Vaccine breakthrough hypoxemic COVID-19 pneumonia in patients with auto-Abs neutralizing type I IFNs
Bibliographic record
Abstract
Life-threatening "breakthrough" cases of critical COVID-19 are attributed to poor or waning antibody (Ab) response to SARS-CoV-2 vaccines in individuals already at risk. Preexisting auto-Abs neutralizing type I IFNs underlie at least 15% of critical COVID-19 pneumonia cases in unvaccinated individuals; their contribution to hypoxemic breakthrough cases in vaccinated people is unknown. We studied a cohort of 48 individuals (aged 20 to 86 years) who received two doses of a messenger RNA (mRNA) vaccine and developed a breakthrough infection with hypoxemic COVID-19 pneumonia 2 weeks to 4 months later. Ab levels to the vaccine, neutralization of the virus, and auto-Abs to type I IFNs were measured in the plasma. Forty-two individuals had no known deficiency of B cell immunity and a normal Ab response to the vaccine. Among them, 10 (24%) had auto-Abs neutralizing type I IFNs (aged 43 to 86 years). Eight of these 10 patients had auto-Abs neutralizing both IFN-α2 and IFN-ω, whereas two neutralized IFN-ω only. No patient neutralized IFN-β. Seven neutralized type I IFNs at 10 ng/ml and three at 100 pg/ml only. Seven patients neutralized SARS-CoV-2 D614G and Delta efficiently, whereas one patient neutralized Delta slightly less efficiently. Two of the three patients neutralizing only type I IFNs at 100 pg/ml neutralized both D614G and Delta less efficiently. Despite two mRNA vaccine inoculations and the presence of circulating Abs capable of neutralizing SARS-CoV-2, auto-Abs neutralizing type I IFNs may underlie a notable proportion of hypoxemic COVID-19 pneumonia cases, highlighting the importance of this particularly vulnerable population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".