ReviewMikrochimica acta2026
Biosensing technologies for foodborne pathogen detection and healthcare: principles, emerging materials, and intelligent platforms.
Review in Mikrochimica acta, 2026. 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.
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Recent Progress in Nanoparticle-Based Biosensors for MonitoringBiosensors · 2026Review
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Foodborne Pathogen Detection from Sensing to Food Safety Systems: A Systematic Review.Foods (Basel, Switzerland) · 2026Review
- Sustainable Nanotechnology Approaches for Rapid Food Contaminant Detection and Future Food Safety Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Advances in rapid detection technologies for zoonotic diseases: a one health-oriented review.Frontiers in cellular and infection microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Foodborne pathogens such as Escherichia coli (E. Coli), Salmonella, and Listeria monocytogenes continue to pose a major potential threat to global public health and therefore rapid, accurate, and field-deployable detection methods are still extremely desirable. This review describes cutting-edge examples of advanced biosensing platforms for the strategy of detecting these priority pathogens, focusing on clinical detection and highlighting electrochemical, optical, and microfluidic sensing modalities. This has been enabled by recent advances in functional nanomaterials, molecular recognition elements (including aptamers and nanozymes), and surface engineering strategies rendering sensors much ‘smarter’/improved in terms of sensitivity, specificity, and behaviour towards complex food matrices. However blending these biosensors with artificial intelligence (AI) and Machine Learning (ML) enabled intelligent pattern recognition, real-time analytics, and multiplexing at high-speed, turning traditional detection systems into smart diagnostic devices. We critically review recent case studies in light of biosensor design, signal transduction mechanisms, models of AI, performance validation, and applicability in different food environments. The principal challenges are identified which include matrix interference, instability of biorecognition elements, limitations in scalability, and the need for regulatory standardization. We discuss these with associated mitigation strategies that are technically sound, including ratiometric sensing, microfluidic pre-treatment techniques, explainable AI, and printable electronics. Forward-looking, we discuss biosensors enabled by being self-powered, biosensor hubs with modular pathogen panels, blockchain incorporation, and standardized validation pipelines. This review offers a prospective view toward enabling intelligent, robust, and regulation-ready biosensing platforms for next-generation food safety monitoring through the bridging of technological innovations with practical implementation.
Indexed as
Identifiers
What Socratic 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 Socratic graph.