Flow cytometric assessment of the viability and functionality of uterine polymorphonuclear leukocytes in postpartum dairy cows

Postpartum dairy cows experience impaired peripheral polymorphonuclear leukocyte (PMN) functionality, which has been associated with reproductive tract inflammatory diseases. However, it has not been elucidated yet whether endometrial PMN functionality is (equally) impaired. We developed a method for endometrial PMN isolation and flow cytometric assessment of their viability and functionality. We also evaluated PMN immunolabeling, using a specific bovine granulocyte marker, CH138A. Blood and endometrial cytobrush samples were collected in duplicate from seventeen clinically healthy Holstein-Friesian cows between 9 and 37 days in milk. The proportion of viable, apoptotic, and necrotic PMN in endometrial samples roughly ranged from 10 to 80%, indicating highly dynamic endometrial PMN populations in the postpartum uteri. Endometrial PMN functionality testing revealed that PMN immunolabeling increased the accuracy, although this protocol might influence the median fluorescence intensity of the sample. Phagocytosis seemed the most stable and reliable endometrial PMN function and could be assessed satisfactorily without prior CH138A immunolabeling. However, the interpretation of oxidative burst and intracellular proteolysis tests remains challenging. The correlation between peripheral and endometrial PMN functionality was poor. Further research is warranted to unravel the role of uterine PMN viability and functionality in bovine uterine health

Ontologies as a Key to Data Challenges in Digital Twinning

The integration of ontologies within digital twinning frameworks presents a significant opportunity to elevate data management and operational capabilities beyond conventional data annotation. Ontologies, traditionally valued for enabling a FAIR (Findable, Accessible, Interoperable, and Reusable) data structure, offer a foundation for advanced data functionalities essential in complex systems such as digital twins. This work discusses how ontologies enable seamless data integration, supporting the convergence of heterogeneous datasets through standardized vocabularies and facilitating interoperability across digital platforms. Beyond data harmonization, ontologies can represent complex relationships and hierarchical structures within datasets, enhancing the precision of model inputs and outputs. Leveraging graph datasets and enabling applications like federated learning, advanced data extraction, and data standardization, ontologies transform digital twins into robust tools for comprehensive data analysis and operational intelligence. This work thus emphasizes the need to reframe ontologies as active components within digital twins