Progress technology in microencapsulation methods for cell therapy
Bibliographic record
Abstract
Cell encapsulation in microcapsules allows the in situ delivery of secreted proteins to treat different pathological conditions. Spherical microcapsules offer optimal surface-to-volume ratio for protein and nutrient diffusion, and thus, cell viability. This technology permits cell survival along with protein secretion activity upon appropriate host stimuli without the deleterious effects of immunosuppressant drugs. Microcapsules can be classified in 3 categories: matrix-core/shell microcapsules, liquid-core/shell microcapsules, and cells-core/shell microcapsules (or conformal coating). Many preparation techniques using natural or synthetic polymers as well as inorganic compounds have been reported. Matrix-core/shell microcapsules in which cells are hydrogel-embedded, exemplified by alginates capsule, is by far the most studied method. Numerous refinement of the technique have been proposed over the years such as better material characterization and purification, improvements in microbead generation methods, and new microbeads coating techniques. Other approaches, based on liquid-core capsules showed improved protein production and increased cell survival. But aside those more traditional techniques, new techniques are emerging in response to shortcomings of existing methods. More recently, direct cell aggregate coating have been proposed to minimize membrane thickness and implants size. Microcapsule performances are largely dictated by the physicochemical properties of the materials and the preparation techniques employed. Despite numerous promising pre-clinical results, at the present time each methods proposed need further improvements before reaching the clinical phase.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".