Evidence map›Paper›PMID 30610448›Full record

ArticleInternational journal of legal medicine2019

Specific microbes of saliva and vaginal fluid of Guangdong Han females based on 16S rDNA high-throughput sequencing.

Hongyan Huang, Ting Yao, Weibin Wu, Chuangyan Zhai, Tianshan Guan, Yali Song, Yunxia Sun, Cheng Xiao, Peiyan Liang, Ling Chen

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Article in International journal of legal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Hongyan Huang *School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
Ting Yao *School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
Weibin Wu *School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
Chuangyan ZhaiSchool of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
Tianshan GuanSchool of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
Yali SongDepartment of health management, Nanfang Hospital, Guangzhou, 510515, People's Republic of China.
Yunxia SunCenter for Reproductive Medicine, Department of Obstetrics and Gynecology, Nanfang Hospital, Guangzhou, 510515, China.
Cheng XiaoSchool of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
Peiyan LiangSchool of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
Ling ChenSchool of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China. lingpzy@163.com.ORCID http://orcid.org/0000-0001-7699-4820

Funding

Innovative training program for College Students 201612121083Innovative training program for College Students 201812121123Natural Science Foundation of China 81501627
6 · The paper itself

Abstract

Vaginal fluid and saliva are of great importance in forensic sciences. The identification of vaginal fluid or saliva is especially important in criminal cases. Microbes are considered as a promising marker for the identification of body fluids. In this study, 18 salivary fluids and 18 vaginal fluid samples were collected from 18 healthy women of the Han population in Guangdong province, China. The microbes of the above samples were analyzed by 16S rDNA high-throughput sequencing. The results showed that the microbes whose proportions are over 1% in saliva samples distributed across 12 genera and 57 operational taxonomic units (OTUs), and in vaginal fluid distributed across 4 genera and 9 OTUs. The microbes that dominated in saliva were quite different from those dominated in vaginal fluids. The linear discriminant analysis (LDA) effect size (LEfSe) algorithm was used to screen out the specific microbes of the studied samples, and the results showed that the specific microbes in saliva samples are Haemophilus parainfluenzae, Veillonella parvula, and Aggregatibacter segnis, while in vaginal fluid is Lactobacillus iners.

Indexed as

High-Throughput Nucleotide SequencingSequence Analysis, DNAAdultCervix MucusChinaDiscriminant AnalysisDNA, BacterialDNA, RibosomalEthnicityFemaleHumansMiddle AgedPolymerase Chain ReactionRNA, Ribosomal, 16SSalivaYoung AdultDNA, BacterialDNA, RibosomalRNA, Ribosomal, 16S16S rDNABody fluid identificationMicrobesSalivaVaginal fluid

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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.