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Record W2099617191 · doi:10.1186/1477-7827-1-66

Chemoresistance in human ovarian cancer: the role of apoptotic regulators

2003· review· en· W2099617191 on OpenAlexafffund
Michael Fraser, Brendan M. Leung, Arezu Jahani‐Asl, Xiaojuan Yan, Winston E. Thompson, Benjamin K. Tsang

Bibliographic record

VenueReproductive Biology and Endocrinology · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsUniversity of Ottawa
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthNational Cancer InstituteCanadian Institutes of Health ResearchNational Center for Research ResourcesNational Institute of General Medical SciencesU.S. Department of Defense
KeywordsApoptosisCancer researchChemotherapyCancerBiologyOvarian cancerBioinformaticsMedicineInternal medicineGenetics

Abstract

fetched live from OpenAlex

Ovarian cancer is among the most lethal of all malignancies in women. While chemotherapy is the preferred treatment modality, chemoresistance severely limits treatment success. Recent evidence suggests that deregulation of key pro- and anti-apoptotic pathways is a key factor in the onset and maintenance of chemoresistance. Furthermore, the discovery of novel interactions between these pathways suggests that chemoresistance may be multi-factorial. Ultimately, the decision of the cancer cell to live or die in response to a chemotherapeutic agent is a consequence of the overall apoptotic capacity of that cell. In this review, we discuss the biochemical pathways believed to promote cell survival and how they modulate chemosensitivity. We then conclude with some new research directions by which the fundamental mechanisms of chemoresistance can be elucidated.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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

Citations169
Published2003
Admission routes2
Has abstractyes

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