Mechanically stable fibrin scaffolds promote viability and induce neurite outgrowth in neural aggregates derived from human induced pluripotent stem cells
Bibliographic record
Abstract
Recent work demonstrated that 3D fibrin scaffolds function as an effective substrate for engineering tissues from pluripotent stem cells. However, the rapid degradation rate of fibrin remains a major limitation when differentiating human pluripotent stem cells for tissue engineering applications. The addition of crosslinking agents, such as genipin, during the polymerization process increases scaffold stability while decreasing the degradation rate of fibrin. Genipin crosslinking alters the physical characteristics of the fibrin scaffolds, which influences the behaviour of the differentiating cells seeded inside. It also possesses neuritogenic and neuroprotective properties, making it particularly attractive for engineering neural tissue from pluripotent stem cells. Here we show that genipin enhances neuronal differentiation of neural progenitors derived from human induced pluripotent stem cells (hiPSCs) in 2D culture and genipin concentration influences the morphological and mechanical properties of 3D fibrin scaffolds. These mechanically stable genipin-crosslinked fibrin scaffolds support hiPSC-derived neural aggregates and induce neurite outgrowth while remaining intact for 2 weeks as opposed to 5 days for unmodified fibrin scaffolds.
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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".