Protein-based encapsulation systems for codelivery of bioactive compounds: Recent studies and potential applications
Bibliographic record
Abstract
Functional food development faces a considerable hurdle due to the poor bioavailability of incorporated bioactive compounds, which are caused by poor aqueous solubility, rapid release, low circulation time, physical and chemical instability, and cytotoxicity of bioactive compounds. Encapsulation emerges as a pivotal strategy to address this challenge by protecting bioactives. Protein as a wall material for encapsulation plays a significant role due to its biocompatibility, non-toxicity, surface activity, amphiphilic nature, and diverse range of functional groups. Researchers are currently exploring the co-encapsulation of multiple compounds to earn synergistic health benefits, enhanced functionality, and cost-effectiveness but face several challenges due to the diverse solubilities and chemical properties of bioactives. Proteins are crucial as encapsulation wall materials with their nutritional value and abundant availability. The diversity arising from the 20 different amino acids allows proteins to interact effectively with various compounds through various interactions. Emulsions, nano, micro solid particles, and gels are the most common protein-based fabricated systems used for encapsulation and co-encapsulation. However, as delivery systems, proteins face some drawbacks and challenges, such as rapid release and diffusion, low loading capacity, and instability in gastric environments. This review critically explores protein-based co-encapsulation studies, highlighting research gaps and proposing future directions in this field.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".