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| http://dx.doi.org/10.4103/ijpvm.IJPVM_73_20 | DOI Listing |
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Influenza Virus Surveillance from the 1918 Influenza Pandemic to the 2020 Coronavirus Pandemic in New York State, USA.
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December 2024
Wadsworth Center, David Axelrod Institute, New York State Department of Health, Albany, NY 12208, USA.
A historical perspective of more than one hundred years of influenza surveillance in New York State demonstrates the progression from anecdotes and case counts to next-generation sequencing and electronic database management, greatly improving pandemic preparedness and response. Here, we determined if influenza virologic surveillance at the New York State public health laboratory (NYS PHL) tests sufficient specimen numbers within preferred confidence limits to assess situational awareness and detect novel viruses that pose a pandemic risk. To this end, we analyzed retrospective electronic data on laboratory test results for the influenza seasons 1997-1998 to 2021-2022 according to sample sizes recommended in the Influenza Virologic Surveillance Right Size Roadmap issued by the Association of Public Health Laboratories and Centers for Disease Control and Prevention.
View Article and Find Full Text PDFDevelopment and Evaluation of a Newcastle Disease Virus-like Particle Vaccine Expressing SARS-CoV-2 Spike Protein with Protease-Resistant and Stability-Enhanced Modifications.
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December 2024
Animal Infectious Disease Laboratory, College of Veterinary Medicine, Yangzhou University, Yangzhou 225012, China.
The ongoing global health crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates the continuous development of innovative vaccine strategies, especially in light of emerging viral variants that could undermine the effectiveness of existing vaccines. In this study, we developed a recombinant virus-like particle (VLP) vaccine based on the Newcastle Disease Virus (NDV) platform, displaying a stabilized prefusion form of the SARS-CoV-2 spike (S) protein. This engineered S protein includes two proline substitutions (K986P, V987P) and a mutation at the cleavage site (RRAR to QQAQ), aimed at enhancing both its stability and immunogenicity.
View Article and Find Full Text PDFChronic Hepatitis C Infection Treated with Direct-Acting Antiviral Agents and Occurrence/Recurrence of Hepatocellular Carcinoma: Does It Still Matter?
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December 2024
Department of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, Italy.
Hepatitis C virus (HCV) infection is a significant risk factor for liver cirrhosis and hepatocellular carcinoma (HCC). Traditionally, the primary prevention strategy for HCV-associated HCC has focused on removing infection through antiviral regimes. Currently, highly effective direct-acting antivirals (DAAs) offer extraordinary success across all patient categories, including cirrhotics.
View Article and Find Full Text PDFHIV Replication Under High-Level Cabotegravir Is Associated with the Appearance of 3'-PPT Mutations, Circular DNA Transcription and Recombination.
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November 2024
Laboratory Branch, Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA.
The HIV integrase inhibitor, dolutegravir (DTG), in the absence of eliciting integrase (int) resistance, has been reported to select mutations in the virus 3'-polypurine tract (3'-PPT) adjacent to the 3'-LTR U3. An analog of DTG, cabotegravir (CAB), has a high genetic barrier to drug resistance and is used in formulations for treatment and long-acting pre-exposure prophylaxis. We examined whether mutations observed for DTG would emerge in vitro with CAB.
View Article and Find Full Text PDFIntegrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.
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November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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