COVID TCL:A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population.Which reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people.To solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class compactness.Therefore,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period.Meanwhile,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation efficiency.The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference models.The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.

Nasopharyngeal Local Transcriptional Profile upon SARS-CoV-2 Omicron BA.2.2 Breakthrough Infection

The Omicron variants have continued to cause severe acute respiratory syndrome coronavirus 2 infections.To better understand the anti-viral effects of vaccination on host-virus interactions during the outbreak of BA.2.2 Omicron,we conducted RNA-seq transcriptome analysis on nasopharyngeal swabs from COVID-19 patients in Shanghai.This study was performed on selected cases from unvaccinated,fully vaccinated,and booster groups with the same founder virus infection background.We observed predominant immune cell chemotaxis and interleukin-1 production,as well as mucosal keratinization and epidermis development,in unvaccinated patients.In contrast,fully vaccinated subjects exhibited an obvious T-cell activation in the local immune response,whereas B-cell activation was higher in booster-vaccinated cases.In conclusion,our findings suggest that full or booster vaccination provides better adaptive immunity and relieve inflammation at the nasopharyngeal site,thereby reducing the risk of cytokine storm during breakthrough infection.