2025
Validity of Diagnostic Codes and Laboratory Tests to Identify Cholangiocarcinoma and Its Subtypes
Ferrante N, Hubbard R, Weinfurtner K, Mezina A, Newcomb C, Furth E, Bhattacharya D, Njei B, Taddei T, Singal A, Hoteit M, Park L, Kaplan D, Re V. Validity of Diagnostic Codes and Laboratory Tests to Identify Cholangiocarcinoma and Its Subtypes. Pharmacoepidemiology And Drug Safety 2025, 34: e70154. PMID: 40328444, PMCID: PMC12055315, DOI: 10.1002/pds.70154.Peer-Reviewed Original ResearchConceptsPositive predictive valueVeterans Health AdministrationExtrahepatic cholangiocarcinomaValidity of diagnostic codesInternational Classification of Diseases for OncologyUS Veterans Health AdministrationConfidence intervalsPharmacoepidemiological studiesICD-O-3Days of diagnosisVA dataHealth AdministrationIntrahepatic cholangiocarcinomaDiagnostic codesHistology codesCholangiocarcinomaUnique patientsInclusion criteriaCholangiocarcinoma subtypesTopography codesPredictive valuePatientsEvaluate medicationsSubtypesEvaluate determinantsExamining the Factor Structure of the Acquired Capability for Suicide Scale (ACSS) in a Military Population: Initial Development and Validation of a Four-Factor Version of the ACSS
Thomas K, Hoyt W, Goldberg S, Abbas M, Schultz M, Hiserodt M, Wyman M. Examining the Factor Structure of the Acquired Capability for Suicide Scale (ACSS) in a Military Population: Initial Development and Validation of a Four-Factor Version of the ACSS. Psychological Services 2025, 22: 312-323. PMID: 39636588, PMCID: PMC12011536, DOI: 10.1037/ser0000917.Peer-Reviewed Original ResearchMeSH KeywordsAdultFactor Analysis, StatisticalFemaleHumansMaleMiddle AgedMilitary PersonnelPsychometricsReproducibility of ResultsSuicidal IdeationSuicideWisconsinYoung AdultConceptsInterpersonal theory of suicideTheory of suicideFour-factor versionInterpersonal theorySuicide ScaleAcquired CapabilityFactor structureMilitary populationNational Guard service membersReturn from deploymentConfirmatory factor analysisMonths postdeploymentMilitary sampleSuicidal behaviorPreliminary supportPredictive validityPostdeployment dataAssessment pointsSuicideService membersTheory-relevantVeteran populationLife experiencesFour-factorAdequate validityThe NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering
Carlson D, Chavarriaga R, Liu Y, Lotte F, Lu B. The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering. Journal Of Neural Engineering 2025, 22: 021002. PMID: 40073450, PMCID: PMC11948487, DOI: 10.1088/1741-2552/adbfbd.Peer-Reviewed Original ResearchAdaptation and validation of perceived HIV and TB stigma scales among persons with TB.
