Evidence map›Paper›PMID 25563835›Full record

ArticleEnvironmental science and pollution research international2015

Antibiotic resistance pattern and gene expression of non-typhoid Salmonella in riversheds.

Chao-Yu Hsu, Bing-Mu Hsu, Wen-Tsai Ji, Jung-Sheng Chen, Tsui-Kang Hsu, Dar-Der Ji, Shao-Feng Tseng, Yi-Chou Chiu, Po-Min Kao, Yu-Li Huang

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Article in Environmental science and pollution research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

6 citing papers in PubMed.

  1. Whole Genome Sequencing Reveals High Prevalence of Antimicrobial Resistance Genes inThe Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2026
    Article
  2. Retrospective analysis of antimicrobial resistance ofFrontiers in veterinary science · 2025
    Article
  3. Article
  4. Revisiting the Biological Behavior ofFrontiers in microbiology · 2022
    Article
  5. Presence of Tetracycline and Sulfonamide Resistance Genes inAntibiotics (Basel, Switzerland) · 2021
    Review
  6. Review
4 · The record

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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

10 authors.

Chao-Yu HsuDivision of Urology, Department of Surgery, Tungs' Taichung Metro Harbor Hospital, Taichung, Taiwan, Republic of China.
Bing-Mu Hsu
Wen-Tsai Ji
Jung-Sheng Chen
Tsui-Kang Hsu
Dar-Der Ji
Shao-Feng Tseng
Yi-Chou Chiu
Po-Min Kao
Yu-Li Huang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, antibiotic resistance and major phenol and genotypes of non-typhoid Salmonella spp. from riversheds in Taiwan were examined. In 236 water samples tested, 54 (22.9%) contained Salmonella spp. Fifteen Salmonella serovars were identified from the Salmonella isolates, and some common serovars are associated with infections of human and livestock, including Albany (27.8%), Newport (14.8%), Bareilly (13.0%), Derby (11.1%), and Typhimurium (7.4%). Various environmental factors may also affect the presence and proportion of different serovars in the receiving waters. In contrast, serovars with narrower range of hosts, e.g., Dublin, were rarely detected. The Salmonella isolates were subjected to eight antibiotics for drug resistance, and 51.9% of the samples were resistant to at least one tested antibiotics. Tetracycline and sulfadiazine were the two most ineffective antibiotics against the Salmonella isolates, and the results were indicative of long-term antibiotics abuse as fodder supplements in animal husbandry. The more commonly detected serovars such as Albany, Derby, and Typhimurium were also more likely to be resistant to multiple antibiotics. Finally, a significant correlation was observed between resistance to chloramphenicol and the resistance gene cmlA, suggesting that the resistance genotypes could persist in the environment even long after prohibition of the drug use. The high prevalence of antibiotic-resistant Salmonella spp. infers elevated infection risks that must be further examined.

Indexed as

Drug Resistance, Multiple, BacterialWater MicrobiologyAnti-Bacterial AgentsGene Expression Regulation, BacterialSalmonellaTaiwanAnti-Bacterial Agents

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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.