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Chromosome segregation machinery and cancer

2009· review· en· W2034532685 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

VenueCancer Science · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsInstitute of Aging
FundersJapan Science and Technology AgencyUehara Memorial FoundationTakeda Science Foundation
KeywordsChromosome segregationKinetochoreSister chromatidsSpindle checkpointAnaphaseChromosome instabilityBiologyMitosisChromosomeCarcinogenesisCancerGeneticsChromatidCell biologyCancer researchCell cycleGene

Abstract

fetched live from OpenAlex

Loss or gain of chromosomes is associated with many cancer cells. This property, called chromosome instability, might arise from a lesion in the chromosome segregation machinery. Essential for chromosome segregation are the proper connection of microtubules to kinetochores, and the synchronous segregation of sister chromatids in anaphase. Accuracy of these processes is ensured by two sophisticated machineries called the correction mechanism and the spindle assembly checkpoint. Here we outline the current understanding of the underlying mechanisms, and highlight recent challenging experiments to address how chromosome segregation failure might relate to tumorigenesis. Understanding these mechanisms may lead to the discovery of new and improved anticancer therapies.

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 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 score0.997
Threshold uncertainty score0.759

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 head0.020
GPT teacher head0.335
Teacher spread0.315 · 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