In Vitro Framework to Assess the Anti-Helicobacter pylori Potential of Lactic Acid Bacteria Secretions as Alternatives to Antibiotics
Bibliographic record
Abstract
Helicobacter pylori is a prevalent bacterium that can cause gastric ulcers and cancers. Lactic acid bacteria (LAB) ameliorate treatment outcomes against H. pylori, suggesting that they could be a source of bioactive molecules usable as alternatives to current antibiotics for which resistance is mounting. We developed an in vitro framework to compare the anti-H. pylori properties of 25 LAB and their secretions against H. pylori. All studies were done at acidic and neutralized pH, with or without urea to mimic various gastric compartments. Eighteen LAB strains secreted molecules that curtailed the growth of H. pylori and the activity was urea-resistant in five LAB. Several LAB supernatants also reduced the urease activity of H. pylori. Pre-treatment of H. pylori with acidic LAB supernatants abrogated its flagella-mediated motility and decreased its ability to elicit pro-inflammatory IL-8 cytokine from human gastric cells, without reverting the H. pylori-induced repression of other pro-inflammatory cytokines. This study identified the LAB that have the most anti-H. pylori effects, decreasing its viability, its production of virulence factors, its motility and/or its ability to elicit pro-inflammatory IL-8 from gastric cells. Once identified, these molecules can be used as alternatives or complements to current antibiotics to fight H. pylori infections.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".