Recent advances in lipid-protein conjugate-based delivery systems in nutraceutical, drug, and gene delivery
Bibliographic record
Abstract
Lipid and protein-based delivery systems have long been used to deliver active compounds such as drugs, genes, and nutraceuticals. These delivery systems are fabricated to overcome issues of pure active compounds, which include rapid release and metabolism, poor solubility, low stability, poor bioavailability, poor bioaccessibility, and toxicity. However, there are limitations of lipids and proteins that restrict their efficient use in the delivery systems. Lipid-protein conjugation is an emerging technique for fabricating novel delivery systems that provide the advantages of having both proteins and lipids in one delivery system. In addition, these conjugates have a much better synergistic effect and desirable properties inside the body than single carriers. Among them, colloidal and biological stability, enhanced mechanical strength, controlled release, higher circulation time, targeted delivery, less cytotoxicity, higher loading capacity, co-encapsulation, and enhanced bioavailability are key outcomes. Despite recent technological advancement, there are still drawbacks to lipid-protein conjugate-based delivery systems that should be addressed in future research studies. This review is focused on critically evaluating the importance of lipid and protein as delivery systems, benefits of lipid-protein conjugation, conjugation methods, various applications of lipid-protein conjugation in drugs, genes, and nutraceutical delivery, and identifying research challenges and future research directions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".