Evidence map›Paper›PMID 42145955›Full record

ArticlePeerJ2026

A retrospective comparison of CMV DNA detection in reproductive tract secretions

Lingwei Hu, Yongying Bai, Peihao Wu, Shuai Li, Kaiqi Wu, Bo Zhu, Yin Binbin

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2026. 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

7 authors.

Lingwei Hu *Department of Genetics and Metabolism, The Children's Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Yongying Bai *Department of Clinical Laboratory, The Women's Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Peihao WuDepartment of Clinical Laboratory, The Women's Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Shuai LiDepartment of Clinical Laboratory, The Women's Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Kaiqi WuDepartment of Clinical Laboratory, The Women's Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Bo ZhuDepartment of Clinical Laboratory, The Women's Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Yin BinbinDepartment of Clinical Laboratory, The Women's Hospital of Zhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aims to compare cytomegalovirus (CMV) DNA detection rates in reproductive tract secretions (RTS) and urine samples and to further evaluate whether multiple specimen testing improves overall CMV DNA detection rates. Methods: We conducted a retrospective cohort study involving 3,804 female participants who provided 4,383 samples (10.95% RTS; 89.05% urine) between January 2020 and June 2024. Analyses included: (1) CMV DNA detection rates across sample types and population subgroups, (2) the impact of single versus multiple specimen testing on CMV DNA detection rates. Result: CMV DNA was detected in 3.03% (133/4383) of all samples. Positivity rates differed significantly: 9.79% (47/480) for RTS versus 2.20% (86/3903) for urine ( Conclusions: The detection rate of CMV DNA in RTS is higher than that in urine. The combined sample testing increases the detection rate of CMV DNA, potentially helping identify women with active viral shedding. These findings inform the development of optimized CMV DNA testing protocols.

Indexed as

CytomegalovirusCytomegalovirus InfectionsDNA, ViralAdultFemaleHumansRetrospective StudiesSpecimen HandlingUrineViral LoadDNA, ViralCytomegalovirusDNA detectionReproductive tract secretionsUrine

Identifiers

PMID42145955
PMCPMC13175060

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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.