CRISPR/Cas12a-Enabled Multiplex Biosensing Strategy Via an Affordable and Visual Nylon Membrane Readout.

Most multiplex nucleic acids detection methods require numerous reagents and high-priced instruments. The emerging clustered regularly interspaced short palindromic repeats (CRISPR)/Cas has been regarded as a promising point-of-care (POC) strategy for nucleic acids detection. However, how to achieve CRISPR/Cas multiplex biosensing remains a challenge. Here, an affordable means termed CRISPR-RDB (CRISPR-based reverse dot blot) for multiplex target detection in parallel, which possesses the advantages of high sensitivity and specificity, cost-effectiveness, instrument-free, ease to use, and visualization is reported. CRISPR-RDB integrates the trans-cleavage activity of CRISPR-Cas12a with a commercial RDB technique. It utilizes different Cas12a-crRNA complexes to separately identify multiple targets in one sample and converts targeted information into colorimetric signals on a piece of accessible nylon membrane that attaches corresponding specific-oligonucleotide probes. It has demonstrated that the versatility of CRISPR-RDB by constructing a four-channel system to simultaneously detect influenza A, influenza B, respiratory syncytial virus, and SARS-CoV-2. With a simple modification of crRNAs, the CRISPR-RDB can be modified to detect human papillomavirus, saving two-thirds of the time compared to a commercial PCR-RDB kit. Further, a user-friendly microchip system for convenient use, as well as a smartphone app for signal interpretation, is engineered. CRISPR-RDB represents a desirable option for multiplexed biosensing and on-site diagnosis.

SlowFast GCN Network for Quantification of Parkinsonian Gait Using 2D Videos

Parkinson's disease(PD) is a progressive neu-rodegenerative disease defined by clinical syndrome including bradykinesia, tremor and postural instability. The PD-related disability and impairment are usually monitored by clinicals using the MDS-UPDRS scale. However, due to COVID-19, it became much harder for the patients to reach hospitals and obtain necessary assessment and treatment. Nowadays, 2D videos are easily accessible and can be a promising so-lution for on-site and remote diagnosis of movement disorder. Inspired by the frequency-based video processing mechanism of human visual system, we propose a video-based SlowFast GCN network to quantify the gait disorder. The model consists of two parts: the fast pathway and the slow pathway. The former detects characteristics such as tremor and bilateral asymmetry, while the latter extracts characteristics such as bradykinesia and freezing of gait. Furthermore, in order to investigate the influence of age on the model performance, an aged control group and a young control group were set up for verification. The proposed model was evaluated on a video dataset collected from 68 participants. We achieved a balanced accuracy of 87.5% and precision of 87.9%, which outperformed existing competing methods. When replacing the young healthy controls with the same number of older controls, the balanced accuracy and precision were decreased by 10.4% and 9.7%, which indicates that age has a significant effect on the model perfomance. © 2022 IEEE.