Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images (preprint)

Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue to be serious global health concerns that affect millions of people worldwide. In medical practice, chest X-ray examinations have emerged as the norm for diagnosing diseases, particularly chest infections such as COVID-19. Paramedics and scientists are working intensively to create a reliable and precise approach for early-stage COVID-19 diagnosis in order to save lives. But with a variety of symptoms, medical diagnosis of these disorders poses special difficulties. It is essential to address their identification and timely diagnosis in order to successfully treat and prevent these illnesses. In this research, a multiclass classification approach using state-of-the-art methods for deep learning and image processing is proposed. This method takes into account the robustness and efficiency of the system in order to increase diagnostic precision of chest diseases. A comparison between a brand-new convolution neural network (CNN) and several transfer learning pre-trained models including VGG19, ResNet, DenseNet, EfficientNet, and InceptionNet is recommended. Publicly available and widely used research datasets like Shenzen, Montogomery, the multiclass Kaggle dataset and the NIH dataset were used to rigorously test the model. Recall, precision, F1-score, and Area Under Curve (AUC) score are used to evaluate and compare the performance of the proposed model. An AUC value of 0.95 for COVID-19, 0.99 for TB, and 0.98 for pneumonia is obtained using the proposed network. Recall and precision ratings of 0.95, 0.98, and 0.97, respectively, likewise met high standards.

Hepatitis C virus drugs that inhibit SARS-CoV-2 papain-like protease synergize with remdesivir to suppress viral replication in cell culture.

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2. We assessed 10 hepatitis C virus (HCV) protease-inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 main protease (Mpro) and HCV NS3/4A protease. Virtual docking experiments show that these HCV drugs can potentially bind into the Mpro substrate-binding cleft. We show that seven HCV drugs inhibit both SARS-CoV-2 Mpro protease activity and SARS-CoV-2 virus replication in Vero and/or human cells. However, their Mpro inhibiting activities did not correlate with their antiviral activities. This conundrum is resolved by demonstrating that four HCV protease inhibitor drugs, simeprevir, vaniprevir, paritaprevir, and grazoprevir inhibit the SARS CoV-2 papain-like protease (PLpro). HCV drugs that inhibit PLpro synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir's antiviral activity as much as 10-fold, while those that only inhibit Mpro do not synergize with remdesivir.

Hepatitis C Virus Drugs Simeprevir And Grazoprevir Synergize with Remdesivir to Suppress SARS-CoV2 Replication (preprint)

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2 virus. Here we assess ten available HCV protease inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 Mpro and HCV NS3/4A proteases, and virtual docking experiments show that all ten HCV drugs can potentially bind into the Mpro binding cleft. Seven of these HCV drugs inhibit SARS-CoV-2 Mpro protease activity, while four dock well into the PLpro substrate binding cleft and inhibit PLpro protease activity. These same seven HCV drugs inhibit SARS-CoV-2 virus replication in Vero and/or human cells, demonstrating that HCV drugs that inhibit Mpro, or both Mpro and PLpro, suppress virus replication. Two HCV drugs, simeprevir and grazoprevir synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, thereby increasing remdesivir inhibitory activity as much as 10-fold.Funding: This research was supported by grants from the National Institutes of Health (R01-GM120574 to GTM) and RPI Center for Computational Innovations (to KB and GTM). This research was also partly funded by CRIP (Center for Research for Influenza Pathogenesis), a NIAID supported Center of Excellence for Influenza Research and Surveillance (CEIRS, contract #,HHSN272201400008C), by DARPA grant HR0011-19-2-0020, by supplements to NIAID grant U19AI142733 U19AI135972 and DoD grant W81XWH-20-1-0270, and by the generous support of the JPB Foundation, the Open Philanthropy Project (research grant 2020-215611 (5384)), and anonymous donors to AG-S.Conflict of Interest: A provisional patent application related to these, studies has been filed. GTM is a founder of Nexomics Biosciences, Inc. This, relationship has no conflict of interest with respect to this study. GTM and RMK are inventors in patents owned jointly by Rutgers University and the University of Texas at Austin concerning the use of specific compounds as antivirals against influenza virus. These patents have no conflict of interest for this study. AG-S is inventor in patents and patent application owned by the Icahn School of Medicine concerning the use of specific antiviral compounds. This inventorship has no conflict of interest with respect to this study.