Integrated device for plasma separation and nucleic acid extraction from whole blood toward point-of-care detection of bloodborne pathogens.

Sample preparation presents a major challenge in point-of-care (POC) diagnostic assays, including ones requiring whole blood as the starting specimen. This study presents an integrated sample preparation device - which we call PRECISE - that performs both plasma separation and nucleic acid extraction, enabling streamlined sample preparation from whole blood requiring only a commercially available blood collection tool and a syringe, and no other external equipment or electricity. Plasma separation is performed using a dual-membrane filter (which filters out blood components while limiting membrane clogging) integrated into the cartridge, and nucleic acid extraction is performed by users moving magnets (to mix the samples, and along a guided track).

Multiplexed reverse-transcriptase quantitative polymerase chain reaction using plasmonic nanoparticles for point-of-care COVID-19 diagnosis.

Quantitative polymerase chain reaction (qPCR) offers the capabilities of real-time monitoring of amplified products, fast detection, and quantitation of infectious units, but poses technical hurdles for point-of-care miniaturization compared with end-point polymerase chain reaction. Here we demonstrate plasmonic thermocycling, in which rapid heating of the solution is achieved via infrared excitation of nanoparticles, successfully performing reverse-transcriptase qPCR (RT-qPCR) in a reaction vessel containing polymerase chain reaction chemistry, fluorescent probes and plasmonic nanoparticles. The method could rapidly detect SARS-CoV-2 RNA from human saliva and nasal specimens with 100% sensitivity and 100% specificity, as well as two distinct SARS-CoV-2 variants.

Rapid video-based deep learning of cognate versus non-cognate T cell-dendritic cell interactions.

Identification of cognate interactions between antigen-specific T cells and dendritic cells (DCs) is essential to understanding immunity and tolerance, and for developing therapies for cancer and autoimmune diseases. Conventional techniques for selecting antigen-specific T cells are time-consuming and limited to pre-defined antigenic peptide sequences. Here, we demonstrate the ability to use deep learning to rapidly classify videos of antigen-specific CD8 T cells.