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Background: COVID-19 arise global attention since their first public reporting. Infection prevention and control (IPC) is critical to combat COVID-19, especially at the early stage of pandemic outbreak. This study aimed to measure level of healthcare workers' (HCW') self-reported IPC behaviors with the risk of COVID-19 emerges and increases. Methods: A cross-sectional study was conducted in two tertiary hospitals. A structured self-administered questionnaire was delivered to HCWs in selected hospitals. The dependent variables were self-reported IPC behavior compliance; and independent variables were outbreak risk and three intent of infection risk (risk of contact with suspected patients, high-risk department, risk of affected area). Chi-square tests and multivariable negative binomial regression models were employed. Results: A total of 1386 participants were surveyed. The risk of outbreak increased self-reported IPC behavior on each item (coefficient varied from 0.029 to 0.151). Considering different extent of risk, HCWs from high-risk department had better self-reported practice in most IPC behavior (coefficient ranged from 0.027 to 0.149). HCWs in risk-affected area had higher self-reported compliance in several IPC behavior (coefficient ranged from 0.028 to 0.113). However, HCWs contacting with suspected patients had lower self-reported compliance in several IPC behavior (coefficient varied from − 0.159 to − 0.087).

Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a target detection task, which may deteriorate the performance of deep detectors. To address this challenge, we propose a novel low-shot transfer detector (LSTD) in this paper, where we leverage rich source-domain knowledge to construct an effective target-domain detector with very few training examples. The main contributions are described as follows. First, we design a flexible deep architecture of LSTD to alleviate transfer difficulties in low-shot detection. This architecture can integrate the advantages of both SSD and Faster RCNN in a unified deep framework. Second, we introduce a novel regularized transfer learning framework for low-shot detection, where the transfer knowledge (TK) and background depression (BD) regularizations are proposed to leverage object knowledge respectively from source and target domains, in order to further enhance fine-tuning with a few target images. Finally, we examine our LSTD on a number of challenging low-shot detection experiments, where LSTD outperforms other state-of-the-art approaches. The results demonstrate that LSTD is a preferable deep detector for low-shot scenarios.

This paper 1 presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically. The analysis of the specific rationale of each prediction made by the CNN presents a key issue of understanding neural networks, but it is also of significant practical values in certain applications. In this study, we propose to distill knowledge from the CNN into an explainable additive model, so that we can use the explainable model to provide a quantitative explanation for the CNN prediction. We analyze the typical bias-interpreting problem of the explainable model and develop prior losses to guide the learning of the explainable additive model. Experimental results have demonstrated the effectiveness of our method.

Dysregulated immune response and abnormal repairment could cause secondary pulmonary fibrosis of varying severity in COVID-19, especially for the elders. The Krebs Von den Lungen-6 (KL-6) as a sensitive marker reflects the degree of fibrosis and this study will focus on analyzing the evaluative efficacy and predictive role of KL-6 in COVID-19 secondary pulmonary fibrosis. The study lasted more than three months and included total 289 COVID-19 patients who were divided into moderate (n=226) and severe groups (n=63) according to the severity of illness. Clinical information such as inflammation indicators, radiological results and lung function tests were collected. The time points of nucleic acid test were also recorded. Furthermore, based on Chest radiology detection, it was identified that 80 (27.7%) patients developed reversible pulmonary fibrosis and 34 (11.8%) patients developed irreversible pulmonary fibrosis. Receiver operating characteristic (ROC) curve analysis shows that KL-6 could diagnose the severity of COVID-19 (AUC=0.862) and predict the occurrence of pulmonary fibrosis (AUC = 0.741) and irreversible pulmonary fibrosis (AUC=0.872). Importantly, the cross-correlation analysis demonstrates that KL-6 rises earlier than the development of lung radiology fibrosis, thus also illuminating the predictive function of KL-6. We set specific values (505U/mL and 674U/mL) for KL-6 in order to assess the risk of pulmonary fibrosis after SARS-CoV-2 infection. The survival curves for days in hospital show that the higher the KL-6 levels, the longer the hospital stay (P<0.0001). In conclusion, KL-6 could be used as an important predictor to evaluate the secondary pulmonary fibrosis degree for COVID-19.

A simple and efficient protocol for nickel-catalyzed regioselective C-H bond difluoroalkylation of 8-aminoquinoline scaffolds with functionalized difluoromethyl bromides was developed. The reaction has broad substrate scope and provides a facile and useful access to the corresponding C5-functionalized difluoromethylated quinolines in good to excellent yields.

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Active angiogenesis is the basic pathological feature of glioma. Tumor angiogenesis is involved in vascular endothelial cell migration to the tumor tissue and in the formation of tube-like structures. The present study aimed to investigate the role of leucine-rich repeats and immunoglobulin-like domains 2 (LRIG2) in glioma angiogenesis. Glioma (n=50) and normal brain (n=20) tissue samples were collected from patients to detect the expression of LRIG2, epidermal growth factor receptor (EGFR), vascular endothelial growth factor A (VEGF-A), and cluster of differentiation 31 (CD31) using immunohistochemistry. In addition, the association between the expression of LRIG2 in glioma tissue and the microvessel density (MVD) was analyzed. In vitro, the expression of LRIG2 in human glioma U87 and U251 cell lines was knocked down. Subsequently, cell migration and tube formation assays of human umbilical vein endothelial cells (HUVECs) were performed using a coculture system. The protein expression levels of LRIG2, EGFR, phosphorylated-EGFR and VEGF-A were determined using western blotting. The results demonstrated that the expression levels of LRIG2, EGFR, VEGF-A and CD31 were highly upregulated in glioma tissue samples. Furthermore, LRIG2 expression in glioma tissue samples was significantly correlated with the MVD. In vitro, the downregulation of LRIG2 inhibited HUVEC migration and tube formation induced by coculture with glioma cells. The downregulation of LRIG2 resulted in decreased expression of EGFR and VEGF-A. The effects of the LRIG2 knockdown were reversed following EGF treatment. These findings suggest that LRIG2 is a potential target for the inhibition of glioma angiogenesis, which is possibly mediated via the EGFR/VEGF-A signaling pathway.