Attention-Enhanced Unpaired xAI-GANs for Transformation of Histological Stain Images.

Histological staining is the primary method for confirming cancer diagnoses, but certain types, such as p63 staining, can be expensive and potentially damaging to tissues. In our research, we innovate by generating p63-stained images from H&E-stained slides for metaplastic breast cancer. This is a crucial development, considering the high costs and tissue risks associated with direct p63 staining. Our approach employs an advanced CycleGAN architecture, xAI-CycleGAN, enhanced with context-based loss to maintain structural integrity. The inclusion of convolutional attention in our model distinguishes between structural and color details more effectively, thus significantly enhancing the visual quality of the results. This approach shows a marked improvement over the base xAI-CycleGAN and standard CycleGAN models, offering the benefits of a more compact network and faster training even with the inclusion of attention.

Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms.

In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research efforts. The integration of various data types, including clinical, demographic, and imaging information, could further refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart transplantation.

Quantitative Assessment of Ciliary Ultrastructure with the Use of Automatic Analysis: PCD Quant.

The ciliary ultrastructure can be damaged in various situations. Such changes include primary defects found in primary ciliary dyskinesia (PCD) and secondary defects developing in secondary ciliary dyskinesia (SCD). PCD is a genetic disease resulting from impaired ciliary motility causing chronic disease of the respiratory tract. SCD is an acquired condition that can be caused, for example, by respiratory infection or exposure to tobacco smoke. The diagnosis of these diseases is a complex process with many diagnostic methods, including the evaluation of ciliary ultrastructure using transmission electron microscopy (the golden standard of examination). Our goal was to create a program capable of automatic quantitative analysis of the ciliary ultrastructure, determining the ratio of primary and secondary defects, as well as analysis of the mutual orientation of cilia in the ciliary border. PCD Quant, a program developed for the automatic quantitative analysis of cilia, cannot yet be used as a stand-alone method for evaluation and provides limited assistance in classifying primary and secondary defect classes and evaluating central pair angle deviations. Nevertheless, we see great potential for the future in automatic analysis of the ciliary ultrastructure.

Norway spruce embryogenesis: changes in carbohydrate profile, structural development and response to polyethylene glycol.

Two unrelated, geographically distinct, highly embryogenic lines of Norway spruce (Picea abies (L.) Karst.) were analysed to identify metabolic traits characteristic for lines with good yields of high-quality embryos. The results were compared with corresponding characteristics of a poorly productive line (low embryo yield, scarce high-quality embryos). The following carbohydrate profiles and spectra during maturation, desiccation and germination were identified as promising characteristics for line evaluation: a gradual decrease in total soluble carbohydrates with an increasing sucrose : hexose ratio during maturation; accumulation of raffinose family oligosaccharides resulting from desiccation and their rapid degradation at the start of germination; and a decrease in sucrose, increase in hexoses and the appearance of pinitol with proceeding germination. We propose that any deviation from this profile in an embryonic line is a symptom of inferior somatic embryo development. We further propose that a fatty acid spectrum dominated by linoleic acid (18 : 2) was a common feature of healthy spruce somatic embryos, although it was quite different from zygotic embryos mainly containing oleic acid (18 : 1). The responses of the lines to osmotic stress were evaluated based on comparison of control (without osmoticum) and polyethylene glycol (PEG)-exposed (PEG 4000) variants. Although genetically distinct, both highly embryogenic lines responded in a very similar manner, with the only difference being sensitivity to high concentrations of PEG. At an optimum PEG concentration (3.75 and 5%), which was line specific, negative effects of PEG on embryo germination were compensated for by a higher maturation efficiency so that the application of PEG at an appropriate concentration improved the yield of healthy germinants per gram of initial embryonal mass and accelerated the process. Polyethylene glycol application, however, resulted in no improvement of the poorly productive line.