AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF).

Background: Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods: Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm2 (2.56 mm2 of epithelium and 24.87 mm2 of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138- cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results: The AI algorithm consistently and reliably distinguished CD138- and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36-0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p < 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (p = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples. Conclusion: Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial CD138+ plasma cells, offering distinct advantages over manual inspection, such as rapid analysis of whole slide images, reduction of intra- and interobserver variations, sparing the valuable time of trained specialists, and consistent productivity. This supports the application of AI technology to help clinical decision-making, for example, in understanding endometrial cycle phase-related dynamics, as well as different reproductive disorders.

Postnatal development and LPS responsiveness of pulmonary adenosine receptor expression and of adenosine-metabolizing enzymes in mice.

BACKGROUND: Adenosine levels are regulated by ecto-5'-nucleotidase/CD73 and adenosine deaminase (ADA). Adenosine regulates endothelial permeability and anti-inflammatory responses via adenosine receptors. Here, the adenosine receptors and purine-converting enzymes were studied during postnatal development and inflammation. METHODS: Newborn, 1-, 10-, 14-d-old and adult C57BL/6 mice were challenged intraperitoneally (i.p.) with lipopolysaccharide (LPS) for 6 h. The inflammatory response was evaluated by histochemistry. Expression levels of adenosine receptors (A1, A2A, A2B, and A3), CD73, and ADA were measured by quantitative reverse transcription polymerase chain reaction. A1 was studied by immunohistochemistry, and enzyme activities were analyzed by thin-layer chromatography. RESULTS: LPS caused respiratory distress in newborns within 24 h. LPS induced neutrophils at the basal stage and alveolar congestion. Low activity and expression of CD73 increased after birth. Expression of ADA after LPS increased 16-fold in adults and 2-fold in newborns (P < 0.05). A1 expression was high in newborns and increased after LPS (P < 0.05). A1 was localized to endothelial membranes. A2A decreased after LPS in newborns and increased in adults (P < 0.05). The expression of A3 increased in newborns and adults after LPS. CONCLUSION: Low pulmonary CD73 expression, LPS-induced suppression of A2A, LPS-induced increase of A1 expression, and severe respiratory distress were distinguishing responses in the newborns from those in the adults.