Manno-oligosaccharides as a promising antimicrobial strategy: pathogen inhibition and synergistic effects with antibiotics
Bibliographic record
Abstract
Infections caused by pathogenic bacteria pose a significant health challenge to humans and animals, especially given the rising incidence of antimicrobial resistance. Addressing this challenge has resulted in initiatives seeking alternatives to traditional antibiotics. Manno-oligosaccharides (MOS) exhibit pathogen-binding properties, due to their ability to prevent bacterial adhesion to epithelial cells, such as those within the urinary tract and intestinal epithelium. This suggests that MOS could offer a promising alternative to antibiotics. In this study, we explore the ability of various β-MOS products to inhibit the growth of Escherichia coli, Klebsiella pneumoniae, Listeria monocytogenes, and Streptococcus mutans, in addition to their ability to render antibiotics more effective. Inhibition profiles were distinct for each bacterial strain and differed according to β-MOS structure. Antibiotics were significantly potentiated by MOS in some cases, such as ceftazidime against K. pneumoniae. This research shows the role of carbohydrate structure in the anti-bacterial properties of non-digestible oligosaccharides such as MOS and positions MOS as a promising strategy in the treatment of bacterial 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".