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Record W3112590295 · doi:10.1126/science.abc8059

Inhibition of prostaglandin-degrading enzyme 15-PGDH rejuvenates aged muscle mass and strength

2020· article· en· W3112590295 on OpenAlexfundno aff
Adelaida R. Palla, Meenakshi Ravichandran, Yu Xin Wang, L. А. Alexandrova, Ann V. Yang, Peggy E. Kraft, Colin Holbrook, Christian M. Schürch, Andrew Tri Van Ho, Helen M. Blau

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Institute of Neurological Disorders and StrokeNational Human Genome Research InstituteNational Institute on AgingNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDonald E. and Delia B. Baxter FoundationCanadian Institutes of Health ResearchLi Ka Shing FoundationCalifornia Institute for Regenerative Medicine
KeywordsEnzymeChemistryProstaglandinMuscle massBiochemistryBiologyEndocrinology

Abstract

fetched live from OpenAlex

Maintaining muscle Prostaglandin E2 (PGE2), an eicosanoid that mediates inflammatory responses, also supports the function of muscle stem cells. Palla et al. found that loss of PGE2 in aging mice contributes to loss of muscle and appears to be a consequence of increased activity of 15-hydroxyprostaglandin dehydrogenase (15-PGDH), an enzyme that degrades PGE2 (see the Perspective by Becker and Rudolph). Restoring PGE2 concentrations by inhibiting 15-PGDH in older mice improved muscle function. Decreased activity of 15-PGDH in older animals had beneficial effects that included decreased proteolysis and transforming growth factor–β signaling and increased mitochondrial function and autophagy. The findings reveal a potential therapeutic strategy to help maintain muscle mass and function during aging. Science , this issue p. eabc8059 ; see also p. 462

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 score0.002
Threshold uncertainty score0.007

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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

Citations216
Published2020
Admission routes1
Has abstractyes

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