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Record W2922585093 · doi:10.3390/cells8030278

Recent Progress on the Localization of the Spindle Assembly Checkpoint Machinery to Kinetochores

2019· review· en· W2922585093 on OpenAlexaff
Zhen Dou, Diogjena Katerina Prifti, Ping Gui, Xing Liu, Sabine Elowe, Xuebiao Yao

Bibliographic record

VenueCells · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsUniversité Laval
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsKinetochoreSpindle checkpointCell biologyAnaphaseSister chromatidsBiologySpindle apparatusChromosome segregationMitosisMetaphaseMicrotubuleMitotic exitCell divisionChromosomeGeneticsCell cycleCell

Abstract

fetched live from OpenAlex

Faithful chromosome segregation during mitosis is crucial for maintaining genome stability. The spindle assembly checkpoint (SAC) is a surveillance mechanism that ensures accurate mitotic progression. Defective SAC signaling leads to premature sister chromatid separation and aneuploid daughter cells. Mechanistically, the SAC couples the kinetochore microtubule attachment status to the cell cycle progression machinery. In the presence of abnormal kinetochore microtubule attachments, the SAC prevents the metaphase-to-anaphase transition through a complex kinase-phosphatase signaling cascade which results in the correct balance of SAC components recruited to the kinetochore. The correct kinetochore localization of SAC proteins is a prerequisite for robust SAC signaling and, hence, accurate chromosome segregation. Here, we review recent progresses on the kinetochore recruitment of core SAC factors.

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 score0.005
Threshold uncertainty score0.015

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)0.0050.003

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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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

Citations40
Published2019
Admission routes1
Has abstractyes

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