0000000000724977

AUTHOR

E Zanon

showing 2 related works from this author

Combined use of antifibrinolytics and activated prothrombin complex concentrate (aPCC) is not related to thromboembolic events in patients with acqui…

2019

Antifibrinolytics combined with aPCC are not routinely administered to patients with acquired hemophilia A due to increased thrombotic risk. This association normalizes clot stability, and improves the efficacy of therapy, but can increase the risk of severe side effects. Due to these premises it has always raised doubts and perplexities in the clinics. We now report the data of the "FEIBA® on acquired haemophilia A Italian Registry (FAIR Registry)", a retrospective-prospective study that included 56 patients. This is the first study that assessed the clinical response of the combination of aPCC and antifibrinolytic agents in patients with acquired haemophilia A. A total of 101 acute bleeds…

medicine.medical_specialtyAntifibrinolyticmedicine.drug_classHemorrhage030204 cardiovascular system & hematologyHemophilia APremises03 medical and health sciences0302 clinical medicineDrug TherapyThromboembolismAntifibrinolytic agentInternal medicineActivated prothrombin complex concentrateAcquired haemophiliaThromboembolic riskHumansMedicineIn patientRegistries030212 general & internal medicineAcquired haemophilia AHematologybusiness.industryHematologyAcquired haemophilia A; Activated prothrombin complex concentrate; Antifibrinolytics; Thromboembolic risk; Antifibrinolytic Agents; Blood Coagulation Factors; Cardiovascular Diseases; Drug Therapy Combination; Hemophilia A; Hemorrhage; Humans; Registries; ThromboembolismAntifibrinolytic AgentsBlood Coagulation FactorsClinical trialTolerabilityCardiovascular DiseasesCombinationAntifibrinolyticDrug Therapy CombinationAntifibrinolyticsCardiology and Cardiovascular Medicinebusiness
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A completely automated CAD system for mass detection in a large mammographic database.

2006

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing secon…

Databases FactualInformation Storage and RetrievalReproducibility of ResultsBreast NeoplasmsSensitivity and SpecificityNeural networkPattern Recognition AutomatedRadiographic Image EnhancementBreast cancerTextural featuresRadiology Information SystemsImage processingComputer-aided detection (CAD)Artificial IntelligenceCluster AnalysisDatabase Management SystemsHumansRadiographic Image Interpretation Computer-AssistedFemaleBreast cancer; Computer-aided detection (CAD); Image processing; Mammographic mass detection; Neural network; Textural featuresMammographic mass detectionAlgorithmsMammographyMedical physics
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