Evidence map›Paper›PMID 42655861›Full record

ArticleVeterinary sciences2026

Development and Validation of an RT-Droplet Digital PCR Assay for Detection of Peste des Petits Ruminants.

Rima Si, Jiao Xu, Xiaohua Wang, Yumeng Liu, Linlin Fang, Jiani Li, Yingli Wang, Jiarong Yu, Yi Zhai, Qinghua Wang and 3 more

Abstract read
In one paragraph

Article in Veterinary sciences, 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
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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

13 authors.

Rima SiReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Jiao XuReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.ORCID 0000-0001-8707-312X
Xiaohua WangReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Yumeng LiuReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Linlin FangReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Jiani LiCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao 266105, China.
Yingli WangReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Jiarong YuReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Yi ZhaiReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Qinghua WangReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Jingyue BaoReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.ORCID 0000-0003-2241-8507
Zhiliang WangReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.
Lin YangReference Laboratory for Peste des Petits Ruminants, China Animal Health and Epidemiology Center, Qingdao 266000, China.

Funding

This research and APC were funded by National Key Research and Development Programof China 2025YFD1800101
6 · The paper itself

Abstract

To achieve rapid, accurate, and quantitative detection of peste des petits ruminants virus (PPRV), we aligned the available PPRV genomic sequences, screened for conserved regions, and designed specific primers and probes accordingly. An RT-droplet digital PCR (RT-ddPCR) method was established, and a series of reaction conditions were optimized. Subsequently, the specificity, sensitivity, and repeatability of the method were subsequently evaluated. The results demonstrated that the RT-ddPCR method exhibited good specificity, with positive results obtained exclusively for PPRV and negative results for all other goat-derived viral or bacterial biological products and seed viruses tested. The method showed promising analytical sensitivity, with a LOD of 1.0 copies/μL for gene copy number. The method also demonstrated good repeatability, with coefficients of variation in all groups not exceeding 5%. In a preliminary clinical evaluation using 150 field samples, RT-ddPCR identified 113 positive cases, compared to 105 by RT-qPCR, with 8 additional low-copy-number positives detected exclusively by RT-ddPCR, and although the sample size is limited for precise diagnostic performance estimation, the assay showed 100% relative diagnostic sensitivity (105/105) and 82.2% relative diagnostic specificity (37/45), with almost perfect agreement between the two methods (κ = 0.87). As the positive signals in these discordant samples were too weak for independent confirmation by Sanger sequencing, they should be interpreted as "presumptive positives" requiring further investigation. The RT-ddPCR method established in this study enables sensitive and rapid detection of PPRV nucleic acid. However, detection of low-copy-number targets should be interpreted with caution, as it does not necessarily indicate active infection or epidemiologically significant virus circulation. This assay shows promise as a valuable research and surveillance tool for PPRV nucleic acid detection, particularly for confirming low-level viral RNA in samples near the detection limit of conventional methods, and could complement existing RT-qPCR-based approaches in PPR eradication programs.

Indexed as

clinical diagnosisdroplet digital PCRmolucular detectionpeste des petits ruminants virus

Identifiers

PMID42655861
PMCPMC13517568

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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.