Exopolysaccharides as Antimicrobial Agents: Mechanism and Spectrum of Activity
Bibliographic record
Abstract
Exopolysaccharides (EPSs) are metabolites synthesized and excreted by a variety of microorganisms, including lactic acid bacteria (LAB). EPS serve several biological functions such as interactions between bacteria and their environments, protection against hostile conditions including dehydration, the alleviation of the action of toxic compounds (bile salts, hydrolyzing enzymes, lysozyme, gastric, and pancreatic enzymes, metal ions, antibiotics), and stresses (changing pH, osmolarity), and evasion of the immune response and phage attack. Bacterial EPSs are considered valuable by the food, pharmaceutical, and nutraceutical industries, owing to their health-promoting benefits and rheological impacts. Numerous studies have reported the unusual antimicrobial activities of various EPS against a wide variety of pathogenic microbes (bacteria, virus, and fungi). This review aims to provide a comprehensive examination of the in vitro and in vivo antimicrobial activities of different EPSs, mainly against foodborne bacterial, fungal, and viral pathogens. The mechanism of EPS action against these pathogens as well as the methods used to measure antimicrobial activities are critically reviewed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".