circuit resistance training (CRT) protocol can increase lean body mass (LM), and reduce fat body mass (FM) and the percent of FM (%FM) of sedentary women, without inducing inflammatory responses, indicated by serum cytokine levels. The initial hypothesis was that CRT would improve body composition, without changing serum cytokine levels. The study consisted of 14 healthy, sedentary women, aged 33-45 years (mean +/- SD, 40.23 +/- 3.98 years), with a normal body mass index. They participated in 3 sessions per week of CRT, which included 2 rounds in 9 stations with 1 set of 8-12 repetition maximum at each station, for 10 weeks. During the 10-week CRT period, participants maintained their pretraining nutritional standard. Body composition was analysed with dual-energy X-ray absorptiometry both pre- and post-training. Blood samples were collected after 96 h of rest pre- and post-training, and 5 min, 24 h, and 48 h after the second and last training sessions to measure serum cytokine levels by flow cytometry. The nutritional standard was accompanied throughout the study period with 24-h dietary recall. Increases in LM (35.937 +/- 4.926 to 39.130 +/- 4.950 kg) and decreases in FM (21.911 +/- 8.150 to 17.824 +/- 4.235 kg) and %FM (37.10 +/- 10.84 to 31.19 +/- 6.06), without concurrent changes in serum cytokine levels, and in the nutritional standard (alpha = 0.05). The proposed CRT improved body composition and did not induce any changes in serum cytokine levels characteristic of the inflammatory response in women.
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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".