Evidence map›Paper›PMID 34205552›Full record

ArticleAntibiotics (Basel, Switzerland)2021

Molecular and Anti-Microbial Resistance (AMR) Profiling of Methicillin-Resistant

Bing-Mu Hsu, Jung-Sheng Chen, I-Ching Lin, Gwo-Jong Hsu, Suprokash Koner, Bashir Hussain, Shih-Wei Huang, Hsin-Chi Tsai

Abstract read
In one paragraph

Article in Antibiotics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The high cross-transmission in methicillin resistantIranian journal of microbiology · 2023
    Article
  6. Article
  7. Article
  8. 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

8 authors.

Bing-Mu HsuDepartment of Earth and Environmental Sciences, National Chung Cheng University, Chiayi County 621, Taiwan.ORCID 0000-0002-2988-8408
Jung-Sheng ChenDepartment of Medical Research, E-Da Hospital, Kaohsiung City 824, Taiwan.ORCID 0000-0003-3187-9479
I-Ching LinDepartment of Family Medicine, Asia University Hospital, Taichung City 413, Taiwan.
Gwo-Jong HsuDivision of Infectious Diseases, Ditmanson Medical Foundation, Chia-Yi Christian Hospital, Chiayi City 600, Taiwan.
Suprokash KonerDepartment of Earth and Environmental Sciences, National Chung Cheng University, Chiayi County 621, Taiwan.
Bashir HussainDepartment of Earth and Environmental Sciences, National Chung Cheng University, Chiayi County 621, Taiwan.
Shih-Wei HuangCenter for Environmental Toxin and Emerging Contaminant Research, Cheng Shiu University, Kaohsiung City 830, Taiwan.ORCID 0000-0002-2410-5320
Hsin-Chi TsaiDepartment of Psychiatry, School of Medicine, Tzu Chi University, Hualien County 970, Taiwan.

Funding

This research was funded by the Ministry of Science and Technology of Taiwan , the Buddhist Tzu Chi Hospital and Asia University Hospital. This research was also supported by Ditmanson Medical Foundation Chia - Yi Christian Hospital and the Center for Inn (MOST 108-2116-M-194 -005 & 108-2811-M-194 -507), (RCN011)
6 · The paper itself

Abstract

To provide evidence of the cross-contamination of emerging pathogenic microbes in a local network between long-term care facilities (LTCFs) and hospitals, this study emphasizes the molecular typing, the prevalence of virulence genes, and the antibiotic resistance pattern of methicillin-resistant

Indexed as

enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR)long term care facilities (LTCF)methicillin-resistant Staphylococcus aureus (MRSA)multidrug resistance (MDR)SCCmec typing

Identifiers

PMID34205552
PMCPMC8235027

What OpenQuestion holds

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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.