Airborne pathogens affect both humans and animals and are often highly and rapidly transmittable. Many problematic airborne pathogens, both viral (influenza A/H1N1, Rubella, and avian influenza/H5N1) and bacterial (Mycobacterium tuberculosis, Streptococcus pneumoniae, and Bacillus anthracis), have huge impacts on health care and agricultural applications, and can potentially be used as bioterrorism agents. Many different laboratory-based methods have been introduced and are currently being used. However, such detection is generally limited by sample collection, including nasal swabs and blood analysis. Direct identification from air (specifically, aerosol samples) would be ideal, but such detection has not been very successful due to the difficulty in sample collection and the extremely low pathogen concentration found in aerosol samples. In this review, we will discuss the portable biosensors and/or micro total analysis systems (µTAS) that can be used for monitoring such airborne pathogens, similar to smoke detectors. Current laboratory-based methods will be reviewed, and possible solutions to convert these lab-based methods into µTAS biosensors will be discussed.

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Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

Saliva specimens have drawn interest for diagnosing respiratory viral infections due to their ease of collection and decreased risk to healthcare providers. However, rapid and sensitive immunoassays have not yet been satisfactorily demonstrated for such specimens due to their viscosity and low viral loads. Using paper microfluidic chips and a smartphone-based fluorescence microscope, we developed a highly sensitive, low-cost immunofluorescence particulometric SARS-CoV-2 assay from clinical saline gargle samples. We demonstrated the limit of detection of 10 ag/µL. With easy-to-collect saline gargle samples, our clinical sensitivity, specificity, and accuracy were 100%, 86%, and 93%, respectively, for n = 27 human subjects with n = 13 RT-qPCR positives.