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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".