Human primary immunodeficiencies causing defects in innate immunity
Bibliographic record
Abstract
PURPOSE OF REVIEW: There have been exciting recent advances in identifying new mutations that cause human primary immunodeficiencies which impact innate immune defences. In this review, we will highlight the most important and influential advances published in the last 18 months related to the defects of the innate immune system. We will also provide clinical context to facilitate the incorporation of these discoveries into clinical practice. RECENT FINDINGS: We will specifically focus on three areas that have seen recent significant advances: defects in Toll-like receptor signalling that enhance susceptibility to viral infection, particularly herpes simplex encephalitis; defects in innate immunity that impact phagocyte function predisposing to mycobacterial infection; and the discovery of genes responsible for isolated congenital asplenia. SUMMARY: The field of innate immunodeficiency has benefited greatly from the recent improvements in genome sequencing technology and has advanced dramatically in the last 18 months. For clinicians confronted with patients with suspected innate immunodeficiency, these new discoveries not only increase the likelihood that a patient will receive a specific molecular diagnosis and tailored therapy, but also add significant complexity to the diagnostic workup. Future challenges will include identifying accurate, cost-effective diagnostic approaches to these novel immunodeficiencies, so these impressive advances in our understanding of innate immunity can be translated into improved health outcomes for our affected patients and their families.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".