PddCas: A Polydisperse Droplet Digital CRISPR/Cas-Based Assay for the Rapid and Ultrasensitive Amplification-Free Detection of Viral DNA/RNA.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based assays have been an emerging diagnostic technology for pathogen diagnosis. In this work, we developed a polydisperse droplet digital CRISPR-Cas-based assay (PddCas) for the rapid and ultrasensitive amplification-free detection of viral DNA/RNA with minimum instruments. LbaCas12a and LbuCas13a were used for the direct detection of viral DNA and RNA, respectively. The reaction mixtures were partitioned with a common vortex mixer to generate picoliter-scale polydisperse droplets in several seconds. The limit of detection (LoD) for the target DNA and RNA is approximately 100 aM and 10 aM, respectively, which is about 3 × 104-105 fold more sensitive than corresponding bulk CRISPR assays. We applied the PddCas to successfully detect severe acute respiratory syndrome coronavirus (SARS-CoV-2) and human papillomavirus type 18 (HPV 18) in clinical samples. For the 23 HPV 18-suspected cervical epithelial cell samples and 32 nasopharyngeal swabs for SARS-CoV-2, 100% sensitivity and 100% specificity were demonstrated. The dual-gene virus detection with PddCas was also established and verified. Therefore, PddCas has potential for point-of-care application and is envisioned to be readily deployed for frequent testing as part of an integrated public health surveillance program.

Development and validation of a clinical prediction model to estimate the risk of critical patients with COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. This study was aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID-19 at admission progressing to critical patients. Patients admitted to the hospital between January 16, 2020, and March 10, 2020, were retrospectively enrolled, and they were observed for at least 14 days after admission to determine whether they developed into severe pneumonia. According to the clinical symptoms, all patients were divided into four groups: mild, normal, severe, and critical. A total of 390 patients with COVID-19 pneumonia were identified, including 212 severe patients and 178 nonsevere patients. The least absolute shrinkage and selection operator (LASSO) regression reduced the variables in the model to 6, which are age, number of comorbidities, computed tomography severity score, lymphocyte count, aspartate aminotransferase, and albumin. The area under curve of the model in the training set is 0.898, and the specificity and sensitivity were 89.7% and 75.5%. The prediction model, nomogram might be useful to access the onset of severe and critical illness among COVID-19 patients at admission, which is instructive for clinical diagnosis.