Identifying central nervous system (CNS) infection is necessary for clinical care. Rapid and accurate diagnosis is essential for timely therapy and better outcome. However, the achieved effort targeting the issue is minimal. On the one hand, it is due to the unsatisfactory performance of traditional pathogen detection approaches. On the other hand, the features of host and pathogen in CNS infection are rarely revealed currently. Therefore, efficient diagnosis and discrimination remain challenging. Establishing a novel diagnostic strategy and understanding should be prioritized for CNS infection.
With the development of emerging technologies in recent years, more attention focused on this field. For example, the increased sensitivity and specificity of PCR or sequencing allow accurate identification. The improvement in omics, including proteomics and transcriptomics, could help us monitor the progress of infection detailly and dynamically. The occurrence of artificial intelligence could help us make the best use of test data. Thus, this topic aims to include excellent studies targeting the diagnostics and pathogenesis of CNS infection to promote the management of CNS infection.
The detailed scopes of the topic are as the following:
- Approaches and tools for identifying pathogens resulting in CNS infection
- Differential tests or methods for discriminating various infection types, including bacteria, virus, fungus, and Mycobacterium tuberculosis
- Emerging investigations targeting the host or pathogen characteristics during CNS infection. These investigations include transcriptomics, proteomics, metabolomics, single-cell-based sequencing, spatial transcriptomics, and other exploration involved in omics
- The application of technologies involved in artificial intelligence for diagnosis and prognosis. These technologies include machine learning, deep learning, image-based neural network, and other concepts involved in algorithms
Identifying central nervous system (CNS) infection is necessary for clinical care. Rapid and accurate diagnosis is essential for timely therapy and better outcome. However, the achieved effort targeting the issue is minimal. On the one hand, it is due to the unsatisfactory performance of traditional pathogen detection approaches. On the other hand, the features of host and pathogen in CNS infection are rarely revealed currently. Therefore, efficient diagnosis and discrimination remain challenging. Establishing a novel diagnostic strategy and understanding should be prioritized for CNS infection.
With the development of emerging technologies in recent years, more attention focused on this field. For example, the increased sensitivity and specificity of PCR or sequencing allow accurate identification. The improvement in omics, including proteomics and transcriptomics, could help us monitor the progress of infection detailly and dynamically. The occurrence of artificial intelligence could help us make the best use of test data. Thus, this topic aims to include excellent studies targeting the diagnostics and pathogenesis of CNS infection to promote the management of CNS infection.
The detailed scopes of the topic are as the following:
- Approaches and tools for identifying pathogens resulting in CNS infection
- Differential tests or methods for discriminating various infection types, including bacteria, virus, fungus, and Mycobacterium tuberculosis
- Emerging investigations targeting the host or pathogen characteristics during CNS infection. These investigations include transcriptomics, proteomics, metabolomics, single-cell-based sequencing, spatial transcriptomics, and other exploration involved in omics
- The application of technologies involved in artificial intelligence for diagnosis and prognosis. These technologies include machine learning, deep learning, image-based neural network, and other concepts involved in algorithms
