The ongoing global novel coronavirus pneumonia COVID‐19 outbreak has engendered numerous cases of infection and death. COVID‐19 diagnosis relies upon nucleic acid detection; however, currently recommended methods exhibit high false‐negative rates and are unable to identify other respiratory virus infections, thereby resulting in patient misdiagnosis and impeding epidemic containment. Combining the advantages of targeted amplification and long‐read, real‐time nanopore sequencing, herein, nanopore targeted sequencing (NTS) is developed to detect SARS‐CoV‐2 and other respiratory viruses simultaneously within 6–10 h, with a limit of detection of ten standard plasmid copies per reaction. Compared with its specificity for five common respiratory viruses, the specificity of NTS for SARS‐CoV‐2 reaches 100%. Parallel testing with approved real‐time reverse transcription‐polymerase chain reaction kits for SARS‐CoV‐2 and NTS using 61 nucleic acid samples from suspected COVID‐19 cases show that NTS identifies more infected patients (22/61) as positive, while also effectively monitoring for mutated nucleic acid sequences, categorizing types of SARS‐CoV‐2, and detecting other respiratory viruses in the test sample. NTS is thus suitable for COVID‐19 diagnosis; moreover, this platform can be further extended for diagnosing other viruses and pathogens.

Background: Central lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. This study aimed to develop predictive models for CLNM by machine learning (ML) algorithms. Methods: Patients with PTC who underwent initial thyroid resection at our hospital between January 2018 and December 2019 were enrolled. A total of 22 variables, including clinical characteristics and ultrasonography (US) features, were used for conventional univariate and multivariate analysis and to construct ML-based models. A 5-fold cross validation strategy was used for validation and a feature selection approach was applied to identify risk factors. Results: The areas under the receiver operating characteristic curve (AUC) of 7 models ranged from 0.680 to 0.731. All models performed significantly better than US (AUC=0.623) in predicting CLNM (P<0.05). In decision curve, most of the models also performed better than US. The gradient boosting decision tree model with 7 variables was identified as the best model because of its best performance in both ROC (AUC=0.731) and decision curves. Based on multivariate analysis and feature selection, young age, male sex, low serum thyroid peroxidase antibody and US features such as suspected lymph nodes, microcalcification and tumor size > 1.1 cm were the most contributing predictors for CLNM. Conclusions: It is feasible to develop predictive models of CLNM in PTC patients by incorporating clinical characteristics and US features. The ML algorithm may be a useful tool for the prediction of lymph node metastasis in thyroid cancer.

24The ongoing novel coronavirus pneumonia COVID-19 outbreak in Wuhan, China, has engendered 25 numerous cases of infection and death. COVID-19 diagnosis relies upon nucleic acid detection; 26 however, current recommended methods exhibit high false-negative rates, low sensitivity, and 27 cannot identify other respiratory virus infections, thereby resulting patient misdiagnosis and 28 impeding epidemic containment. Combining the advantages of target amplification and long-read, 29 real-time nanopore sequencing, we developed nanopore target sequencing (NTS) to detect SARS-30

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