Evidence map›Paper›PMID 34741041›Full record

ArticleScientific reports2021

Efficient nested-PCR-based method development for detection and genotype identification of Acanthamoeba from a small volume of aquatic environmental sample.

Tsui-Kang Hsu, Jung-Sheng Chen, Hsin-Chi Tsai, Chi-Wei Tao, Yu-Yin Yang, Ying-Chin Tseng, Yi-Jie Kuo, Dar-Der Ji, Jagat Rathod, Bing-Mu Hsu

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.0field-weighted citation impact, top 15% 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

3 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Review
  3. Diagnosis ofDiagnostics (Basel, Switzerland) · 2023
    Review
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

10 authors at 7 institutions in 1 country.

Tsui-Kang Hsu *Department of Ophthalmology, Cheng Hsin General Hospital, Taipei, Taiwan.
Jung-Sheng Chen *Department of Medical Research, E-Da Hospital, Kaohsiung, Taiwan.
Hsin-Chi TsaiDepartment of Psychiatry, School of Medicine, Tzu Chi University, Hualien, Taiwan.
Chi-Wei TaoSection of Respiratory Therapy, Cheng Hsin General Hospital, Taipei, Taiwan.
Yu-Yin YangDepartment of Laboratory, Show Chwan Memorial Hospital, Changhua, Taiwan.
Ying-Chin TsengSection of Respiratory Therapy, Cheng Hsin General Hospital, Taipei, Taiwan.
Yi-Jie KuoDepartment of Orthopedic Surgery, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
Dar-Der JiDepartment of Tropical Medicine, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Jagat RathodDepartment of Earth Sciences, National Cheng Kung University, Tainan, Taiwan.
Bing-Mu HsuDepartment of Earth and Environmental Sciences, National Chung Cheng University, No. 168, University Road, Minhsiung, Chiayi County, 621, Taiwan. bmhsu@ccu.edu.tw.ORCID 0000-0002-2988-8408
Cheng Hsin General Hospital · TWNational Chung Cheng University · TWNational Yang Ming Chiao Tung University · TWChang Bing Show Chwan Memorial Hospital · TWNational Cheng Kung University · TWTzu Chi University · TWWan Fang Hospital · TW

Funding

Cheng Hsin General Hospital Foundation CHGH106-04Cheng Hsin General Hospital Foundation CHGH106-13Cheng Hsin General Hospital Foundation CHGH107-2Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan) MOST 105-2116-M-194-014
6 · The paper itself

Abstract

Acanthamoeba spp. are opportunistic human pathogens that cause granulomatous amoebic encephalitis and keratitis, and their accurate detection and enumeration in environmental samples is a challenge. In addition, information regarding the genotyping of Acanthamoeba spp. using various PCR methods is equally critical. Therefore, considering the diverse niches of habitats, it is necessary to develop an even more efficient genotyping method for Acanthamoeba spp. detection. This study improved the sensitivity of detection to avoid underestimation of Acanthamoeba spp. occurrence in aquatic environmental samples, and to accurately define the pathogenic risk by developing an efficient PCR method. In this study, a new nested genotyping method was established and compared with various PCR-based methods using in silico, lab, and empirical tests. The in silico test showed that many PCR-based methods could not successfully align specific genotypes of Acanthamoeba, except for the newly designed nested PCR and real-time PCR method. Furthermore, 52 water samples from rivers, reservoirs, and a river basin in Taiwan were analysed by six different PCR methods and compared for genotyping and detection efficiency of Acanthamoeba. The newly developed nested-PCR-based method of genotyping was found to be significantly sensitive as it could effectively detect the occurrence of Acanthamoeba spp., which was underestimated by the JDP-PCR method. Additionally, the present results are consistent with previous studies indicating that the high prevalence of Acanthamoeba in the aquatic environment of Taiwan is attributed to the commonly found T4 genotype. Ultimately, we report the development of a small volume procedure, which is a combination of recent genotyping PCR and conventional real-time PCR for enumeration of aquatic Acanthamoeba and acquirement of biologically meaningful genotyping information. We anticipate that the newly developed detection method will contribute to the precise estimation, evaluation, and reduction of the contamination risk of pathogenic Acanthamoeba spp., which is regularly found in the water resources utilised for domestic purposes.

Indexed as

Genotyping TechniquesAcanthamoebaEnvironmental MonitoringPolymerase Chain ReactionRivers

Identifiers

PMID34741041
PMCPMC8571327
OpenAlexW3208704632

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LicenceCC BY
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Registered trials

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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.