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Diabetic retinopathy, known as one of the most crucial neurovascular barriers to diabetes, has affected the retina, leading to a potential risk of blindness. The harm is not isolated to personal health, with negative effects felt at the economic and social level in entire communities. The consequences of vision loss secondary to diabetic retinopathy on quality of life, independent living, and employment can be significant. It is necessary to adopt a comprehensive approach for effective management of underlying diabetes, which includes early detection, timely intervention, supportive care, and management of comorbid conditions. Critical to this is a precise classification of its stages for disease analysis. A manual diagnosis, on the other hand, requires qualified individuals to recognize the subtle nuances of the condition, making it a lengthy process. This study aims to develop a computer-assisted system for analyzing retinal fundus images to identify and classify different stages of diabetic retinopathy. A dataset of eye fundus images was used by the proposed network to classify the presence and severity of blindness and its stages. The proposed model uses a non-uniform squash function in capsule neural networks. The motive behind employing a non-uniform squash function was to encourage the model to allocate greater attention and stability to intricate images throughout the training phase. The proposed model's efficiency is assessed meticulously with pre-trained CNN methods like InceptionV3, VGG16, VGG19, and Xception. The proposed capsule network model has been revamped to address the shortcomings of prior research with care. The method stands out with an impressive 99.84 % training accuracy and 98.68 % validation accuracy. Diabetic retinopathy, though rare in occurrence, poses significant challenges due to the high cost of diagnosis, rendering it inaccessible to rural and remote communities with limited access to tests and check-ups.

Simple Summary: Current ultrasound imaging methods for detecting breast cancer often face challenges with image quality, making it tough to accurately spot tumors. This paper introduces UCapsNet, a new model that combines two advanced techniques to enhance breast cancer detection in ultrasound images. By improving how tumors are segmented and classified, UCapsNet aims to deliver clearer and more accurate results compared to traditional methods. Our findings indicate that UCapsNet can greatly improve diagnostic precision, enabling earlier detection and better treatment options for patients. This research could significantly impact future studies and lead to improved breast cancer detection practices in the medical community. Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult. Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network. Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively. Conclusions: By combining the U-Net's powerful segmentation capabilities with the capsule network's high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings. [ABSTRACT FROM AUTHOR]

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems. Its attributes as a non-toxic, low-carbon, and economical substitute for conventional cement concrete, coupled with its elevated compressive strength and reduced shrinkage properties, position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure. In this context, this study sets out the task of using machine learning (ML) algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field. To achieve this goal, a new approach using convolutional neural networks (CNNs) has been adopted. This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes, all containing Class F fly ash. The selection of optimal input parameters is guided by two distinct criteria. The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength. The second criterion scrutinizes the impact of these features within the model's predictive framework. Key to enhancing the CNN model's performance is the meticulous determination of the optimal hyperparameters. Through a systematic trial-and-error process, the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation--a technique vital to the model's robustness. The model's predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses. Furthermore, the model's adaptability is gauged by integrating a secondary dataset into its predictive framework, facilitating a comparative evaluation against conventional prediction methods. To unravel the intricacies of the CNN model's learning trajectory, a loss plot is deployed to elucidate its learning rate. The study culminates in compelling findings that underscore the CNN model's accurate prediction of geopolymer concrete compressive strength. To maximize the dataset's potential, the application of bivariate plots unveils nuanced trends and interactions among variables, fortifying the consistency with earlier research. Evidenced by promising prediction accuracy, the study's outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations, thereby reinforcing its role as an eco-conscious and robust construction material. The findings prove that the CNN model accurately estimated geopolymer concrete's compressive strength. The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes. The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering. [ABSTRACT FROM AUTHOR]

Paroxysmal nocturnal hemoglobinuria (PNH) is a serious blood disorder characterized by dysregulated complement activation on blood cells. Eculizumab, the current standard therapy and a humanized anti-C5 mAb, relieves anemia and thrombosis symptoms of PNH patients by preventing complement-dependent intravascular hemolysis (IVH). However, up to 20% of PNH patients on long-term eculizumab treatment still suffer from significant anemia and are transfusion dependent because of extravascular hemolysis (EVH) of C3-opsonized PNH erythrocytes. In this study, we show that function-blocking anti-properdin (P) mAbs dose-dependently inhibited autologous, complement-mediated hemolysis induced by factor H dysfunction. Furthermore, anti--human P (hP) mAbs potently and dose-dependently inhibited acidified serum--induced hemolysis of PNH erythrocytes (Ham test). In contrast to erythrocytes rescued by anti-C5 mAb, nonlysed PNH erythrocytes rescued by anti-P mAb incurred no activated C3 fragment deposition on their surface. These results suggested that anti-P mAbs may prevent EVH as well as IVH of PNH erythrocytes. To test the in vivo efficacy of anti-hP mAbs in preventing EVH, we generated a P humanized mouse by transgenic expression of hP in P knockout mice (hP-Tg/P-/-). In a murine EVH model, complement-susceptible erythrocytes were completely eliminated within 3 d in control mAb-treated hP-Tg/P-/- mice, whereas such cells were protected and persisted in hP-Tg/P-/- mice treated with an anti-hP mAb. Collectively, these data suggest that anti-P mAbs can inhibit both IVH and EVH mediated by complement and may offer improved efficacy over eculizumab, the current standard therapy for PNH. [ABSTRACT FROM AUTHOR]