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Record W2079195016 · doi:10.1002/bies.201200114

Genome damage in induced pluripotent stem cells: Assessing the mechanisms and their consequences

2012· review· en· W2079195016 on OpenAlex
Samer M. I. Hussein, Judith Elbaz, András Nagy

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

VenueBioEssays · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsInduced pluripotent stem cellReprogrammingSomatic cellBiologyEpigeneticsStem cellGenomeCell biologyCellular differentiationEmbryonic stem cellNeuroscienceGeneticsCellGene

Abstract

fetched live from OpenAlex

In 2006, Shinya Yamanaka and colleagues discovered how to reprogram terminally differentiated somatic cells to a pluripotent stem cell state. The resulting induced pluripotent stem cells (iPSCs) made a paradigm shift in the field, further nailing down the disproval of the long-held dogma that differentiation is unidirectional. The prospect of using iPSCs for patient-specific cell-based therapies has been enticing. This promise, however, has been questioned in the last two years as several studies demonstrated intrinsic epigenetic and genomic anomalies in these cells. Here, we not only review the recent critical studies addressing the genome integrity during the reprogramming process, but speculate about the underlying mechanisms that could create de novo genome damage in iPSCs. Finally, we discuss how much an elevated mutation load really matters considering the safety of future therapies with cells heavily cultured in vitro.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.082
GPT teacher head0.330
Teacher spread0.247 · 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