Evidence map›Paper›PMID 42322398›Full record

ArticleInternational journal of legal medicine2026

Establishment of rapid identification methods of menstrual blood based on SHERLOCK technology.

Xianmiao Wang, Qianwei Yao, Yu Luo, Zhe Wang, Lei Feng, Sheng Hu, Zibo Yin, Jian Lei, Wei Xie, Qifan Sun

Abstract read
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Article in International journal of legal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

10 authors.

Xianmiao WangKey Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science of China, Beijing, 100038, China.
Qianwei YaoKey Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science of China, Beijing, 100038, China.
Yu LuoKey Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science of China, Beijing, 100038, China.
Zhe WangKey Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science of China, Beijing, 100038, China.
Lei FengKey Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science of China, Beijing, 100038, China.
Sheng HuKey Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science of China, Beijing, 100038, China.
Zibo YinHangzhou Medimscience Biomedical Technology Co., Ltd, Hangzhou, Zhejiang, 311200, China.
Jian LeiHangzhou Medimscience Biomedical Technology Co., Ltd, Hangzhou, Zhejiang, 311200, China.
Wei XieCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
Qifan SunKey Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science of China, Beijing, 100038, China. sunqifan@cifs.gov.cn.ORCID http://orcid.org/0000-0002-3282-9652

Funding

Central level public welfare research institutes basic research business fund project 2024JB040Key Technologies Research and Development Program 2022YFC3341002
6 · The paper itself

Abstract

The accurate determination of the tissue origin of body fluid-like forensic samples at crime scenes holds significant value for crime scene reconstruction and case trials. mRNA is the most commonly used molecular marker for body fluid identification. In this study, short crRNA fragments targeting the menstrual blood-specific genes MMP10 and STC1 were designed and screened. A SHERLOCK detection method based on CRISPR/Cas technology was established. Two signal detection methods, SHERLOCK-Fluorescence and SHERLOCK-LFA, were evaluated using 86 samples from five body fluid types (semen, peripheral blood, menstrual blood, saliva, and vaginal secretions). The SHERLOCK-Fluorescence method showed a sensitivity of 10⁻

Indexed as

Forensic GeneticsMenstruationRNA, MessengerCervix MucusCRISPR-Cas SystemsFemaleFluorescenceHumansSalivaSemenSensitivity and SpecificityRNA, MessengerCRISPR/CasForensic geneticsMenstrual blood identificationMRNASHERLOCK

Identifiers

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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.