Bibliographic record
Abstract
Multiple studies have now shown that various species of bacteria can stimulate platelets; many in a strain and donor-dependent manner. The signalling pathways underlying this platelet activation has been the subject of scrutiny for the last decade. The best-delineated pathway is that in response to Streptococcal species, such as Streptococcus sanguinis (S. sanguinis), Streptococcus gordonii (S. gordonii) and Streptococcus oralis (S. oralis), where a pathway is initiated by the engagement of the low affinity IgG receptor, FcγRIIA. This leads to and involves the tyrosine kinase Syk, the adaptor protein Linker of Activated T Cells (LAT) and subsequently both phospholipase Cγ2 (PLCγ2) and phosphatidylinositol-3-kinase (PI-3-K). Finally, this leads to the expression of the αIIbβ3 integrin, the synthesis and release of thromboxane A2 (T × A2) and the exocytosis of PF4, each of which plays a crucial role in secondary signalling and full platelet activation. Roles for other signalling pathways in Streptococcal-induced platelet activation are less clear, although an ADP-mediated inhibition of adenylyl cyclase, a glycoprotein Ib/IX/V-mediated pathway and perhaps a complement-induced pathway have each been proposed. Platelet activation by Porphyromonas gingivalis (P. gingivalis) at least partially shares the FcγRIIA/Syk/PLCγ2/PI-3-K mechanism utilised by Streptococcal species. However, it has also been suggested that P. gingivalis activates platelets by two additional methods; stimulation of the protease-activated receptors leading to activation of phospholipase Cβ (PLCβ), and the engagement of Toll-like receptors 2 and 4 by released lipopolysaccharide leading to an ill-defined pathway which may involve PI-3-K. Consequently, it appears that bacteria can stimulate platelets by eliciting multiple signalling pathways some of which are common, and some unique, to individual species.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".