Prebiotic Mannan-Oligosaccharides and Their Role in the Gut Microbiota
Bibliographic record
Abstract
Pathogenic bacterial infections in the gastrointestinal tract compromise the health and function of the gut microbiome, and the rising incidence of antibiotic-resistant bacterial strains has resulted in initiatives seeking alternative treatments. Some prebiotic fibers, such as mannan-oligosaccharides (MOS), may be a promising alternative. In addition to selectively growing commensal bacteria and creating a diverse gut microbiota, MOS have a high affinity for specific binding arms on the structure of some pathogenic bacteria and can prevent bacterial adhesion to intestinal epithelial cells. The ability of MOS to bind pathogenic bacteria and selectively grow commensal bacteria is influenced by its carbohydrate structure. However, there are no published studies to our knowledge on the optimization of MOS structure for both pathogen binding and commensal bacterial growth. Therefore, the focus of this work is to (1) assess the effectiveness of MOS on in vitro agglutination of various pathogenic bacterial species and, (2) explore the impact of MOS carbohydrate structure on pathogen binding. Characterization of MOS will be performed using high performance liquid chromatography (HPLC) and liquid chromatography mass spectrometry (LCMS). To assess the ability of MOS to bind pathogenic bacteria, in vitro agglutination and growth inhibition assays will be performed. Prebiotic performance of MOS on commensal bacterial growth will be assessed using in vitro mixed culture analyses of both commensal and pathogenic bacterial species. Protocol Abstract This research will inform further investigations of the ability of prebiotics such as MOS to support intestinal health through the selective growth of commensal bacteria and binding of pathogenic bacteria, as a nutritional supplement and as an alternative to antibiotics. Natural Sciences and Engineering Research Council of Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".