Bibliographic record
Abstract
The outbreak of COVID-19 pneumonia caused by a new coronavirus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) is posing a global health emergency and has led to more than 380,000 deaths worldwide. The cell entry of SARS-CoV-2 depends on two host proteins angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2). There is currently no vaccine available and also no effective drug for the treatment of COVID-19. Hydrogen sulfide (H2S) as a novel gasotransmitter has been shown to protect against lung damage via its anti-inflammation, antioxidative stress, antiviral, prosurvival, and antiaging effects. In light of the research advances on H2S signaling in biology and medicine, this review proposed H2S as a potential defense against COVID-19. It is suggested that H2S may block SARS-CoV-2 entry into host cells by interfering with ACE2 and TMPRSS2, inhibit SARS-CoV-2 replication by attenuating virus assembly/release, and protect SARS-CoV-2-induced lung damage by suppressing immune response and inflammation development. Preclinical studies and clinical trials with slow-releasing H2S donor(s) or the activators of endogenous H2S-generating enzymes should be considered as a preventative treatment or therapy for COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".