Artificial Intelligence and mNGS in Pathogenic Microorganism Research, Volume II

This is the second edition of this Research Topic, the first volume can be browsed here.Pathogenic microorganisms include any microorganisms that cause disease in humans or animals, including viruses, parasites, bacteria, fungi, Rickettsia, Mycoplasma, and Chlamydia. They may be transmitted through air, body fluids, food, water, and other routes, often causing major public health concerns and economic burden. Rapid and accurate identification of pathogenic microorganisms is therefore essential for the effective management of infectious diseases. Increasing evidence shows that artificial intelligence has strong potential in outbreak risk assessment, pathogen detection, disease diagnosis, prognosis prediction, and clinical decision support. At the same time, metagenomic next-generation sequencing (mNGS) addresses several limitations of conventional diagnostic assays and has emerged as a promising approach for unbiased pathogen detection and precision medicine.Pathogenic microorganisms remain a major threat to human health and socioeconomic development worldwide. Effective management of these infections depends on the rapid and precise identification of causative agents. Conventional culture-based methods remain the gold standard for pathogen identification, but they are often time-consuming and may fail to detect certain species. Immunological diagnostics are widely used in clinical practice, but may suffer from limited sensitivity, while PCR-based assays can face challenges related to contamination risk and restricted target specificity. In contrast, mNGS offers a powerful and increasingly accessible tool for the detection of clinically unknown or complex infections. Artificial intelligence has also become an important driver of innovation in medicine, with applications in pathogen detection, disease classification, prognosis prediction, and treatment monitoring. Together, AI and mNGS are opening new opportunities for improving the study, diagnosis, and management of infectious diseases.This Research Topic aims to collect the latest advances in pathogenic microorganism research enabled by artificial intelligence and mNGS, with particular interest in studies that improve pathogen detection, clinical interpretation, predictive modelling, and systems-level understanding of infectious processes. By bringing together methodological, computational, and translational perspectives, this Topic may provide useful insights for the future prevention, diagnosis, and management of pathogenic microorganism infections.Pathogenic microorganisms remain a great threat to human health and socioeconomic development and have been accepted as a major global public health concern. Management of pathogenic microorganism infections depends on rapid and precise identification of pathogens. Currently, the conventional culture method remains the gold standard for the identification of pathogenic microorganisms, which is time-consuming and fails in species identification. Immunological diagnostics are commonly used in clinical practices but suffer from a problem of low sensitivity, while PCR assay suffers from problems of high likelihood of pollution and low specificity. Metagenomic next-generation sequencing (mNGS) is a strong tool in precision medicine, which provides a rapid, precise tool for the identification of clinically unknown pathogenic microorganisms. Artificial intelligence is a breakthrough in human society and has been widely applied in modern medicine, including pathogen detection, disease diagnosis, subclassification, prediction of prognosis, and monitoring response to therapy. This Research Topic aims to summarize the latest findings on pathogenic microorganism research as revealed by artificial intelligence and mNGS, which may provide insights into future containment of pathogenic microorganism infections in clinical practices. We welcome the following subjects:• Role of mNGS in the identification of pathogenic microorganisms• mNGS for the precise detection of rare or complex infections• Artificial intelligence for assisting pathogen detection and classification• Artificial intelligence for predicting the spread of pathogenic microorganisms• Artificial intelligence for monitoring treatment response in pathogen-associated infections• Artificial intelligence in vaccine or drug development for pathogenic microorganisms• Artificial intelligence for prognosis prediction in patients with pathogenic microorganism infections Please note that Systems Microbiology does not consider descriptive studies solely based on amplicon profiles (e.g. 16S rRNA, 18S rRNA, ITS, and other marker genes), unless they are accompanied by a clear hypothesis and experimentation, and provide insight into the microbiological system or process being studied. Topic editors: Wei Wang, Nguyen Quoc Khanh Le, Esmaeil Ebrahimie Source: https://www.frontiersin.org/research-topics/80529/artificial-intelligence-and-mngs-in-pathogenic-microorganism-research-volume-ii