Platelets express functional Toll-like receptor-4
Bibliographic record
Abstract
Profound thrombocytopenia occurs in humans with sepsis and in mice administered lipopolysaccharide (LPS). Growing evidence indicates that platelets may contribute to these abnormalities, but whether that is a direct result of LPS activation of platelets or an indirect result of other inflammatory mechanisms remains unclear. Here we demonstrate that although platelets do not increase P-selectin expression in response to LPS, platelets bind more avidly to fibrinogen under flow conditions in a Toll-like receptor-4 (TLR4)-dependent manner. In addition, we find that CD41+ megakaryocytes grown from fetal livers and adult circulating platelets express significant amounts of TLR4. LPS induced thrombocytopenia in wild-type mice but not in TLR4-deficient (TLR4def) mice. Wild-type platelets accumulated in the lungs of wild-type mice in response to LPS; TLR4def platelets did not. However, wild-type platelets did not accumulate in the lungs of LPS-treated TLR4def mice. Neutrophils also accumulated in the lungs, and this preceded platelet accumulation. Neutrophil depletion completely abolished LPS-induced platelet sequestration into the lungs, but platelet depletion did not affect neutrophil accumulation. Thus, our data show for the first time that platelets do express functional levels of TLR4, which contribute to thrombocytopenia through neutrophil-dependent pulmonary sequestration in response to LPS.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".