The seven enigmas of SARS-CoV-2: From the past to the future
Bibliographic record
Abstract
Five years ago, we launched the COVID Human Genetic Effort. Our goal was to explain the clinical variability among SARS-CoV-2-exposed individuals by searching for monogenic inborn errors of immunity (IEI) and their phenocopies. We deciphered the pathogenesis of critical COVID-19 pneumonia and multisystemic inflammatory syndrome in children (MIS-C) in ~15% and 2% of cases, respectively, thereby revealing general mechanisms of severe disease. We also defined neuro-COVID-19 genetically and immunologically in one child, while we delineated the immunological mechanisms of COVID-toes in healthy children and young adults, paving the way for their genetic study. Understanding the human genetic and immunological basis of resistance to SARS-CoV-2 infection, long COVID, and myocarditis post mRNA vaccination, has been challenging and investigations remain ongoing. This work highlights the power of patient-based basic research and large-scale international collaborative efforts to discover human genetic and immunological drivers of infectious disease phenotypes, with implications for the timely development of new medical strategies before the next pandemic arrives.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| 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".