The outcome of haematopoietic stem cell transplantation in a patient with STAT1 gain-of-function: a case report
Bibliographic record
Abstract
Background: The human signal transducer and activator of transcription 1 (STAT1) is a latent cytoplasmic transcription factor and one of seven members of the STAT family. Autosomal dominant (AD) STAT1 gain-of-function (GOF), characterized by enhanced phosphorylation of the tyrosine-701 residue and STAT1 activation, is commonly associated with chronic mucocutaneous candidiasis (CMCC), immunodeficiency, aneurysms, malignancies, and autoimmune phenomena. The best management strategies for patients with STAT1 GOF remain unclear. Typically, the standard care includes supportive treatments such as antimicrobial prophylaxis, either alone or combined with immunoglobulin replacement therapy. Biological therapies such as JAK inhibitors have been shown to alleviate symptoms of infection and autoimmune disorders in patients with STAT1 GOF mutations. However, there is a lack of long-term outcome data for JAK inhibition. Hematopoietic stem cell transplantation (HSCT) is an alternative treatment option for a subset who experience a persistent disease course despite conventional therapy. However, the effectiveness of HSCT for this condition is not yet well established. Methods: Our patient’s medical records were analyzed retrospectively, including her medical history. Results: We present the outcome of HSCT performed on a 27-year-old female with STAT1 GOF, conducted as a treatment for acute myeloid leukemia (AML). Conclusion: HSCT may serve as an alternative and potentially curative treatment for certain STAT1 GOF patients with progressive, life-threatening conditions that do not respond to conventional therapies. Additional research is needed to improve the management of these patients. Statement of Novelty: We present a novel case study of HSCT as a treatment for AML in a 27-year-old patient with STAT1 GOF, highlighting the potential for curative outcomes in progressive, life-threatening conditions unresponsive to conventional therapies. This case contributes to the limited body of evidence supporting the efficacy and challenges of HSCT in STAT1 GOF patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".