Ponticiello M, Nanziri L, Hennein R, Ochom E, Gupta A, Turimumahoro P, White M, Armstrong-Hough M, Katamba A, Davis J. Adaptation and validation of perceived HIV and TB stigma scales among persons with TB. The International Journal Of Tuberculosis And Lung Disease 2025, 29: 127-134. PMID: 40052658, DOI: 10.5588/ijtld.24.0497.Peer-Reviewed Original ResearchConceptsPerceived social supportSocial supportTB stigma scaleConvergent validityCommunity health workersAssess convergent validityEvaluate internal validityTB-affected householdsExperiences of personsCross-sectional studyInternal validityMultidisciplinary research teamMeasure stigmaOne-factor solutionTB stigmaStigma ScaleHIV ScaleHIV stigmaEvidence of convergent validityHealth workersFactor analysisExploratory factor analysisCognitive interviewsPsychometric propertiesMarginal model fitThe Factor Structure and Validity of the Psychopathy Checklist‐Short Version When Used With Autistic Psychiatric Inpatients
Maguire K, Barnoux M, Collins J, Melvin C, Inkson I, Alexander R, Devapriam J, Duggan C, Shepstone L, Staufenburg E, Thompson P, Turner D, Viding E, Langdon P. The Factor Structure and Validity of the Psychopathy Checklist‐Short Version When Used With Autistic Psychiatric Inpatients. Autism Research 2025, 18: 614-631. PMID: 39963077, PMCID: PMC11928917, DOI: 10.1002/aur.70004.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAntisocial Personality DisorderAutistic DisorderChecklistEnglandFactor Analysis, StatisticalFemaleHospitals, PsychiatricHumansInpatientsIntellectual DisabilityMaleMiddle AgedPersonality TestsPrisonersProspective StudiesPsychometricsRegression AnalysisReproducibility of ResultsROC CurveWalesYoung AdultConceptsMental Health ActPCL:SVPersonality disorder diagnosisForensic historyHealth ActIntellectual disabilityAutistic adultsDisorder diagnosisHistory of violent offendingPCL:SV scoresMeasures of psychopathyInpatient psychiatric hospitalizationPart IIInpatient psychiatric carePCL-SVCriminal offendingHCR-20Violent offendersSV scoresPsychiatric inpatientsFactor structurePsychiatric hospitalPsychopathyPsychiatric careLesser securityMeasurement properties of instruments to assess insight in psychosis: A systematic review protocol
Hazan H, Funaro M, Srihari V. Measurement properties of instruments to assess insight in psychosis: A systematic review protocol. PLOS ONE 2025, 20: e0316447. PMID: 39854417, PMCID: PMC11759350, DOI: 10.1371/journal.pone.0316447.Peer-Reviewed Original ResearchMeSH KeywordsHumansPsychometricsPsychotic DisordersReproducibility of ResultsSchizophreniaSystematic Reviews as TopicConceptsMeasurement properties of instrumentsProperties of instrumentsSystematic reviewCOSMIN Risk of Bias checklistRisk of Bias checklistMeasurement propertiesMeasure methodological qualityMeta-Analysis ProtocolsSynthesize current evidenceSystematic review protocolPreferred Reporting ItemsClinical practicePeer-reviewed journalsStrength of evidenceSemi-structured interviewsBias checklistImprove careMental illnessMethodological qualityReporting ItemsPatient awarenessPRISMA-PReview protocolGRADE approachSelf-reportDevelopment of a Clinical and Ultrasonic Parameter-Based Nomogram Model to Predict Restenosis after Superficial Femoral Artery Stenting
