Preparation of 3D Fibrin Scaffolds for Stem Cell Culture Applications
Bibliographic record
Abstract
Stem cells are found in naturally occurring 3D microenvironments in vivo, which are often referred to as the stem cell niche. Culturing stem cells inside of 3D biomaterial scaffolds provides a way to accurately mimic these microenvironments, providing an advantage over traditional 2D culture methods using polystyrene as well as a method for engineering replacement tissues. While 2D tissue culture polystrene has been used for the majority of cell culture experiments, 3D biomaterial scaffolds can more closely replicate the microenvironments found in vivo by enabling more accurate establishment of cell polarity in the environment and possessing biochemical and mechanical properties similar to soft tissue. A variety of naturally derived and synthetic biomaterial scaffolds have been investigated as 3D environments for supporting stem cell growth. While synthetic scaffolds can be synthesized to have a greater range of mechanical and chemical properties and often have greater reproducibility, natural biomaterials are often composed of proteins and polysaccharides found in the extracelluar matrix and as a result contain binding sites for cell adhesion and readily support cell culture. Fibrin scaffolds, produced by polymerizing the protein fibrinogen obtained from plasma, have been widely investigated for a variety of tissue engineering applications both in vitro and in vivo. Such scaffolds can be modified using a variety of methods to incorporate controlled release systems for delivering therapeutic factors. Previous work has shown that such scaffolds can be used to successfully culture embryonic stem cells and this scaffold-based culture system can be used to screen the effects of various growth factors on the differentiation of the stem cells seeded inside. This protocol details the process of polymerizing fibrin scaffolds from fibrinogen solutions using the enzymatic activity of thrombin. The process takes 2 days to complete, including an overnight dialysis step for the fibrinogen solution to remove citrates that inhibit polymerization. These detailed methods rely on fibrinogen concentrations determined to be optimal for embryonic and induced pluripotent stem cell culture. Other groups have further investigated fibrin scaffolds for a wide range of cell types and applications - demonstrating the versatility of this approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".