CD8+ T cells induce platelet clearance in the liver via platelet desialylation in immune thrombocytopenia
Bibliographic record
Abstract
In addition to antiplatelet autoantibodies, CD8(+) cytotoxic T lymphocytes (CTLs) play an important role in the increased platelet destruction in immune thrombocytopenia (ITP). Recent studies have highlighted that platelet desialylation leads to platelet clearance via hepatocyte asialoglycoprotein receptors (ASGPRs). Whether CD8(+) T cells induce platelet desialylation in ITP remains unclear. Here, we investigated the cytotoxicity of CD8(+) T cells towards platelets and platelet desialylation in ITP. We found that the desialylation of fresh platelets was significantly higher in ITP patients with positive cytotoxicity of CD8(+) T cells than those without cytotoxicity and controls. In vitro, CD8(+) T cells from ITP patients with positive cytotoxicity induced significant platelet desialylation, neuraminidase-1 expression on the platelet surface, and platelet phagocytosis by hepatocytes. To study platelet survival and clearance in vivo, CD61 knockout mice were immunized and their CD8(+) splenocytes were used. Platelets co-cultured with these CD8(+) splenocytes demonstrated decreased survival in the circulation and increased phagocytosis in the liver. Both neuraminidase inhibitor and ASGPRs competitor significantly improved platelet survival and abrogated platelet clearance caused by CD8(+) splenocytes. These findings suggest that CD8(+) T cells induce platelet desialylation and platelet clearance in the liver in ITP, which may be a novel mechanism of 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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".