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Enregistrement W4385872074 · doi:10.1093/eurheartj/ehad495

Barbara Casadei receives award for outstanding research into atrial fibrillation

2023· article· en· W4385872074 sur OpenAlexaboutno aff
Judith Ozkan

Notice bibliographique

RevueEuropean Heart Journal · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueAtrial Fibrillation Management and Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineAtrial fibrillationCardiology

Résumé

récupéré en direct d'OpenAlex

All correspondence relating to this article should be sent to [email protected] Barbara Casadei, MD, DPhil, FMedSci, Professor at the British Heart Foundation (BHF) Centre of Research Excellence at the University of Oxford, UK, has been awarded the Lucian Award for outstanding research in the field of circulatory diseases in recognition of her pioneering work into atrial fibrillation (AF). She has been a BHF Professor at Oxford since 2012 and served as President of the European Society of Cardiology from 2018 to 2020 (Figure 1). Barbara Casadei, winner of the 2022 Lucian Award. Prof. Casadei and her team have a long-term focus on understanding the underlying mechanisms of AF and identifying potential targets for treatment which has led to cataloguing key processes behind the genesis and maintenance of this common arrhythmia, including the part played by nitric oxide (NO), reactive oxygen species, and inflammation. Her work has also put potential AF treatments to the test in clinical studies that may ultimately lead to better treatment and prevention of AF. These include investigations of the characteristics of blood flow in the left atrium by magnetic resonance imaging and their relationship with brain infarcts and the use of genetically informed biological pathways to understand the aetiological heterogeneity of AF and to refine prediction of AF-related complications, in collaboration with Prof. Jemma Hopewell in Oxford. The group’s investigations into the effects of myocardial NO production in AF-induced electrical remodelling goes back over 20 years following a 2002 study which described a reduction in NO availability in the atrial endocardium of animal models with pacing-induced AF. This prompted questions and further investigation. Prof. Casadei says: ‘When we started looking at atrial tissue samples from patients with AF, we confirmed that there was a dramatic reduction in NO production due to the near disappearance of the neuronal NO synthase (nNOS) from the fibrillating atrial myocardium in humans and animal models. We then asked ourselves what the mechanisms might be underpinning such a dramatic reduction and looked at what may affect the stability of the nNOS protein.’ To answer this question, the team drew inspiration from studies in patients and animal models of Duchenne muscular dystrophy. Prof. Casadei says: ‘From work on Duchenne Muscular Dystrophy, we knew that lack of dystrophin displaces nNOS from the cell membrane and, in the skeletal muscle, leads to its near disappearance. We found that AF was associated with a reduction in dystrophin in the atrial myocardium, and then found that there was an upregulation of a microRNA that was already known in Duchenne’s to inhibit the translation of the dystrophin messenger RNA. Furthermore, upregulation of that particular microRNA was also accelerating the decay of nNOS mRNA. The dramatic reduction in atrial nNOS that followed, altered the function of several myocardial ion channels which contribute to the atrial electrical remodelling that begets AF.’ Prof. Svetlana Reilly, MD, DPhil, who was one of the Prof. Casadei’s graduate students and is now a BHF Senior Research Fellow, is taking this work forward into investigations of the mechanisms responsible for atrial structural remodelling and fibrosis in the presence of AF. Understanding the mechanisms behind atrial fibrosis, Prof. Casadei says, may open up new avenues for preventing AF, greatly increasing the efficacy of ablation and possibly even reducing the risk of cardioembolic stroke. ‘The mechanisms that regulate the fibrotic process in the human atrium can uncover, as Prof. Reilly demonstrated, novel therapeutic targets that could be relevant to a host of conditions, from HFpEP to pulmonary hypertension, for which we only have partially effective treatment.’ Prof. Casadei acknowledges that translating science discovery into clinical practice is a long and challenging process that requires a breadth of techniques and models and sustained funding from many sources. ‘Failure to take a basic discovery into the clinic is much more common than success’ she says, ‘but rigorously conducted research always makes an important contribution to our knowledge, whether it proves or disproves our original hypothesis’. The Lucian Award is an annual accolade for research in circulatory disease, established by a bequest to McGill University, Canada, in 1965 in memory of brothers Louis and Artur Lucian. Prof. Casadei was the sole recipient of the 2022 award; however, she emphasizes the recognition of her team’s work rather than personal accolades. ‘It’s always a great honour to be given awards and I was very happy to receive the Lucian Award on this occasion. However, this is not just an award for me, it’s also a recognition for the work that my graduate students, clinical fellows and collaborators have undertaken with passion and determination, and of everything we have been able to achieve together as a “team of explorers”.’ All authors declare no conflict of interest for this contribution.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,100
Score d'incertitude au seuil0,333

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,012
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,001
Communication savante0,0040,002
Science ouverte0,0010,002
Intégrité de la recherche0,0040,006
Charge utile insuffisante (le modèle a refusé de juger)0,1000,066

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,335
Tête enseignante GPT0,478
Écart entre enseignants0,142 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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