The 3-component mixture of power distributions under Bayesian paradigm with application of life span of fatigue fracture

Abstract Mixture distributions are naturally extra attractive to model the heterogeneous environment of processes in reliability analysis than simple probability models. This focus of the study is to develop and Bayesian inference on the 3-component mixture of power distributions. Under symmetric and asymmetric loss functions, the Bayes estimators and posterior risk using priors are derived. The presentation of Bayes estimators for various sample sizes and test termination time (a fact of time after that test is terminated) is examined in this article. To assess the performance of Bayes estimators in terms of posterior risks, a Monte Carlo simulation along with real data study is presented

Dual contrastive learning based image-to-image translation of unstained skin tissue into virtually stained H&E images

Abstract Staining is a crucial step in histopathology that prepares tissue sections for microscopic examination. Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models. These models have the potential to improve staining accuracy, reduce staining time, and minimize the use of hazardous chemicals, making histopathology a safer and more efficient field. In this study, we introduce a novel three-stage, dual contrastive learning-based, image-to-image generative (DCLGAN) model for virtually applying an "H&E stain" to unstained skin tissue images. The proposed model utilizes a unique learning setting comprising two pairs of generators and discriminators. By employing contrastive learning, our model maximizes the mutual information between traditional H&E-stained and virtually stained H&E patches. Our dataset consists of pairs of unstained and H&E-stained images, scanned with a brightfield microscope at 20 × magnification, providing a comprehensive set of training and testing images for evaluating the efficacy of our proposed model. Two metrics, Fréchet Inception Distance (FID) and Kernel Inception Distance (KID), were used to quantitatively evaluate virtual stained slides. Our analysis revealed that the average FID score between virtually stained and H&E-stained images (80.47) was considerably lower than that between unstained and virtually stained slides (342.01), and unstained and H&E stained (320.4) indicating a similarity virtual and H&E stains. Similarly, the mean KID score between H&E stained and virtually stained images (0.022) was significantly lower than the mean KID score between unstained and H&E stained (0.28) or unstained and virtually stained (0.31) images. In addition, a group of experienced dermatopathologists evaluated traditional and virtually stained images and demonstrated an average agreement of 78.8% and 90.2% for paired and single virtual stained image evaluations, respectively. Our study demonstrates that the proposed three-stage dual contrastive learning-based image-to-image generative model is effective in generating virtual stained images, as indicated by quantified parameters and grader evaluations. In addition, our findings suggest that GAN models have the potential to replace traditional H&E staining, which can reduce both time and environmental impact. This study highlights the promise of virtual staining as a viable alternative to traditional staining techniques in histopathology

Advances in the Optimization of Fe Nanoparticles: Unlocking Antifungal Properties for Biomedical Applications

In recent years, nanotechnology has achieved a remarkable status in shaping the future of biological applications, especially in combating fungal diseases. Owing to excellence in nanotechnology, iron nanoparticles (Fe NPs) have gained enormous attention in recent years. In this review, we have provided a comprehensive overview of Fe NPs covering key synthesis approaches and underlying working principles, the factors that influence their properties, essential characterization techniques, and the optimization of their antifungal potential. In addition, the diverse kinds of Fe NP delivery platforms that command highly effective release, with fewer toxic effects on patients, are of great significance in the medical field. The issues of biocompatibility, toxicity profiles, and applications of optimized Fe NPs in the field of biomedicine have also been described because these are the most significant factors determining their inclusion in clinical use. Besides this, the difficulties and regulations that exist in the transition from laboratory to experimental clinical studies (toxicity, specific standards, and safety concerns) of Fe NPs-based antifungal agents have been also summarized