Typical herpes simplex virus (HSV) capsids contain seven proteins that form a T=16 icosahedron of 1,250-Å diameter. Infection of cells with recombinant baculoviruses expressing two of these proteins, VP5 (which forms the pentons and hexons in typical HSV capsids) and VP19C (a component of the triplexes that connect adjacent capsomeres), results in the formation of spherical particles of 880-Å diameter. Electron cryomicroscopy and computer reconstruction revealed that these particles possess a T=7 icosahedral symmetry, having 12 pentons and 60 hexons. Among the characteristic structural features of the particle are the skewed appearance of the hexons and the presence of intercapsomeric mass densities connecting the middle domain of one hexon subunit to the lower domain of a subunit in the adjacent hexon. We interpret these connecting masses as being formed by VP19C. Comparison of the connecting masses with the triplexes, which occupy equivalent positions in the T=16 capsid, reveals the probable locations of the single VP19C and two VP23 molecules that make up the triplex. Their arrangement suggests that the two triplex proteins have different roles in controlling intercapsomeric interactions and capsid stability. The nature of these particles and of other aberrant forms made in the absence of scaffold demonstrates the conformational adaptability of the capsid proteins and illustrates how VP23 and the scaffolding protein modulate the nature of the VP5-VP19C network to ensure assembly of the functional T=16 capsid.

Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arias supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the “BraTS 2017 challenge”. The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management.

This study introduces a novel deep-learning methodology that is customized to automatically diagnose Alzheimer’s disease (AD) through the analysis of MRI datasets. The process of diagnosing AD via the visual examination of magnetic resonance imaging (MRI) presents considerable challenges. The visual diagnosis of mild to very mild stages of AD is challenging due to the MRI similarities observed between a brain that is aging normally and one that has AD. The detection of AD with extreme precision is critical during its early stages. Deep-learning techniques have recently been shown to be significantly more effective than human detection in identifying various stages of AD, enabling early-stage diagnosis. The aim of this research is to develop a deep-learning approach that utilizes pre-trained convolutional neural networks (CNNs) to accurately detect the severity levels of AD, particularly in situations where the quantity and quality of available datasets are limited. In this approach, the AD dataset is preprocessed via a refined image processing module prior to the training phase. The proposed method was compared to two well-known deep-learning algorithms (VGG16 and ResNet50) using four Kaggle AD datasets: one for the normal stage of the disease and three for the mild, very mild, and moderate stages, respectively. This allowed us to evaluate the effectiveness of the classification results. The three models were compared using six performance metrics. The results achieved with our approach indicate an overall detection accuracy of 99.3%, which is superior to the other existing models.