Human norovirus (HuNoV) is a leading cause of acute viral gastroenteritis worldwide and represents a major unmet challenge in antiviral drug and vaccine development. HuNoV is a non-enveloped, positive-sense RNA virus characterized by extensive genetic diversity and rapid evolution, which contribute to recurrent outbreaks in the absence of effective licensed therapeutics. Despite substantial progress in understanding HuNoV molecular biology, effective antiviral strategies remain limited, in part due to historical limitations in experimental model systems and the virus’s ability to evade host antiviral responses. Recent advances in HuNoV in vitro culture systems, particularly human intestinal enteroids, and inhibitor screening platforms have improved the identification of antiviral candidates, while parallel efforts in vaccine development have yielded immunogenicity data in preclinical and early-stage clinical studies. However, significant challenges persist, including antigenic diversity, strain-specific immunity, and limited correlates of protection. This review provides a critical analysis of the molecular mechanisms governing HuNoV infection, immune evasion, and replication, with a focused emphasis on key antiviral targets, inhibitory strategies, and therapeutic development. Moreover, this review outlines key limitations and future directions for the development of effective therapeutic and preventive measures against HuNoV infection.
Sadia Islam, Sabbir Zia, Simonto Mirza et al.· Therapeutic Advances in Infe...· 0 citations
Driver behavior detection is essential for improving road safety in Intelligent Transportation Systems (ITSs). This paper proposes an attention-enhanced CNN integrated with Explainable Artificial Intelligence (XAI) to achieve reliable and interpretable driver behavior classification. Furthermore, the model also uses a multi-head attention mechanism to ensure that it captures local spatial features as well as long-range dependencies in the activities of the driver. The State Farm Distracted Driver dataset is experimented with using stratified 5-fold cross-validation. The proposed MHA-CNN model has a mean accuracy of 99.48% on all folds and an overall high levels of precision, recall, and F1-score in all ten distraction classes. Grad-CAM is used to produce attribution maps to improve interpretability by showing semantically important parts of the image, including hands, face, and mobile devices, and can give a visual explanation of why models make decisions. In addition, an ablation investigation is performed with k-fold validation to assess the value of the multi-head attention mechanism. Its model can run at 122 frames per second with 42.92 million parameters, showing that the suggested method is a reasonable tradeoff between accuracy, efficiency, and transparency and can be applied in real-time driver monitoring applications.
A. Al mamun, Md Shahidul Islam Shabuz, Mohamed N. Rahaman et al.· Algorithms· 0 citations
An integrated machine learning framework combining evolutionary optimization, multi-objective optimization, and explainable artificial intelligence (XAI) for BFRC strength prediction and mix design optimization is proposed and incorporates a graphical user interface (GUI) deployment to enhance practical usability and support engineering decision-making.
A. Al mamun, M. Aburizaiza, W. Sindi et al.· Materials· 0 citations
The proposed framework provides an accurate and explainable approach for rice leaf disease diagnosis in smart agriculture applications and effectively integrates global semantic information with local disease-specific features for improved classification performance.
Mohamed N. Rahaman, A. al Mamun, Md. Kamal Hossen et al.· Computers· 0 citations
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