Evidence map›Paper›PMID 24896087›Full record

ArticlePloS one2014

Prevalence of WHO transmitted drug resistance mutations by deep sequencing in antiretroviral-naïve subjects in Hunan Province, China.

Zou Xiaobai, Chen Xi, Hongping Tian, Ann B Williams, Honghong Wang, Jianmei He, Jun Zhen, Jennifer Chiarella, Lisebeth A Blake, Gregory Turenchalk and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
1.6field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.

  1. Pooled it
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  3. Trial
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 2 countries.

Zou XiaobaiHunan Provincial Center for Disease Control and Prevention, Changsha, Hunan Province, China.
Chen XiHunan Provincial Center for Disease Control and Prevention, Changsha, Hunan Province, China.
Hongping TianYale-China Association, New Haven, Connecticut, United States of America.
Ann B WilliamsUCLA School of Nursing, Los Angeles, California, United States of America.
Honghong WangCentral South University, Hunan Province, China.
Jianmei HeHunan Provincial Center for Disease Control and Prevention, Changsha, Hunan Province, China.
Jun ZhenHunan Provincial Center for Disease Control and Prevention, Changsha, Hunan Province, China.
Jennifer ChiarellaYale School of Medicine, New Haven, Connecticut, United States of America.
Lisebeth A Blake454 Life Science, Branford, Connecticut, United States of America.
Gregory Turenchalk454 Life Science, Branford, Connecticut, United States of America.
Michael J KozalYale School of Medicine, New Haven, Connecticut, United States of America.
Hunan Provincial Center for Disease Control and Prevention · CNEnzo Life Sciences (United States) · USYale University · USCentral South University · CNUniversity of California, Los Angeles · US

Funding

Developing an ART Adherence Intervention in South Central ChinaR34MH083564 · NIMH · YALE UNIVERSITY · PI WILLIAMS, ANN BARTLEY · 2009 to 2011
$671k
NIMH NIH HHS R34 MH 083564NIMH NIH HHS R34 MH083564
6 · The paper itself

Abstract

backgroundThere are few data on the prevalence of WHO transmitted drug resistance mutations (TDRs) that could affect treatment responses to first line antiretroviral therapy (ART) in Hunan Province, China.

objectiveDetermine the prevalence of WHO NRTI/NNRTI/PI TDRs in ART-naïve subjects in Hunan Province by deep sequencing.

methodsART-naïve subjects diagnosed in Hunan between 2010-2011 were evaluated by deep sequencing for low-frequency HIV variants possessing WHO TDRs to 1% levels. Mutations were scored using the HIVdb.stanford.edu algorithm to infer drug susceptibility.

resultsDeep sequencing was performed on samples from 90 ART-naïve subjects; 83.3% were AE subtype. All subjects had advanced disease (average CD4 count 134 cells/mm3). Overall 25.6%(23/90) of subjects had HIV with major WHO NRTI/NNRTI TDRs by deep sequencing at a variant frequency level ≥ 1%; 16.7%(15/90) had NRTI TDR and 12.2%(11/90) had a major NNRTI TDR. The majority of NRTI/NNRTI mutations were identified at variant levels <5%. Mutations were analyzed by HIVdb.stanford.edu and 7.8% of subjects had variants with high-level nevirapine resistance; 4.4% had high-level NRTI resistance. Deep sequencing identified 24(27.6%) subjects with variants possessing either a PI TDR or hivdb.stanford.edu PI mutation (algorithm value ≥ 15). 17(19.5%) had PI TDRs at levels >1%.

conclusionsART-naïve subjects from Hunan Province China infected predominantly with subtype AE frequently possessed HIV variants with WHO NRTI/NNRTI TDRs by deep sequencing that would affect the first line ART used in the region. Specific mutations conferring nevirapine high-level resistance were identified in 7.8% of subjects. The majority of TDRs detected were at variant levels <5% likely due to subjects having advanced chronic disease at the time of testing. PI TDRs were identified frequently, but were found in isolation and at low variant frequency. As PI/r use is infrequent in Hunan, the existence of PI mutations likely represent AE subtype natural polymorphism at low variant level frequency.

Indexed as

Drug Resistance, ViralMutationAdultAgedAnti-HIV AgentsCD4 Lymphocyte CountFemaleGenotypeHigh-Throughput Nucleotide SequencingHIV-1HIV InfectionsHumansMaleMiddle AgedPrevalenceViral LoadAnti-HIV Agents

Identifiers

PMID24896087
PMCPMC4045886
OpenAlexW2064097482

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.