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Record W4361925007 · doi:10.1158/1078-0432.22464621.v1

Supplementary Data from High-throughput Chemical Screening Identifies Focal Adhesion Kinase and Aurora Kinase B Inhibition as a Synergistic Treatment Combination in Ewing Sarcoma

2023· preprint· en· W4361925007 on OpenAlexaff
Sarah Wang, Elizabeth E. Hwang, Rajarshi Guha, Allison F. O’Neill, Nicole Melong, Chansey J. Veinotte, Amy Conway Saur, Kellsey Wuerthele, Min Shen, Crystal McKnight, Gabriela Alexe, Madeleine E. Lemieux, Amy Wang, E. D. Hughes, Xin Xu, Matthew B. Boxer, Matthew D. Hall, Andrew L. Kung, Jason N. Berman, Mindy I. Davis, Kimberly Stegmaier, Brian D. Crompton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health Centre
Fundersnot available
KeywordsSarcomaAurora kinaseAurora inhibitorCancer researchEwing's sarcomaZebrafishFocal adhesionKinaseCell growthCell cultureMedicineBiologyCell biologyCellCell cyclePathologyGenetics

Abstract

fetched live from OpenAlex

Supplemental Methods and Figures Supplemental Figure S1. Aurora kinase expression in Ewing sarcoma Supplemental Figure S2. Effects of cell growth on response to AZD-1152 as a function of duration of treatment. Supplemental Figure S3. Aurora kinase and FAK inhibitor combinations are synergistic in Ewing sarcoma cell lines Supplemental Figure S4. Response of Ewing cell lines treated with combinations of Aurora kinase and FAK inhibitors Supplemental Figure S5. Cell cycle and apoptotic effects of Aurora kinase B knock out in Ewing sarcoma cell lines Supplemental Figure S6. PYK2 is poorly expressed in Ewing sarcoma cells Supplemental Figure S7. Zebrafish and Murine studies

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.544
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.5440.155

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 head0.042
GPT teacher head0.298
Teacher spread0.257 · 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.

Study designBench or experimental
Domainnot available
GenreDataset

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

Citations0
Published2023
Admission routes1
Has abstractyes

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