Wang Y, Gao M, Zhao X, Han P, Zhang L, Dardik A. Development of a Clinical and Ultrasonic Parameter-Based Nomogram Model to Predict Restenosis after Superficial Femoral Artery Stenting. Annals Of Vascular Surgery 2025, 113: 175-185. PMID: 39855385, DOI: 10.1016/j.avsg.2024.12.068.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAlloysDecision Support TechniquesEndovascular ProceduresFemaleFemoral ArteryHumansMaleMiddle AgedNomogramsPeripheral Arterial DiseasePredictive Value of TestsRecurrenceReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsSelf Expandable Metallic StentsStentsTime FactorsTreatment OutcomeConceptsIn-stent restenosisSuperficial femoral arteryDecision curve analysisSuperficial femoral artery stentingLogistic regression analysisPeripheral arterial diseaseReceiver operating characteristicNomogram modelClinical utilityCurve analysisMultivariate logistic regression analysisSelf-expanding bare nitinol stentsTreated with stentingReceiver operating characteristic curveRisk of in-stent restenosisNomogram's clinical utilityBare nitinol stentsPrediction of in-stent restenosisExcellent discriminatory powerRegression analysisFemoral artery stentingArtery flow rateRetrospective studyTraining cohortSFA stentingReliability of Central Vein Sign Imaging With 3T FLAIR* in a Multicenter Study
Martin M, Cao Q, Luskin E, Renner B, Daboul L, O'Donnell C, Rodrigues P, Derbyshire J, Azevedo C, Bar‐Or A, Caverzasi E, Calabresi P, Cree B, Freeman L, Henry R, Longbrake E, Oh J, Papinutto N, Pelletier D, Prchkovska V, Ramos M, Samudralwar R, Schindler M, Sotirchos E, Sicotte N, Solomon A, Reich D, Ontaneda D, Shinohara R, Sati P. Reliability of Central Vein Sign Imaging With 3T FLAIR* in a Multicenter Study. Journal Of Neuroimaging 2025, 35: e70011. PMID: 39838609, DOI: 10.1111/jon.70011.Peer-Reviewed Original ResearchMeSH KeywordsAdultBrainCerebral VeinsContrast MediaCross-Sectional StudiesFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedMultiple SclerosisReproducibility of ResultsConceptsCentral vein signMulticenter studyMultiple sclerosisT2-weighted fluid-attenuated inversion recoveryClinical settingMRI vendorsFluid-attenuated inversion recoveryDiagnosis of MSPost-GdContrast-to-noise ratioGd-based contrast agentsNo significant differenceT2*-weightedImaging sitesDiagnostic imaging biomarkersImaging biomarkersInversion recoverySignificant differenceMRIFLAIRContrast agentsMeasurement of Blood Pressure in Children and Adolescents Outside the Office for the Diagnosis of Hypertension
Nugent J. Measurement of Blood Pressure in Children and Adolescents Outside the Office for the Diagnosis of Hypertension. Current Cardiology Reports 2025, 27: 27. PMID: 39826056, DOI: 10.1007/s11886-024-02178-4.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentBlood PressureBlood Pressure DeterminationBlood Pressure Monitoring, AmbulatoryChildHumansHypertensionPractice Guidelines as TopicReproducibility of ResultsConceptsAmbulatory blood pressure monitoringBlood pressure monitoringHome blood pressure monitoringDiagnosis of hypertensionBlood pressurePressure monitoringUsefulness of ambulatory blood pressure monitoringOffice blood pressureOut-of-office blood pressure measurementsTarget organ damageNovel care pathwayHome blood pressureMeasurement of blood pressureBlood pressure measurementsOrgan damageHypertensionHypertension statusSubspecialty settingsCare pathwaysDiagnosisPredicting Mortality in Patients Hospitalized With Acute Myocardial Infarction: From the National Cardiovascular Data Registry
Faridi K, Wang Y, Minges K, Smilowitz N, McNamara R, Kontos M, Wang T, Connors A, Clary J, Osborne A, Pereira L, Curtis J, Blankinship K, Mayfield J, Abbott J. Predicting Mortality in Patients Hospitalized With Acute Myocardial Infarction: From the National Cardiovascular Data Registry. Circulation Cardiovascular Quality And Outcomes 2025, 18: e011259. PMID: 39801472, PMCID: PMC11919567, DOI: 10.1161/circoutcomes.124.011259.Peer-Reviewed Original ResearchConceptsNational Cardiovascular Data Registry Chest Pain-MI RegistryIn-hospital mortalityAcute myocardial infarctionChest Pain-MI RegistryOut-of-hospital cardiac arrestRisk scoreRisk-standardized modelsNational Cardiovascular Data RegistryAcute MI hospitalizationsPatient characteristicsMyocardial infarctionContemporary risk modelsIn-hospital mortality rateMI hospitalizationST-segment elevation MIIndependent predictor of mortalityMortality risk predictionSystolic blood pressureMortality riskPredictors of mortalityHospital volumePatient prognosticationData RegistryRisk modelSimplified risk score
