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Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control
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DOI：10.1002/wnan.70050.png)
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Abstract
En 中文
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.
Keywords:
biosensors
machine learning
microbial diagnostics
nanotechnology
zoonotic pathogens
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