Systemic Inflammatory Markers and Disease Severity in Chronic Obstructive Pulmonary Disease—The Effect of Acute Exercise and Pulmonary Rehabilitation
Bibliographic record
Abstract
Background: Decreased physical capacity and increased systemic inflammatory response are frequently observed in patients with chronic obstructive pulmonary disease (COPD). The relationship between the inflammatory response and disease severity and the immunological response to exercise were addressed in COPD. Objective: The first objective was to identify systemic biomarkers and their relationship with COPD severity. The second objective was to examine the effect of both acute exercise and pulmonary rehabilitation on these biomarkers. Methods: Forty subjects participated in the study. Thirty-two patients with moderate or severe COPD and 8 healthy non-smokers completed the study. Spirometry was preformed. Physical capacity was determined by a progressive symptom-limited cycle ergo meter (incremental) test. Blood samples were analyzed for C-reactive protein (CRP), pro-inflammatory cytokines (IL-6, TNF-α), pro-fibrotic cytokines (TGF-β) and oxidative burst in circulating leukocytes before and after exercise, and before and after pulmonary rehabilitation. Results: IL-6, CRP, WCC and TGF-β were higher in COPD (p α, CRP and TGF-β were negatively related to forced expiratory volume in 1 s (FEV1) (r = 0.4054, 0.3221, 0.1528, 0.1846 and 0.1187, respectively). Acute exercise increased circulating leucocytes and oxidative stress in both groups (p = 0.000, 0.0049 respectively), while IL-6 was increased in COPD group ((p = 0.0115) and circulating TNF-α in healthy control (p = 0.0369). Pulmonary rehabilitation didn’t modify the levels of inflammatory mediators. Conclusions: Reduced lung function is associated with increased levels of systemic inflammatory markers and acute exercise can further increase this inflammatory response. However pulmonary rehabilitation is unlikely to exacerbate systemic inflammation in COPD.
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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) | 0.000 | 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 | 0.001 |
| 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".