About this Research Topic

Background

Infectious diseases caused by bacterial pathogens are important public issues. Meanwhile, the overuse of antibiotics in recent years has resulted in the emergence of many multidrug-resistant bacterial pathogens such as Methicillin-Resistant Staphylococcus aureus (MRSA) and Carbapenem-resistant Klebsiella pneumoniae (CRKP), which have been widely encountered in clinical settings and lead to serious consequences. Therefore, accurate identification of bacteria pathogens and profiling of antibiotic resistance are urgently needed to facilitate the rapid diagnosis of bacterial infections and precise treatment of infectious diseases. Although there are many conventional tests and molecular methods developed for the in vitro diagnostics of bacterial infection that are widely used in clinical laboratories, they suffer a variety of limitations. Therefore, novel methods for bacterial diagnosis are urgently needed.

Rapid and reliable detection of pathogenic microbes at an early stage is essential for infectious disease control and treatment, which is also highly important for public health. However, most traditional approaches for microbial pathogen identification are time-consuming and labor-intensive, which may cause physicians to make untimely decisions and administer inappropriate treatment based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of microbial detection and identification in clinical settings. As a state-of-the-art analytical technique, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate identification of microbial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential by incorporating both classical metallic nanoparticles (silver, gold, copper) and many novel and chemically-modified nanomaterials. In particular, SERS is a non-destructive chemical analysis based on interactions between the laser light and chemical bonds within samples, which could generate detailed characteristic fingerprinting spectra for (specific) microbes and achieve rapid and accurate recognition of microbial pathogens. In addition, innovative SERS method combined with lateral flow assay (SERS-LFA) has also been developed for the convenient and accurate detection of microbial pathogens, e.g., SARS-CoV-2, which shows that SERS has great capacity for in-field detection of microbial pathogens. Due to the complexity of the raw SERS spectral data, traditional statistical methods are not sufficient for data analysis and pattern recognition, which hinders SERS application in the field of infectious diseases. With the assistance of advanced computational methods like machine learning algorithms, it would be possible for the promising technique to overcome current challenges and gradually realize its real-world applications in clinical laboratories for the detection of microbial pathogens, profiling of bacterial antibiotic resistance, and discrimination of microbial virulence phenotypes. Taken together, this research topic welcomes investigators to contribute original studies, systematic reviews, methodology, perspectives and opinions within the scope of application of Raman spectroscopy in the detection of microbial pathogens and diagnosis of infectious diseases from a variety of experimental and computational aspects so as to facilitate the real-world application of this important technique.

The types of manuscripts in this research topic include original research articles, reviews and mini-reviews, methods, perspectives, and opinions.
Topics of interest are in particular:
1. Novel methods, nanomaterials, and devices developed for the application of Raman spectroscopy in microbial pathogen detection
2. Raman spectroscopy in the detection and identification of microbial pathogens e.g., viruses, bacteria, fungi, etc.
3. Raman spectroscopy in the profiling of microbial antibiotic resistance and virulence phenotypes
4. Raman spectroscopy in the direct analysis of clinical samples with microbial infections
5. Novel computational methods and algorithms for Raman spectral studies
6. Novel Raman spectral databases for microbial pathogens e.g., viruses, bacteria, and fungi, etc.

Keywords: Raman spectroscopy, Machine learning algorithms, Microbial pathogen, Infectious Disease, SERS, Nanoparticles

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