ReviewSustainable microbiology2024
Antimicrobial resistance detection methods in water environments: a scoping review.
Review in Sustainable microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Antimicrobial resistance in wastewater-impacted coastal waters: implications for environmental surveillance and public health.Microbiology (Reading, England) · 2026Article
- Effects of PVA/TiOMembranes · 2026Article
- From Host-Derived Pressures to the Environmental Anti-Antimicrobial Peptides Resistome: Mechanisms, Reservoirs and Implications for Therapeutic Peptide Design.Marine drugs · 2026Review
- Advances in the detection of antimicrobial resistance in aquatic environments: a methodological perspective.Biology methods & protocols · 2026Review
- Microbial innovations for sustainable water treatment.Sustainable microbiology · 2026Article
- A Practical Framework for Environmental Antibiotic Resistance Monitoring in Freshwater Ecosystems.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) in water environments poses a significant threat to public health, ecosystem stability, and the effectiveness of antimicrobial treatments. This review aims to provide a comprehensive overview of the methods used to detect AMR in various water environments. A literature search was conducted following the PRISMA guidelines. Original articles published in English relating to AMR in water environments were included. Reviews, protocols, and abstracts were excluded. A total of 115 publications were selected for full-text evaluation. Overall, river water samples were the most commonly assessed samples across all of the reviewed studies (49/115 studies, 42%). The top 3 countries investigating AMR genes in water samples were the USA (19 studies, 17%), China (11 studies, 10%), and Brazil (10 studies, 9%). The review revealed that polymerase chain reaction and metagenomic methods are increasingly preferred for their high sensitivity, specificity, and comprehensive detection capabilities, appearing in 65/115 (57%) and 31/115 (27%) studies, respectively. Despite higher costs and technical complexity, these methods provide valuable insights into the resistome of water environments. Culture-dependent methods, while most cost effective and straightforward, are limited by their time-consuming nature and inability to detect non-viable resistant organisms, reducing their effectiveness in comprehensive AMR surveillance. The review addresses the challenges and limitations of current detection methods and proposes directions for future research to develop more robust, cost-effective, and user-friendly detection methods. The review highlights the urgent need for integrated approaches to monitor and mitigate AMR in water environments, ensuring better public health and environmental protection.
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What OpenQuestion holds
Registered trials
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.