Search results for "Receiver"

showing 10 items of 308 documents

Software-based analysis of 1-hour Holter ECG to select for prolonged ECG monitoring after stroke.

2020

Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean …

0301 basic medicineMalemedicine.medical_specialtyTime Factorsmedicine.medical_treatmentNeurosciences. Biological psychiatry. NeuropsychiatryBrain Ischemia03 medical and health sciencesElectrocardiography0302 clinical medicineRisk FactorsInternal medicineAtrial FibrillationMedicineHumansIn patientSinus rhythmcardiovascular diseasesProspective StudiesRC346-429Medical History TakingStrokeResearch ArticlesAgedAged 80 and overReceiver operating characteristicbusiness.industryGeneral NeuroscienceThrombolysisMiddle Agedmedicine.diseaseEcg monitoringStroke030104 developmental biologyMulticenter studyCardiologyElectrocardiography AmbulatoryFemaleNeurology. Diseases of the nervous systemNeurology (clinical)business030217 neurology & neurosurgeryRC321-571Holter ecgResearch ArticleAnnals of clinical and translational neurology
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Integrating Liquid Biopsy and Radiomics to Monitor Clonal Heterogeneity of EGFR-Positive Non-Small Cell Lung Cancer

2020

BackgroundEGFR-positive Non-small Cell Lung Cancer (NSCLC) is a dynamic entity and tumor progression and resistance to tyrosine kinase inhibitors (TKIs) arise from the accumulation, over time and across different disease sites, of subclonal genetic mutations. For instance, the occurrence of EGFR T790M is associated with resistance to gefitinib, erlotinib, and afatinib, while EGFR C797S causes osimertinib to lose activity. Sensitive technologies as radiomics and liquid biopsy have great potential to monitor tumor heterogeneity since they are both minimally invasive, easy to perform, and can be repeated over patient’s follow-up, enabling the extraction of valuable information. Yet, to date, t…

0301 basic medicineOncologyCancer Researchmedicine.medical_specialtyAfatinibEGFRprecision medicinelcsh:RC254-282cell free DNA; EGFR; liquid biopsy; non-small cell lung cancer; precision medicine; radiomics; tyrosine kinase inhibitors03 medical and health sciencesT790M0302 clinical medicineGefitinibInternal medicinetyrosine kinase inhibitorsmedicineOsimertinibLiquid biopsynon-small cell lung cancerOriginal ResearchReceiver operating characteristiccell free DNAliquid biopsybusiness.industrylcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens030104 developmental biologyOncologyTumor progressionradiomics030220 oncology & carcinogenesisErlotinibbusinessmedicine.drug
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Interleukin-6 and Lymphocyte Count Associated and Predicted the Progression of Frailty Syndrome in Prostate Cancer Patients Undergoing Antiandrogen T…

2020

Frailty syndrome is a functional state that includes a loss of ability to react to stressors, and is associated with poor outcomes, morbidity and premature mortality. The first line treatment in many men with prostate cancer (PCa) consists of an androgen-deprivation therapy (ADT) which can promote or favor frailty syndrome and ADT may therefore favor the progression of frailty over time. Among the pathophysiological bases of frailty, the presence of chronic low-grade inflammation has been associated with its adverse outcomes, but longitudinal studies are needed to validate these biomarkers. In this study, we prospectively evaluate frailty syndrome and blood inflammatory markers (IL1-beta, I…

0301 basic medicineOncologyCancer Researchmedicine.medical_specialtyleukocytesgeriatric assessmentLymphocyteFrailty syndromeInflammationinterleukin-1 betalcsh:RC254-282Article03 medical and health sciencesProstate cancer0302 clinical medicineInternal medicinemedicineInterleukin 6Receiver operating characteristicbiologybusiness.industryinterleukin-6C-reactive proteinlcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogensmedicine.disease030104 developmental biologymedicine.anatomical_structureOncologyinflammation030220 oncology & carcinogenesisbiology.proteinBiomarker (medicine)biomarkermedicine.symptombusinessCancers
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MicroRNA-30a-5pme: a novel diagnostic and prognostic biomarker for clear cell renal cell carcinoma in tissue and urine samples

