Does autoimmune vitiligo protect against COVID‐19 disease?
Bibliographic record
Abstract
The SARS-CoV-2 pandemic has evolved to a global health problem with a dramatic morbidity and mortality rate impacting our daily life and those of many patients. While there is evidence that some diseases are associated with an increased risk for development of a more severe course of COVID-19, little is known on protective conditions. Importantly, clearance of viral infection and protection against disease manifestation crucially depends on functional innate and adaptive immunity and the interferon signalling axis. Here, we hypothesize that patients with non-segmental vitiligo (NSV), an autoimmune skin (and mucosal) disorder, may clear SARS-CoV-2 infection more efficiently and have a lower risk of COVID-19 development. Conversely, in case of COVID-19 development, vitiligo autoimmunity may influence the cytokine storm-related disease burden. In addition, immune activation during SARS-CoV-2 infection or COVID-19 disease might increase vitiligo disease activity. Our hypothesis is based on the shift of the immune system in NSV towards adaptive type 1 (IFNγ and CD8 T cells) and innate immune responses. Identified susceptibility genes of NSV patients may further confer increased antiviral activity. To validate our hypothesis, we suggest an international consortium to perform a retrospective data registry and patient-reported study on a large number of NSV patients worldwide during the COVID-19 pandemic.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".