The effect of different strains of<i>Helicobacter pylori</i>on platelet aggregation
Bibliographic record
Abstract
BACKGROUND AND AIMS: Helicobacter pylori is the major causative agent in peptic ulcer disease and is strongly implicated in the development of gastric cancer. It has also been linked, less strongly, to cardiovascular disease. The mechanisms by which certain strains of H pylori induce platelet aggregation through interactions with platelet glycoprotein Ib have been previously described. METHODS: In the present study, 21 different strains of H pylori, varying in their vacuolating toxin gene, cytotoxic-associated gene A status and other pathogenicity factors, were tested for their ability to induce platelet aggregation. RESULTS: Ten of the 21 strains induced platelet aggregation, a response that appeared to be independent of their vacuolating toxin gene and cytotoxic-associated gene A status. CONCLUSIONS: Platelet aggregation has been suggested to be one of the possible mechanisms involved in the effects on the cardiovascular system induced by H pylori. Our results suggest that any putative role H pylori plays in cardiovascular disease may be strain dependent. Further work to identify the H pylori factors involved in induction of platelet aggregation may allow for identification of 'higher risk' strains for cardiovascular disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".