2020

Abstract Background The rising incidence of renal cell carcinomas (RCC) constitutes a significant challenge owing to risk of overtreatment. Because aberrant microRNA (miR) promoter methylation contributes to cancer development, we investigated whether altered miR-30a-5p expression associates with DNA promoter methylation and evaluated the usefulness as clear cell RCC (ccRCC) diagnostic and prognostic markers. Methods Genome-wide methylome and RNA sequencing data from a set of ccRCC and normal tissue samples from The Cancer Genome Atlas (TCGA) database were integrated to identify candidate CpG loci involved in cancer onset. MiR-30a-5p expression and promoter methylation were quantitatively a…

0301 basic medicineOncologyClear cell renal cell carcinomaCancer Researchmedicine.medical_specialty610Urinelcsh:RC254-28203 medical and health sciences0302 clinical medicineInternal medicinemicroRNADiagnosisMedicineDNA methylationReceiver operating characteristicmicroRNAbusiness.industryResearchBiomarkermedicine.diseaselcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogensPrognosisLog-rank testClear cell renal cell carcinoma030104 developmental biologyOncologyCpG site030220 oncology & carcinogenesisDNA methylationbusinessClear cellJournal of Experimental & Clinical Cancer Research : CR
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The diagnostic accuracy of circulating tumor DNA for the detection of EGFR-T790M mutation in NSCLC: a systematic review and meta-analysis

2018

AbstractThis pooled analysis aims at evaluating the diagnostic accuracy of circulating tumor (ct) DNA for the detection of EGFR-T790M mutation in NSCLC patients who progressed after EGFR-TKIs. Data from all published studies, reporting both sensitivity and specificity of plasma-based EGFR-T790M mutation testing by ctDNA were collected by searching in PubMed, Cochrane Library, American Society of Clinical Oncology, European Society of Medical Oncology and World Conference of Lung Cancer meeting proceedings. A total of twenty-one studies, with 1639 patients, were eligible. The pooled sensitivity of ctDNA analysis was 0.67 (95% CI: 0.64–0.70) and the pooled specificity was 0.80 (95% CI: 0.77–0…

0301 basic medicineOncologyMalemedicine.medical_specialtyLung NeoplasmsctDNA EGFR-T790M NSCLCMutation Missenselcsh:MedicineCochrane LibraryLikelihood ratios in diagnostic testingArticleCirculating Tumor DNA03 medical and health sciencesAmino Acid Substitution; ErbB Receptors; Female; Humans; Male; Predictive Value of Tests; Carcinoma Non-Small-Cell Lung; Circulating Tumor DNA; Lung Neoplasms; Mutation Missense; Neoplasm Proteins0302 clinical medicinePredictive Value of TestsInternal medicineCarcinoma Non-Small-Cell LungmedicineHumansLung cancerNon-Small-Cell Lunglcsh:ScienceMultidisciplinaryReceiver operating characteristicbusiness.industryCarcinomalcsh:RArea under the curvemedicine.diseasePublisher CorrectionNeoplasm ProteinsErbB Receptors030104 developmental biologyAmino Acid Substitution030220 oncology & carcinogenesisPredictive value of testsMeta-analysisMutationDiagnostic odds ratioFemalelcsh:QMissensebusinessScientific Reports
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Liquid biopsy as surrogate for tissue for molecular profiling in pancreatic cancer: A meta-analysis towards precision medicine

2019

Liquid biopsy (LB) is a non-invasive approach representing a promising tool for new precision medicine strategies for cancer treatment. However, a comprehensive analysis of its reliability for pancreatic cancer (PC) is lacking. To this aim, we performed the first meta-analysis on this topic. We calculated the pooled sensitivity, specificity, positive (LR+) and negative (LR-) likelihood ratio, and diagnostic odds ratio (DOR). A summary receiver operating characteristic curve (SROC) and area under curve (AUC) were used to evaluate the overall accuracy. We finally assessed the concordance rate of all mutations detected by multi-genes panels. Fourteen eligible studies involving 369 patients wer…

0301 basic medicineOncologymedicine.medical_specialtyCancer ResearchConcordanceprecision medicinepancreatic cancerReviewlcsh:RC254-282Circulating tumor cells (CTC)03 medical and health sciences0302 clinical medicineInternal medicinePancreatic cancermedicineLiquid biopsycfDNALiquid biopsyReceiver operating characteristicliquid biopsybusiness.industryCfDNAPrecision medicinePancreatic cancerPrecision medicinemedicine.diseaselcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens3. Good healthCancer treatment030104 developmental biologyOncologycirculating tumor cells (CTC)030220 oncology & carcinogenesisMeta-analysisDiagnostic odds ratiobusinesscfDNA; circulating tumor cells (CTC); liquid biopsy; pancreatic cancer; precision medicine
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Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning.

