Impact of a yogurt matrix and cell microencapsulation on the survival of Lactobacillus reuteri in three in vitro gastric digestion procedures
Bibliographic record
Abstract
The goal of this study was to assess the interaction between microencapsulation and a yogurt food matrix on the survival of Lactobacillus reuteri NCIMB 30242 in four different in vitro systems that simulate a gastric environment. The four systems were: United States Pharmacopeia (USP) solutions, a static two-step (STS) procedure which included simulated food ingredients, a constantly dynamic digestion procedure (IViDiS), as well a multi-step dynamic digestion scheme (S'IViDiS). The pH profiles of the various procedures varied between systems with acidity levels being: USP > STS > IViDiS = S'IVIDiS. Addition of a food matrix increased the pH in all systems except for the USP methodology. Microencapsulation in alginate-based gels was effective in protecting the cells in model solutions when no food ingredients were present. The stability of the probiotic culture in the in vitro gastric environments was enhanced when (1) yoghurt or simulated food ingredient were present in the medium in sufficient quantity, (2) pH was higher. The procedure-comparison data of this study will be helpful in interpreting the literature with respect to viable counts of probiotics obtained from different static or dynamic in vitro gastric systems.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".