The COVID-19 pandemic has created huge damage to society and brought panic around the world. Such panic can be ascribed to the seemingly deceptive features of COVID-19: Compared to other deadly viral outbreaks, it has medium transmission and mortality rates. As a result, the severity of the causative coronavirus, SARS-CoV-2, was deeply underestimated by society at the beginning of the COVID-19 outbreak. Based on this, in this review, we define the viruses with features similar to those of SARS-CoV-2 as the Panic Zone viruses. To contain those viruses, accurate and fast diagnosis followed by effective isolation and treatment of patients are pivotal at the early stage of virus breakouts. This is especially true when there is no cure or vaccine available for a transmissible disease, which is the case for the current COVID-19 pandemic. As of July 2020, more than 100 kits for COVID-19 diagnosis on the market have been surveyed in this review, while emerging sensing techniques for SARS-CoV-2 are also discussed. It is of critical importance to rationally use these kits for efficient management and control of the Panic Zone viruses. Therefore, we discuss guidelines to select diagnostic kits at different outbreak stages of the Panic Zone viruses, SARS-CoV-2 in particular. While it is of utmost importance to use nucleic acid based detection kits with low false negativity (high sensitivity) at the early stage of an outbreak, the low false positivity (high specificity) gains importance at later stages of the outbreak. When society is set to reopen from the lockdown stage of the COVID-19 pandemic, it becomes critical to have immunoassay based kits with high specificity to identify people who can safely return to society after their recovery from SARS-CoV-2 infections. Finally, since a massive attack from a viral pandemic requires a massive defense from the whole society, we urge both government and the private sector to research and develop affordable and reliable point-of-care testing (POCT) kits, which can be used massively by the general public (and therefore called massive POCT) to contain Panic Zone viruses in the future.

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For proteins and DNA secondary structures such as G-quadruplexes and i-motifs, nanoconfinement can facilitate their folding and increase structural stabilities. However, the properties of the physiologically prevalent B-DNA duplex have not been elucidated inside the nanocavity. Using a 17-bp DNA duplex in the form of a hairpin stem, here, we probed folding and unfolding transitions of the hairpin DNA duplex inside a DNA origami nanocavity. Compared to the free solution, the DNA hairpin inside the nanocage with a 15 × 15 nm cross section showed a drastic decrease in mechanical (20 → 9 pN) and thermodynamic (25 → 6 kcal/mol) stabilities. Free energy profiles revealed that the activation energy of unzipping the hairpin DNA duplex decreased dramatically (28 → 8 kcal/mol), whereas the transition state moved closer to the unfolded state inside the nanocage. All of these indicate that nanoconfinement weakens the stability of the hairpin DNA duplex to an unexpected extent. In a DNA hairpin made of a stem that contains complementary telomeric G-quadruplex (GQ) and i-motif (iM) forming sequences, formation of the Hoogsteen base pairs underlining the GQ or iM is preferred over the Watson−Crick base pairs in the DNA hairpin. These results shed light on the behavior of DNA in nanochannels, nanopores, or nanopockets of various natural or synthetic machineries. It also elucidates an alternative pathway to populate noncanonical DNA over B-DNA in the cellular environment where the nanocavity is abundant.

In Tau protein condensates formed by the Liquid-Liquid Phase Separation (LLPS) process, liquidto-solid transitions lead to the formation of fibrils implicated in Alzheimer's disease. Here, by tracking two contacting Tau-rich droplets using a simple and nonintrusive video microscopy, we found that the halftime of the liquid-to-solid transition in the Tau condensate is affected by the Hofmeister series according to the solvation energy of anions. After dissecting functional groups of physiologically relevant small molecules using a multivariate approach, we found that charged groups facilitate the liquid-to-solid transition in a manner similar to the Hofmeister effect, whereas hydrophobic alkyl chains and aromatic rings inhibit the transition. Our results not only elucidate the driving force of the liquid-to-solid transition in Tau condensates, but also provide guidelines to design small molecules to modulate this important transition for many biological functions for the first time.

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary, resulting in less accurate results. Recently, machine-learning (ML)-assisted models have been used in image analysis. We evaluated a combination of four ML models—logistic regression, support vector machine (SVM), random forest, and artificial neural network (ANN)—as well as three image color spaces, RGB, HSV, and LAB, for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras and users for food color and enzyme inhibition assays to create training and test datasets. The prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML models and color space combinations. All models better predicted coarse-level classifications than fine-grained concentration classes. ML models using the sample color along with a reference color increased the models’ ability to predict the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration class prediction accuracy obtained for food color was 0.966 when using the ANN model and LAB color space. The accuracy for enzyme inhibition assay was 0.908 when using the SVM model and LAB color space. Appropriate models and color space combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.