CD19 chimeric antigen receptor‐T cell therapy in murine immune thrombocytopenia
Bibliographic record
Abstract
Summary Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by antiplatelet autoantibodies, with many patients refractory or relapsing on conventional treatments. GPIbα, an important autoantigen in ITP, is notably linked to refractoriness, highlighting the need for novel treatments. We assessed CD19 chimeric antigen receptor (CAR)‐T cell therapy's potential in a modified murine model targeting GPIbα. CD19 CAR‐T cell infusion accelerated platelet count recovery compared to the control group, effectively depleted CD19 + B cells and CD138 + plasma cells, and markedly reduced anti‐GPIbα autoantibodies in vivo. In vitro CD19 CAR‐T cells reduced both plasma cells and B cells in the spleens of mice and ITP patients. CD19 CAR‐T cell therapy significantly altered T‐cell subsets, increasing regulatory T cells, T helper 1 and T helper 17 populations, suggesting a role in modulating the immune response for sustained ITP remission. Monitoring of body/spleen weights and temperature showed no significant cytokine release syndrome, indicating a favourable safety profile. These promising results support the potential of CD19 CAR‐T cell therapy as a novel treatment option for refractory ITP, particularly in GPIbα‐positive autoantibody patients. Further clinical studies are warranted to assess the safety and efficacy of this approach in human 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".