2025
Diagnosis, screening, and follow-up of patients with familial interstitial lung disease: Results from an international survey
Moen E, Prior T, Kreuter M, Wuyts W, Molina-Molina M, Wijsenbeek M, Morais A, Tzouvelekis A, Ryerson C, Caro F, Buendia-Roldan I, Magnusson J, Lee J, Morisett J, Oldham J, Troy L, Funke-Chambour M, Alberti M, Borie R, Walsh S, Rajan S, Kondoh Y, Khor Y, Bendstrup E. Diagnosis, screening, and follow-up of patients with familial interstitial lung disease: Results from an international survey. BMC Pulmonary Medicine 2025, 25: 59. PMID: 39901224, PMCID: PMC11792556, DOI: 10.1186/s12890-025-03532-0.Peer-Reviewed Original ResearchConceptsGenetic testing methodsMultidisciplinary teamGenetic testingFamily historyInterstitial lung diseaseHistory of interstitial lung diseaseRecommended genetic testingScreening of first-degree relativesFamilial interstitial lung diseaseEvidence-based guidelinesGenetic screening of relativesScreening of relativesFirst-degree relativesCharacteristics of respondentsInternational surveyPathogenic genetic variantsScreening toolLung transplantationInsufficient evidenceLung diseaseConsensus statementGenetic variantsFollow-up of patientsStandard programRespondents
2020
High genetic burden in 163 Chinese children with status epilepticus
Wang T, Wang J, Ma Y, Zhou H, Ding D, Li C, Du X, Jiang YH, Wang Y, Long S, Li S, Lu G, Chen W, Zhou Y, Zhou S, Wang Y. High genetic burden in 163 Chinese children with status epilepticus. Seizure 2020, 84: 40-46. PMID: 33278787, DOI: 10.1016/j.seizure.2020.10.032.Peer-Reviewed Original ResearchConceptsNon-genetic aetiologyGenetic etiologyMonogenic mutationsNumber variation analysisMolecular dataSingle geneNext-generation sequencingGene mutationsPathogenic genetic variantsUncertain significance variantsCausative variantsGenetic variantsMutationsDe novoGenetic burdenStatus epilepticusGenetic testing methodsHigher genetic burdenGenesMedical GeneticsMonogenic variantsVariation analysisVariantsTSC2GeneticsDetection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma
AlDubayan S, Conway J, Camp S, Witkowski L, Kofman E, Reardon B, Han S, Moore N, Elmarakeby H, Salari K, Choudhry H, Al-Rubaish A, Al-Sulaiman A, Al-Ali A, Taylor-Weiner A, Van Allen E. Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma. JAMA 2020, 324: 1957-1969. PMID: 33201204, PMCID: PMC7672519, DOI: 10.1001/jama.2020.20457.Peer-Reviewed Original ResearchMeSH KeywordsCross-Sectional StudiesDeep LearningDNA Mutational AnalysisFemaleGenetic Predisposition to DiseaseGenetic TestingGerm-Line MutationHigh-Throughput Nucleotide SequencingHumansMaleMelanomaMiddle AgedNeural Networks, ComputerPredictive Value of TestsProstatic NeoplasmsSensitivity and SpecificityConceptsAmerican College of Medical Genetics and GenomicsGermline genetic testingCancer predisposition genesDetection of pathogenic variantsVariant detection performanceACMG genesNegative predictive valuePositive predictive valuePathogenic variantsGenetic testingProstate cancer cohortCriterion reference standardVariant detectionMendelian genesProstate cancerCancer cohortGermline pathogenic variantsCross-sectional studyGenetic testing methodsGermline variant detectionMelanoma cohortPathogenic germline alterationsMain OutcomesConvenience cohortClinical data collection
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