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Record W4409678241 · doi:10.1080/15384101.2025.2485842

Regulation of microtubule growth rates and their impact on chromosomal instability

2024· review· en· W4409678241 on OpenAlexafffund
Lia Mara Gomes Paim, Susanne Bechstedt

Bibliographic record

VenueCell Cycle · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsBiologyChromosome instabilityMicrotubuleCell biologyGenome instabilityInstabilityGeneticsDNA damageChromosomeDNAGene

Abstract

fetched live from OpenAlex

Microtubules are polymers of α/β tubulin dimers that build the mitotic spindle, which segregates duplicated chromosomes during cell division. Microtubule function is governed by dynamic instability, whereby cycles of growth and shrinkage contribute to the forces necessary for chromosome movement. Regulation of microtubule growth velocity requires cell cycle-dependent changes in expression, localization and activity of microtubule-associated proteins (MAPs) as well as tubulin post-translational modifications that modulate microtubule dynamics. It has become clear that optimal microtubule growth velocities are required for proper chromosome segregation and ploidy maintenance. Suboptimal microtubule growth rates can result from altered activity of MAPs and could lead to aneuploidy, possibly by disrupting the establishment of microtubule bundles at kinetochores and altering the mechanical forces required for sister chromatid segregation. Future work using high-resolution, low-phototoxicity microscopy and novel fluorescent markers will be invaluable in obtaining deeper mechanistic insights into how microtubule processes contribute to chromosome segregation.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.277
Teacher spread0.266 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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