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Record W2762965851 · doi:10.1089/scd.2017.0090

Optimizing Human Induced Pluripotent Stem Cell Expansion in Stirred-Suspension Culture

2017· article· en· W2762965851 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.

Bibliographic record

VenueStem Cells and Development · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInduced pluripotent stem cellBiologyBioreactorEmbryonic stem cellHuman Induced Pluripotent Stem CellsRegenerative medicineGerm layerSuspension cultureSeedingChemically defined mediumSuspension (topology)Cell cultureCell biologyBiotechnologyStem cellBotanyIn vitroGeneticsGene

Abstract

fetched live from OpenAlex

Human induced pluripotent stem cells (hiPSCs) hold great hopes for application in regenerative medicine due to their inherent capacity to self-renew and differentiate into cells from the three embryonic germ layers. For clinical applications, a large quantity of hiPSCs produced in standardized and scalable culture processes is required. Several groups, including ours, have developed methodologies for scaled-up hiPSC production in stirred bioreactors in chemically defined medium. In this study, we optimized the critical steps and factors that affect hiPSC expansion and yield in stirred-suspension cultures, including inoculation conditions, seeding density, aggregate size, agitation rate, and cell passaging method. After multiple passages in stirred-suspension bioreactors, hiPSCs remained pluripotent, karyotypically normal, and capable of differentiating into all three germ layers.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.031
GPT teacher head0.274
Teacher spread0.243 · 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