CpG oligonucleotides induce acute murine thrombocytopenia dependent on toll-like receptor 9 and spleen tyrosine kinase pathways
Bibliographic record
Abstract
BACKGROUND: CpG oligonucleotides (ODNs) are synthetic single-stranded DNA sequences that act as immunostimulants. They have been increasingly used to treat several cancers; however, thrombocytopenia is a potential recognized side effect of some sequences. OBJECTIVES: We tested the ability of 2 CpG ODNs (ODN 2395 and ISIS 120704) to induce thrombocytopenia when administered to BALB/c mice and determined mechanisms associated with thrombocytopenia. METHODS: BALB/c mice were prebled and then injected with titrated doses of CpG ODNs, and platelet counts were determined. The mice were treated with intravenous immunoglobulin (IVIg) or various inhibitors and antagonists of toll-like receptor 9 (TLR9) and spleen tyrosine kinase (Syk) to determine their effects on thrombocytopenia. RESULTS: Compared with saline-treated mice or mice treated with 2'-O-methoxyethyl-modified antisense ODN, both ODN 2395 and ISIS 120704 induced acute dose-dependent thrombocytopenia within 3 and 24 hours, respectively. The thrombocytopenia was associated with significant increases in plasma monocyte chemoattractant protein 1. IVIg administration significantly rescued the CpG ODN-induced thrombocytopenia, as did treatment with either a Syk inhibitor or TLR9 antagonists. In vitro, CpG ODN could activate human platelets and this correlated significantly with enhanced IVIg- and Syk-dependent phagocytosis by THP-1 monocytes. CONCLUSION: These results suggest that CpG ODNs induce acute inflammatory-associated (IVIg-sensitive) thrombocytopenia that can be alleviated by Syk- or TLR9-blockade, and an IVIg- and Syk-dependent platelet clearance pathway appears primarily responsible for the thrombocytopenia.
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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) | 0.002 | 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".