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PRRSV-2 variant classification: a dynamic nomenclature for enhanced monitoring and surveillance.
mSphere
January 2025
Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA.
Existing genetic classification systems for porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2), such as restriction fragment length polymorphisms and sub-lineages, are unreliable indicators of close genetic relatedness or lack sufficient resolution for epidemiological monitoring routinely conducted by veterinarians. Here, we outline a fine-scale classification system for PRRSV-2 genetic variants in the United States. Based on >25,000 U.
View Article and Find Full Text PDFAn integrative approach to generating explainable safety assessment scenarios for autonomous vehicles based on Vision Transformer and SHAP.
Accid Anal Prev
January 2025
Department of Computer Engineering, Hongik University, Seoul, 04066, Republic of Korea. Electronic address:
Automated Vehicles (AVs) are on the cusp of commercialization, prompting global governments to organize the forthcoming mobility phase. However, the advancement of technology alone cannot guarantee the successful commercialization of AVs without insights into the accidents on the read roads where Human-driven Vehicles (HV) coexist. To address such an issue, The New Car Assessment Program (NCAP) is currently in progress, and scenario-based approaches have been spotlighted.
View Article and Find Full Text PDFToward efficient slide-level grading of liver biopsy via explainable deep learning framework.
Med Biol Eng Comput
January 2025
Pathology Department, Beijing Youan Hospital, Capital Medical University, Beijing, 100000, China.
In the context of chronic liver diseases, where variability in progression necessitates early and precise diagnosis, this study addresses the limitations of traditional histological analysis and the shortcomings of existing deep learning approaches. A novel patch-level classification model employing multi-scale feature extraction and fusion was developed to enhance the grading accuracy and interpretability of liver biopsies, analyzing 1322 cases across various staining methods. The study also introduces a slide-level aggregation framework, comparing different diagnostic models, to efficiently integrate local histological information.
View Article and Find Full Text PDFUse and limitations of clinical data in the identification and classification of low molecular weight chemicals (LMWCs) as respiratory sensitizers: recommendations for improvement.
Crit Rev Toxicol
January 2025
Syngenta, Bracknell, United Kingdom.
While progress has been made in recent years, there are still no suitable and accepted , or models that can be used to accurately predict whether a chemical substance has the intrinsic property to cause immune-mediated chemical respiratory allergy, typically manifested as allergic asthma or allergic rhinitis which represents a severe health hazard. Regulatory authorities have relied primarily on clinical evidence (case reports, clinical databases, worker exposure studies) to classify substances as respiratory sensitizers, but this evidence can lack a proven immunological mechanism which is necessary to identify substances which can cause life-long sensitization and clinically relevant allergic symptoms in the respiratory tract in an exposed population (such respiratory allergens may be considered as "true" sensitizers, in analogy to the definition of skin sensitization, and in contrast to respiratory irritants). In light of this, the European Center for Ecotoxicology and Toxicology of Chemicals convened a Task Force to evaluate the types of clinical methods and data sources and the implications of relying on such data for regulatory decision making from a scientific perspective.
View Article and Find Full Text PDFAffinity Peptide-Based Circularly Permuted Fluorescent Protein Biosensors for Non-Small Cell Lung Cancer Diagnosis.
Sensors (Basel)
December 2024
Central Hospital of Dalian University of Technology, Dalian 116021, China.
Non-small cell lung cancer (NSCLC) is the predominant form of lung cancer and poses a significant public health challenge. Early detection is crucial for improving patient outcomes, with serum biomarkers such as carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCCAg), and cytokeratin fragment 19 (CYFRA 21-1) playing a critical role in early screening and pathological classification of NSCLC. However, due to being mainly based on corresponding antibody binding reactions, existing detection technologies for these serum biomarkers have shortcomings such as complex operations, high false positive rates, and high costs.
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