Human leukocyte antigens (HLA) HLA-DQA1 and -DQB1 genotyping in Helicobacter pylori -seropositive individuals
Bibliographic record
Abstract
This work aimed at studying the HLA-DQA1 and HLA-DQB1 genotyping in gastritis patients with positive rapid urease test (RUT). This study was carried out in College of Medicine, University of Basrah. HLA-DQA1 and HLA-DQB1 genotyping was done in College of Medicine, University of Manitoba, Winnpeg, Canada during the period from 17th of April 2009 to 15th of July 2010. A total of 70 gastritis patients (29 males and 41 females) and 30 controls were included in this study. A significant increased frequency of DQA1*050101 and DQB1*020101 alleles was found in individuals (patients + controls) who showed positive rapid diagnostic test (+RDT), but the association was weak (odds ratio = 0.39 and 0.33), as compared with individuals (patients + controls) with (-RDT). A significant decreased frequency of DQB1*050201 allele was found in individuals (patients + controls) with (+RDT). The association was very strong (odds ratio = 5.31), as compared with individuals (patients + controls) with (-RDT). A significant decreased frequencies of DQA1*0201 and DQB1*020101 alleles were found in gastritis patients with (+RDT). The association in DQA1*0201 was very strong (odds ratio = 6.38) and for -DQB1*020101 allele, the association was weak (odds ratio = 0.08), as compared with controls with (RDT). A significant increased frequency of DQA1*0201 allele was found in controls with (+RDT) and the association was very strong (odds ratio = 6.18), as compared with controls with (-RDT). A significant increased frequency of DQB1*020101 allele was found in controls with (+RDT) but with weak association (odds ratio = 0.09), as compared with controls with (-RDT). Key Words : HLA-DQA1, HLA-DQB1, DQA1, HLA-DQB1, +RDT, RDT
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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 | 0.001 | 0.001 |
| 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".