DNMT1 Gene Expression in Patients with Helicobacter pylori Infection
Bibliographic record
Abstract
DNMT1, as a critical enzyme affecting epigenetics through methylation of DNA cytosine-rich sequences, regulates gene expression. Exterior factors including long-term infections, in this study Helicobacter pylori infection, could change host cells’ epigenetics by affecting DNMT1 gene expression. This study investigated the statistical correlation between H. pylori virulence genes and DNMT1 gene expression in gastric antral epithelial cells of gastric adenocarcinoma and gastritis patients. In a case-control study, 50 and 53 gastritis and gastric adenocarcinoma antral biopsies, including 23 and 21 patients with H. pylori infection, respectively, were collected from hospitals in the west of Iran. Having extracted total RNA from gastric biopsy samples, cDNA was synthesized and virulence genes of H. pylori were detected by using the PCR method. Relative real-time RT PCR was used to detect ΔΔCt fold changes of the DNMT1 gene expression in divided groups of patients based on H. pylori infection and clinical manifestations. The results showed that along with increasing patients’ age, the DNMT1 gene expression will increase in gastric antral epithelial cells of gastric cancer patients ( P ≤ 0.05 ). On the other hand, the biopsy samples with infection of H. pylori cagA, cagY, and cagE genotypes revealed a direct correlation along with increased DNMT1 gene expression. This study revealed the correlations of H. pylori cag pathogenicity island genes with increased DNMT1 gene expression.
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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 | 0.000 |
| 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".