Highlights d SARS-CoV-2 genome sequencing and phylogenetic analyses identify 35 recurrent mutations d Association with 117 clinical phenotypes reveals potentially important mutations d D500-532 in Nsp1 coding region correlates with lower viral load and serum IFN-b d Viral isolates with D500-532 mutation induce lower IFN-I response in the infected cells

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global health emergency, and its gene mutation and evolution further posed uncertainty of epidemic risk. Herein, we reported a light-up CRISPR-Cas13 transcription amplification method, which enables the detection of SARS-CoV-2 and its mutated variants. Sequence specificity was ensured by both the ligation process and Cas13a/crRNA recognition, allowing us to identify viral RNA mutation. Light-up RNA aptamer allows sensitive output of amplification signals via target-activated ribonuclease activity of CRISPR-Cas13a. The RNA virus assay has been designed to detect coronavirus, SARS-CoV-2, Middle East respiratory syndrome (MERS), and SARS, as well as the influenza viruses such as, H1N1, H7N9, and H9N2. It was accommodated to sense as low as 82 copies of SARS-CoV-2. Particularly, it allowed us to strictly discriminate key mutation of the SARS-CoV-2 variant, D614G, which may induce higher epidemic and pathogenetic risk. The proposed RNA virus assays are promising for point-of-care monitoring of SARS-CoV-2 and its risking variants.

2 0 2 1 2 2 All rights reserved. No reuse allowed without permission. ABSTRACT 2 5Background: COVID-19 has been spreading globally since emergence, but the 2 6 diagnostic resources are relatively insufficient. 7Results: In order to effectively relieve the resource deficiency of diagnosing 2 8 COVID-19, we developed a machine learning-based diagnosis model on basis of 2 9 laboratory examinations indicators from a total of 620 samples, and subsequently 3 0implemented it as a COVID-19 diagnosis aid APP to facilitate promotion. 1Conclusions: External validation showed satisfiable model prediction performance 3 2 (i.e., the positive predictive value and negative predictive value was 86.35% and 3 3 84.62%, respectively), which guarantees the promising use of this tool for extensive 3 4 screening.3 5

Background Tuberculosis (TB) is difficult to diagnose under complex clinical conditions as electronic health records (EHRs) are often inadequate in making an affirmative diagnosis. As exosomal miRNAs emerged as promising biomarkers, we investigated the potential of using exosomal miRNAs and EHRs in TB diagnosis.MethodsA total of 370 individuals, including pulmonary tuberculosis (PTB), tuberculous meningitis (TBM), non-TB disease controls and healthy state controls, were enrolled. Exosomal miRNAs were profiled in the exploratory cohort using microarray and miRNA candidates were selected in the selection cohort using qRT-PCR. EHRs and follow-up information of the patients were collected accordingly. miRNAs and EHRs were used to develop diagnostic models for PTB and TBM in the selection cohort with the Support Vector Machine (SVM) algorithm. These models were further evaluated in an independent testing cohort.FindingsSix exosomal miRNAs (miR-20a, miR-20b, miR-26a, miR-106a, miR-191, miR-486) were differentially expressed in the TB patients. Three SVM models, "EHR+miRNA", "miRNA only" and "EHR only" were compared, and "EHR + miRNA" model achieved the highest diagnostic efficacy, with an AUC up to 0.97 (95% CI 0.80–0.99) in TBM and 0.97 (0.87–0.99) in PTB, respectively. However, "EHR only" model only showed an AUC of 0.67 (0.46–0.83) in TBM. After 2-month anti-tuberculosis therapy, overexpressed miRNAs presented a decreased expression trend (p= 4.80 × 10−5).InterpretationOur results showed that the combination of exosomal miRNAs and EHRs could potentially improve clinical diagnosis of TBM and PTB.FundFunds for the Central Universities, the National Natural Science Foundation of China.

Author order was determined both alphabetically and in order of increasing seniority.