A novel linear displacement isothermal amplification with strand displacement probes (LDIA-SD) in a pocket-size device for point-of-care testing of infectious diseases.

Nucleic acid amplification is crucial for disease diagnosis, especially lethal infectious diseases such as COVID-19. Compared with PCR, isothermal amplification methods are advantageous for point-of-care testing (POCT). However, complicated primer design limits their application in detecting some short targets or sequences with abnormal GC content. Herein, we developed a novel linear displacement isothermal amplification (LDIA) method using two pairs of conventional primers and Bacillus stearothermophilus (Bst) DNA polymerase, and reactions could be accelerated by adding an extra primer. Pseudorabies virus gE (high GC content) and Salmonella fimW (low GC content) genes were used to evaluate the LDIA assay. Using strand displacement (SD) probes, a LDIA-SD method was developed to realize probe-based specific detection. Additionally, we incorporated a nucleic acid-free extraction step and a pocket-sized device to realize POCT applications of the LDIA-SD method. The LDIA-SD method has advantages including facile primer design, high sensitivity and specificity, and applicability for POCT, especially for amplification of complex sequences and detection of infectious diseases.

Virome in the cloaca of wild and breeding birds revealed a diversity of significant viruses.

BACKGROUND: Wild birds may harbor and transmit viruses that are potentially pathogenic to humans, domestic animals, and other wildlife. RESULTS: Using the viral metagenomic approach, we investigated the virome of cloacal swab specimens collected from 3182 birds (the majority of them wild species) consisting of > 87 different species in 10 different orders within the Aves classes. The virus diversity in wild birds was higher than that in breeding birds. We acquired 707 viral genomes from 18 defined families and 4 unclassified virus groups, with 265 virus genomes sharing < 60% protein sequence identities with their best matches in GenBank comprising new virus families, genera, or species. RNA viruses containing the conserved RdRp domain with no phylogenetic affinity to currently defined virus families existed in different bird species. Genomes of the astrovirus, picornavirus, coronavirus, calicivirus, parvovirus, circovirus, retrovirus, and adenovirus families which include known avian pathogens were fully characterized. Putative cross-species transmissions were observed with viruses in wild birds showing > 95% amino acid sequence identity to previously reported viruses in domestic poultry. Genomic recombination was observed for some genomes showing discordant phylogenies based on structural and non-structural regions. Mapping the next-generation sequencing (NGS) data respectively against the 707 genomes revealed that these viruses showed distribution pattern differences among birds with different habitats (breeding or wild), orders, and sampling sites but no significant differences between birds with different behavioral features (migratory and resident). CONCLUSIONS: The existence of a highly diverse virome highlights the challenges in elucidating the evolution, etiology, and ecology of viruses in wild birds. Video Abstract.

Automatic pulmonary auscultation grading diagnosis of Coronavirus Disease 2019 in China with artificial intelligence algorithms: A cohort study.

BACKGROUND AND OBJECTIVE: Research on automatic auscultation diagnosis of COVID-19 has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis. METHODS: 172 confirmed cases of COVID-19 in Tongji Hospital were divided into moderate, severe and critical group. Pulmonary auscultation were recorded in 6-10 sites per patient through 3M littmann stethoscope and the data were transferred to computer to construct the dataset. Convolutional neural network (CNN) were designed to generate classifications of the auscultation. F1 score, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity and specificity were quantified. Another 45 normal patients were served as control group. RESULTS: There are about 56.52%, 59.46% and 78.85% abnormal auscultation in the moderate, severe and critical groups respectively. The model showed promising performance with an averaged F1 scores (0.9938 95% CI 0.9923-0.9952), AUC ROC score (0.9999 95% CI 0.9998-1.0000), sensitivity (0.9938 95% CI 0.9910-0.9965) and specificity (0.9979 95% CI 0.9970-0.9988) in identifying the COVID-19 patients among normal, moderate, severe and critical group. It is capable in identifying crackles, wheezes, phlegm sounds with an averaged F1 scores (0.9475 95% CI 0.9440-0.9508), AUC ROC score (0.9762 95% CI 0.9848-0.9865), sensitivity (0.9482 95% CI 0.9393-0.9578) and specificity (0.9835 95% CI 0.9806-0.9863). CONCLUSIONS: Our model is accurate and efficient in automatically diagnosing COVID-19 according to different categories, laying a promising foundation for AI-enabled auscultation diagnosing systems for lung diseases in clinical applications.

