Multifunctional Thermoresponsive Microcarriers for High‐Throughput Cell Culture and Enzyme‐Free Cell Harvesting
Bibliographic record
Abstract
An effective treatment of human diseases using regenerative medicine and cell therapy approaches requires a large number of cells. Cultivation of cells on microcarriers is a promising approach due to the high surface-to-volume ratios that these microcarriers offer. Here, multifunctional temperature-responsive microcarriers (cytoGel) made of an interpenetrating hydrogel network composed of poly(N-isopropylacrylamide) (PNIPAM), poly(ethylene glycol) diacrylate (PEGDA), and gelatin methacryloyl (GelMA) are developed. A flow-focusing microfluidic chip is used to produce microcarriers with diameters in the range of 100-300 μm and uniform size distribution (polydispersity index of ≈0.08). The mechanical properties and cells adhesion properties of cytoGel are adjusted by changing the composition hydrogel composition. Notably, GelMA regulates the temperature response and enhances microcarrier stiffness. Human-derived glioma cells (U87) are grown on cytoGel in static and dynamic culture conditions with cell viabilities greater than 90%. Enzyme-free cell detachment is achieved at room temperature with up to 70% detachment efficiency. Controlled release of bioactive molecules from cytoGel is accomplished for over a week to showcase the potential use of microcarriers for localized delivery of growth factors to cell surfaces. These microcarriers hold great promise for the efficient expansion of cells for the industrial-scale culture of therapeutic cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".