Displaying all 4 publications

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  1. Jiamsakul A, Sungkanuparph S, Law M, Kantor R, Praparattanapan J, Li PC, et al.
    J Int AIDS Soc, 2014;17:19053.
    PMID: 25141905 DOI: 10.7448/IAS.17.1.19053
    First-line antiretroviral therapy (ART) failure often results from the development of resistance-associated mutations (RAMs). Three patterns, including thymidine analogue mutations (TAMs), 69 Insertion (69Ins) and the Q151M complex, are associated with resistance to multiple-nucleoside reverse transcriptase inhibitors (NRTIs) and may compromise treatment options for second-line ART.
    Matched MeSH terms: Asia
  2. Jiamsakul A, Kumarasamy N, Ditangco R, Li PC, Phanuphak P, Sirisanthana T, et al.
    J Int AIDS Soc, 2014;17:18911.
    PMID: 24836775 DOI: 10.7448/IAS.17.1.18911
    Adherence to antiretroviral therapy (ART) plays an important role in treatment outcomes. It is crucial to identify factors influencing adherence in order to optimize treatment responses. The aim of this study was to assess the rates of, and factors associated with, suboptimal adherence (SubAdh) in the first 24 months of ART in an Asian HIV cohort.
    Matched MeSH terms: Asia/epidemiology
  3. Sungkanuparph S, Oyomopito R, Sirivichayakul S, Sirisanthana T, Li PC, Kantipong P, et al.
    Clin Infect Dis, 2011 Apr 15;52(8):1053-7.
    PMID: 21460324 DOI: 10.1093/cid/cir107
    Of 682 antiretroviral-naïve patients initiating antiretroviral therapy in a prospective, multicenter human immunodeficiency virus type 1 (HIV-1) drug resistance monitoring study involving 8 sites in Hong Kong, Malaysia, and Thailand, the prevalence of patients with ≥1 drug resistance mutation was 13.8%. Primary HIV drug resistance is emerging after rapid scaling-up of antiretroviral therapy use in Asia.
  4. Jiamsakul A, Chaiwarith R, Durier N, Sirivichayakul S, Kiertiburanakul S, Van Den Eede P, et al.
    J Med Virol, 2016 Feb;88(2):234-43.
    PMID: 26147742 DOI: 10.1002/jmv.24320
    HIV drug resistance assessments and interpretations can be obtained from genotyping (GT), virtual phenotyping (VP) and laboratory-based phenotyping (PT). We compared resistance calls obtained from GT and VP with those from PT (GT-PT and VP-PT) among CRF01_AE and subtype B HIV-1 infected patients. GT predictions were obtained from the Stanford HIV database. VP and PT were obtained from Janssen Diagnostics BVBA's vircoType(TM) HIV-1 and Antivirogram®, respectively. With PT assumed as the "gold standard," the area under the curve (AUC) and the Bland-Altman plot were used to assess the level of agreement in resistance interpretations. A total of 80 CRF01_AE samples from Asia and 100 subtype B from Janssen Diagnostics BVBA's database were analysed. CRF01_AE showed discordances ranging from 3 to 27 samples for GT-PT and 1 to 20 samples for VP-PT. The GT-PT and VP-PT AUCs were 0.76-0.97 and 0.81-0.99, respectively. Subtype B showed 3-61 discordances for GT-PT and 2-75 discordances for VP-PT. The AUCs ranged from 0.55 to 0.95 for GT-PT and 0.55 to 0.97 for VP-PT. Didanosine had the highest proportion of discordances and/or AUC in all comparisons. The patient with the largest didanosine FC difference in each subtype harboured Q151M mutation. Overall, GT and VP predictions for CRF01_AE performed significantly better than subtype B for three NRTIs. Although discrepancies exist, GT and VP resistance interpretations in HIV-1 CRF01_AE strains were highly robust in comparison with the gold-standard PT.
    Matched MeSH terms: Asia
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