Endometriosis is a common gynecological condition affecting approximately 10% of women of reproductive age, characterized by the abnormal presence of endometrial-like tissue outside the uterus. Although endometriosis was first described over 300 years ago, its underlying mechanisms remain poorly understood, and accurate, prompt diagnosis continues to be challenging. Currently, there is a lack of effective, non-invasive diagnostic methods, and available treatments often come with significant side effects and high recurrence rates. This has spurred interest in investigating the role of pro- and anti-inflammatory molecules, particularly cytokines, in endometriosis, as these molecules play a key role in its progression by influencing cell growth and differentiation. Previous studies suggest that various cytokines could serve as potential biomarkers for diagnosing endometriosis, as they are detectable in both serum and peritoneal fluid. This review provides an overview of the expression, origin, function, and regulation of specific cytokines in endometriosis, along with a brief discussion on their potential clinical implications for diagnosis. Due to the complexity of endometriosis, a panel of multiple biomarkers may ultimately be necessary for accurate diagnosis. It is essential to consider factors such as patient selection, sample collection, and analytical variability when initiating or evaluating biomarker studies.

This paper presents a technique that can potentially help to determine the receptivity stage of the endometrium from histology images by automatically measuring the stromal nuclear changes. The presented technique is composed of an image segmentation model and the statistical evolution of segmented areas in hematoxylin and eosin (HE)-stained histology images. Three different endometrium receptivity stages, namely pre-receptive, post-receptive, and receptive, were compared. An ensemble-based AI model was proposed for histology image segmentation, which is based on individual UNet++, UNet, and ResNet34-UNet segmentation models. The performance of the ensemble-based technique was assessed using the Dice score and intersection over unit (IoU) values. In comparison to alternative segmentation architectures that were applied singly, the current ensemble-based method obtained higher Dice score (0.95) and IoU (0.90) values. The statistical comparison highlighted a noticeable difference in the number of nuclei and the size of the stroma tissue. The proposed technique demonstrated the positive potential for practical implementation for automatic endometrial tissue analysis.

Frequent employee resistance to innovation is one of the main barriers of change failure in a health care organization and one of the negative stimuli of employment relationships. Identifying the reasons of resistance is a topical issue for every organization, as the speed of change can affect their competitiveness. Consequently, it is helpful not only to know the causes of potential resistance but also to be ready to control any implicit opposition. The organizational climate and the attitude of the staff play an important role in understanding and accepting innovation. Purpose of the study is to develop a model, which would facilitate the choice of an appropriate strategy necessary to enable the health care organization to eliminate or at least to reduce resistance to often essential innovative changes. The article analyses the root causes of resistance and identifies strategies that help to mitigate or eliminate staff resistance for innovation. Use of suggested model can make easier reducing staff resistance to change processes and thus speed up the implementation of innovations. This methodology can be used to eliminate the reasons for staff resistance to change in health care institutions of different countries, but it was tested in Lithuania and achieved good enough results.

Assessing human embryo quality is a critical step in in vitro fertilization (IVF), yet traditional manual grading remains subjective and physically limited by the shallow depth-of-field in conventional microscopy. This study develops a novel “soft optical sensor” architecture that transforms standard optical microscopy into an automated, high-precision instrument for embryo quality assessment. The proposed system integrates two key computational innovations: (1) a multi-focal image fusion module that reconstructs lost morphological details from Z-stack focal planes, effectively creating a 3D-aware representation from 2D inputs; and (2) a retrieval-augmented generation (RAG) framework coupled with a Swin Transformer to provide both high-accuracy classification and explainable clinical rationales. Validated on a large-scale clinical dataset of 102,308 images (prior to augmentation), the system achieves a diagnostic accuracy of 94.11%. This performance surpasses standard single-plane analysis methods by 9.43%, demonstrating the critical importance of fusing multi-focal data. Furthermore, the RAG module successfully grounds model predictions in standard ESHRE consensus guidelines, generating natural language explanations. The results demonstrate that this soft sensor approach significantly reduces inter-observer variability and offers a robust tool for standardized morphological assessment, though prospective validation against live birth outcomes remains essential for clinical adoption.