A role for immunoglobulin G in donor-specific Streptococcus sanguis-induced platelet aggregation
Bibliographic record
Abstract
There is increasing evidence for a relationship between bacterial infections and several cardiovascular disorders. Although the precise mechanism(s) underlying this association is unknown, the direct activation of platelets by bacteria is one possibility. Individual strains of S. sanguis activate platelets in a non-uniform, donor-dependent manner. In the current study, platelet aggregation profiles were obtained for fourteen donors in response to four strains of S. sanguis (2017-78, 133-79, SK112, SK108a) and one of S. gordonii (SK8) . The platelets from all donors responded to strains 2017-78 and 133-79, whereas strains SK112, SK8 and SK108a caused aggregation in one, five and twelve donors, respectively. Immunoglobulin G (IgG) binding to strains 2017-78, 133-79 and SK108a were significantly greater than to strains SK112 and SK8. Absorption of IgG by strain 2017-78 caused significant decreases in IgG binding, and platelet aggregation in response, to all strains. Single-strand conformational polymorphisms were observed in the Fcgamma RIIA gene from four donors. Sequencing revealed two known and two novel point mutations, none of which correlated with the aggregation profile. Thus, platelet activation to the various strains depends on a common IgG and, while in most cases the level of IgG binding to S. sanguis determines platelet responsiveness, neither the levels of IgG nor FcgammaRIIA polymorphisms can fully account for donor variability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".