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Record W2071975518 · doi:10.1126/science.1215327

The Ancient Drug Salicylate Directly Activates AMP-Activated Protein Kinase

2012· article· en· W2071975518 on OpenAlexafffund
Simon A. Hawley, Morgan D. Fullerton, Fiona A. Ross, Jonathan D. Schertzer, Cyrille Chevtzoff, Katherine J. Walker, Mark Peggie, Darya Zibrova, Kevin A. Green, Kirsty J. Mustard, Bruce E. Kemp, Kei Sakamoto, Gregory R. Steinberg, D. Grahame Hardie

Bibliographic record

VenueScience · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchNational Institutes of HealthWellcome TrustYale University
KeywordsAMPKAMP-activated protein kinaseProtein kinase AAdenosine monophosphateAdenosine triphosphateAdenosineAdenosine diphosphateChemistryAspirinKinaseAdenylate kinasePhosphorylationBiochemistryPharmacologyCell biologyBiologyEnzymeInternal medicineMedicine

Abstract

fetched live from OpenAlex

Salicylate, a plant product, has been in medicinal use since ancient times. More recently, it has been replaced by synthetic derivatives such as aspirin and salsalate, both of which are rapidly broken down to salicylate in vivo. At concentrations reached in plasma after administration of salsalate or of aspirin at high doses, salicylate activates adenosine monophosphate-activated protein kinase (AMPK), a central regulator of cell growth and metabolism. Salicylate binds at the same site as the synthetic activator A-769662 to cause allosteric activation and inhibition of dephosphorylation of the activating phosphorylation site, threonine-172. In AMPK knockout mice, effects of salicylate to increase fat utilization and to lower plasma fatty acids in vivo were lost. Our results suggest that AMPK activation could explain some beneficial effects of salsalate and aspirin 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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations733
Published2012
Admission routes2
Has abstractyes

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