Search results for "3202 Clinical Sciences"

showing 5 items of 5 documents

Clinical and molecular diagnosis, screening and management of Beckwith-Wiedemann syndrome: An international consensus statement

2018

Beckwith-Wiedemann syndrome (BWS), a human genomic imprinting disorder, is characterized by phenotypic variability that might include overgrowth, macroglossia, abdominal wall defects, neonatal hypoglycaemia, lateralized overgrowth and predisposition to embryonal tumours. Delineation of the molecular defects within the imprinted 11p15.5 region can predict familial recurrence risks and the risk (and type) of embryonal tumour. Despite recent advances in knowledge, there is marked heterogeneity in clinical diagnostic criteria and care. As detailed in this Consensus Statement, an international consensus group agreed upon 72 recommendations for the clinical and molecular diagnosis and management …

0301 basic medicineBeckwith-Wiedemann SyndromeConsensusDNA Copy Number VariationsReproductive Techniques AssistedEndocrinology Diabetes and MetabolismLibrary science32 Biomedical and Clinical SciencesTranslational research030105 genetics & heredityPolymorphism Single NucleotideBildung03 medical and health sciencesRare DiseasesEndocrinologyPrenatal DiagnosisHumansMedicinemedia_common.cataloged_instancePediatric nephrologyChild growthEuropean union3202 Clinical Sciencesmedia_commonPediatricbusiness.industryEuropean researchExpert consensusDNA MethylationNeoplasms Germ Cell and EmbryonalNational health service3. Good healthMolecular Diagnostic Techniquesbusiness
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Protocol for the EARCO Registry

2020

Rationale and objectives Alpha-1 antitrypsin deficiency (AATD) is a genetic condition that leads to an increased risk of emphysema and liver disease. Despite extensive investigation, there remain unanswered questions concerning the natural history, pathophysiology, genetics and the prognosis of the lung disease in association with AATD. The European Alpha-1 Clinical Research Collaboration (EARCO) is designed to bring together researchers from European countries and to create a standardised database for the follow-up of patients with AATD. Study design and population The EARCO Registry is a non-interventional, multicentre, pan-European, longitudinal observational cohort study enrolling patie…

Pulmonary and Respiratory Medicinemedicine.medical_specialtyChronic Obstructive Pulmonary DiseasePopulation1MEDLINElcsh:Medicine61032 Biomedical and Clinical Sciences610 Medicine & health[SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract03 medical and health sciencesLiver diseaseStudy Protocol0302 clinical medicineRare DiseasesClinical ResearchmedicineGenetics030212 general & internal medicineIntensive care medicineeducation3202 Clinical SciencesLungProtocol (science)Emphysemaeducation.field_of_studybusiness.industryPreventionlcsh:Rmedicine.disease3. Good healthNatural historyClinical research030228 respiratory systemObservational study10178 Clinic for PneumologybusinessCohort study
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Immunosenescence and Cytomegalovirus

2010

Since Looney at al. published their seminal paper a decade ago [1] it has become clear that many of the differences in T cell immunological parameters observed between young and old people are related to the age-associated increasing prevalence of infection with the persistent β-herpesvirus HHV-5 (Cytomegalovirus). Ten years later, studies suggest that hallmark age-associated changes in peripheral blood T cell subset distribution may not occur at all in people who are not infected with this virus [[2]; Derhovanessian et al., in press]. Whether the observed changes are actually caused by CMV is an open question, but very similar, rapid changes observed in uninfected patients receiving CMV-in…

lcsh:Immunologic diseases. AllergyAgingCMV ImmunosenescenceageingT cellImmunologyCongenital cytomegalovirus infectionYellow fever vaccine32 Biomedical and Clinical Scienceslcsh:GeriatricsVirusImmune systemMedicine3202 Clinical Sciencesbiologybusiness.industryvirus diseasesImmunosenescenceBiological Sciencesmedicine.disease3204 Immunologylcsh:RC952-954.6Ageingmedicine.anatomical_structureImmunologyT cell subsetQR180biology.proteinCommentaryAntibodylcsh:RC581-607businessmedicine.drugImmunity & ageing
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CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

2020

Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability o…

Urologic DiseasesComputer scienceContext (language use)32 Biomedical and Clinical Sciences-Convolutional neural networkDeep convolutional neural networks Prostate zonal segmentation Cross-dataset generalizationProstate cancer46 Information and Computing SciencesProstateDeep convolutional neural networksmedicineAnatomical MRISegmentationProstate zonal segmentation; Prostate cancer; Anatomical MRI; Deep convolutional neural networks; Cross-dataset generalization;3202 Clinical SciencesCancerSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniProstate cancerSettore INF/01 - Informaticamedicine.diagnostic_testbusiness.industryDeep learningINF/01 - INFORMATICAMagnetic resonance imagingPattern recognitionmedicine.disease3211 Oncology and Carcinogenesismedicine.anatomical_structureCross-dataset generalizationProstate zonal segmentationBiomedical ImagingArtificial intelligenceDeep convolutional neural networkbusinessT2 weightedAnatomical MRI; Cross-dataset generalization; Deep convolutional neural networks; Prostate cancer; Prostate zonal segmentation
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Medicine of the future: How and who is going to treat us?

2023

Medicine’s ability to quickly respond to challenges raises questions from researchers, practitioners, and society as a whole. Our task in this study was to identify key and atypical current factors influencing the development of medicine and to predict the development of medicine in the short, medium, and long term. To implement our study, we selected 22 medical experts and applied the three-level Delphi method. The current trends caused by COVID-19 have a short-term impact, but they will launch other drivers that will transform the healthcare industry. Well-being technologies, data-informed personalization, and climate change will become key drivers for the development of medicine over the…

futuremedicineSociology and Political ScienceterveydenhuoltoforecasthealthcareGeneral Social SciencesGeneral Decision Sciencesennusteet32 Biomedical and Clinical Sciences3 Good Health and Well BeinglääketiedeDevelopmentDelphiGeneric health relevancetulevaisuusBusiness and International Management3202 Clinical Sciences48 Law and Legal StudiesFutures
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