The clinical, immunological and genetic features of 12 Chinese patients with STAT3 mutations
Bibliographic record
Abstract
BACKGROUND: Loss-of-function (LOF) mutations in signal transducer and activator of transcription 3 (STAT3) is one of the causes of STAT3 hyperimmunoglobulin E (IgE) syndrome (STAT3-HIES), while gain-of-function (GOF) mutations in STAT3 lead to immune dysregulation diseases. We retrospectively analyzed the age, common clinical symptoms, immunologic and molecular manifestations in 11 patients with LOF STAT3 mutations and 1 patient with a GOF STAT3 mutation. METHODS: Twelve patients were enrolled in our study. Serum immunoglobulin measurements, lymphocyte subset detection and whole-exome sequencing were performed. RESULTS: infections were the classic manifestations. Elevated serum IgE levels are not always observed in conjunction with high eosinophil counts. A moderate viral DNA load was also measured in peripheral blood mononuclear cells. We noticed that c. 1144C>T was the most common mutation site, followed by c.1311C>A. Additionally, c.1311C>A and c. 1826G>C are two novel mutations. Eight patients achieved notable improvement after receiving intravenous immunoglobulin. CONCLUSION: We updated the current knowledge of this topic. We found an earlier median age at diagnosis, a higher survival rate, and a general lack of nonimmunological abnormalities; we also described the treatment details and novel mutations involve in STAT3-HIES and compared STAT3 LOF and GOF mutations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".