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AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides.
Viruses
December 2024
Beijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, China.
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides.
View Article and Find Full Text PDFEvaluating Neural Network Performance in Predicting Disease Status and Tissue Source of JC Polyomavirus from Patient Isolates Based on the Hypervariable Region of the Viral Genome.
Viruses
December 2024
Life Sciences, Health, and Engineering Department, The Roux Institute, Northeastern University, Portland, ME 04101, USA.
JC polyomavirus (JCPyV) establishes a persistent, asymptomatic kidney infection in most of the population. However, JCPyV can reactivate in immunocompromised individuals and cause progressive multifocal leukoencephalopathy (PML), a fatal demyelinating disease with no approved treatment. Mutations in the hypervariable non-coding control region (NCCR) of the JCPyV genome have been linked to disease outcomes and neuropathogenesis, yet few metanalyses document these associations.
View Article and Find Full Text PDFAn Experimental Approach for Investigating Fatigue-Induced Debonding Propagation in Composite Stiffened Panels Using Thermographic Phase Mapping.
Polymers (Basel)
January 2025
Institute for Polymers Composites and Biomaterials, Italian National Research Council, Piazzale Enrico Fermi, 80055 Portici, NA, Italy.
This work introduces an experimental approach focused on investigating fatigue-driven debonding in a composite structure designed to simulate the complexity of a typical aeronautical panel. The debonding is placed between the skin and the stringer, and the structure has been tested under fatigue compression conditions. Using lock-in thermography, the damage evolution during fatigue cycles has been detailed monitored.
View Article and Find Full Text PDFCharacterization of Fatigue Properties of Fiber-Reinforced Polymer Composites Based on a Multiscale Approach.
Polymers (Basel)
January 2025
Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Republic of Korea.
This study presents a methodology for characterizing the constituent properties of composite materials by back-calculating from the laminate behavior under fatigue loading. Composite materials consist of fiber reinforcements and a polymer matrix, with the fatigue performance of the laminate governed by the interaction between these constituents. Due to the challenges in directly measuring the properties of individual fibers and the polymer matrix, a reverse-engineering approach was employed.
View Article and Find Full Text PDFEvaluation of Prediction Models for the Capping and Breaking Force of Tablets Using Machine Learning Tools in Wet Granulation Commercial-Scale Pharmaceutical Manufacturing.
Pharmaceuticals (Basel)
December 2024
College of Pharmacy, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si 31116, Republic of Korea.
: This study aimed to establish a predictive model for critical quality attributes (CQAs) related to tablet integrity, including tablet breaking force (TBF), friability, and capping occurrence, using machine learning-based models and nondestructive experimental data. : The machine learning-based models were trained on data to predict the CQAs of metformin HCl (MF)-containing tablets using a commercial-scale wet granulation process, and five models were each compared for regression and classification. We identified eight input variables associated with the process and material parameters that control the tableting outcome using feature importance analysis.
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