Platelet Toll-like receptor expression modulates lipopolysaccharide-induced thrombocytopenia and tumor necrosis factor-α production in vivo
Bibliographic record
Abstract
Toll-like receptors (TLRs) play a critical role in stimulating innate immunity by recognizing pathogen-associated molecular patterns (PAMPs) on invading microorganisms. Platelets also play a role in innate immunity, and we studied whether they express TLR. Results show that human and murine platelets variably expressed TLR2, TLR4, and TLR9 by flow cytometry and Western blotting. TLR4 expression was confirmed by demonstrating murine platelet binding to lipopolysaccharide (LPS). Thrombin activation of the platelets significantly enhanced the expression of TLR9, suggesting that at least some TLRs may derive from intracellular compartments. When LPS was administered to LPS-sensitive C3H/HeN and LPS-resistant C3H/HeJ mice, functional TLR4 expression in vivo was shown to be responsible for LPS-induced thrombocytopenia. However, when the C3H/HeN mice were first rendered thrombocytopenic by an antiplatelet antibody and then administered LPS, a significant reduction occurred in their ability to produce TNF-alpha. The decreased cytokine production in the thrombocytopenic mice was restored with platelet transfusion. These results suggest that platelets express various TLRs and that the functional significance of one of these, TLR4, appears to be a role in the modulation of LPS-induced thrombocytopenia and TNF-alpha production. This work implicates platelets as important mediators of innate immune responses against invading microorganisms.
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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 | 0.000 | 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".