2021

Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2′-o-ribose methyltransferase). Supported by…

0301 basic medicineSimeprevirArtificial intelligencevirusesMERS Middle East Respiratory SyndromeHealth InformaticsBiologyMachine learningcomputer.software_genremedicine.disease_causeAntiviral AgentsArticleWHO World Health OrganizationAUC area under the curve03 medical and health sciences0302 clinical medicinessRNA single-stranded RNA virusmedicineChemotherapyHumansSARS severe acute respiratory syndromeCOVID-19 coronavirus disease 2019CoronavirusNatural productsVirtual screeningACE2 angiotensin converting enzyme 2Drug discoverybusiness.industrySARS-CoV-2COVID-19LBE lowest binding energyFDA Food and Drug AdministrationROC receiver operating characteristicComputer Science ApplicationsHIV human immunodeficiency virusMolecular Docking SimulationDrug repositioning030104 developmental biologyDrug developmentSevere acute respiratory syndrome-related coronavirusParitaprevirInfectious diseasesRespiratory virusArtificial intelligenceSupervised Machine Learningbusinesscomputer030217 neurology & neurosurgeryComputers in biology and medicine
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Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

2020

International audience; A major trend in the epitranscriptomics field over the last 5 years has been the high-throughput analysis of RNA modifications by a combination of specific chemical treatment(s), followed by library preparation and deep sequencing. Multiple protocols have been described for several important RNA modifications, such as 5-methylcytosine (m5C), pseudouridine (ψ), 1-methyladenosine (m1A), and 2'-O-methylation (Nm). One commonly used method is the alkaline cleavage-based RiboMethSeq protocol, where positions of reads' 5'-ends are used to distinguish nucleotides protected by ribose methylation. This method was successfully applied to detect and quantify Nm residues in vari…

0301 basic medicinebioinformatic pipelinelcsh:QH426-470Computer scienceComputational biologyDeep sequencingPseudouridine03 medical and health scienceschemistry.chemical_compound0302 clinical medicine[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]ribose methylationEpitranscriptomicsGeneticsGenetics (clinical)receiver operating characteristic2'-O-methylation2′-O-methylationhigh-throughput sequencingRNA[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyBrief Research Reportlcsh:Genetics030104 developmental biologychemistry030220 oncology & carcinogenesisTransfer RNARNAMolecular MedicineSmall nuclear RNAReference genomeFrontiers in Genetics
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Machine learning–XGBoost analysis of language networks to classify patients with epilepsy

2017

Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing ‘atypical’ (compared to ‘typical’ in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. The neurosurgeon should only remove the zone generating seizures and must preserve cognitive functions to avoid deficits. To preserve functions, one sho…

0301 basic medicinemedicine.medical_specialtyCognitive Neuroscience[SCCO.COMP]Cognitive science/Computer scienceAudiologyExtreme Gradient Boostinglcsh:Computer applications to medicine. Medical informaticsArticle03 medical and health sciencesEpilepsy0302 clinical medicineText miningMachine learningmedicineLanguagelcsh:Computer softwareEpilepsyCognitive mapReceiver operating characteristicbusiness.industryCognitionNeurophysiologymedicine.diseaseMLComputer Science ApplicationsStatistical classificationlcsh:QA76.75-76.765030104 developmental biologyNeurologyBinary classification[ SCCO.COMP ] Cognitive science/Computer sciencelcsh:R858-859.7Artificial intelligencePsychologybusiness030217 neurology & neurosurgeryAtypicalXGBoost
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Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

2020

Background Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsoni…

0301 basic medicinemedicine.medical_specialtyParkinson's diseaseParkinson's diseasemultiple system atrophyProgressive supranuclear palsyDiagnosis Differential03 medical and health sciences0302 clinical medicinePhysical medicine and rehabilitationParkinsonian DisordersmedicineHumansmultimodal magnetic resonance imagingReceiver operating characteristicmedicine.diagnostic_testbusiness.industryParkinsonismMagnetic resonance imagingprogressive supranuclear palsymedicine.diseaseMagnetic Resonance Imaging3. Good healthnervous system diseasesmachine learning algorithm030104 developmental biologyDiffusion Tensor ImagingNeurologyCategorizationnervous systemCohort[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Neurology (clinical)Supranuclear Palsy Progressivebusiness030217 neurology & neurosurgeryDiffusion MRI
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