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Record W2125040825 · doi:10.2174/1871520611313070004

Hitting the Golden TORget: Curcumin’s Effects on mTOR Signaling

2013· review· en· W2125040825 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

VenueAnti-Cancer Agents in Medicinal Chemistry · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCurcumin's Biomedical Applications
Canadian institutionsUniversity of New Brunswick
FundersNational Cancer Institute
KeywordsCurcuminPI3K/AKT/mTOR pathwayChemistryPharmacologySignal transductionCell biologyBiology

Abstract

fetched live from OpenAlex

The polyphenol natural product curcumin possesses a plethora of biological and pharmacological properties. For years, much interest has been placed in the development and use of curcumin and its derivatives for the prevention and treatment of cardiovascular, diabetic, and neurodegenerative diseases, as well as cancer. Increasing evidence suggests that curcumin displays amazing molecular versatility, and the number of its proposed cellular targets grows as the research continues. The mammalian target of rapamycin (mTOR) is a master kinase, regulating cell growth/proliferation, survival, and motility. Dysregulated mTOR signaling occurs frequently in cancer, and targeting mTOR signaling is a promising strategy for cancer therapy. Recent studies have identified mTOR as a novel target of curcumin. Here we focus on reviewing current knowledge regarding the effects of curcumin on mTOR signaling for better understanding the anticancer mechanism of curcumin. The emerging studies of mTOR signaling and clinical studies on curcumin with cancer patients are also discussed here.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.039
GPT teacher head0.364
Teacher spread0.325 · 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