Toll‐like receptor 4 ligand can differentially modulate the release of cytokines by human platelets
Bibliographic record
Abstract
Blood platelets link the processes of haemostasis and inflammation. This study examined the immunomodulatory factors released by platelets after Toll-Like Receptor 4 (TLR4) engagement on their surfaces. Monoclonal anti-human FcgammaRII Ab (IV.3)-treated human platelets were cultured with TLR4 ligands in the presence or absence of blocking monoclonal antibody to human TLR4. The release of sCD62p, epidermal growth factor (EGF), transforming growth factor beta (TGFbeta), interleukin (IL)-8, platelet activating factor 4 (PAF4), platelet-derived growth factor, alpha, beta polypeptide (PDGF-AB), Angiogenin, RANTES (regulated upon activation, normal T-cell expressed, and presumably secreted) and sCD40L were measured by specific enzyme-linked immunosorbent assay. TLR4 ligand [Escherichia coli lipopolysaccharide (LPS)] bound platelet TLR4, which differentially modulates the release of cytokines by platelets. It was noted that (i) sCD62p, IL-8, EGF and TGFbeta release were each independent of platelet activation after TLR4 engagement; (ii) RANTES, Angiogenin and PDGF-AB concentration were weaker in platelet supernatant after TLR4 engagement; (iii) sCD40L and PAF4 are present in large concentration in the releaseate of platelets stimulated by TLR4 ligand. The effects of LPS from E. coli on the modulation of secretory factors were attenuated by preincubation of platelets with an anti-TLR4 monoclonal antibody, consistent with the immunomodulation being specifically mediated by the TLR4 receptor. We propose that platelets adapt the subsequent responses, with polarized cytokine secretion, after TLR4 involvement.
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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) | 0.001 | 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".