A novel <i>STAT3</i> splice-site variant in a kindred with autosomal dominant hyper IgE syndrome
Bibliographic record
Abstract
Background: Dominant negative STAT3 loss-of-function is the most common genetic cause of hyper-IgE syndrome (HIES). Patients may present with a host of both immune and non-immune manifestations, including connective tissue abnormalities, recurrent infections, malignant predisposition, and biochemical evidence of elevated serum IgE or eosinophilia. Aim: To describe a novel splice-site variant in STAT3 resulting in HIES. Methods: Case report of two family members with HIES. Results: A proband and his son presented with neonatal-onset pustular rash, recurrent skin and sinopulmonary infections and elevated serum IgE and were diagnosed with AD-HIES. They were identified to harbor a novel splice-site variant in the DNA-binding domain (DBD) of STAT3: c.1110-3C>G, predicted to result in defective splicing in exon 12. Interestingly, a number of other patients with AD-HIES have mutations affecting the same splice-site, suggesting this may be a hot-spot for mutagenesis. Conclusion: Splice-site mutations in the DBD of STAT3 are increasingly identified as a cause of AD-HIES. Future work is required to delineate whether patients with splice-site mutations have unique clinical characteristics, supporting efforts for genotype-phenotype correlation in this disease. Statement of Novelty: We present a novel splice-site mutation in the DNA-binding domain of STAT3 leading to autosomal dominant hyper-IgE syndrome.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".