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Record W2067025407 · doi:10.1038/srep01978

Cartilage tissue engineering identifies abnormal human induced pluripotent stem cells

2013· article· en· W2067025407 on OpenAlexafffund
Akihiro Yamashita, Shiying Liu, Knut Woltjen, Bradley Thomas, Guoliang Meng, Akitsu Hotta, Kazutoshi Takahashi, James Ellis, Shinya Yamanaka, Derrick E. Rancourt

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenUniversity of AlbertaUniversity of Calgary
FundersAlberta Innovates
KeywordsInduced pluripotent stem cellReprogrammingCartilageEmbryonic stem cellCell biologyBiologyIn vitroTissue engineeringCell cultureHuman Induced Pluripotent Stem CellsStem cellCancer researchAnatomyCellGenetics

Abstract

fetched live from OpenAlex

Safety is the foremost issue in all human cell therapies, but human induced pluripotent stem cells (iPSCs) currently lack a useful safety indicator. Studies in chimeric mice have demonstrated that certain lines of iPSCs are tumorigenic; however a similar screen has not been developed for human iPSCs. Here, we show that in vitro cartilage tissue engineering is an excellent tool for screening human iPSC lines for tumorigenic potential. Although all human embryonic stem cells (ESCs) and most iPSC lines tested formed cartilage safely, certain human iPSCs displayed a pro-oncogenic state, as indicated by the presence of secretory tumors during cartilage differentiation in vitro. We observed five abnormal iPSC clones amoungst 21 lines derived from five different reprogramming methods using three cellular origins. We conclude that in vitro cartilage tissue engineering is a useful approach to identify abnormal human iPSC lines.

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.

How this classification was reachedexpand

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 score0.024
Threshold uncertainty score0.936

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 communication0.0010.000
Open science0.0000.000
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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2013
Admission routes2
Has abstractyes

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