Analysis of endometrial myeloid and lymphoid dendritic cells during mouse estrous cycle

This study was performed to evaluate the frequency and localization of endometrial myeloid (CD11c(+) CD11b(+)) and lymphoid (CD11c(+) CD8alpha(+)) dendritic cells (DCs) at different stages of murine estrous cycle. To address the systemic effect of ovarian hormones fluctuations during estrous cycle, the same variables were studied in splenic DCs as well. Stages of the estrous cycle of Balb/c mice were determined by examination of vaginal smears. Frozen sections of uterus and spleen at each stage of estrous cycle were stained for CD11c and MHC-II. Two-color immunohistochemistry was also carried out using anti-CD11c with one of the antibodies against CD11b, CD8alpha, CD86, and DEC-205. The average density of DCs and relative percentage of myeloid and lymphoid DCs (MDCs and LDCs) were determined at each stage of estrous cycle by morphometric analysis. Our results showed that DCs were present throughout the estrous cycle in mice endometrium, but their frequency was highest at estrus and lowest at proestrus (P<0.005). The lymphoid subset of DCs was more prominent at estrus relative to those at other stages (P<0.005). Conversely, the relative percentage of myeloid DCs at estrus was significantly lower compared to other stages (P<0.005). Nearly all endometrial and splenic DCs expressed CD86 and MHC-II. At proestrus, and particularly at estrus, DCs were more concentrated subadjacent to the luminal and glandular epithelial layers with some scattered throughout the stroma whereas, at metestrus and diestrus, DCs were randomly distributed in stroma and around the glandular and luminal epithelial layers. The number and immunophenotype of splenic DCs were not statistically different between stages of estrous cycle. Our results suggest that endometrial but not splenic myeloid and lymphoid DCs are influenced by steroid hormones during estrous cycle.     

Structural Performance of EB-FRP-Strengthened RC T-Beams Subjected to Combined Torsion and Shear Using ANN

This research study applied Artificial Neural Networks (ANNs) to predict and evaluate the structural responses of externally bonded FRP (EB-FRP)-strengthened RC T-beams under combined torsion and shear. Previous studies proved that, compared to reinforced concrete (RC) rectangular beams, RC T-beams performance in shear is significantly higher in structural analysis and design. The structural response of RC beams experiences a critical change while torsion moments are applied in load conditions. Fiber Reinforced Polymer (FRP) is used to retrofit the structural elements due to changing structural design codes and loadings, especially in earthquake-prone countries. We applied Finite Element Method (FEM) software, ABAQUS, to provide a precise numerical database of a set of experimentally tested FRP-retrofitted RC T-beams in previous research works. ANN predicted structural analysis results and Mean Square Error (MSE) and Multiple Determination Coefficients (R2) proved the accuracy of this study. The MSE values that were less than 0.0009 and R2 values greater than 0.9960 showed that the ANN precisely fits the data. The consistency between analyzed experimental and numerical results demonstrated the accurate implication of ANN, MSE, and R2 in predicting the structural responses of EB-FRP- strengthened RC T-beams.     

Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique

Journal of Digital Imaging - Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further...