2024
Robust, fully-automated assessment of cerebral perivascular spaces and white matter lesions: a multicentre MRI longitudinal study of their evolution and association with risk of dementia and accelerated brain atrophy
Barisano G, Iv M, Choupan J, Hayden-Gephart M, Weiner M, Aisen P, Petersen R, Jack C, Jagust W, Trojanowki J, Toga A, Beckett L, Green R, Saykin A, Morris J, Shaw L, Liu E, Montine T, Thomas R, Donohue M, Walter S, Gessert D, Sather T, Jiminez G, Harvey D, Donohue M, Bernstein M, Fox N, Thompson P, Schuff N, DeCarli C, Borowski B, Gunter J, Senjem M, Vemuri P, Jones D, Kantarci K, Ward C, Koeppe R, Foster N, Reiman E, Chen K, Mathis C, Landau S, Cairns N, Householder E, Reinwald L, Lee V, Korecka M, Figurski M, Crawford K, Neu S, Foroud T, Potkin S, Shen L, Kelley F, Kim S, Nho K, Kachaturian Z, Frank R, Snyder P, Molchan S, Kaye J, Quinn J, Lind B, Carter R, Dolen S, Schneider L, Pawluczyk S, Beccera M, Teodoro L, Spann B, Brewer J, Vanderswag H, Fleisher A, Heidebrink J, Lord J, Petersen R, Mason S, Albers C, Knopman D, Johnson K, Doody R, Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig L, Bell K, Ances B, Morris J, Carroll M, Leon S, Householder E, Mintun M, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geld-macher D, Brockington J, Roberson E, Grossman H, Mitsis E, deToledo-Morrell L, Shah R, Duara R, Varon D, Greig M, Roberts P, Albert M, Onyike C, D’Agostino D, Kielb S, Galvin J, Pogorelec D, Cerbone B, Michel C, Rusinek H, de Leon M, Glodzik L, De Santi S, Doraiswamy P, Petrella J, Wong T, Arnold S, Karlawish J, Wolk D, Smith C, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Lopez O, Oakley M, Simpson D, Porsteinsson A, Goldstein B, Martin K, Makino K, Ismail M, Brand C, Mulnard R, Thai G, Mc Adams Ortiz C, Womack K, Mathews D, Quiceno M, Arrastia R, King R, Weiner M, Cook K, DeVous M, Levey A, Lah J, Cellar J, Burns J, Anderson H, Swerdlow R, Apostolova L, Tingus K, Woo E, Silverman D, Lu P, Bartzokis G, Radford N, Parfitt F, Kendall T, Johnson H, Farlow M, Hake A, Matthews B, Herring S, Hunt C, van Dyck C, Carson R, MacAvoy M, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung G, Feldman H, Mudge B, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Kerwin D, Mesulam M, Lipowski K, Wu C, Johnson N, Sadowsky C, Martinez W, Villena T, Turner R, Johnson K, Reynolds B, Sperling R, Johnson K, Marshall G, Frey M, Yesavage J, Taylor J, Lane B, Rosen A, Tinklenberg J, Sabbagh M, Belden C, Jacobson S, Sirrel S, Kowall N, Killiany R, Budson A, Norbash A, Johnson P, Obisesan T, Wolday S, Allard J, Lerner A, Ogrocki P, Hudson L, Fletcher E, Carmichael O, Olichney J, DeCarli C, Kittur S, Borrie M, Lee T, Bartha R, Johnson S, Asthana S, Carlsson C, Potkin S, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre D, Kataki M, Adeli A, Zimmerman E, Celmins D, Brown A, Pearlson G, Blank K, Anderson K, Santulli R, Kitzmiller T, Schwartz E, Sink K, Williamson J, Garg P, Watkins F, Ott B, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen H, Miller B, Mintzer J, Spicer K, Bachman D, Finger E, Pasternak S, Rachinsky I, Rogers J, Kertesz A, Drost D, Pomara N, Hernando R, Sarrael A, Schultz S, Ponto L, Shim H, Smith K, Relkin N, Chaing G, Raudin L, Smith A, Fargher K, Raj B. Robust, fully-automated assessment of cerebral perivascular spaces and white matter lesions: a multicentre MRI longitudinal study of their evolution and association with risk of dementia and accelerated brain atrophy. EBioMedicine 2024, 111: 105523. PMID: 39721217, PMCID: PMC11732520, DOI: 10.1016/j.ebiom.2024.105523.