Microentrapment of probiotic bacteria in a Ca<sup>2 +</sup>-induced whey protein gel and effects on their viability in a dynamic gastro-intestinal model
Bibliographic record
Abstract
Entrapping probiotic bacteria in gels with ionic cross-linking is typically achieved with polysaccharides (alginate, pectin, carraghenan). In this study, whey proteins were used for this purpose by carrying out the Ca(2+)-induced gelation of pre-heated whey protein isolate (WPI). A Lactobacillus rhamnosus cell suspension was added in a denatured WPI solution in a 30 : 70 volume ratio. Gelation was carried out by extrusion of the cell suspension in a CaCl(2) solution. Beads of approximately 3 mm diameter were formed. The population in the beads was 8.0 x 10(8) cells g(-1). Entrapment efficiency in gel beads was 96%, with a survival level of 23%. Scanning electron microscopy of beads before freeze-drying showed a tight protein network containing encapsulated Lb. rhamnosus cells homogeneously distributed throughout the matrix. The survival to freeze-drying of the bead-entrapped cells was 41%. Viability of microentrapped cells in a dynamic gastro-intestinal (GI) model was studied and the results were compared to free cells freeze-dried in a milk-based cryoprotective solution, as well as in a pre-denatured WPI solution. Results showed that protein gelation provided protection against acidic conditions in the stomach after 90 min, as well as against bile after 30, 60 and 90 min in the duodenum. Moreover, the milk-based cryoprotective solution was equally effective after 90 min in the duodenum. It is concluded that the gelation of whey proteins induced by Ca(2+) ions can protect the cells against adverse conditions of the GI system. However, certain stages in the entrapment process, particularly extrusion in the solution of CaCl(2), still need to be optimized in order to reduce the mortality of the cells during gelation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".