Evidence map›Paper›PMID 28622345›Full record

Trial reportPloS one2017

The infection staging and profile of genotypic distribution and drug resistance mutation among the human immunodeficiency virus-1 infected blood donors from five Chinese blood centers, 2012-2014.

Peibin Zeng, Yu Liu, Miao He, Jingxing Wang, Sheila Keating, Wei Mao, Mei Huang, Hongli Ma, Weilan He, Xinhong Bi and 6 more

Open access · goldAbstract readClinical TrialMulticenter Study
In one paragraph

Trial report in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

10 citing papers in PubMed, 17 citations in OpenAlex.

  1. Trial
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  7. Observational
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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

16 authors at 7 institutions in 2 countries.

Peibin ZengWest China School of Public Health, Sichuan University, Chengdu, Sichuan, China.
Yu LiuInstitute of Blood Transfusion, Chinese Academic of Medical Science, Chengdu, Sichuan, China.
Miao HeInstitute of Blood Transfusion, Chinese Academic of Medical Science, Chengdu, Sichuan, China.
Jingxing WangInstitute of Blood Transfusion, Chinese Academic of Medical Science, Chengdu, Sichuan, China.
Sheila KeatingBlood System Research Institute, San Francisco, California, United States of America.
Wei MaoChongqing Blood Center, Chongqing, China.
Mei HuangMianyang Blood Center, Mianyang, Sichuan, China.
Hongli MaLouyang Blood Center, Luoyang, Henan, China.
Weilan HeGuangxi Blood Center, Liuzhou, Guangxi, China.
Xinhong BiXinjiang Blood Center, Urumqi, Xinjiang, China.
Dan LiaoResearch Triangle Institute, Research Triangle Park, North Carolina, United States of America.
Michael BuschBlood System Research Institute, San Francisco, California, United States of America.
Paul NessDepartment of Transfusion Medicine, Johns Hopkins Hospital, Baltimore, Maryland, United States of America.
Jing LiuDepartment of Transfusion Medicine, Johns Hopkins Hospital, Baltimore, Maryland, United States of America.
Hua ShanDepartment of Pathology, Stanford University, Palo Alto, California, United States of America.
NHLBI Recipient Epidemiology and Donor Evaluation Study-III program
Guangzhou Blood Center · CNBlood Systems Research Institute · USJohns Hopkins Hospital · USRTI International · USSichuan Mianyang 404 Hospital · CNSichuan University · CNStanford University · US

Funding

NHLBI NIH HHS HHSN268201100001INHLBI NIH HHS HHSN268201100008CNHLBI NIH HHS HHSN268201100008IWHI NIH HHS HHSN268201100001C
6 · The paper itself

Abstract

The increasing complexity and diversity of the human immunodeficiency virus-1 (HIV-1) infections challenge the disease control and anti-retrovirus treatment in China. The infection stages and molecular characteristics of HIV-1 from infected Chinese blood donors were examined to shed light on the HIV genotype distribution and the status of drug resistance mutations (DRMs) in the changing HIV epidemic in China. Western blot (WB) confirmed HIV-1 positive plasma samples were collected from blood donors at five Chinese blood centers from April 16, 2012, through June 30, 2014. The HIV infection stages were determined using the Lag-avidity assay. HIV Pol regions including whole protease and partial reverse transcriptase (RT) were amplified and sequenced to establish the profile of genotype distribution and drug resistance mutations (DRMs). Viral loads were determined using the ROCHE COBAS system. Of the 259 HIV-1 positive samples tested by the Lag-avidity assay, 23.6% (61/259) were identified as recent infections. A total of 205 amplified sequences displayed the following genotype distributions: circulating recombinant form (CRF) 07_BC (61.5%), CRF08_BC (8.3%), CRF01_AE (20%), B (6.3%), and 01B (3.9%). There was no significant difference in genotype distribution between recent and long-term infections. 31 DRMs were identified from 27 samples including four protease inhibitors (PIs) accessory DRMs, two PIs major DRMs (M46I), two nucleoside RT inhibitors DRMs (K219R and K70Q), and 23 nonnucleoside RT inhibitors DRMs. 27 samples had DRMs, yielding a drug resistance prevalence of 13.2% (27/205). Our findings provide important information for developing strategies for comprehensive HIV control and improving anti-retroviral treatment in China.

Indexed as

Blood BanksBlood DonorsGenotypeMutationAdolescentAdultAsian PeopleChinaDrug Resistance, ViralFemaleHIV-1HIV InfectionsHIV ProteaseHIV Protease InhibitorsHIV Reverse TranscriptaseHumansHIV ProteaseHIV Protease InhibitorsHIV Reverse Transcriptasereverse transcriptase, Human immunodeficiency virus 1Reverse Transcriptase Inhibitors

Identifiers

PMID28622345
PMCPMC5473534
OpenAlexW2625128626

What OpenQuestion holds

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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.