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Record W2115220076 · doi:10.2478/s11658-010-0039-8

Identification of molecules derived from human fibroblast feeder cells that support the proliferation of human embryonic stem cells

2010· article· en· W2115220076 on OpenAlexaff
Sergey V. Anisimov, Nicolaj S. Christophersen, Ana Sofia Correia, Vanessa Jane Hall, Ingrid Sandelin, Jiayi Li, Patrik Brundin

Bibliographic record

VenueCellular & Molecular Biology Letters · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersRussian Academy of SciencesNordForskLundbeckfonden
KeywordsEmbryonic stem cellCell biologyStem cellFibroblastIdentification (biology)BiologyFibroblast growth factorAdult stem cellCell cultureBiochemistryGeneticsGeneReceptor

Abstract

fetched live from OpenAlex

The majority of human embryonic stem cell lines depend on a feeder cell layer for continuous growth in vitro, so that they can remain in an undifferentiated state. Limited knowledge is available concerning the molecular mechanisms that underlie the capacity of feeder cells to support both the proliferation and pluripotency of these cells. Importantly, feeder cells generally lose their capacity to support human embryonic stem cell proliferation in vitro following long-term culture. In this study, we performed large-scale gene expression profiles of human foreskin fibroblasts during early, intermediate and late passages using a custom DNA microarray platform (NeuroStem 2.0 Chip). The microarray data was validated using RT-PCR and virtual SAGE analysis. Our comparative gene expression study identified a limited number of molecular targets potentially involved in the ability of human neonatal foreskin fibroblasts to serve as feeder cells for human embryonic stem cell cultures. Among these, the C-KIT, leptin and pigment epithelium-derived factor (PEDF) genes were the most interesting candidates.

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 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 score0.014
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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.

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

Citations16
Published2010
Admission routes1
Has abstractyes

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