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAlgorithmsAtrophyBrainDementiaFemaleGlymphatic SystemHumansLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsWhite MatterConceptsRisk of dementiaDementia riskAccelerated brain atrophyLower risk of dementiaMeasures of cognitive functionLongitudinal studyUS National Institutes of HealthNational Institutes of HealthCombat dementiaInstitutes of HealthPerivascular spaces countAlzheimer's disease biomarkersUS National InstitutesClinical measures of cognitive functioningDementiaBrain healthMixed-effects modelsScreening toolClinical measuresConfounding factorsObservational studyCognitive declineLongitudinal trajectoriesParticipantsBrain atrophyA meta‐analysis assessing reliability of the Yale Food Addiction Scale: Implications for compulsive eating and obesity
Jahrami H, Husain W, Trabelsi K, Ammar A, Pandi‐Perumal S, Saif Z, Potenza M, Lin C, Pakpour A. A meta‐analysis assessing reliability of the Yale Food Addiction Scale: Implications for compulsive eating and obesity. Obesity Reviews 2024, 26: e13881. PMID: 39715731, PMCID: PMC11884959, DOI: 10.1111/obr.13881.Peer-Reviewed Original ResearchMeSH KeywordsCompulsive BehaviorFeeding and Eating DisordersFood AddictionHumansObesityPsychometricsReproducibility of ResultsConceptsYale Food Addiction ScaleRandom-effects meta-analysisReliability generalization meta-analysisMeta-analysisFood addictionTest-retest correlation coefficientTest-retest reliabilityComprehensive systematic reviewSixty-five studiesIntraclass coefficientSystematic reviewAddiction ScaleDisordered eatingCompulsive eatingObesitySample sizeEatingCorrelation coefficientEating disordersIntraclassReliability measuresReliabilityParticipantsScaleInceptionComparing the Accuracy and Reliability of ABC/2 and Planimetry for Vestibular Schwannoma Volume Assessment
Singh K, Abdou H, Panth N, Chiang V, Buono F, Schwartz N, Mahajan A. Comparing the Accuracy and Reliability of ABC/2 and Planimetry for Vestibular Schwannoma Volume Assessment. Otology & Neurotology 2024, 46: 196-200. PMID: 39792983, DOI: 10.1097/mao.0000000000004392.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeuroma, AcousticReproducibility of ResultsRetrospective StudiesTumor BurdenConceptsTumor volume assessmentABC/2 methodVestibular schwannomaVolume assessmentPlanimetry methodTumor volumeTumor volume changesFollow-up scansOverestimate tumor volumePatients' quality of lifeTumor sizeRetrospective reviewIntracranial tumorsImprove clinical decision makingSubgroup analysisVolumetric assessmentTumorClinical decision makingQuality of lifePatients' qualityPatientsClinical settingPlanimetryImaging techniquesPositive correlationParental considerations about their childs’ mental health: Validating the German adaptation of the Parental Reflective Functioning Questionnaire
