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Record W3209603066 · doi:10.1242/dev.199909

Microtubule-dependent subcellular organisation of pluripotent cells

2021· review· en· W3209603066 on OpenAlexfundno aff
Azelle Hawdon, Asma Aberkane, Jennifer Zenker

Bibliographic record

VenueDevelopment · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsnot available
FundersMedical Research CouncilState Government of VictoriaAustralian GovernmentNational Health and Medical Research CouncilCanadian Institute for Advanced Research
KeywordsBiologyMicrotubuleInduced pluripotent stem cellCell biologyReprogrammingOrganelleEmbryonic stem cellCellGenetics

Abstract

fetched live from OpenAlex

With the advancement of cutting-edge live imaging technologies, microtubule remodelling has evolved as an integral regulator for the establishment of distinct differentiated cells. However, despite their fundamental role in cell structure and function, microtubules have received less attention when unravelling the regulatory circuitry of pluripotency. Here, we summarise the role of microtubule organisation and microtubule-dependent events required for the formation of pluripotent cells in vivo by deciphering the process of early embryogenesis: from fertilisation to blastocyst. Furthermore, we highlight current advances in elucidating the significance of specific microtubule arrays in in vitro culture systems of pluripotent stem cells and how the microtubule cytoskeleton serves as a highway for the precise intracellular movement of organelles. This Review provides an informed understanding of the intrinsic role of subcellular architecture of pluripotent cells and accentuates their regenerative potential in combination with innovative light-inducible microtubule techniques.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.258
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2021
Admission routes1
Has abstractyes

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