The global COVID-19 pandemic has oversaturated many intensive care units to the point of collapse, leading to enormous spikes in death counts. Before critical care becomes a necessity, identifying patients who are likely to become critically ill and providing prompt treatment is a strategy to avoid ICU oversaturation. There is a consensus that a hyperinflammatory syndrome or a "cytokine storm" is responsible for poor outcomes in COVID-19. Measuring cytokine levels at the point of care is required in order to better understand this process. In this Perspective, we summarize the main events behind the cytokine storm in COVID-19 as well as current experimental treatments. We advocate for a new biosensor-enabled paradigm to personalize the management of COVID-19 and stratify patients. Biosensorguided dosing and timing of immunomodulatory therapies could maximize the benefits of these anti-inflammatory treatments while minimizing deleterious effects. Biosensors will also be essential in order to detect complications such as coinfections and sepsis, which are common in immunosuppressed patients. Finally, we propose the ideal features of these biosensors using some prototypes from the recent literature as examples. Multisensors, lateral flow tests, mobile biosensors, and wearable biosensors are seen as key players for precision medicine in COVID-19.

Detecting small variations in the levels of IL-6 is crucial for the early diagnosis of sepsis.

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Colorimetric tests are becoming increasingly popular in point-of-need analyses due to the possibility of detecting the signal with the naked eye, which eliminates the utilization of bulky and costly instruments only available in laboratories. However, colorimetric tests may be interpreted incorrectly by nonspecialists due to disparities in color perception or a lack of training. Here we solve this issue with a method that not only detects colorimetric signals but also interprets them so that the test outcome is understandable for anyone. It consists of an augmented reality (AR) app that uses a camera to detect the colored signals generated by a nanoparticle-based immunoassay, and that yields a warning symbol or message when the concentration of analyte is higher than a certain threshold. The proposed method detected the model analyte mouse IgG with a limit of detection of 0.3 μg mL, which was comparable to the limit of detection afforded by classical densitometry performed with a nonportable device. When adapted to the detection of E. coli, the app always yielded a "hazard" warning symbol when the concentration of E. coli in the sample was above the infective dose (10 cfu mL or higher). The proposed method could help nonspecialists make a decision about drinking from a potentially contaminated water source by yielding an unambiguous message that is easily understood by anyone. The widespread availability of smartphones along with the inexpensive paper test that requires no enzymes to generate the signal makes the proposed assay promising for analyses in remote locations and developing countries.

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Identifying the pathogen responsible for an infection is a requirement in order to personalize antimicrobial treatments. Detecting bacterial enzymes, such as proteases, lipases, and oxidoreductases, is a winning approach for detecting pathogens at the point of care. In this Article, a new method for detecting urease-producing bacteria rapidly and at ultralow concentrations is reported. In this method, longsome bacteriological culture steps are substituted for a 10 min capture procedure with positively charged magnetic beads. The presence of urease-positive bacteria on the particles is then queried with a plasmonic signal generation step that generates blue-or red-colored nanoparticle suspensions upon addition of the enzyme substrate. These colorimetric signals, which can be easily identified by eye, are generated by the NH 3 -dependent assembly of gold nanoparticles in the presence of bovine serum albumin (BSA). The proposed method can detect Proteus mirabilis with a limit of detection of 10 1 cells mL −1 , with a total assay time of 40 min, even in the presence of a large excess of urease-negative bacteria (Pseudomonas aeruginosa). Furthermore, it does not require bulky equipment, and it can detect P. mirabilis at clinically relevant concentrations within minutes, making it suitable for detecting urease-positive pathogens at the point of care.

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