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The effect of Young’s modulus on the neuronal differentiation of mouse embryonic stem cells

2015· article· en· W965674395 on OpenAlexaff
Shahzad Ali, Ivan Wall, Chris Mason, Andrew E. Pelling, Farlan Veraitch

Bibliographic record

VenueActa Biomaterialia · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Ottawa
FundersIntelligent Manufacturing Research CenterEngineering and Physical Sciences Research CouncilInnovative electronics Manufacturing Research Centre
KeywordsEmbryonic stem cellMaterials scienceStem cellCell biologyModulusCellular differentiationBiomedical engineeringBiologyComposite materialMedicineGenetics

Abstract

fetched live from OpenAlex

There is substantial evidence that cells produce a diverse response to changes in ECM stiffness depending on their identity. Our aim was to understand how stiffness impacts neuronal differentiation of embryonic stem cells (ESC's), and how this varies at three specific stages of the differentiation process. In this investigation, three effects of stiffness on cells were considered; attachment, expansion and phenotypic changes during differentiation. Stiffness was varied from 2 kPa to 18 kPa to finally 35 kPa. Attachment was found to decrease with increasing stiffness for both ESC's (with a 95% decrease on 35 kPa compared to 2 kPa) and neural precursors (with a 83% decrease on 35 kPa). The attachment of immature neurons was unaffected by stiffness. Expansion was independent of stiffness for all cell types, implying that the proliferation of cells during this differentiation process was independent of Young's modulus. Stiffness had no effect upon phenotypic changes during differentiation for mESC's and neural precursors. 2 kPa increased the proportion of cells that differentiated from immature into mature neurons. Taken together our findings imply that the impact of Young's modulus on attachment diminishes as neuronal cells become more mature. Conversely, the impact of Young's modulus on changes in phenotype increased as cells became more mature.

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)0.000
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 score0.002
Threshold uncertainty score0.217

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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

Citations60
Published2015
Admission routes1
Has abstractyes

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