Thermally induced gelable polymer networks for living cell encapsulation
Bibliographic record
Abstract
We report the encapsulation of MIN6 cells, a pancreatic beta-cell line, using thermally induced gelable materials. This strategy uses aqueous solvent and mild temperatures during encapsulation, thereby minimizing adverse effects on cell function and viability. Using a 2:1 mixture of PNIPAAm-PEG-PNIPAAm tri-block copolymer and PNIPAAm homopolymer that exhibit reversible sol-to-gel transition at approximately 30 degrees C, gels were formed that exhibit mechanical integrity, and are stable in H(2)O, PBS and complete DMEM with negligible mass loss at 37 degrees C for 60 days. MTT assays showed undetectable cytotoxicity of the polymers towards MIN6 cells. A simple microencapsulation process was developed using vertical co-extrusion and a 37 degrees C capsule collection bath containing a paraffin layer above DMEM. Spherical capsules with diameters ranging from 500 to 900 microm were formed. SEM images of freeze-dried capsules with PBS as the core solution showed homogenous gel capsule membranes. Confocal microscopy revealed that the encapsulated cells tended to form small aggregates over 5 days, and staining for live and dead cells showed high viability post-encapsulation. A static glucose challenge with day-5 cultured microencapsulated cells exhibited glucose-dependent insulin secretion comparable to controls of free MIN6 cells grown in monolayers. These results demonstrate the potential use of these thermo-responsive polymers as cell encapsulation membranes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".