The early detection of low-concentrations of bacteria and rapid antibiotic susceptibility testing (AST) are of great importance for the diagnosis and treatment of infectious diseases, as well as ensuring microbial safety in food, water, and soil. As antibiotic drug-resistant bacterial infections continue to pose significant challenges globally, there is a growing demand for simple, rapid, sensitive, portable and reliable new analytical methods to provide rapid and accurate results. Advances in nano/micro analytical methods including nanoparticle-driven analytical systems, surface plasmon resonance (SPR), surface-enhanced Raman spectroscopy (SERS), fluorescence spectroscopy/microscopy, DNA-microarrays, aptamer-sensors, microfluidic lab-on-a-chip platforms, label-free single-cell resolution imaging and analysis techniques, offers promising solutions for bacteria detection and AST. Moreover, recent advances in artificial intelligence (AI) have led to enhanced high-throughput testing, image analysis, label-free bacteria sorting, and data analysis, which holds the potential to improve the analytical performance of the current techniques.
The objective of this article collection is to collect original research articles or review papers in the field of emerging nano/micro-analytical methods for bacteria detection and AST. These articles will include the progress in nanoparticle-driven analytical methods highlighting the advantages of functional nanoparticles such as magnetic nanoparticles, gold nanomaterials, quantum dots, and bio-recognition surfaces including aptamers, antibodies, enzymes, or bacteriophages with their specific and highly-sensitive detection features. The benefits of wearable or portable biosensors for point-of-care testing and microfluidic-assisted imaging approaches will be discussed, along with the AI-assisted object detection and sorting methods for rapid and accurate bacteria identification. These studies are expected to address challenges in the detection of low-concentrations of bacteria as well as rapid identification and antibiotic susceptibility testing of bacteria in diverse settings.
This Research Topic will accept original research and review articles that cover emerging bacteria detection and antibiotic susceptibility testing methods including, but not limited to:
1. Nanoparticle-driven analytical methods such as surface plasmon resonance, surface-enhanced Raman spectroscopy, and fluorescence spectroscopy.
2. Point-of-care testing platforms such as paper-based microfluidics, lab-on-a-chip devices, mobile-phone-based sensors, and wearable biosensors.
3. Bacteria visualization and characterization methods including scanning electron microscopy (SEM), atomic force microscopy (AFM), and single-cell microfluidics-assisted imaging approaches.
4. Computer image analysis and artificial intelligence (AI)-assisted label-free bacteria sorting
Keywords:
Nanoparticles, microfluidics, analytical methods, bacteria detection, antibiotic resistance
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
The early detection of low-concentrations of bacteria and rapid antibiotic susceptibility testing (AST) are of great importance for the diagnosis and treatment of infectious diseases, as well as ensuring microbial safety in food, water, and soil. As antibiotic drug-resistant bacterial infections continue to pose significant challenges globally, there is a growing demand for simple, rapid, sensitive, portable and reliable new analytical methods to provide rapid and accurate results. Advances in nano/micro analytical methods including nanoparticle-driven analytical systems, surface plasmon resonance (SPR), surface-enhanced Raman spectroscopy (SERS), fluorescence spectroscopy/microscopy, DNA-microarrays, aptamer-sensors, microfluidic lab-on-a-chip platforms, label-free single-cell resolution imaging and analysis techniques, offers promising solutions for bacteria detection and AST. Moreover, recent advances in artificial intelligence (AI) have led to enhanced high-throughput testing, image analysis, label-free bacteria sorting, and data analysis, which holds the potential to improve the analytical performance of the current techniques.
The objective of this article collection is to collect original research articles or review papers in the field of emerging nano/micro-analytical methods for bacteria detection and AST. These articles will include the progress in nanoparticle-driven analytical methods highlighting the advantages of functional nanoparticles such as magnetic nanoparticles, gold nanomaterials, quantum dots, and bio-recognition surfaces including aptamers, antibodies, enzymes, or bacteriophages with their specific and highly-sensitive detection features. The benefits of wearable or portable biosensors for point-of-care testing and microfluidic-assisted imaging approaches will be discussed, along with the AI-assisted object detection and sorting methods for rapid and accurate bacteria identification. These studies are expected to address challenges in the detection of low-concentrations of bacteria as well as rapid identification and antibiotic susceptibility testing of bacteria in diverse settings.
This Research Topic will accept original research and review articles that cover emerging bacteria detection and antibiotic susceptibility testing methods including, but not limited to:
1. Nanoparticle-driven analytical methods such as surface plasmon resonance, surface-enhanced Raman spectroscopy, and fluorescence spectroscopy.
2. Point-of-care testing platforms such as paper-based microfluidics, lab-on-a-chip devices, mobile-phone-based sensors, and wearable biosensors.
3. Bacteria visualization and characterization methods including scanning electron microscopy (SEM), atomic force microscopy (AFM), and single-cell microfluidics-assisted imaging approaches.
4. Computer image analysis and artificial intelligence (AI)-assisted label-free bacteria sorting
Keywords:
Nanoparticles, microfluidics, analytical methods, bacteria detection, antibiotic resistance
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.