Biofunctionalization of Cellulose Microcarriers Using a Carbohydrate Binding Module Linked with Fibroblast Growth Factor for the Expansion of Human Umbilical Mesenchymal Stromal Cells in Stirred Suspension Bioreactors
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) have the potential to be used as autologous or allogenic cell therapy in several diseases due to their beneficial secretome and capacity for immunomodulation and differentiation. However, clinical trials using MSCs require a large number of cells. As an alternative to traditional culture flasks, suspension bioreactors provide a scalable platform to produce clinically relevant quantities of cells. When cultured in bioreactors, anchorage-dependent cells like MSCs require the addition of microcarriers, which provide a surface for cell attachment while in suspension. The best performing microcarriers are typically coated in animal derived proteins, which increases cellular attachment and proliferation but present issues from a regulatory perspective. To overcome this issue, a recombinant fusion protein was generated linking basic fibroblast growth factor (bFGF) to a cellulose-specific carbohydrate binding module (CBM) and used to functionalize the surface of cellulose microcarriers for the expansion of human umbilical MSCs in suspension bioreactors. The fusion protein was shown to support the growth of MSCs when used as a soluble growth factor in the absence of cellulose, readily bound to cellulose microcarriers in a dose-dependent manner, and ultimately improved the expansion of MSCs when grown in bioreactors using cellulose microcarriers. The use of CBM fusion proteins offers a simple method for the surface immobilization of growth factors to animal component-free substrates such as cellulose, which can be used alongside bioreactors to increase growth factor lifespan, decrease culture medium cost, and increase cell production in the manufacturing 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".