Regular exercise improves airway inflammation in cystic fibrosis patients
Bibliographic record
Abstract
Neutrophilic inflammation, mainly from recurrent bacterial infection, characterizes cystic fibrosis (CF) airways. Repeated inflammatory and oxidative stress insults may lead to progressive lung function decline. Exercise is part of a healthy lifestyle, but its relationship with inflammatory pattern has been poorly studied and could trigger an inflammatory response. We aim to depict the modulation of airway inflammation after a mouth of physical training. 10 subjects with mild to moderate stable CF (FEV1>50%) underwent an ergocycle constant load exercise test at 80% of their maximal load until exhaustion. After this test, they underwent one month’s non-supervised physical fitness program, custom build for each of them by a certified physiotherapist, based on muscle tone and cardiovascular reinforcement. At the end of this training, they underwent once again the same ergocycle test. Sputum was sampled before and 1 hour after the two exercises and analysed for leucocyte counts and for cytokines. 5 females and 5 males completed the study with a mean exercise time of 4.6 minutes. No desaturation was reported. 8 subjects improved their mean FEV1%. The sputum neutrophils show the decrease from baseline level by 82% after one month of fitness program. The sputum IL-8 decrease by 22%. Both MMP-9 and TIMP-1 were also decreased after the one-month training. The MMP-9/TIMP-1 proportion demonstrates a trend in an up-regulation. We show that regular exercise diminished airway inflammation in stable CF subjects by down regulating the neutrophilic chemokine IL-8 and modifying the MMP-9/TIMP-1 to a non-destructive balance. We hope that this data will help our patients to be more pro-active in physical training.
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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) | 0.001 | 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) | 0.002 | 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".