Evidence map›Paper›PMID 37835748›Full record

ArticleAnimals : an open access journal from MDPI2023

Detecting Forest Musk Deer Abscess Disease Pathogens Using 16S rRNA High-Throughput Sequencing Technology.

Guanjie Lu, Zhe Wang, Baofeng Zhang, Zhichao Zhou, Defu Hu, Dong Zhang

Open access · goldAbstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 21% of its field
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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Insights into the Gut Microbial Diversity of Wild Siberian Musk Deer (Animals : an open access journal from MDPI · 2024
    Article
  4. Article
  5. Characterization ofFrontiers in microbiology · 2024
    Article
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

6 authors at 1 institution in 1 country.

Guanjie LuSchool of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China.
Zhe WangSchool of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China.
Baofeng ZhangSchool of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China.
Zhichao ZhouSchool of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0001-8136-4794
Defu HuSchool of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China.
Dong ZhangSchool of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0001-6427-7867
Beijing Forestry University · CN

Funding

the Key Research and Development Project Fund of the National Forestry and Grassland Administration GLM[2021]45the National Key R&D Program of China 2022YFC2601601
6 · The paper itself

Abstract

Currently, researchers use bacterial culture and targeted PCR methods to classify, culture, and identify the pathogens causing abscess diseases. However, this method is limited by factors such as the type of culture medium and culture conditions, making it challenging to screen and proliferate many bacteria effectively. Fortunately, with the development of high-throughput sequencing technology, pathogen identification at the genetic level has become possible. Not only can this approach overcome the limitations of bacterial culture, but it can also accurately identify the types and relative abundance of pathogens. In this study, we used high-throughput sequencing of 16S rRNA to identify the pathogens in purulent fluid samples. Our results not only confirmed the presence of the main pathogen reported by previous researchers,

Indexed as

16S rRNA high-throughput sequencing technologyabscessforest musk deerpathogenic bacteria

Identifiers

PMID37835748
PMCPMC10572063
OpenAlexW4387458110

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.