CD44 Antibodies and Immune Thrombocytopenia in the Amelioration of Murine Inflammatory Arthritis
Bibliographic record
Abstract
Antibodies to CD44 have been used to successfully ameliorate murine models of autoimmune disease. The most often studied disease model has been murine inflammatory arthritis, where a clear mechanism for the efficacy of CD44 antibodies has not been established. We have recently shown in a murine passive-model of the autoimmune disease immune thrombocytopenia (ITP) that some CD44 antibodies themselves can induce thrombocytopenia in mice, and the CD44 antibody causing the most severe thrombocytopenia (IM7), also is known to be highly effective in ameliorating murine models of arthritis. Recent work in the K/BxN serum-induced model of arthritis demonstrated that antibody-induced thrombocytopenia reduced arthritis, causing us to question whether CD44 antibodies might primarily ameliorate arthritis through their thrombocytopenic effect. We evaluated IM7, IRAWB14.4, 5035-41.1D, KM201, KM114, and KM81, and found that while all could induce thrombocytopenia, the degree of protection against serum-induced arthritis was not closely related to the length or severity of the thrombocytopenia. CD44 antibody treatment was also able to reverse established inflammation, while thrombocytopenia induced by an anti-platelet antibody targeting the GPIIbIIIa platelet antigen, could not mediate this effect. While CD44 antibody-induced thrombocytopenia may contribute to some of its therapeutic effect against the initiation of arthritis, for established disease there are likely other mechanisms contributing to its efficacy. Humans are not known to express CD44 on platelets, and are therefore unlikely to develop thrombocytopenia after CD44 antibody treatment. An understanding of the relationship between arthritis, thrombocytopenia, and CD44 antibody treatment remains critical for continued development of CD44 antibody therapeutics.
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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 | 0.001 | 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) | 0.001 | 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".