Impaired Endothelial Function in Individuals With Post-Acute Sequelae of COVID-19
Bibliographic record
Abstract
PURPOSE: We investigated the presence of impaired endothelial function in individuals with post-acute sequelae of coronavirus disease-2019 (PASC) compared to healthy individuals and explored the efficacy of combined exercise training in restoring or improving endothelial function in those with PASC. METHODS: Study I was a cross-sectional study which compared endothelial function between individuals with PASC (n = 29, mean age 22.9 ± 3.9 year) and healthy individuals (n = 42, mean age 21.7 ± 2.0 year). Study II, an intervention design, explored if combined exercise training (n = 14) could reverse the decline in endothelial function associated with PASC compared to controls (n = 14). The combined exercise program included aerobic, resistance, and inspiratory muscle training administered for 8 weeks. We measured endothelial function using flow-mediated dilation of the brachial artery and assessed peak oxygen uptake (VO2peak), dyspnea, and fatigue before and after the intervention. RESULTS: Individuals with PASC exhibited significantly lower endothelial function compared to healthy controls (4.95 ± 2.0% vs 8.00 ± 2.4%, P < .001). The exercise group showed a significant increase in endothelial function (4.73 ± 1.5% to 7.98 ± 2.4%) as opposed to the control group (5.31 ± 2.5% to 6.30 ± 2.5%) (interaction effect: P = .008), reaching levels similar to those in healthy individuals. Additionally, the exercise group demonstrated improvement in VO2peak (38.3 ± 6.4 ml/min/kg to 42.8 ± 7.3 ml/min/kg, P < .001) and a reduction in dyspnea and fatigue compared to the control group (P < .001). CONCLUSIONS: Having PASC is associated with impaired endothelial function, but combined exercise training effectively restores it, making it a promising lifestyle intervention for vascular function in PASC.
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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 | 0.001 |
| 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 | 0.000 |
| 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".