Identification of asymptomatic COVID-19 patients on chest CT images using Transformer-or CNN-based deep learning models (preprint)

Backgroud To evaluate the feasibility of deep learning (DL) models in identifying asymptomatic COVID-19 patients, based on chest CT images.Methods In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs)-or Transformer-architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou was randomly split into the training set (n = 2,140) and the internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Models’ performance was assessed by accuracy, recall and specificity and was compared with that of two radiologists.Results A total of 2,700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by EfficientNet model (0.954). The recall and precision of the Swin model were 0.989 and 1.000. In the test dataset, the Swin model still was the best that achieved the highest accuracy (0.980). All the DL models performed remarkable than two experts. Lastly, the time on the test set diagnosis spent by two experts 42min17s (Junior) and 29min43s (Senior), was significantly higher than that of those DL models (all below 2min).Conclusions This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found a Transformer model, the Swin model, performed best.

Influencing factors of humanistic care ability and its dimensions among mental health workers: an online cross-sectional study (preprint)

Background: In psychiatric services, humanistic care ability significantly affects the quality of therapeutic relationship and thus affects therapeutic outcomes of patients. Mental health workers may face more obstacles in humanistic care during the COVID-19 pandemic, we aimed to explore the level of humanistic care ability among mental health workers and its potential influencing factors.Method(s) An online cross-sectional survey was conducted among 262 mental health workers working in Chongqing, China, from December 2020 to January 2021. Data were collected by the Caring Ability Inventory (CAI), the Psychological Capital Questionnaire (PCQ-24) and the Eysenck Personality Questionnaire-Revised, Short Scale for Chinese (EPQ-RSC). Multiple linear regression analysis was used to explore the influencing factors of humanistic care ability.Results Mental health workers’ humanistic care ability was at a low level, with a score of 186.47 ± 21.34. Psychological capital was positively associated with humanistic care ability (β[95%CI] = 0.411 [0.464, 0.772], p<0.001) and its two dimensions (cognition: β[95%CI] = 0.514 [0.304, 0.465], p<0.001; patience: β[95%CI] = 0.478 [0.171, 0.283], p<0.001). Psychoticism was negatively associated with humanistic care ability (β[95%CI] = -0.277 [-5.179, -2.513], p<0.001) and its three dimensions (cognition: β[95%CI] = -0.124 [-1.567, -0.165], p<0.05; courage: β[95%CI] = -0.166 [-1.698, -0.316], p<0.01; patience: β[95%CI] = -0.185 [-1.330, -0.358], p<0.01). Extroversion was positively associated with humanistic care ability (β[95%CI] = 0.189 [0.686, 2.080], p<0.001) and its two dimensions (cognition: β[95%CI] = 0.188 [0.323, 1.051], p<0.001; courage: β[95%CI] = 0.268 [0.496, 1.230], p<0.001). Neuroticism was negatively associated with humanistic care ability (β[95%CI] = -0.130[-1.366, -0.193], p<0.01) and its one dimension (courage: β[95%CI] = -0.252 [-0.977, -0.352], p<0.001).Conclusion(s) Research has found that the humanistic care ability of mental health workers is at a low level, and psychological capital and personality traits are significant factors influencing the humanistic care ability and its sub-dimensions. Interventions to improve psychological capital of mental health workers or to promote the change of personality traits that they want are recommended, thereby promoting humanistic practice.

Influencing Factors of Humanistic Care Ability in Mental Health Workers: an Online Cross-Sectional Study (preprint)

Background: Mental health workers may face more obstacles in humanistic care during the COVID-19 pandemic, we aimed to explore the humanistic care ability of mental health workers and its potential influencing factors. Method: (s) An online cross-sectional survey was conducted among 262 mental health workers working in Chongqing, China from December 1 to 31, 2020. Self-administered questionnaires were used to collect data from the participants. Results: Mental health workers’ humanistic care ability score was 186.47 ± 21.34, and most of the them (79.8%) were at a low level. Psychological capital and personality traits were significant influencing factors of humanistic care ability and its sub-dimensions. Conclusion: (s) Research has found that the humanistic care ability of mental health workers is at a low level, and psychological capital and personality traits are significant factors influencing the humanistic care ability and its sub-dimensions. Interventions to improve psychological capital of mental health workers or to promote the change of personality traits that they want are recommended, thereby promoting humanistic practice.

A novel biparatopic antibody-ACE2 fusion that blocks SARS-CoV-2 infection: implications for therapy (preprint)

In the absence of a proven effective vaccine preventing infection by SARS-CoV-2, or a proven drug to treat COVID-19, the positive results of passive immune therapy using convalescent serum provides a strong lead. We have developed a new class of tetravalent, biparatopic therapy, 89C8-ACE2. It combines the specificity of a monoclonal antibody (89C8) that recognizes the relatively conserved N-terminal domain (NTD) of the viral S glycoprotein, and the ectodomain of ACE2, which binds to the receptor-binding domain (RBD) of S. This molecule shows exceptional performance in vitro, inhibiting the interaction of recombinant S1 to ACE2 and transduction of ACE2-overexpressing cells by S-pseudotyped lentivirus with IC50s substantially below 100 pM, and with potency approximately 100-fold greater than ACE2-Fc itself. Moreover, 89C8-ACE2 was able to neutralize authentic virus infection in a standard assay at low nanomolar concentrations, making this class of molecule a promising lead for therapeutic applications.