Automatic pulmonary auscultation grading diagnosis of Coronavirus Disease 2019 in China with artificial intelligence algorithms: a cohort study

Background and Objective Research on automatic auscultation diagnosis of COVID-19 has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis. Methods 172 confirmed cases of COVID-19 in Tongji Hospital were divided into moderate, severe and critical group. Pulmonary auscultation were recorded in 6-10 sites per patient through 3M littmann stethoscope and the data were transferred to computer to construct the dataset. Convolutional neural network (CNN) were designed to generate classifications of the auscultation. F1 score, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity and specificity were quantified. Another 45 normal patients were served as control group. Results There are about 56.52%, 59.46% and 78.85% abnormal auscultation in the moderate, severe and critical groups respectively. The model showed promising performance with an averaged F1 scores (0.9938 95% CI 0.9923–0.9952), AUC ROC score (0.9999 95% CI 0.9998–1.0000), sensitivity (0.9938 95% CI 0.9910–0.9965) and specificity (0.9979 95% CI 0.9970–0.9988) in identifying the COVID-19 patients among normal, moderate, severe and critical group. It is capable in identifying crackles, wheezes, phlegm sounds with an averaged F1 scores (0.9475 95% CI 0.9440–0.9508), AUC ROC score (0.9762 95% CI 0.9848–0.9865), sensitivity (0.9482 95% CI 0.9393–0.9578) and specificity (0.9835 95% CI 0.9806–0.9863). Conclusions Our model is accurate and efficient in automatically diagnosing COVID-19 according to different categories, laying a promising foundation for AI-enabled auscultation diagnosing systems for lung diseases in clinical applications.

[Impact of public health emergency on public psychology: analysis of mental health assistance hotlines during COVID-19 in Zhejiang province].

OBJECTIVE: To analyze the usage of mental health assistance hotline during COVID-19 in Zhejiang province from January 25th to February 29th 2020, and summarize the characteristics of the demand for mental health services and the dynamic changes of public mental health status during COVID-19 pandemic. METHODS: Both quantitative and qualitative methods were used. The calls related to pandemic were divided into four categories: medical, psychological, information and the others. The secondary categories of psychological calls were determined by text analysis. The number of calls were calculated weekly and the number of various types of calls over time were analyzed. We used stratified random sampling method to extract 600 cases of all kinds of calls related to pandemic and conducted a semantic analysis, through marking new, similar combination to form a feature set, then summed up the call content characteristics of each stage. Two hundred callers were followed up to understand how they felt about the call process in four aspects: the waiting time, call duration, the degree of problem-solving and the way to end the call. RESULTS: In a total of 13 746 calls, 8978 were related to pandemic, among which 12.59%(1130/8978) were about medical issues, 26.50%(2379/8978) were about mental health, 27.18%(2440/8978) were about information regarding the pandemic and 33.74%(3029/8978) were about other pandemic related issues. Pandemic situation, relevant policy release, frequency of advertising campaigns were predictors of the number of calls per day during the pandemic (P<0.05 or P<0.01). The number of calls differed by gender and identities of callers (both P<0.05). Finally 181 callers accepted telephone follow-up. Among them, 51.38%(93/181) of the callers thought that the waiting time was too long, 33.15%(60/181) of the callers thought that the call time was insufficient, 80.66%(146/181) of callers believed that the hotline could partially or completely resolve their concerns, and 39.23%(71/181) of the callers said the operator proposed to end the call. CONCLUSIONS: s The changes of the number and content of the mental health assistance hotline calls reflected that the public mental health status experienced four stages during the pandemic: confusion, panic, boredom, and adjustment. The specialized mental health assistance hotlines should be further strengthened, and the efficiency should be improved. Mental health interventions should be tailored and adopted according to the characteristics of the public mental health status at different stages of the pandemic.