Blood flow restriction impairs the inflammatory adaptations of strength training in overweight men: a clinical randomized trial
Bibliographic record
Abstract
The aim of this study was to evaluate the impact of high-intensity strength training (ST) or low-intensity strength training with blood flow restriction (ST-BFR) on monocyte subsets, the expression of C-C chemokine receptor 5 (CCR5), and CD16 on monocytes, and tumor necrosis factor alpha (TNF-α) production of overweight men. Thirty overweight men were randomly assigned to conventional ST or ST-BFR. Both groups performed exercises of knee extension and biceps curl with equal volume (3 sessions/week) over 8 weeks, and the peripheral frequency of monocytes (CD14+CD16−, classical monocytes; CD14+CD16+, intermediate monocytes; CD14−CD16+, nonclassical monocytes), the mean fluorescence intensity (MFI) of CCR5 and CD16 on CD14+ monocytes; and the production of TNF-α by lipopolysaccharide (LPS)-stimulated cells were quantified. Eight weeks of ST increased the frequency of CD14+CD16− monocytes (p = 0.04) and reduced the percentage of CD14−CD16+ (p = 0.02) and the production of TNF-α by LPS-stimulated cells (p = 0.03). The MFI of CD16 on CD14+ monocytes decreased after the ST intervention (p = 0.02). No difference in monocyte subsets, CCR5 or CD16 expression, and TNF-α production were identified after ST-BFR intervention (p > 0.05). The adoption of ST promotes anti-inflammatory effects on monocyte subsets of overweight men, but this effect was lost when BFR was adopted. Novelty High-intensity strength training reduces the production of TNF-α and the peripheral frequency of CD16+ monocytes in overweight men. Blood flow restriction method blunts the strength training adaptations on monocyte subsets and pro-inflammatory TNF-α production in overweight men.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".