Biomedical Applications of Layer‐by‐Layer Self‐Assembly for Cell Encapsulation: Current Status and Future Perspectives
Bibliographic record
Abstract
Encapsulating living cells within multilayer functional shells is a crucial extension of cellular functions and a further development of cell surface engineering. In the last decade, cell encapsulation has been widely utilized in many cutting-edge biomedical fields. Compared with other techniques for cell encapsulation, layer-by-layer (LbL) self-assembly technology, due to the versatility and tunability to fabricate diverse multilayer shells with controllable compositions and structures, is considered as a promising approach for cell encapsulation. This review summarizes the state-of-the-art and potential future biomedical applications of LbL cell encapsulation. First of all, a brief introduction to the LbL self-assembly technique, including assembly mechanisms and technologies, is made. Next, different cell encapsulation strategies by LbL self-assembly techniques are explained. Then, the biomedical applications of LbL cell encapsulation in cell-based biosensors, cell transplantation, cell/molecule delivery, and tissue engineering, are highlighted. Finally, discussions on the current limitations and future perspectives of LbL cell encapsulation are also provided.
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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 | 0.000 |
| 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".