Evidence map›Paper›PMID 42075335›Full record

ReviewMicroorganisms2026

Progress of Rapid Detection Technology for Aquatic Microorganisms: A Comprehensive Review.

Qin Liu, Zhuangzhuang Qiu, Mengli Yao, Boyan Jiao, Yu Zhou, Chenghua Li, Haipeng Liu, Lusheng Xin

Abstract readReview
In one paragraph

Review in Microorganisms, 2026. 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
–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

2 citing papers in PubMed.

  1. Review
  2. 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

8 authors.

Qin LiuCollege of Agricultural Engineering, Guangxi Vocational University of Agriculture, Nanning 530007, China.ORCID 0000-0002-2549-3425
Zhuangzhuang QiuSchool of Public Health, Jining Medical University, Jining 272002, China.
Mengli YaoSchool of Public Health, Jining Medical University, Jining 272002, China.
Boyan JiaoDepartment of Laboratory, Jining Center for Disease Control and Prevention, Jining 272000, China.
Yu ZhouSchool of Public Health, Jining Medical University, Jining 272002, China.
Chenghua LiCollege of Marine Science, Ningbo University, Ningbo 315832, China.ORCID 0000-0003-2978-8762
Haipeng LiuCollege of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, China.
Lusheng XinSchool of Public Health, Jining Medical University, Jining 272002, China.ORCID 0000-0002-0131-7697

Funding

National Key Research and Development Program of China No. 2023YFD2403000the Ministry of Science and Technology of the People's Republic of China, Shandong Provincial University Youth Innovation Team Project 2024KJN029
6 · The paper itself

Abstract

Microbial contamination in aquatic environments poses severe threats to aquaculture sustainability, ecological balance and public health. Traditional culture-based detection methods, while standardized, are time-consuming and labor-intensive, often failing to meet the urgent need for rapid on-site monitoring required to prevent disease outbreaks and manage water quality effectively. By integrating latest research advances (2020-2025), this study reviews advances in rapid detection technologies for aquatic microorganisms, including the evolution of nucleic acid amplification strategies, with a focused comparison of the analytical sensitivity and field deployability of quantitative polymerase chain reaction (qPCR) and mainstream isothermal amplification techniques (loop-mediated isothermal amplification, LAMP; recombinase polymerase amplification, RPA). Furthermore, this study reports on the emergence of Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-associated protein (Cas) systems as next-generation diagnostic tools, highlighting their integration with microfluidic Lab-on-a-Chip (LOC) platforms to achieve attomolar sensitivity. We also consider the application of portable nanopore sequencing for real-time pathogen identification and the growing role of Artificial Intelligence (AI) in analyzing complex diagnostic datasets. Advanced molecular methods have achieved significant reductions in time consumption-from days to less than one hour-while challenges regarding sample preparation and environmental matrix inhibition remain. The future of aquatic monitoring lies in integrated, automated systems that combine the specificity of CRISPR-Cas diagnostics with the connectivity of IoT-enabled biosensors. Comparative analysis indicates that isothermal amplification methods (LAMP, RPA) coupled with CRISPR-Cas systems offer the optimal balance of sensitivity, speed, and field deployability for point-of-care aquaculture diagnostics, while qPCR/dPCR remain indispensable for quantitative regulatory applications. We propose a structured technology selection framework to guide researchers and practitioners in choosing appropriate detection modalities based on specific sensitivity, cost, throughput, and deployment requirements.

Indexed as

aquatic environmentdetection methodmicroorganismsnucleic acid level

Identifiers

PMID42075335
PMCPMC13119226

What OpenQuestion holds

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LicenceCC BY
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.