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Record W2132808326 · doi:10.1039/c4bm00299g

Engineering personalized neural tissue by combining induced pluripotent stem cells with fibrin scaffolds

2014· article· en· W2132808326 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomaterials Science · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInduced pluripotent stem cellHuman Induced Pluripotent Stem CellsTissue engineeringFibrinStem cellCell biologyNeural stem cellChemistryBiologyBiomedical engineeringEmbryonic stem cellMedicineBiochemistryImmunologyGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.242
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it