Impact of Dietary Patterns on H. pylori Infection and the Modulation of Microbiota to Counteract Its Effect. A Narrative Review
Bibliographic record
Abstract
Helicobacter pylori (H. pylori) is a Gram-negative bacterium that colonizes the stomach and can induce gastric disease and intra-gastric lesions, including chronic gastritis, peptic ulcers, gastric adenocarcinoma, and mucosa-associated lymphoid tissue lymphoma. This bacterium is responsible for long-term complications of gastric disease. The conjunction of host genetics, immune response, bacterial virulence expression, diet, micronutrient availability, and microbiome structure influence the disease outcomes related to chronic H. pylori infection. In this regard, the consumption of unhealthy and unbalanced diets can induce microbial dysbiosis, which infection with H. pylori may contribute to. However, to date, clinical trials have reported controversial results and current knowledge in this field is inconclusive. Here, we review preclinical studies concerning the changes produced in the microbiota that may be related to H. pylori infection, as well as the involvement of diet. We summarize and discuss the last approaches based on the modulation of the microbiota to improve the negative impact of H. pylori infection and their potential translation from bench to bedside.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".