An interview with Saadet Mahmutoglu, 2016 Epilepsia prize winner for Clinical Research
Notice bibliographique
Résumé
Saadet Mahmutoglu I am a biochemical geneticist and neurometabolic specialist. I completed my pediatric residency in Istanbul, Turkey, and moved to Vienna, Austria, for training in neurometabolics. Later I moved from Vienna to Vancouver (British Columbia, Canada) to complete fellowship training in biochemical genetics, which was accredited by the Canadian College of Medical Geneticists at British Columbia Children's Hospital, University of British Columbia. Following this, I also completed a 1-year pediatric neurology fellowship at the University of British Columbia. These trainings bridged my knowledge between biochemical genetics and neurology and enabled me to practice as a neurometabolic physician. Since 2011, I have been working as a neurometabolic specialist at The Hospital for Sick Children, where I established a complex epilepsy genetics clinic for the investigation of underlying genetic causes in patients with epilepsy. I have been following more than 200 patients in my clinic and performing intensive diagnostic investigations. During my training in biochemical genetics and early on in my faculty position at University of British Columbia, I had the opportunity to diagnose and treat patients with treatable metabolic epilepsies such as creatine deficiency disorders and pyridoxine dependent epilepsy. I was fascinated by seizure freedom in patients treated with oral vitamins, cofactors, and amino acid supplementations. During my work at The Hospital for Sick Children in Toronto, I found that treatable metabolic epilepsies were underdiagnosed, and I determined that expanded investigations would identify more patients and give them more opportunities for treatment. For this reason, I started an epilepsy genetics research study in newborns and children using whole-exome sequencing. At the same time, a targeted next-generation-sequencing epilepsy genetics panel was introduced at our clinical practice. This gave me opportunity to investigate my patients, and I quickly discovered that the proportion of treatable metabolic epilepsies is very small and that there are other genetic disorders causing seizures. Despite these disorders not being treatable, parents wanted to know the cause of seizures in their children. In response, I expanded my clinical practice to epilepsy genetics. My study's main question was to determine the percentage of underlying genetic causes in patients with epilepsy. I decided to undertake a retrospective cohort study including all patients seen in my complex epilepsy genetics clinic. I divided genetic causes into two groups including inherited metabolic disorders and other genetic causes. I identified an underlying genetic cause in 28% of the 110 patients including 7% with one of the inherited metabolic disorders and 21% with one of the other genetic causes. Forty-five percent of patients obtained a genetic diagnosis with use of targeted next-generation-sequencing epileptic encephalopathy panels. It is notable that 4.5% of patients had a treatable inherited metabolic disease. Targeted next-generation-sequencing panels increased the genetic diagnostic yield from <10% to >25% in patients with epileptic encephalopathy.1 Currently I have a research study looking at underlying genetic causes of epilepsy using whole-exome sequencing to identify novel epilepsy genes. I have been treating 11 patients who have pyridoxine-dependent epilepsy with lysine-restricted diet and arginine therapy in addition to pyridoxine. The accumulating metabolites do not normalize in the central nervous system despite maximum therapy. For this reason, I developed the zebra fish knockout model for drug screening of pyridoxine-dependent epilepsy. There are only two inherited metabolic disorders for which the effect of the ketogenic diet on seizure control is well known: glucose transporter 1 (GLUT1) deficiency and pyruvate dehydrogenase complex (PDHC) deficiency. We recently published a report on (mediator complex subunit 23) MED23-associated refractory epilepsy as a third genetic disorder to exhibit responsiveness to the ketogenic diet.2 I have been applying for grants to investigate patients who are being treated with the ketogenic diet using an omics approach: whole exome sequencing, proteomics, and metabolomics. This future research aims to identify biomarkers that can identify patients as responders (using positive biomarkers). I am honored to be nominated for the 2016 Epilepsia Prize, and I am thrilled to receive this prize. The Epilepsia Prize is a prestigious award that demonstrates my success in epilepsy research. It will help me to further develop my laboratory research by supporting my funding applications. Read this article online at http://onlinelibrary.wiley.com/doi/10.1111/epi.12954/abstract.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,011 | 0,006 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,011 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,007 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».