Search results for "Variability"

showing 10 items of 904 documents

Oxygenation and Bioenergetic Status of Murine Fibrosarcomas

1992

The heterogeneity of cellular response to therapy is a major problem in non-surgical cancer therapy. This heterogeneity is influenced by both the genetic variability between different tumor cells and by epigenetic, physiological factors, such as the local metabolic milieu. A restriction of tumor microcirculation concomitant with regional hypoxia, nutrient depletion, accumulation of lactate, and an intensified tumor acidosis becomes evident during growth of many solid tumors1. These critical factors can greatly influence the efficiency of various non-surgical tumor therapies.

BioenergeticsCritical factorsmedicineCancer therapyCancer researchGenetic variabilityEpigeneticsOxygenationmedicine.symptomBiologyHypoxia (medical)Acidosis
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Global variability in seawater Mg:Ca and Sr:Ca ratios in the modern ocean

2020

12 pages, 5 figures, supporting information https://doi.org/10.1073/pnas.1918943117.-- Data Availability. Our published databases are publicly accessible for readers, and they are deposited at the NOAA NCEI at https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.nodc:0171017.-- Correction for Lebrato et al., Global variability in seawater Mg:Ca and Sr:Ca ratios in the modern ocean; Proceedings of the National Academy of Sciences of the USA 118(49): e2119099118 (2021); doi: 10.1073/pnas.2119099118; http://hdl.handle.net/10261/258054.-- This is Pacific Marine Environmental Laboratory contribution number 5046

Biogeochemical cycleMedio Marino y Protección Ambiental010504 meteorology & atmospheric sciencesHigh variabilityAlkalinitySede Central IEO010502 geochemistry & geophysics01 natural sciencesCA [MG]CA [SR]//purl.org/becyt/ford/1 [https]//purl.org/becyt/ford/1.5 [https]14. Life underwater0105 earth and related environmental sciencesMultidisciplinarySEAWATERCorrectionBiogeochemistryBIOGEOCHEMISTRYEnvironmental effect13. Climate actionEnvironmental chemistry[SDE]Environmental SciencesUpwellingSeawaterEarth (classical element)GLOBAL
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Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.

2007

A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of…

Biomedical EngineeringBlood PressureBivariate analysisDirectionalitySensitivity and SpecificitySurrogate dataFeedbackNonlinear parametric modelGranger causalityControl theoryHeart RateOptimal parameter searchStatisticsAnimalsHumansComputer SimulationPredictabilityHeart rate variabilityMathematicsNonlinear autoregressive exogenous modelCardiovascular regulationSystem identificationModels CardiovascularNonlinear systemAutoregressive modelNonlinear DynamicsAutoregressive exogenous modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaRegression AnalysisSurrogate dataArterial pressure variabilityAlgorithmsAnnals of biomedical engineering
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Spatial patterns of bacterial taxa in nature reflect ecological traits of deep branches of the 16S rRNA bacterial tree

2009

International audience; Whether bacteria display spatial patterns of distribution and at which level of taxonomic organization such patterns can be observed are central questions in microbial ecology. Here we investigated how the total and relative abundances of eight bacterial taxa at the phylum or class level were spatially distributed in a pasture by using quantitative PCR and geostatistical modelling. The distributions of the relative abundance of most taxa varied by a factor of 2.5–6.5 and displayed strong spatial patterns at the field scale. These spatial patterns were taxon-specific and correlated to soil properties, which indicates that members of a bacterial clade defined at high t…

BiostatisticsBiologySpatial distributionMicrobiology03 medical and health sciencesMicrobial ecologyRNA Ribosomal 16SCladeRelative species abundancePhylogenySoil MicrobiologyEcology Evolution Behavior and Systematics030304 developmental biology2. Zero hungerSOIL MICROBIAL COMMUNITY CLASSIFICATION0303 health sciencesBacteriaEcologyGeography030306 microbiologyEcologyPhylumSPATIAL PATTERNS15. Life on landMODELTaxon[SDV.MP]Life Sciences [q-bio]/Microbiology and ParasitologySpatial ecologySpatial variabilityEnvironmental Monitoring
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Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability.

