ReviewWiley interdisciplinary reviews. Nanomedicine and nanobiotechnology
Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.
Review in Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Phenotype-Guided Nanotherapeutic Strategies for Carbapenem-ResistantPharmaceutics · 2026Review
- Nanomaterial-nucleic acid probe synergy: accelerating rapid pathogen detection and antimicrobial susceptibility testing in bloodstream infections.Folia microbiologica · 2026Review
- Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Zoonotic diseases pose significant global health threats, with microbial pathogens, including bacteria, viruses, fungi, and protozoa, responsible for severe outbreaks. The rapid identification and control of zoonotic pathogens remain a major challenge due to their complex transmission dynamics and environmental persistence. Recent advances in molecular microbiology, nanotechnology, and artificial intelligence (AI) have revolutionized diagnostic and therapeutic strategies, enhancing the detection, monitoring, and prevention of diseases caused by pathogens. In machine learning (ML), it is possible to predict outbreaks and classify pathogens with high precision using genomic, proteomics, and epidemiological data, which can be analyzed with machine learning methods. Molecular-level detection is possible with nanotechnology-based biosensors, enabling rapid diagnosis even in areas with limited resources. Machine learning-driven computational models and nanotechnology-based detection tools can drive further advancements in microbial diagnostics, zoonotic disease surveillance, and host-pathogen interactions. Bioinformatics will be discussed along with new trends in microbial resistance and molecular mechanisms underlying pathogen identification in relation to zoonotic spillover events. By combining artificial intelligence with nanoscale biosensors, microbiology can develop more effective diagnostic platforms, real-time surveillance tools, and targeted antimicrobials. The standardization of data, the elimination of biosafety concerns, and the development of regulatory frameworks are all essential steps in advancing this cutting-edge approach to controlling zoonotic disease. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies.
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.