Natural anti-adhesive components against pathogenic bacterial adhesion and infection in gastrointestinal tract: case studies of <i>Helicobacter pylori</i> , <i>Salmonella enterica</i> , <i>Clostridium difficile</i> , and diarrheagenic <i>Escherichia coli</i>
Bibliographic record
Abstract
Antimicrobial resistance (AMR) poses a global public health concern. Recognizing the critical role of bacterial adhesion in pathogenesis of infection, anti-adhesive therapy emerges as a promising approach to impede initial bacterial attachment, thus preventing pathogenic colonization and infection. Natural anti-adhesive agents derived from food sources are generally safe and have the potential to inhibit the emergence of resistant bacteria. This comprehensive review explored diverse natural dietary components exhibiting anti-adhesive activities against several model enteric pathogens, including Helicobacter pylori, Salmonella enterica, Clostridium difficile, and three key diarrheagenic Escherichia coli (i.e., enterotoxigenic E. coli, enteropathogenic E. coli, and enterohemorrhagic E. coli). Investigating various anti-adhesive products will advance our understanding of current research of the field and inspire further development of these agents as potential nutraceuticals or adjuvants to improve the efficacy of conventional antibiotics.
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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) | 0.001 | 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) | 0.003 | 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".