Exploring the relationship between exposure to technological and gastrointestinal stress and probiotic functional properties of lactobacilli and bifidobacteria
Bibliographic record
Abstract
Strains of Lactobacillus and Bifidobacterium are considered probiotic because of their associated potential health benefits. Probiotics are commonly administered orally via incorporation into food products. Microorganisms for use as probiotics encounter stress conditions, which include acid, bile, osmotic, oxidative, heat and cold stresses. These can occur during processing and storage and during passage through the gastrointestinal tract, and can affect viability. Probiotic bacteria have to remain viable to confer any health benefits. Therefore, the ability to withstand technological and gastrointestinal stresses is crucial probiotic selection criteria. While the stress tolerance mechanisms of lactobacilli and bifidobacteria are largely understood, the impact of exposure to stressful conditions on the functional properties of surviving probiotic microorganisms is not clear. This review explores the potentially positive and negative relationships between exposure to stress conditions and probiotic functional properties, such as resistance to gastric acid and bile, adhesion and colonization potential, and tolerance to antibiotics. Protective strategies can be employed to combat negative effects of stress on functional properties. However, further research is needed to ascertain synergistic relationships between exposure to stress and probiotic properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".