<scp>CD</scp>44 antibody–mediated amelioration of murine immune thrombocytopenia (ITP): mouse background determines the effect of <scp>Fc</scp>γRIIb genetic disruption
Bibliographic record
Abstract
BACKGROUND: Several monoclonal antibodies to CD44 can successfully ameliorate murine immune thrombocytopenia (ITP). As these antibodies may be a potential replacement for intravenous immune globulin (IVIG) in the treatment of ITP and other autoimmune diseases, an understanding of their mechanisms of action is important. The role of the inhibitory Fc receptor (FcγRIIb) in the mechanism of action of IVIG and therapeutic CD44 antibodies remains uncertain. To assess if FcγRIIb in splenic macrophages plays a critical role in the action of these two therapeutics, splenectomized mice and mice genetically deficient in FcγRIIb on different backgrounds were evaluated. STUDY DESIGN AND METHODS: Thrombocytopenia was induced in FcγRIIb-deficient mice on B6;129S, C57BL/6, and BALB/C backgrounds, as well as splenectomized mice and control mice by platelet (PLT) antibody. PLT counts were enumerated before and after treatment with anti-CD44, red blood cell antibodies, or IVIG. RESULTS: Anti-CD44 is ineffective at inhibiting thrombocytopenia in B6;129S FcγRIIb-deficient mice but, like IVIG, is effective in splenectomized mice and FcγRIIb-deficient mice on the BALB/C and C57BL/6 background. CONCLUSION: These data suggest that 1) the B6;129S background itself is unlikely to be the sole reason for anti-CD44's inability to function in B6;129S FcγRIIb-deficient mice, 2) the simple loss of macrophage FcγRIIb expression alone is insufficient to explain anti-CD44 ameliorative function, and 3) a combination of mouse background genes in addition to FcγRIIb genetic disruption may affect the ability of anti-CD44 to function therapeutically. Similarities between IVIG and anti-CD44 mechanisms suggest that patients responsive to IVIG may also potentially respond to anti-CD44 treatment.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".