Transcription factor defects in inborn errors of immunity with atopy
Bibliographic record
Abstract
Transcription factors (TFs) are critical components involved in regulating immune system development, maintenance, and function. Monogenic defects in certain TFs can therefore give rise to inborn errors of immunity (IEIs) with profound clinical implications ranging from infections, malignancy, and in some cases severe allergic inflammation. This review examines TF defects underlying IEIs with severe atopy as a defining clinical phenotype, including STAT3 loss-of-function, STAT6 gain-of-function, FOXP3 deficiency, and T-bet deficiency. These disorders offer valuable insights into the pathophysiology of allergic inflammation, expanding our understanding of both rare monogenic and common polygenic allergic diseases. Advances in genetic testing will likely uncover new IEIs associated with atopy, enriching our understanding of molecular pathways involved in allergic inflammation. Identification of monogenic disorders profoundly influences patient prognosis, treatment planning, and genetic counseling. Hence, the consideration of IEIs is essential for patients with severe, early-onset atopy. This review highlights the need for continued investigation into TF defects to enhance our understanding and management of allergic diseases.
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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) | 0.003 | 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".