Possible association between <i>Helicobacter pylori</i> infection and nonalcoholic fatty liver disease
Bibliographic record
Abstract
Possible association between Helicobacter pylori infection (HPI) and nonalcoholic fatty liver disease (NAFLD) has been proposed by several studies with inconsistent conclusions. Here, we studied the association between HPI and NAFLD at 3 levels: (i) genetic level; (ii) small molecular level; and (iii) clinical level. Relation data between diseases, genes, and small molecules were acquired from Pathway Studio ResNet Mammalian database. Clinical data were acquired from 2263 elderly South Chinese subjects, including 603 NAFLD patients and 1660 subjects without NAFLD. Results showed that HPI and NAFLD present significantly shared genetic bases (95 genes, p value = 2.5E-72), demonstrating multiple common genetic pathways (enrichment p value ≤ 4.38E-20 for the top 10 pathways). Genetic network analysis suggested that mutual regulation may exist between HPI and NAFLD through 21 out of 95 genes. Furthermore, 85 out of the 95 genes manifested strong interaction with 12 small molecules/drugs that demonstrate effectiveness in treating both diseases. Clinical results showed that HPI rate in the NAFLD group was significantly higher than that in the group without NAFLD (51.9% vs. 43.6%; p value = 4.9E-4). Multivariate logistic regression results supported the observations and suggested that HPI served as a risk factor for NAFLD in the experiment data studied (odds ratio: 1.387, p value = 0.018). Results from this study support the hypothesis that complex biological association may exist between HPI and NAFLD, which partially explains the significant clinical co-incidence in the elderly population of south China.
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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 | 0.001 |
| 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) | 0.002 | 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".