Exploring lactic acid bacteria in food, human health, and agriculture
Bibliographic record
Abstract
Lactic acid bacteria (LAB) are ubiquitous multifaceted microorganisms widely used in food and agricultural industries due to their metabolic adaptability, safety and beneficial bioactivities. This review provides a comprehensive synthesis of recent advances in LAB applications focusing on their roles in food fermentation, value-added compound production, human health, and biocontrol and bioremediation in sustainable agriculture. In food systems, LAB not only can improve preservation or shelf life, but also contributes to improved nutritional profiles through the production of functional biomolecules (e.g., exopolysaccharides, bacteriocins, and vitamins). Furthermore, LAB-derived extracellular vesicles, lipoteichoic acids, and exopolysaccharides have demonstrated immunomodulatory, anti-inflammatory effects, and hypoglycemic and hypocholesterolemic properties, highlighting their therapeutic and nutraceutical potential. Meanwhile, in agriculture LAB can promote plant growth, soil health, and pathogen suppression through antimicrobial properties and nutrient solubilization. These microorganisms have also demonstrated capabilities in degrading contaminants in the environmental and food sectors showcasing its diverse biotechnological utility. Altogether, this review emphasizes emerging biotechnological applications in addressing global challenges related to food safety, agri-environmental sustainability, and human health.
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 | 0.000 |
| 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 | 0.002 |
| 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".