Contribution of polymeric materials to progress in xenotransplantation of microencapsulated cells: a review
Bibliographic record
Abstract
Cell microencapsulation and subsequent transplantation of the microencapsulated cells require multidisciplinary approaches. Physical, chemical, biological, engineering, and medical expertise has to be combined. Several natural and synthetic polymeric materials and different technologies have been reported for the preparation of hydrogels, which are suitable to protect cells by microencapsulation. However, owing to the frequent lack of adequate characterization of the hydrogels and their components as well as incomplete description of the technology, many results of in vitro and in vivo studies appear contradictory or cannot reliably be reproduced. This review addresses the state of the art in cell microencapsulation with special focus on microencapsulated cells intended for xenotransplantation cell therapies. The choice of materials, the design and fabrication of the microspheres, as well as the conditions to be met during the cell microencapsulation process, are summarized and discussed prior to presenting research results of in vitro and in vivo studies. Overall, this review will serve to sensitize medically educated specialists for materials and technological aspects of cell microencapsulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".