Engineering personalized neural tissue by combining induced pluripotent stem cells with fibrin scaffolds
Bibliographic record
Abstract
Induced pluripotent stem cells (iPSCs) are generated from adult somatic cells through the induction of key transcription factors that restore the ability to become any cell type found in the body. These cells are of interest for tissue engineering due to their potential for developing patient-specific therapies. As the technology for generating iPSCs advances, it is important to concurrently investigate protocols for the efficient differentiation of these cells to desired downstream phenotypes in combination with biomaterial scaffolds as a way of engineering neural tissue. For such applications, the generation of neurons within three dimensional fibrin scaffolds has been well characterized as a cell-delivery platform for murine embryonic stem cells (ESCs) but has not yet been applied to murine iPSCs. Given that iPSCs have been reported to differentiate less effectively than ESCs, a key objective of this investigation is to maximize the proportion of iPSC-derived neurons in fibrin through the choice of differentiation protocol. To this end, this study compares two EB-mediated protocols for generating neurons from murine iPSCs and ESCs: an 8 day 4-/4+ protocol using soluble retinoic acid in the last 4 days and a 6 day 2-/4+ protocol using soluble retinoic acid and the small molecule sonic hedgehog agonist purmorphamine in the last 4 days. EBs were then seeded in fibrin scaffolds for 14 days to allow further differentiation into neurons. EBs generated by the 2-/4+ protocol yielded a higher percentage of neurons compared to those from the 4-/4+ protocol for both iPSCs and ESCs. The results demonstrate the successful translation of the fibrin-based cell-delivery platform for use with murine iPSCs and furthermore that the proportion of neurons generated from murine iPSC-derived EBs seeded in fibrin can be maximized using the 2-/4+ differentiation protocol. Together, these findings validate the further exploration of 3D fibrin-based scaffolds as a method of delivering neuronal cells derived from iPSCs - an important step toward the development of iPSC-based tissue engineering strategies for spinal cord injury repair.
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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 | 0.001 | 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".