Allogeneic platelet transfusions prevent murine T-cell–mediated immune thrombocytopenia
Bibliographic record
Abstract
Platelet transfusions are life-saving treatments for many patients with thrombocytopenia; however, their use is generally discouraged in the autoimmune disorder known as immune thrombocytopenia (ITP). We examined whether allogeneic platelet major histocompatibility complex (MHC) class I transfusions affected antiplatelet CD61-induced ITP. BALB/c CD61 knockout mice (CD61(-)/H-2(d)) were immunized against platelets from wild-type syngeneic BALB/c (CD61(+)/H-2(d)), allogeneic C57BL/6 (CD61(+)/H-2(b)), or C57BL/6 CD61 KO (CD61(-)/H-2(b)) mice, and their splenocytes were transferred into severe combined immunodeficient (SCID) mice to induce ITP. When nondepleted splenocytes were transferred to induce antibody-mediated ITP, both CD61(+) platelet immunizations generated immunity that caused thrombocytopenia independently of allogeneic MHC molecules. In contrast, when B-cell-depleted splenocytes were transferred to induce T-cell-mediated ITP, transfer of allogeneic MHC-immunized splenocytes completely prevented CD61-induced ITP development. In addition, allogeneic platelet transfusions into SCID mice with established CD61-induced ITP rescued the thrombocytopenia. Compared with thrombocytopenic mice, bone marrow histology in the rescued mice showed normalized megakaryocyte morphology, and in vitro CD61-specific T-cell cytotoxicity was significantly suppressed. These results indicate that antibody-mediated ITP is resistant to allogeneic platelet transfusions, while the T-cell-mediated form of the disease is susceptible, suggesting that transfusion therapy may be beneficial in antibody-negative ITP.
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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 | 0.000 | 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) | 0.001 | 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".