2005

A nonlinear prediction method for investigating the dynamic interdependence between short length time series is presented. The method is a generalization to bivariate prediction of the univariate approach based on nearest neighbor local linear approximation. Given the input and output series x and y, the relationship between a pattern of samples of x and a synchronous sample of y was approximated with a linear polynomial whose coefficients were estimated from an equation system including the nearest neighbor patterns in x and the corresponding samples in y. To avoid overfitting and waste of data, the training and testing stages of the prediction were designed through a specific out-of-sampl…

Bivariate time seriePhysics::Medical PhysicsBiomedical EngineeringBlood PressureBivariate analysisOverfittingCross-validationk-nearest neighbors algorithmCardiovascular Physiological PhenomenaHealth Information ManagementHeart RateTilt-Table TestStatisticsApplied mathematicsHumansComputer SimulationPredictabilityHeart rate variabilityMathematicsHealth InformaticBaroreflex controlSystolic arterial pressure variabilityUnivariateModels CardiovascularNonlinear predictionComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science ApplicationsNonlinear systemComputational Theory and MathematicsNonlinear DynamicsLinear approximationMedicalbiological engineeringcomputing
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Long-term parameters of heart rate variability in patients with insulin-resistance.

2019

Heart rate variability (HRV) is defined as the oscillation in both the interval between consecutive heartbeats (considered RR intervals) and consecutives measures of instantaneous heart rates.1 HRV measures the cardiac autonomic function noninvasively1,2 and its reduction is an independent risk factor for cardiovascular events.3 Insulin-resistance is a pathological condition, in which the body’s cells become resistant to insulin effects.4 The aim of our study was to evaluate the relationship between insulin-resistance and the reduction of HRV parameters.

Blood GlucoseMalemedicine.medical_specialtyTime FactorsMEDLINEPilot ProjectsHeart rate variability insulin-resistanceInsulin resistanceHeart RateInternal medicineHeart ratemedicineHeart rate variabilityHumansInsulinIn patientAgedRetrospective StudiesMetabolic SyndromeInsulin bloodbusiness.industryRetrospective cohort studyGeneral MedicineMiddle Agedmedicine.diseaseTerm (time)CardiologyFemaleInsulin ResistanceCardiology and Cardiovascular MedicinebusinessBiomarkersJournal of cardiovascular medicine (Hagerstown, Md.)
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Associations of cardiometabolic risk factors with heart rate variability in 6‐ to 8‐year‐old children: The PANIC Study

2019

BACKGROUND Associations of cardiometabolic risk factors with heart rate variability (HRV) in children are unclear. We examined associations of cardiometabolic risk score (CRS) and individual cardiometabolic risk factors with HRV variables in 6- to 8-year-olds. METHODS The participants were a population-based sample of 443 children participating in baseline measurements of the Physical Activity and Nutrition in Children trial. Cardiometabolic risk factors included waist circumference (WC), insulin, glucose, triglycerides, HDL cholesterol, systolic blood pressure (SBP), and diastolic blood pressure (DBP). CRS was calculated as WC + insulin + glucose + triglycerides - HDL cholesterol + the mea…

Blood GlucoseMalemedicine.medical_specialtyWaistEndocrinology Diabetes and Metabolismmedicine.medical_treatmentPopulationBlood Pressurechemistry.chemical_compoundHeart RateInternal medicineotorhinolaryngologic diseasesInternal MedicinemedicineHumansInsulinHeart rate variabilityChildeducationeducation.field_of_studyCholesterolbusiness.industryInsulinCardiometabolic Risk FactorsCardiorespiratory fitnessmedicine.diseaseCross-Sectional StudiesBlood pressurechemistryPediatrics Perinatology and Child HealthCardiologyFemaleMetabolic syndromebusinesscirculatory and respiratory physiologyPediatric Diabetes
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Blood pressure and cardiac autonomic nervous system in obese type 2 diabetic patients: effect of metformin administration

