Exosomes, nano‐sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface‐enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label‐free Raman spectroscopy method's prediction ratio correlates with the ratio of HT‐1080 exosomes in the mixture. This machine learning‐assisted SERS method enables a new direction through label‐free investigation of EV preparations by differentiating cancer cell‐derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

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Since the discovery of coronavirus disease 2019 (COVID-19) in December 2019, it has been mainly diagnosed with quantitative reverse transcription polymerase chain reaction (PCR) of nasal swabs in clinics. A very sensitive and rapid detection technique using easily collected fluids such as saliva is needed for safer and more practical, precise mass testing. Here, we introduce a computationally screened gold-nanopatterned metasurface platform out of a pattern space of 2 100 combinations for strongly enhanced light–virus interaction using a genetic algorithm and apply them to investigate the presence and concentration of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In our approach, the gold metasurface with the nanopattern that provides the highest plasmonic enhancement is modified with the primary DNA aptamer for COVID-19 sensing from unprocessed saliva. A fluorescently tagged secondary aptamer was used to bind the virus that was then captured on the surface with the primary aptamer. By incorporating machine learning to identify the virus from Raman spectra, we achieved 95.2% sensitivity and specificity on 36 SARS-CoV-2 PCR-positive and 33 SARS-CoV-2 PCR-negative samples collected in the clinics. In addition, we demonstrated that our nanoplasmonic aptasensor could distinguish wild-type, Alpha, and Beta variants through the machine learning analysis of their spectra. Our results may help pave the way for effective, safe, and quantitative preventive screening and identification of variants.

The human eye and tear provide essential physiological information for the detection of ocular dysfunctions and therapy monitoring. The measurements of biomarkers in tear composition are critical for disease diagnosis and early interventions. Hence, significant efforts are dedicated to the development of functional contact lenses that can quantify tear analytes and ocular physiological condition. The combination of microfluidics and contact lens technologies offer real-time monitoring of ocular physiology and timely detection of eye disorders through wireless components. This review discusses the fundamentals of microfluidic contact lenses and their diverse applications in ophthalmic diagnostics and drug delivery. It also elucidates the strategies for the commercialization of microfluidic contact lenses to create clinical and point-of-care products.

COVID-19 is detected using reverse transcription polymerase chain reaction (RT-PCR) of nasal swabs. A very sensitive and rapid detection technique using easily-collected fluids like saliva must be developed for safe and precise mass testing. Here, we introduce a metasurface platform for direct sensing of COVID-19 from unprocessed saliva. We computationally screen gold metasurfaces out of a pattern space of 2100 combinations for strongly-enhanced light-virus interaction with machine learning and use it to investigate the presence and concentration of the SARS-CoV-2. We use machine learning to identify the virus from Raman spectra with 95.2% sensitivity and specificity on 36 PCR positive and 33 negative clinical samples and to distinguish wild-type, alpha, and beta variants. Our results could pave the way for effective, safe and quantitative preventive screening and identification of variants.