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Record W2055678496 · doi:10.2147/bctt.s26437

Targeting γ-secretase in breast cancer

2012· review· en· W2055678496 on OpenAlexaff
Jianxun Han, Shen

Bibliographic record

VenueBreast Cancer Targets and Therapy · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDevelopmental Biology and Gene Regulation
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsNotch signaling pathwayBreast cancerGamma secretaseMedicineTransmembrane proteinCancer researchCarcinogenesisCancerBioinformaticsReceptorInternal medicineBiologyCell biology

Abstract

fetched live from OpenAlex

γ-secretase complexes are multisubunit protease complexes that perform the intramembrane cleavage of more than 60 type-I transmembrane proteins, including Notch receptors. Since dysregulated Notch signaling has been implicated in the tumorigenesis and progression of breast cancer, small molecule γ-secretase inhibitors (GSIs) are being tested for their therapeutic potential in breast cancer treatment in several clinical trials. Here, the structure of γ-secretase complex and the development of GSIs are briefly reviewed, the roles of Notch and several other γ-secretase substrates in breast cancer are discussed, and the difference between γ-secretase inhibition and Notch inhibition, as well as the side effects associated with GSIs, are described. A better understanding of molecular mechanisms that affect the responsiveness of breast cancer to GSI might help to develop strategies to enhance the antitumor activity and, at the same time, alleviate the side effects of GSI.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.005

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 head0.024
GPT teacher head0.310
Teacher spread0.285 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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