Evidence map›Paper›PMID 36233330›Full record

ArticleInternational journal of molecular sciences2022

Integrative Analysis of the Nasal Microbiota and Serum Metabolites in Bovines with Respiratory Disease by 16S rRNA Sequencing and Gas Chromatography/Mass Selective Detector-Based Metabolomics.

Ying Zhang, Chunji Ma, Yang Han, Hua Jin, Haixia Luo, Xiujing Hao, Min Li

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 45% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

7 authors at 1 institution in 1 country.

Ying ZhangLife Science School, Ningxia University, Yinchuan 750021, China.
Chunji MaLife Science School, Ningxia University, Yinchuan 750021, China.
Yang HanLife Science School, Ningxia University, Yinchuan 750021, China.
Hua JinLife Science School, Ningxia University, Yinchuan 750021, China.
Haixia LuoLife Science School, Ningxia University, Yinchuan 750021, China.
Xiujing HaoLife Science School, Ningxia University, Yinchuan 750021, China.
Min LiLife Science School, Ningxia University, Yinchuan 750021, China.
Ningxia University · CN

Funding

National Natural Science Foundation of China 31960036
6 · The paper itself

Abstract

Bovine respiratory disease (BRD) continues to pose a serious threat to the cattle industry, resulting in substantial economic losses. As a multifactorial disease, pathogen infection and respiratory microbial imbalance are important causative factors in the occurrence and development of BRD. Integrative analyses of 16S rRNA sequencing and metabolomics allow comprehensive identification of the changes in microbiota and metabolism associated with BRD, making it possible to determine which pathogens are responsible for the disease and to develop new therapeutic strategies. In our study, 16S rRNA sequencing and metagenomic analysis were used to describe and compare the composition and diversity of nasal microbes in healthy cattle and cattle with BRD from different farms in Yinchuan, Ningxia, China. We found a significant difference in nasal microbial diversity between diseased and healthy bovines; notably, the relative abundance of

Indexed as

Cattle DiseasesMicrobiotaRespiration DisordersRespiratory Tract DiseasesAnimalsCattleChromatography, GasLactic AcidMetabolomicsRNA, Ribosomal, 16SSarcosineLactic AcidRNA, Ribosomal, 16SSarcosinebovine respiratory diseasemetabolitesnasal microbiota

Identifiers

PMID36233330
PMCPMC9569885
OpenAlexW4304136394

What OpenQuestion holds

Textmetadata
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.