Helicobacter pylori infection and iron deficiency in patients with coronary artery disease.
Bibliographic record
Abstract
The aim of this study was to investigate whether impact of the seropositivity to Helicobacter pylori (H pylori) infection on ferritin and iron levels is an independent risk factor for atherosclerosis in patients with cardiovascular disease. The anti H pylori IgG, IgA levels, serum ferritin and iron concentration of 86 patients with cardiovascular disease and 64 participants free of cardiovascular disease as control subjects were determined by ELISA assay. The results of present study showed that seropositivity to H pylori IgG and IgA levels of coronary artery disease (CAD) patients was higher than controls and CAD patients with negative anti H pylori IgG and IgA significantly. A significant negative correlation was found between seropositivity to H pylori IgG and IgA, ferritin and iron levels of CAD patients with seronegativity and seronegativity to H pylori IgG and IgA in comparison with controls. The achieved results from present study suggest that the involvement of H pylori infection in atherosclerosis process is based on the chronic inflammation which might facilitate the CAD-related pathologies. Moreover, impact of the presence of H pylori infection on reduction of the ferritin and iron levels of CAD patients as a risk factor independent of other classic factors including lipid profiles and inflammatory factors was remarkable.
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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 | 0.001 |
| 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 | 0.001 |
| 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".