Exercise-induced expression of SARS-CoV-2 entry receptors: impact of mask modality, sex, and exercise intensity
Bibliographic record
Abstract
BACKGROUND: Wearing a facemask affects physiological responses to exercise. We explored how exercising with a facemask affects the expression of SARS-CoV-2 entry receptor (angiotensin-converting enzyme 2 [ACE2]) and some associated genes (angiotensin type-1 receptors [AT1R]; Mas receptor [MasR]; hypoxia-inducible factor 1α [HIF-1α]; endothelial nitric oxide synthase [eNOS]) among healthy males and females. METHODS: One hundred forty-four apparently healthy individuals (72 females; age: 30±6) were allocated to three mask groups of 48 (N95, Surgical, No Mask) with two exercise subgroups for each mask for both sexes. Participants in each experimental group performed either a submaximal (walking with no grade) or maximal (a modified Bruce Protocol) treadmill exercise test. Blood samples were collected before and after each exercise test and used to analyze the mRNA expression of the genes studied. RESULTS: The post-exercise expression of genes examined were comparable between Surgical, N95, and No Mask (P>0.05). ACE2 was significantly greater in Surgical and N95 against No Mask at baseline and following moderate-intensity exercise (P<0.05). Whilst similar expressions were noted for MasR and eNOS (P>0.05), AT1R was greater in N95 than Surgical following high-intensity exercise (P<0.05). HIF-1α following either exercise intensity was significantly lower in N95 than Surgical (P<0.05). AT1R and HIF-1α were similar between Surgical and N95 against No Mask (P>0.05). ACE2 and AT1R were significantly higher in either mask modality than No Mask in males at baseline and postexercise (P<0.05). HIF-1α, MasR, and eNOS expressions were comparable between all mask groups in either sex (P<0.05). CONCLUSIONS: Our findings suggest that wearing a facemask does not differentiate the gene expression of SARS-CoV-2 entry receptor following exercise among both sexes.
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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".