Intestinal Delivery of Probiotics: Materials, Strategies, and Applications
Bibliographic record
Abstract
Probiotics with diverse and crucial properties and functions have attracted broad interest from many researchers, who adopt intestinal delivery of probiotics to modulate the gut microbiota. However, the major problems faced for the therapeutic applications of probiotics are the viability and colonization of probiotics during their processing, oral intake, and subsequent delivery to the gut. The challenges of simple oral delivery (stability, controllability, targeting, etc.) have greatly limited the use of probiotics in clinical therapies. Nanotechnology can endow the probiotics to be delivered to the intestine with improved survival rate and increased resistance to the adverse environment. Additionally, the progress in synthetic biology has created new opportunities for efficiently and purposefully designing and manipulating the probiotics. In this article, a brief overview of the types of probiotics for intestinal delivery, the current progress of different probiotic encapsulation strategies, including the chemical, physical, and genetic strategies and their combinations, and the emerging single-cell encapsulation strategies using nanocoating methods, is presented. The action mechanisms of probiotics that are responsible for eliciting beneficial effects are also briefly discussed. Finally, the therapeutic applications of engineered probiotics are discussed, and the future trends toward developing engineered probiotics with advanced features and improved health benefits are proposed.
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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) | 0.001 | 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".