SCARF Genes in COVID-19 and Kidney Disease: A Path to Comorbidity-Specific Therapies
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which has killed ~7 million persons worldwide. Chronic kidney disease (CKD) is the most common risk factor for severe COVID-19 and one that most increases the risk of COVID-19-related death. Moreover, CKD increases the risk of acute kidney injury (AKI), and COVID-19 patients with AKI are at an increased risk of death. However, the molecular basis underlying this risk has not been well characterized. CKD patients are at increased risk of death from multiple infections, to which immune deficiency in non-specific host defenses may contribute. However, COVID-19-associated AKI has specific molecular features and CKD modulates the local (kidney) and systemic (lung, aorta) expression of host genes encoding coronavirus-associated receptors and factors (SCARFs), which SARS-CoV-2 hijacks to enter cells and replicate. We review the interaction between kidney disease and COVID-19, including the over 200 host genes that may influence the severity of COVID-19, and provide evidence suggesting that kidney disease may modulate the expression of SCARF genes and other key host genes involved in an effective adaptive defense against coronaviruses. Given the poor response of certain CKD populations (e.g., kidney transplant recipients) to SARS-CoV-2 vaccines and their suboptimal outcomes when infected, we propose a research agenda focusing on CKD to develop the concept of comorbidity-specific targeted therapeutic approaches to SARS-CoV-2 infection or to future coronavirus infections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".