Obesogenic and Ketogenic Diets Distinctly Regulate the SARS-CoV-2 Entry Proteins ACE2 and TMPRSS2 and the Renin-Angiotensin System in Rat Lung and Heart Tissues
Bibliographic record
Abstract
BACKGROUND: Obesity increases the severity of SARS-CoV-2 outcomes. Thus, this study tested whether obesogenic and ketogenic diets distinctly affect SARS-CoV-2 entry proteins and the renin-angiotensin system (RAS) in rat pulmonary and cardiac tissues. METHODS: Male Sprague-Dawley rats were fed either standard chow (SC), a high-fat sucrose-enriched diet (HFS), or a ketogenic diet (KD) for 16 weeks. Afterwards, levels of angiotensin converting enzyme 2 (ACE2), transmembrane protease serine 2 (TMPRSS2), RAS components, and inflammatory genes were measured in the lungs and hearts of these animals. RESULTS: In the lungs, HFS elevated ACE2 and TMPRSS2 levels relative to SC diet, whereas the KD lowered the levels of these proteins and the gene expressions of toll-like receptor 4 and interleukin-6 receptor relative to HFS. The diets did not alter ACE2 and TMPRSS2 in the heart, although ACE2 was more abundant in heart than lung tissues. CONCLUSION: Diet-induced obesity increased the levels of viral entry proteins in the lungs, providing a mechanism whereby SARS-CoV-2 infectivity can be enhanced in obese individuals. Conversely, by maintaining low levels of ACE2 and TMPRSS2 and by exerting an anti-inflammatory effect, the KD can potentially attenuate the severity of infection and migration of SARS-CoV-2 to other ACE2-expressing tissues.
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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 | 0.001 |
| 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".