Layer-by-Layer Cell Encapsulation for Drug Delivery: The History, Technique Basis, and Applications
Bibliographic record
Abstract
The encapsulation of cells with various polyelectrolytes through layer-by-layer (LbL) has become a popular strategy in cellular function engineering. The technique sprang up in 1990s and obtained tremendous advances in multi-functionalized encapsulation of cells in recent years. This review comprehensively summarized the basis and applications in drug delivery by means of LbL cell encapsulation. To begin with, the concept and brief history of LbL and LbL cell encapsulation were introduced. Next, diverse types of materials, including naturally extracted and chemically synthesized, were exhibited, followed by a complicated basis of LbL assembly, such as interactions within multilayers, charge distribution, and films morphology. Furthermore, the review focused on the protective effects against adverse factors, and bioactive payloads incorporation could be realized via LbL cell encapsulation. Additionally, the payload delivery from cell encapsulation system could be adjusted by environment, redox, biological processes, and functional linkers to release payloads in controlled manners. In short, drug delivery via LbL cell encapsulation, which takes advantage of both cell grafts and drug activities, will be of great importance in basic research of cell science and biotherapy for various diseases.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".