ReviewEnvironmental monitoring and assessment2025
Recent developments in waterborne pathogen detection technologies.
Review in Environmental monitoring and assessment, 2025. 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.
- Graphene Oxide (GO) and Gold Nanoparticles (AuNP) Facilitated Electrochemical Biosensing for Lung Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Progress of Rapid Detection Technology for Aquatic Microorganisms: A Comprehensive Review.Microorganisms · 2026Review
- Flexible polymeric materials and wearable biosensors for smart diabetes mellitus diagnostics and monitoring.iScience · 2026Review
- Multispectral Infrared Colony Phenotyping for High-Throughput Microbiological Control of Waters.Journal of biophotonics · 2026Article
- Recent Developments onFoods (Basel, Switzerland) · 2025Review
- Mapping total microbial communities and waterborne pathogens in household drinking water in China by citizen science and metabarcoding.Frontiers in microbiology · 2025Article
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
Waterborne pathogens find their way into water bodies through contamination of fecal discharge, stormwater run-offs, agriculture and industrial activities, and poor water infrastructure. These organisms are responsible for causing diarrheal, gastroenteritis, cholera, and typhoid diseases which raise an alarming sense on public human health due to the high mortality rate, especially in children. Several studies have indicated that these waterborne diseases can be managed by monitoring pathogens in water using traditional culture-based and molecular techniques. However, these methods have shown several setbacks such as the longer duration for detection and the inability to detect pathogens at low concentrations. Effective management of these diseases requires rapid, sensitive, highly selective, fast, and efficient economic methods to monitor pathogens in water. Since the creation of biosensors, these tools have been applied and shown the ability to detect pathogens at low concentrations. The highlights of biosensor systems are that they are fast, portable, easy to use, highly sensitive, and specific. The capabilities of biosensors have given these tools exposure to be widely applied in detecting pharmaceutical pollutants, pesticides, toxins, residues of detergents, and cosmetics from household activities in soil and water. With such difficulties faced for detecting waterborne pathogens, this review evaluates the effectiveness of technologies for waterborne pathogens detection and their drawbacks. It further highlights biosensors as the current reliable method available for detecting pathogens in water and its future capabilities in sustaining safe potable water.
Indexed as
Identifiers
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.