Renal consequences of the novel coronavirus disease 2019 (COVID-19) and hydrogen sulfide as a potential therapy
Bibliographic record
Abstract
The novel coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is a global pandemic which is primarily considered a respiratory illness. However, emerging reports show that the virus exhibits both pulmonary and extra-pulmonary manifestations in humans, with the kidney as a major extra-pulmonary target due to its abundant expression of angiotensin-converting enzyme 2 and transmembrane protease serine 2, which facilitate entry of the virus into cells. Acute kidney injury has become prevalent in COVID-19 patients without prior any history of kidney dysfunction. In addition, the virus also worsens kidney conditions and increases mortality of COVID-19 patients with pre-existing chronic kidney disease, renal cancer, diabetic nephropathy, end-stage kidney disease as well as dialysis and kidney transplant patients. In the search for antiviral agents for the treatment of COVID-19, hydrogen sulfide (H2S), the third established member of gasotransmitter family, is emerging as a potential candidate, possessing important therapeutic properties including antiviral, anti-inflammatory, anti-thrombotic and antioxidant properties. A recent clinical study revealed higher serum H2S levels in survivors of COVID-19 pneumonia with reduced interleukin-6 levels compared to fatal cases. In this review, we summarize the global impact of COVID-19 on kidney conditions and discuss the emerging role of H2S as a potential COVID-19 therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".