Sialylation‐independent mechanism involved in the amelioration of murine immune thrombocytopenia using intravenous gammaglobulin
Bibliographic record
Abstract
BACKGROUND: Sialylation of the N-linked glycan on asparagine-297 within the Fc region of intravenous gammaglobulins (IVIgs) was shown to be necessary for efficacy of IVIg in the amelioration of experimental inflammatory arthritis. To test the role for Fc sialylation of IVIg in immune modulating therapies beyond the K/BxN arthritis model, we examined the efficacy of sialylated compared to nonsialylated IVIg for the ability to attenuate immune thrombocytopenia (ITP) in a mouse model that approximates the clinical setting of human ITP. STUDY DESIGN AND METHODS: We used a published, passive anti-platelet (PLT) dose-escalation mouse model of ITP that approximates clinical ITP. PLT counts were followed over time before and after IVIg treatment. IVIg from two different manufacturers was used to prepare untreated and neuraminidase-treated IVIg. Sambucus nigra agglutinin (SNA) affinity chromatography was used to obtain sialic acid-enriched and -depleted IVIg. Sialic acid content was determined using Western blot, enzyme-linked immunosorbent assay, and high-performance liquid chromatography. RESULTS: Results were the same using sialylated and desialylated (neuraminidase-treated) IVIg from two different manufacturers. No differences were observed between sialic acid-enriched and -depleted IVIg compared to normal IVIg in their efficacy to alleviate ITP. Using quantitative reverse transcription-polymerase chain reaction, no increase in the spleen FcγRIIB mRNA was detectable, but a pronounced increase of FcγRIIB mRNA in the marrow was seen after IVIg administration. CONCLUSIONS: We conclude that IVIg ameliorates experimental ITP by a mechanism that is independent of sialylation either in the Fc or the Fab region of IVIg.
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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 | 0.000 | 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".