Thermoresponsive BrushGel Microcarriers for Efficient Cell Expansion and Enzyme‐Reduced Harvesting
Bibliographic record
Abstract
Scaling up cell therapy requires efficient expansion of high-quality cells. Microcarrier(MC)-based systems offer high surface-to-volume ratios and reduce culture media usage. In this study, we developed BrushGel, a temperature-responsive MC composed of gelatin methacryloyl (GelMA) hydrogel particles coated with poly(N-isopropyl acrylamide) (PNIPAM) polymer brushes via covalent grafting. BrushGel was fabricated using a flow-focusing droplet microfluidic device and functionalized using carbodiimide chemistry ( 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide-N-Hydroxysuccinimide, EDC-NHS). The degree of PNIPAM coating was tuned by varying the degree of methacrylation (DOM) of GelMA and the concentration of PNIPAM. Human dermal fibroblast (HNDF) cells cultured on the BrushGel under dynamic conditions showed a 4.9 fold increase in cell density, 12-fold upregulation in COL1A1 gene expression and elevated procollagen protein secretion compared to static culture. Low temperature detachment (4 °C) yeilded up to 65% detachment efficiency with >95% post-detachment viability. Clinical grade human bone marrow-derived mesenchymal stromal/stem cells (MSCs) expnaded 5.3 fold over five days on BrushGel with 69% detachment efficiency and 80% post-harvest viability using 10-fold less enzyme. BrushGel supported over 10 days of culture in spinner flasks, enabling enzyme-minimized, scalable cell expansion. These findings position BrushGel as a promising platfrom for dyanmic cell culture systems in regenerative medicine.
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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".