Evidence map›Paper›PMID 36582726›Full record

ArticleHeliyon2022

Microbial profile of T-shirts after a fitness session of Chinese students.

Huizhen Yan, Yuxing Ren, Bihong Zhou, Fang Ye, Zhigang Wu

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 24% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. 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

5 authors at 2 institutions in 1 country.

Huizhen YanDepartment of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
Yuxing RenDepartment of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
Bihong ZhouGuangzhou Blue Moon Industrial Co., Ltd., Guangzhou, Guangdong, China.
Fang YeDepartment of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
Zhigang WuDepartment of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
Huazhong University of Science and Technology · CNMinistry of Ecology and Environment · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clothing textiles could protect our human skin against external factors, but the microbial population, including conditional pathogens, in clothing, would cause unpleasant odor, Skin inflammation, and textile deterioration. Several studies have reported that microbiomes on clothes are affected by skin microorganisms of individuals, the local environment and the types of textile fabrics, but little is known about how the textile microbial community is shaped in the Chinese population. In this study, 10 healthy young students were recruited to successfully wear the T-shirts made with 3 different fabrics (polyester, cotton, and blending fabrics of polyester and cotton) during physical exercise. Total deoxyribonucleic acid (DNA) was extracted from 30 T-shirts and 16s rRNA gene amplicon sequencing was applied to estimate the absolute abundances of bacteria in the samples. The main bacteria on wore T-shirts were

Indexed as

16S rRNA gene sequencingBacteriaMicrobiomeTextile

Identifiers

PMID36582726
PMCPMC9793269
OpenAlexW4311765026

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.