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Record W2951923805 · doi:10.1073/pnas.1900152116

Apelin protects against abdominal aortic aneurysm and the therapeutic role of neutral endopeptidase resistant apelin analogs

2019· article· en· W2951923805 on OpenAlexafffund
Wang Wang, Mengcheng Shen, Conrad Fischer, Ratnadeep Basu, Saugata Hazra, Pierre Couvineau, Manish Paul, Faqi Wang, Sandra Toth, Doran Mix, Marko Poglitsch, Norma P. Gerard, Michel Bouvier, John C. Vederas, Josef Penninger, Zamaneh Kassiri, Gavin Y. Oudit

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicApelin-related biomedical research
Canadian institutionsUniversity of British ColumbiaUniversité de MontréalUniversity of Alberta
FundersCanadian Institutes of Health ResearchGovernment of CanadaCanadian Institute for Advanced Research
KeywordsApelinNeprilysinPathogenesisMedicineAbdominal aortic aneurysmTherapeutic effectPharmacologyInternal medicineEnzymeBiologyAneurysmReceptorSurgeryBiochemistry

Abstract

fetched live from OpenAlex

Significance Vascular diseases remain a major health burden, and AAAs lack effective medical therapy. We demonstrate a seminal role for APLN in AAA pathogenesis based on loss-of-function and gain-of-function approaches and included human vascular SMCs and AA tissue obtained from patients. We identified NEP as a dominant inactivating enzyme for native APLN-17. This allowed us to design and synthesize a stable and bioactive APLN analog resistant to NEP degradation that showed profound therapeutic effects against AAA. Our study clearly defines the APLN pathway as a central node in the pathogenesis of AAA and elucidate a therapeutic strategy of enhancing the APLN pathway by using a stable APLN analog to treat AAA.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.025
GPT teacher head0.311
Teacher spread0.286 · 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

Citations63
Published2019
Admission routes2
Has abstractyes

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Same venueProceedings of the National Academy of SciencesSame topicApelin-related biomedical researchFrench-language works237,207