2004

Background: Hyperinsulinemia/insulin resistance and elevated plasma free fatty acids (FFA) levels are involved in the hypertension and cardiac sympathetic overactivity. Metformin improves insulin action and lower plasma FFA concentrations. We investigate the possible effect of metformin on arterial blood pressure (BP) and cardiac sympathetic nervous system. Methods: One hundred twenty overweight type 2 diabetic patients were treated by placebo (n = 60) + diet or metformin (850 mg twice daily) (n = 60) + diet for 4 months, to evaluate the effect of metformin treatment on the cardiac autonomic nervous system. Insulin resistance was measured by the Homeostasis Model Assessment (HOMA) index. He…

Blood GlucoseMalemedicine.medical_specialtyendocrine system diseasesmedicine.medical_treatmentBlood PressureType 2 diabetesFatty Acids NonesterifiedAutonomic Nervous SystemInsulin resistanceHeart RateDiabetes mellitusInternal medicineInternal MedicineHyperinsulinemiaDiabetes MellitusMedicineHumansHypoglycemic AgentsObesityHeart rate variabilityTriglyceridesAgedGlycated HemoglobinFree fatty acidAnthropometrybusiness.industryInsulinnutritional and metabolic diseasesInsulin resistanceMiddle Agedmedicine.diseaseMetforminMetforminEndocrinologyBlood pressureTreatment OutcomeDiabetes Mellitus Type 2Multivariate AnalysisFemalebusinessHyperinsulinismBiomarkersmedicine.drug
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ABSENCE OF AN ASSOCIATION BETWEEN SHORT-TERM BLOOD PRESSURE VARIABILITY AND MILD RENAL DYSFUNCTION IN ESSENTIAL HYPERTENSIVE PATIENTS.

2014

Introduction: Studies investigating the prognostic implications of short-term blood pressure (BP) variability (STBPV), expressed as standard deviation (SD) and assessed by noninvasive 24-h ambulatory BP monitoring (ABPM), yielded conflicting results. In last years further indices of STBPV have been proposed. Among these, the 24-h BP average real variability (ARV) seems to be associated with an increased cardiovascular risk more closely than the SD. Little is known about the association between mild renal dysfunction (MRD) and STBPV, and particularly between 24-h BP ARV and MRD. Aim: To analyse, in a group of essential hypertensives, the relationships between MRD and STBPV, expressed as SD o…

Blood pressure variability Renal dysfunctionCKD Essential hypertension
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Average real variability of 24-h systolic blood pressure is associated with microalbuminuria in patients with primary hypertension.

2015

Limited and conflicting data are available about the association between short-term blood pressure (BP) variability and urinary albumin excretion rate (uAER). The objective of our study was to analyze the relationships between microalbuminuria (MAU), defined as an uAER between 20 and 200 μg min(-1), and short-term BP variability (BPV), assessed as average real variability (ARV), weighted s.d. of 24-h BP and as s.d. of daytime and night-time BP. The study population consisted of 315 untreated essential hypertensives with normal estimated glomerular filtration rate (>60 ml min(-1) per 1.73 m(2)), who underwent 24-h ambulatory BP monitoring and 24-h uAER determination. MAU was detected in 82 (…

Blood pressure variabilityAdultMalemedicine.medical_specialtyAmbulatory blood pressureSettore MED/09 - Medicina InternamicroalbuminuriaDiastoleRenal functionBlood Pressure030204 cardiovascular system & hematologyEssential hypertension03 medical and health sciences0302 clinical medicineInternal medicinetarget organ damageInternal MedicinemedicineAlbuminuriaHumans030212 general & internal medicineambulatory blood pressureSettore MED/14 - Nefrologiabusiness.industryMiddle Agedmedicine.diseaseSettore MED/11 - Malattie Dell'Apparato CardiovascolareEndocrinologyBlood pressureHypertensionAlbuminuriaCardiologyPopulation studyMicroalbuminuriaFemalemedicine.symptombusinessJournal of human hypertension
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