Physical exercise as cytokine modulator in inflammatory immune response: a systematic review
Bibliographic record
Abstract
Physical exercise contributes to maintain our health, through its role in inflammatory immune response. Cytokines are proteins that mediate communication between immune cells, either as pro- or anti-inflammation agents. Nonetheless, the dominance of pro- over anti-inflammatory cytokines during a workout, is harmful to health. This systematic review aimed at determining the effect of physical activity in modulating pro- and anti-inflammatory cytokines during immune response. Following Preferred Reporting Items for Systematic Reviews (PRISMA) guideline, literature searching was conducted in 3 databases which were PUBMED/MEDLINE, DOAJ and GARUDA, using keywords, citation tracking and snowballing. Inclusion and exclusion criterias were used to screen, while the Newcastle Ottawa Scale (NOS) was used to assess the quality of the articles. Data extraction and analysis were conducted descriptively. There were 376 articles identified, of which 329 were sorted and 82 were retrieved. Thirty articles underwent quality assessment, resulting to 12 inclusion studies. In conclusion, physical exercise influences the modulation of cytokine, whereby IL-6 and TNF-α (pro-) which increase at the beginning of exercise, are balanced by the increase of IL-10 (anti-inflammatory), which appears later in exercise or during recovery. For this purpose, physical exercise is recommended as a combination of aerobic and resistance exercise performed regularly at moderate intensity.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".