CD20+ B-cell depletion therapy suppresses murine CD8+ T-cell–mediated immune thrombocytopenia
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is an autoimmune bleeding disorder with a complex pathogenesis, which includes both antibody- and T-cell-mediated effector mechanisms. Rituximab (an anti-human CD20 monoclonal antibody [mAb]) is one of the treatments for ITP and is known to deplete B cells but may also work by affecting the T-cell compartments. Here, we investigated the outcome of B-cell depletion (Bdep) therapy on CD8(+) T-cell-mediated ITP using a murine model. CD61 knockout (KO) mice were immunized with CD61(+) platelets, and T-cell-mediated ITP was initiated by transfer of their splenocytes into severe combined immunodeficiency (SCID) mice. The CD61 KO mice were administrated an anti-mouse CD20 mAb either before or after CD61(+) platelet immunization. This resulted in efficient Bdep in vivo, accompanied by significant increases in splenic and lymph node CD4(+) and CD8(+) T cells and proportional increases of FOXP3(+) in CD4(+)and CD8(+) T cells. Moreover, Bdep therapy resulted in significantly decreased splenic CD8(+) T-cell proliferation in vitro that could be rescued by interleukin-2. This correlated with normalization of in vivo platelet counts in the transferred SCID mice suggesting that anti-CD20 therapy significantly reduces the ability of CD8(+) T cells to activate and mediate ITP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".