Wildner A, Küçükakyüz S, Marx A, Nolte T, Reck C, Fonagy P, Luyten P, von Tettenborn A, Müller M, Zietlow A, Woll-Weber C. Parental considerations about their childs’ mental health: Validating the German adaptation of the Parental Reflective Functioning Questionnaire. PLOS ONE 2024, 19: e0314074. PMID: 39630624, PMCID: PMC11616854, DOI: 10.1371/journal.pone.0314074.Peer-Reviewed Original ResearchConceptsParental Reflective Functioning QuestionnaireReflective Functioning QuestionnaireParental reflective functioningMental healthMental statesChild mental healthReflective functioningParental attachment dimensionsInfant attachment statusFunction QuestionnaireMothers of childrenSelf-report measuresGerman validation studyThree-factor structureConfirmatory factor analysisAttachment dimensionsConcurrent validityCommunity sampleAttachment statusEmotional availabilityGerman adaptationParenting stressAssess model fitOriginal validationCronbach's aTherapy Mode Preference Scale: Preliminary Validation Methodological Design
Cerrito B, Xiao J, Fialk A, Buono F. Therapy Mode Preference Scale: Preliminary Validation Methodological Design. JMIR Formative Research 2024, 8: e65477. PMID: 39612373, PMCID: PMC11623782, DOI: 10.2196/65477.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultCOVID-19FemaleHumansMaleMental Health ServicesPatient PreferencePsychometricsReproducibility of ResultsSARS-CoV-2Surveys and QuestionnairesTelemedicineYoung AdultConceptsZoom Exhaustion and Fatigue scaleIn-person careHierarchical linear regressionHealth careIn-personExploratory factor analysisInternal consistencyMental health treatment programsSatisfactory levels of internal consistencyFactor analysisLevels of internal consistencyIn-person therapyKaiser-Meyer-Olkin measure of sampling adequacyClient Satisfaction QuestionnaireKaiser-Meyer-Olkin measureBartlett's test of sphericityHealth care demandBarriers to treatmentMeasure of sampling adequacyTest of sphericityIncremental validityFactor structureFatigue ScaleLinear regressionCare demandsGeneralizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness
Chopra S, Dhamala E, Lawhead C, Ricard J, Orchard E, An L, Chen P, Wulan N, Kumar P, Rubenstein A, Moses J, Chen L, Levi P, Holmes A, Aquino K, Fornito A, Harpaz-Rotem I, Germine L, Baker J, Yeo B, Holmes A. Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness. Science Advances 2024, 10: eadn1862. PMID: 39504381, PMCID: PMC11540040, DOI: 10.1126/sciadv.adn1862.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedBrainCognitionCognitive DysfunctionFemaleHumansMagnetic Resonance ImagingMaleMental DisordersMiddle AgedReproducibility of ResultsConceptsPrediction of cognitionCognitive functionPrediction of cognitive functionFunctional neuroimaging dataTransdiagnostic sampleComputational psychiatryPsychiatric illnessNeuroimaging dataCognitive impairmentCognitionPopulation-level datasetsPsychiatryAssociated with poor outcomesUK BiobankImpairmentBrainIllnessSymptomsPrediction studiesParticipantsPoor outcomeClinical studiesSamplesInternational multi-institutional external validation of preoperative risk scores for 30-day in-hospital mortality in paediatric patients
Tangel V, Hoeks S, Stolker R, Brown S, Pryor K, de Graaff J, Committee M, Pace N, Domino K, Muehlschlegel J, Kheterpal S, Vaughan M, Mathis M, Jiang S, Obembe S, Freundlich R, Schonberger R, Kim D. International multi-institutional external validation of preoperative risk scores for 30-day in-hospital mortality in paediatric patients. British Journal Of Anaesthesia 2024, 133: 1222-1233. PMID: 39477712, DOI: 10.1016/j.bja.2024.09.003.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentChildChild, PreschoolFemaleHospital MortalityHumansInfantInfant, NewbornMaleNetherlandsRegistriesReproducibility of ResultsRisk AssessmentROC CurveConceptsSurgical risk scoresPediatric risk assessmentArea under the receiver operating characteristic curveIn-hospital mortalityRisk scoreMulticenter Perioperative Outcomes GroupPreoperative risk scoreDecision curve analysisReceiver operating characteristic curvePhysical status scoreExternal validationAssess model discriminationRisk prediction scoreASA physical status scoreClinical decision-makingPaediatric patientsLow probability of mortalityDutch hospitalsOutcome groupPatient-specific risk scoresPrimary outcomeClustering of casesCurve analysisStatus scoreCharacteristic curveModel selection to achieve reproducible associations between resting state EEG features and autism
