Evidence map›Paper›PMID 40895045›Full record

ArticlePeerJ2025

MultNAT HIV-1 Viral Load Assay: a simple, rapid and sensitive cross-primer amplification assay for detecting the HIV-1 virus in the field.

Lili Huang, Zhongbao Zuo, Lanlan Hu, Jing Wu, Qingting Bu, Yueping Li, Xiuhui Li, Yanqiong Zhou, Aifang Xu

Abstract read
In one paragraph

Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Lili HuangClinical Laboratory, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China.
Zhongbao ZuoClinical Laboratory, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China.
Lanlan HuClinical Laboratory, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China.
Jing WuClinical Laboratory, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China.
Qingting BuClinical Laboratory, Ustar Biotechnologies, Hangzhou, Zhejiang Province, China.
Yueping LiClinical Laboratory, Ustar Biotechnologies, Hangzhou, Zhejiang Province, China.
Xiuhui LiClinical Laboratory, Ustar Biotechnologies, Hangzhou, Zhejiang Province, China.
Yanqiong ZhouClinical Laboratory, Ustar Biotechnologies, Hangzhou, Zhejiang Province, China.
Aifang XuClinical Laboratory, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: A large number of new human immunodeficiency virus (HIV) patients are diagnosed worldwide every year. This study aimed to design a rapid, efficient, and convenient kit for detecting HIV-1 viral load. Methods: The sensitivity and specificity of the kit were determined, and serum samples were collected from HIV-negative and HIV-positive individuals who underwent medical checkups at Xixi Hospital in Hangzhou City, Zhejiang Province, China, between June and August 2023 for testing. The presence of HIV-1 in the clinical samples was assessed Results: HIV-1 RNA was measured with a 100 copies/mL sensitivity Conclusion: Due to its simplicity, rapid response time, and high sensitivity and specificity, the MultNAT HIV-1 Viral Load Assay is suitable for primary care and epidemiological screening, facilitating rapid and accurate HIV-1 detection.

Indexed as

HIV-1HIV InfectionsViral LoadAdultChinaFemaleHumansMaleReagent Kits, DiagnosticRNA, ViralSensitivity and SpecificityReagent Kits, DiagnosticRNA, ViralEpidemiological screeningHuman immunodeficiency virusMultNAT HIV-1 viral load assayPrimary care

Identifiers

PMID40895045
PMCPMC12399072

What OpenQuestion holds

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LicenceCC BY
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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.