Effects of presurgical exercise training on systemic inflammatory markers among patients with malignant lung lesions
Bibliographic record
Abstract
Systemic inflammation plays an important role in the initiation, promotion, and progression of lung carcinogenesis. The effects of interventions to lower inflammation have not been explored. Accordingly, we conducted a pilot study to explore the effects of exercise training on changes in biomarkers of systemic inflammation among patients with malignant lung lesions. Using a single-group design, 12 patients with suspected operable lung cancer were provided with structured exercise training until surgical resection. Participants underwent cardiopulmonary exercise testing, 6 min walk testing, pulmonary function testing, and blood collection at baseline and immediately prior to surgical resection. Systemic inflammatory markers included intracellular adhesion molecule (ICAM)-1, macrophage inflammatory protein-1alpha, interleukin (IL)-6, IL-8, monocyte chemotactic protein-1, C-reactive protein, and tumor necrosis factor-alpha. The overall exercise adherence rate was 78%, with patients completing a mean of 30 +/- 25 sessions. Mean peak oxygen consumption increased 2.9 mL.kg-1.min-1 from baseline to presurgery (p = 0.016). Results indicate that exercise training resulted in a significant reduction in ICAM-1 (p = 0.041). Changes in other inflammatory markers did not reach statistical significance. Change in cardiorespiratory fitness was not associated with change in systemic inflammatory markers. This exploratory study provides an initial step for future studies to elucidate the potential role of exercise, as well as identify the underlying mechanisms of action, as a means of modulating the relationship between inflammation and cancer pathogenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".