Carson W, Major S, Akkineni H, Fung H, Peters E, Carpenter K, Dawson G, Carlson D. Model selection to achieve reproducible associations between resting state EEG features and autism. Scientific Reports 2024, 14: 25301. PMID: 39455733, PMCID: PMC11511871, DOI: 10.1038/s41598-024-76659-5.Peer-Reviewed Original ResearchMeSH KeywordsAutistic DisorderBrainChildChild, PreschoolElectroencephalographyFemaleHumansMachine LearningMaleReproducibility of ResultsRestConceptsElectroencephalography spectral powerCustom machine learning modelsPredictive performanceGamma powerMachine learning modelsRegularized generalized linear modelModel selectionBiomarker discoverySpectral powerMidline regionMultiple featuresLearning modelsFunctional connectivity featuresPosterior midline regionsDevelopment of a 31P magnetic resonance spectroscopy technique to quantify NADH and NAD+ at 3 T
Mevenkamp J, Bruls Y, Mancilla R, Grevendonk L, Wildberger J, Brouwers K, Hesselink M, Schrauwen P, Hoeks J, Houtkooper R, Buitinga M, de Graaf R, Lindeboom L, Schrauwen-Hinderling V. Development of a 31P magnetic resonance spectroscopy technique to quantify NADH and NAD+ at 3 T. Nature Communications 2024, 15: 9159. PMID: 39443469, PMCID: PMC11499639, DOI: 10.1038/s41467-024-53292-4.Peer-Reviewed Original ResearchConceptsPhysically active older adultsActive older adultsMetabolic healthHuman skeletal musclePhosphorous magnetic resonance spectroscopySedentary individualsOlder adultsStimulate mitochondrial biogenesisHealthSkeletal muscleMitochondrial biogenesisNAD+Physiological decreaseNADH contentNADHQuantify NADHClinical 3Magnetic resonance spectroscopy techniquesMR sequencesAdultsMeasurement reproducibilityAssessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes
Bogaerts J, Steenbeek M, Bokhorst J, van Bommel M, Abete L, Addante F, Brinkhuis M, Chrzan A, Cordier F, Devouassoux‐Shisheboran M, Fernández‐Pérez J, Fischer A, Gilks C, Guerriero A, Jaconi M, Kleijn T, Kooreman L, Martin S, Milla J, Narducci N, Ntala C, Parkash V, de Pauw C, Rabban J, Rijstenberg L, Rottscholl R, Staebler A, Van de Vijver K, Zannoni G, van Zanten M, Bart J, Bentz J, Bosse T, Bulten J, Desouki M, Lastra R, Numan T, Schoolmeester J, Schwartz L, Shih I, Soong T, Turashvili G, Vang R, Volchek M, Aliredjo R, Kusters‐Vandevelde H, de Hullu J, Simons M, van der Laak J. Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes. The Journal Of Pathology Clinical Research 2024, 10: e70006. PMID: 39439213, PMCID: PMC11496567, DOI: 10.1002/2056-4538.70006.Peer-Reviewed Original ResearchMeSH KeywordsCarcinoma in SituCystadenocarcinoma, SerousDeep LearningFallopian Tube NeoplasmsFemaleHumansImage Interpretation, Computer-AssistedObserver VariationReproducibility of ResultsConceptsDeep learning modelsSerous tubal intraepithelial carcinomaArtificial intelligenceAI assistanceDiagnosis of serous tubal intraepithelial carcinomaTubal intraepithelial carcinomaReview timeFallopian tubeIntraepithelial carcinomaAI supportHigh-grade serous ovarian carcinomaSerous ovarian carcinomaStandalone performanceAverage sensitivityGroup of pathologistsAccuracyOvarian carcinomaHistopathological diagnosisPathologist performanceMixed-model analysisDiagnostic certaintyCarcinomaDiagnostic setting
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