Angiotensin‐Converting Enzyme 2 (SARS‐CoV‐2 receptor) expression in human skeletal muscle
Bibliographic record
Abstract
The study aimed to determine the levels of skeletal muscle angiotensin‐converting enzyme 2 (ACE2, the SARS‐CoV‐2 receptor) protein expression in men and women and assess whether ACE2 expression in skeletal muscle is associated with cardiorespiratory fitness and adiposity. The level of ACE2 in vastus lateralis muscle biopsies collected in previous studies from 170 men (age: 19–65 years, weight: 56–137 kg, BMI: 23–44) and 69 women (age: 18–55 years, weight: 41–126 kg, BMI: 22–39) was analyzed in duplicate by western blot. VO2max was determined by ergospirometry and body composition by DXA. ACE2 protein expression was 1.8‐fold higher in women than men (p = 0.001, n = 239). This sex difference disappeared after accounting for the percentage of body fat (fat %), VO2max per kg of legs lean mass (VO2max‐LLM) and age (p = 0.47). Multiple regression analysis showed that the fat % (β = 0.47) is the main predictor of the variability in ACE2 protein expression in skeletal muscle, explaining 5.2% of the variance. VO2max‐LLM had also predictive value (β = 0.09). There was a significant fat % by VO2max‐LLM interaction, such that for subjects with low fat %, VO2max‐LLM was positively associated with ACE2 expression while as fat % increased the slope of the positive association between VO2max‐LLM and ACE2 was reduced. In conclusion, women express higher amounts of ACE2 in their skeletal muscles than men. This sexual dimorphism is mainly explained by sex differences in fat % and cardiorespiratory fitness. The percentage of body fat is the main predictor of the variability in ACE2 protein expression in human skeletal muscle.
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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) | 0.001 | 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".