Impact of a tailored exercise regimen on physical capacity and plasma proteome profile in post-COVID-19 condition
Bibliographic record
Abstract
Background: Individuals affected by the post-covid condition (PCC) show an increased fatigue and the so-called post-exertion malaise (PEM) that led health professionals to advise against exercise although accumulating evidence indicates the contrary. The goal of this study is to determine the impact of a closely monitored 8-week mixed exercise program on physical capacity, symptoms, fatigue, systemic oxidative stress and plasma proteomic profiles of PCC cases. Methods: = 10) groups. Individuals with no PCC served as a control group. The exercise program included cardiovascular and resistance exercises. Physical capacity, physical activity level and the presence of common PCC symptoms were measured before and after the intervention. Fatigue was measured the day following each exercise session. Plasma and PBMC samples were collected at the beginning and end of the training program. Glutathione and deoxyguanosine levels in PBMC and plasma proteomic profiles were evaluated. Results: =0.007) with a positive effect of the program on their daily functioning and without any adverse effects on general or post-effort fatigue. After exercise, glutathione levels in PBMCs increased in women but remained unchanged in men. Discernable changes were observed in the plasma proteomics profile with certain proteins involved in inflammatory response decreasing in the exercise group. Conclusions: Supervised exercise adapted to the level of fatigue and ability is safe and effective in PCC patients in improving their general physical capacity and wellbeing. Systemic molecular markers that accompany physical improvement can be monitored by analyzing plasma proteomics and markers of oxidative stress. Large-scale studies will help identify promising molecular markers to objectively monitor patient improvement.
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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) | 0.000 | 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 | 0.000 |
| 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".