Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: From the perspective of upstream and downstream wastewater-based epidemiology (WBE) (preprint)

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants . Wastewater-based epidemiology (WBE) is considered as a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman' r = 0.23 - 0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTPs influent (> 90 %). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hot spot model combined with geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from sewer system. In addition, socio-economic factors namely population density, land-use, and tax-income were successfully identified as the potentials drivers which substantially affect the onset of COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also other epidemic issues in the future.

Structural insights into Nirmatrelvir (PF-07321332)-3C-like SARS-CoV-2 protease complexation: a ligand Gaussian accelerated molecular dynamics study.

Coronavirus 3C-like protease (3CLpro) is found in SARS-CoV-2 virus, which causes COVID-19. 3CLpro controls virus replication and is a major target for target-based antiviral discovery. As reported by Pfizer, Nirmatrelvir (PF-07321332) is a competitive protein inhibitor and a clinical candidate for orally delivered medication. However, the binding mechanisms between Nirmatrelvir and 3CLpro complex structures remain unknown. This study incorporated ligand Gaussian accelerated molecular dynamics, the one-dimensional and two-dimensional potential of mean force, normal molecular dynamics, and Kramers' rate theory to determine the binding and dissociation rate constants (koff and kon) associated with the binding of the 3CLpro protein to the Nirmatrelvir inhibitor. The proposed approach addresses the challenges in designing small-molecule antiviral drugs.

A Self-Attention Mechanism Neural Network for Detection and Diagnosis of COVID-19 from Chest X-Ray Images (preprint)

The new type of coronavirus is called COVID-19. The virus can cause respiratory diseases, accompanied by cough, fever, difficulty breathing, and in severe cases, it can also cause symptoms such as pneumonia. It began to spread at the end of 2019 and has now spread to all parts of the world. The limited test kits and increasing number of cases encourage us to propose a deep learning model that can help radiologists and clinicians use chest X-rays to detect COVID-19 cases and show the diagnostic features of pneumonia. In this study, our methods are: 1) Propose a data enhancement method to increase the diversity of the data set, thereby improving the generalization performance of the network. 2) Using the deep convolutional neural network model DPN-SE, an attention mechanism is added on the basis of the DPN network, which greatly improves the performance of the network. 3) Use the lime interpretable library to mark the X-ray, the characteristic area on the medical image that is helpful for the doctor to make a diagnosis. The model we proposed can obtain better results with the least amount of data preprocessing given limited data. In general, the proposed method and model can effectively become a very useful tool for clinical practitioners and radiologists.

DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images (preprint)

Background and Objective: The new type of coronavirus is also called COVID-19. It began to spread at the end of 2019 and has now spread across the world. Until October 2020, It has infected around 37 million people and claimed about 1 million lives. We propose a deep learning model that can help radiologists and clinicians use chest X-rays to diagnose COVID-19 cases and show the diagnostic features of pneumonia. Methods: The approach in this study is: 1) we propose a data enhancement method to increase the diversity of the data set, thereby improving the generalization performance of the model. 2) Our deep convolution neural network model DPN-SE adds a self-attention mechanism to the DPN network. The addition of a self-attention mechanism has greatly improved the performance of the network. 3) Use the Lime interpretable library to mark the feature regions on the X-ray medical image that helps doctors more quickly diagnose COVID-19 in people. Results: Under the same network model, the data with and without data enhancement is put into the model for training respectively. At last, comparing two experimental results: among the 10 network models with different structures, 7 network models have improved their effects after using data enhancement, with an average improvement of 1% in recognition accuracy. We propose that the accuracy and recall rates of the DPN-SE network are 93% and 98% of cases (COVID vs. pneumonia bacteria vs. viral pneumonia vs. normal). Compared with the original DPN, the respective accuracy is improved by 2%. Conclusion: The data augmentation method we used has achieved effective results on a small amount of data set, showing that a reasonable data augmentation method can improve the recognition accuracy without changing the sample size and model structure. Overall, the proposed method and model can effectively become a very useful tool for clinical radiologists.

Cancer patients/survivors, lymphocytes, and severe COVID-19 (preprint)

There is a heated debate on whether the cancer survivors have worse outcomes in corona virus disease 2019 (COVID-2019). This study showed that both cancer survivors and cancer patients have decreased lymphocytes, partially explaining why these patients were associated with poorer prognosis in severe acute respiratory syndrome coronavirus 2 infection (SARS-CoV-2) in principle. Therefore, patients with cancer history, whether they are going active treatment or not, deserve special attention.

Comorbidity and its impact on 1,590 patients with COVID-19 in China: A Nationwide Analysis (preprint)

Objective: To evaluate the spectrum of comorbidities and its impact on the clinical outcome in patients with coronavirus disease 2019 (COVID-19). Design: Retrospective case studies Setting: 575 hospitals in 31 province/autonomous regions/provincial municipalities across China Participants: 1,590 laboratory-confirmed hospitalized patients. Data were collected from November 21st, 2019 to January 31st, 2020. Main outcomes and measures: Epidemiological and clinical variables (in particular, comorbidities) were extracted from medical charts. The disease severity was categorized based on the American Thoracic Society guidelines for community-acquired pneumonia. The primary endpoint was the composite endpoints, which consisted of the admission to intensive care unit (ICU), or invasive ventilation, or death. The risk of reaching to the composite endpoints was compared among patients with COVID-19 according to the presence and number of comorbidities. Results: Of the 1,590 cases, the mean age was 48.9 years. 686 patients (42.7%) were females. 647 (40.7%) patients were managed inside Hubei province, and 1,334 (83.9%) patients had a contact history of Wuhan city. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints. 399 (25.1%) reported having at least one comorbidity. 269 (16.9%), 59 (3.7%), 30 (1.9%), 130 (8.2%), 28 (1.8%), 24 (1.5%), 21 (1.3%), 18 (1.1%) and 3 (0.2%) patients reported having hypertension, cardiovascular diseases, cerebrovascular diseases, diabetes, hepatitis B infections, chronic obstructive pulmonary disease, chronic kidney diseases, malignancy and immunodeficiency, respectively. 130 (8.2%) patients reported having two or more comorbidities. Patients with two or more comorbidities had significantly escalated risks of reaching to the composite endpoint compared with those who had a single comorbidity, and even more so as compared with those without (all P<0.05). After adjusting for age and smoking status, patients with COPD (HR 2.681, 95%CI 1.424-5.048), diabetes (HR 1.59, 95%CI 1.03-2.45), hypertension (HR 1.58, 95%CI 1.07-2.32) and malignancy (HR 3.50, 95%CI 1.60-7.64) were more likely to reach to the composite endpoints than those without. As compared with patients without comorbidity, the HR (95%CI) was 1.79 (95%CI 1.16-2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61-4.17) among patients with two or more comorbidities. Conclusion: Comorbidities are present in around one fourth of patients with COVID-19 in China, and predispose to poorer clinical outcomes.