COVID-19 and X-linked agammaglobulinemia (XLA) – insights from a monogenic antibody deficiency
Bibliographic record
Abstract
PURPOSE OF REVIEW: The clinical outcomes from COVID-19 in monogenic causes of predominant antibody deficiency have pivotal implications for our understanding of the antiviral contribution of humoral immunity. This review summarizes the lessons learned from COVID-19 infection in X-linked agammaglobulinemia (XLA) due to genetic defects in Bruton's tyrosine kinase (BTK). RECENT FINDINGS: Key molecular pathways underlying the development of severe COVID-19 are emerging, highlighting the possible contribution of BTK to hyperinflammation. SARS-CoV-2 specific T-cell responses and complement activation appear insufficient to achieve viral clearance in some B-cell deficient individuals. Whilst appearing efficacious in this group, use of convalescent plasma has been recently associated with the evolution of viral escape variants. Early data suggests individuals with XLA can mount a viral-specific T-cell vaccine response, however, the clinical significance of this is still emerging. SUMMARY: In contrast to reports made early in the pandemic, we show XLA patients remain susceptible to severe disease. Persistent infection was common and is likely to carry a significant symptom burden and risk of novel variant evolution. COVID-19 infection in this vulnerable, antibody deficient group due to genetic, therapeutic or disease causes may require prompt and specific intervention for both patient and societal benefit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".