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Record W1997694334 · doi:10.1172/jci41158

Enabling stem cell therapies through synthetic stem cell–niche engineering

2010· review· en· W1997694334 on OpenAlex
Raheem Peerani, Peter W. Zandstra

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

VenueJournal of Clinical Investigation · 2010
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsHeart and Stroke FoundationUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchStem Cell Network
KeywordsStem cellNicheStem cell nicheBiologyCell biologyProgenitor cellEcology

Abstract

fetched live from OpenAlex

Enabling stem cell-targeted therapies requires an understanding of how to create local microenvironments (niches) that stimulate endogenous stem cells or serve as a platform to receive and guide the integration of transplanted stem cells and their derivatives. In vivo, the stem cell niche is a complex and dynamic unit. Although components of the in vivo niche continue to be described for many stem cell systems, how these components interact to modulate stem cell fate is only beginning to be understood. Using the HSC niche as a model, we discuss here microscale engineering strategies capable of systematically examining and reconstructing individual niche components. Synthetic stem cell-niche engineering may form a new foundation for regenerative therapies.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.006
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 head0.156
GPT teacher head0.395
Teacher spread0.239 · 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