2022
Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods
You S, Chitwood MH, Gunasekera KS, Crudu V, Codreanu A, Ciobanu N, Furin J, Cohen T, Warren JL, Yaesoubi R. Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods. PLOS Digital Health 2022, 1: e0000059. PMID: 36177394, PMCID: PMC9518704, DOI: 10.1371/journal.pdig.0000059.Peer-Reviewed Original ResearchDrug susceptibility testXpert MTB/RIFMachine learning-based modelsLearning-based modelsMachine learning methodsRifampicin-resistant tuberculosisTime of diagnosisRifampin-resistant tuberculosisMTB/RIFNeural network modelLearning methodsNetwork modelMulti-drug resistant tuberculosisNational TB surveillanceDrug-resistant tuberculosisOptimism-corrected areaSelection of antibioticsAnti-TB agentsDistrict-level prevalenceLow-resource settingsPatient characteristicsResistant tuberculosisTB surveillanceAppropriate treatmentDST results
2013
Isoniazid-resistant Tuberculosis in Children
Yuen CM, Tolman AW, Cohen T, Parr JB, Keshavjee S, Becerra MC. Isoniazid-resistant Tuberculosis in Children. The Pediatric Infectious Disease Journal 2013, 32: e217-e226. PMID: 23348808, PMCID: PMC3709006, DOI: 10.1097/inf.0b013e3182865409.Peer-Reviewed Original ResearchConceptsIsoniazid-resistant tuberculosisLatent tuberculosis infectionTuberculosis diseaseTuberculosis infectionIsoniazid resistancePediatric tuberculosis patientsTreatment of childrenIsoniazid-resistant strainsReports of childrenEffective regimensTuberculosis patientsTuberculosis treatmentAppropriate treatmentInclusion criteriaMedian proportionSystematic reviewRifampin resistanceDiseaseTuberculosisRegimensChildrenTreatmentInfectionIsoniazidHigher proportion