Evidence map›Paper›PMID 36082397›Full record

ArticleExperimental biology and medicine (Maywood, N.J.)2022

Classification of salivary bacteriome in asymptomatic COVID-19 cases based on long-read nanopore sequencing.

Rungrat Jitvaropas, Oraphan Mayuramart, Vorthon Sawaswong, Pornchai Kaewsapsak, Sunchai Payungporn

Abstract read
In one paragraph

Article in Experimental biology and medicine (Maywood, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Trial
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  7. Microbiota of Saliva: A Non-invasive Diagnostic Tool.Indian journal of microbiology · 2024
    Review
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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

5 authors.

Rungrat JitvaropasDivision of Biochemistry, Department of Preclinical Science, Faculty of Medicine, Thammasat University, Pathum Thani 10120, Thailand.ORCID 0000-0001-7555-0048
Oraphan MayuramartResearch Unit of Systems Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
Vorthon SawaswongProgram in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand.
Pornchai KaewsapsakResearch Unit of Systems Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
Sunchai PayungpornResearch Unit of Systems Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.ORCID 0000-0003-2668-110X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus (COVID-19) global pandemic has impacted the health of almost everyone, including changes in their salivary microbiota. Since 2019, there has been an increase in the number of new COVID-19 cases in Thailand. Therefore, COVID-19 active case finding is important for early detection and epidemic control. Moreover, the dynamic changes of salivary bacteriome in asymptomatic COVID-19 cases are largely unknown. This research aimed to investigate and compare the salivary bacteriome and the co-infectious bacterial pathogens in the asymptomatic COVID-19 positive group to the negative group, based on novel nanopore sequencing. This cohort was a cross-sectional study including saliva samples collected from 82 asymptomatic participants (39 COVID-19 positive and 43 COVID-19 negative cases). All samples were sequenced for the full-length bacterial 16S rDNA. The alpha and beta diversity analyses were not significantly different between groups. The three major species in salivary bacteriome including

Indexed as

COVID-19Cross-Sectional StudiesHumans16S rRNAfull-lengthmicrobiotaMinIONoral bacteriomeSARS-CoV-2

Identifiers

PMID36082397
PMCPMC9742750

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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.