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Record W4293920955 · doi:10.54097/hset.v11i.1386

Production of Lovastatin and its Lipid-lowering and Anti-Cancer Effects

2022· article· en· W4293920955 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

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsMcGill University
Fundersnot available
KeywordsLovastatinHMG-CoA reductaseCancerCancer cellCancer researchBreast cancerMevalonate pathwayOvarian cancerCholesterolPharmacologyReductaseStatinMedicineBiologyInternal medicineEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Lovastatin is traditionally used to reduce the amount of cholesterol and lipid levels in many diseases, but its anti-cancer properties are now discovered. By regulating and modulating crucial signaling small G-proteins of cancer cell including Rho, Rac, and Ras, lovastatin can alter cancer cell division, migration, and induce cell death. Lovastatin has a similar structure to HMG-CoA and thus can competitively bind to HMG-CoA reductase (HMGR) and work as a hypolipidemic medicine. The anti-cancer effect of lovastatin had led to extensive research. It had been confirmed based on many in-vitro studies that lovastatin had obvious inhibitory effects on different kinds of cancer. In addition, lovastatin can increase therapeutic effect since it regulates the cell signaling pathway which induces cell cycle arrests. This article covers the application of lovastatin and cancer treatment. Lovastatin has shown promising anti-cancer properties in breast cancers, ovarian cancers and breast cancers, but more evidence is needed to determine its anti-cancer properties in-vivo and in humans.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.217
Teacher spread0.213 · 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