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Adenosine as a Marker and Mediator of Cardiovascular Homeostasis: A Translational Perspective

2018· article· en· W2895821445 on OpenAlexaff
Trevor Simard, Richard G. Jung, Alisha Labinaz, Mohammad Ali Faraz, F. Daniel Ramirez, Pietro Di Santo, Ian Pitcher, Pouya Motazedian, Chantal Gaudet, Rebecca Rochman, Jeffrey A. Marbach, Paul Boland, Kiran Sarathy, Saleh Alghofaili, Juan Russo, Étienne Couture, Rob Beanlands, Benjamin Hibbert

Bibliographic record

VenueCardiovascular & Haematological Disorders - Drug Targets · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAdenosinePurinergic signallingAdenosine receptorPurinergic receptorAdenosine A2B receptorMediatorAdenosine A3 receptorMedicinePharmacologyNucleosidePurineReceptorNeuroscienceBiologyInternal medicineAgonistBiochemistry

Abstract

fetched live from OpenAlex

Adenosine, a purine nucleoside, is produced broadly and implicated in the homeostasis of many cells and tissues. It signals predominantly via 4 purinergic adenosine receptors (ADORs) - ADORA1, ADORA2A, ADORA2B and ADOosine signaling, both through design as specific ADOR agonists and antagonists and as offtarget effects of existing anti-platelet medications. Despite this, adenosine has yet to be firmly established as either a therapeutic or a prognostic tool in clinical medicine to date. Herein, we provide a bench-to-bedside review of adenosine biology, highlighting the key considerations for further translational development of this proRA3 in addition to non-ADOR mediated effects. Through these signaling mechanisms, adenosine exerts effects on numerous cell types crucial to maintaining vascular homeostasis, especially following vascular injury. Both in vitro and in vivo models have provided considerable insights into adenosine signaling and identified targets for therapeutic intervention. Numerous pharmacologic agents have been developed that modulate adenmising molecule.

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 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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations8
Published2018
Admission routes1
Has abstractyes

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