AIM: To investigate the influence of mifepristone on ultrastucture of human endometrium in the early secretory phase. METHODS: Endometrial tissue was obstained from 10 patients of reproductive age, who underwent a hysterectomy within 1 week postovulatory for gynecologic diseases not involving the endometrium. Patients were divided into mifepristone group ( n =5) and control group ( n =5) randomly. Each patient in the mifepristone group had taken 25 mg mifepristone per os 24 h before the operation was performed, while none of the control group had taken mifepristone. After removal of uterus, endometrial tissue was immediately acquired and prepared for electron microscopic examination. RESULTS: In comparison with the control group, the endometrial tissue in mifepristone group displayed the following distinctly morphological changes: (1) In the endometrial epithelium neither nucleolar channel system nor giant mitochondrium was seen, and subnuclear glycogen accumulation was seldom observed, but giant lysosomes were frequently found. (2) The intercellular spaces of the epithelium were narrow and straight, the indigitations of lateral plasma membranes were rarely visible. (3) Cytolysis and karyopyknosis of stroma cells and extravasal red cells were repeatedly observed. CONCLUSION: The above mentioned morphological changes in endometrium in the early secretory phase caused by mifepristone are undoubtedly sufficient to prevent implantation. Consequently, mifepristone may have a contraception effect.

AIM: To obverse the expression and localization of urocortin on ultrathin cryosections of syncytiotrophoblast of human term placenta with immunocytochemistry technique under transmission electron microscope. METHODS: The human term placenta tissue from Cesarean delivery and normal labor were fixed in 4% paraformaldehyde, and then divided into two parts. One part was for regular immunocytochemistry under microscope, and the other part was used to prepare ultrathin cryosections for immunocytochemistry under transmission electron microscope. RESULTS: 1.Uroncortin mainly distributed in cytoplasm of syncytiotrophoblast of human term placenta under microscope. Urocortin also appeared in cytoplasm in some stromal cells. 2. Under transmission electron microscope, the anti-urocortin gold particles were observed in cytoplasm of syncytioptrophoblast ultrathin cryosections and sited on rough-surfaced endoplasmic reticulum. The anti-urocortin gold particles also appeared on nucleus and nuclear membrane of syncytiotrophoblast. CONCLUSION: Syncytiotrophoblast of human term placenta synthesized and secreted urocortin. The internalization of urocortin within syncytiotrophoblast nuclear indicates that urocortin may act as intracrine.

AIM: The purpose of this study was to observe the morphological features of neuroendocrine cells(NECs),their proliferation and apoptosis in ovarian epithelial tumors,and to discuss their biological and clinical significance.METHODS: 79 specimens of ovarian epithelial tumor samples were collected,of them 20 benign,18 boderline,41 milignant tumors,and 22 normal ovaries were investigated immunohistochemically.Chromogranin A was used to detect NECs and their proliferation and apoptosis were examined by double-label staining of chromogranin A and Ki67 or TUNEL.RESULTS: The positive rate of CgA,distribution and staining intensity in ovarian epithelial tumors were higher than those in normal ovary.NECs showed various shapes with neuronoid protuberances stretching to the neighboring cells or basement membrane.Occasionally,they might touch together.No TUNEL positive coexpression in all NECs was observed by double-label staining,but some NECs were coexpressed with Ki67.CONCLUSION: NECs of ovarian epithelial tumors like cancer cells showed a proliferation,but no apoptosis.Their secretion might promote their neighboring non-NECs to proliferate and prevent them from apoptosis.

Objective:To identify tooth number abnormalities on pediatric panoramic radiographs based on deep learning.Methods:Eight hundred panoramic radiographs of children aged 4 to 11 years meeting the inclusion and exclusion criteria were selected and randomly assigned by writing programs in Python (version 3.9) to the training set (480 images), verification set (160 images) and internal test set (160 images), taken in Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology between November 2012 to August 2020. And all panoramic radiographs of children aged 4 to 11 years taken in the First Outpatient Department of Peking University School and Hospital of Stomatology from June 2022 to December 2022 were collected as the external test set (907 images). All of the 1 707 images were obtained by operators to determine the outline and to label the tooth position of each deciduous tooth, permanent tooth, permanent tooth germ and additional tooth. The deep learning model with ResNet-50 as the backbone network was trained on the training set, validated on the verification set, tested on the internal test set and external test set. The images of test sets were divided into two categories according to whether there was abnormality of tooth number, to calculate sensitivity, specificity, positive predictive value and negative predictive value, and then divided into four types of extra teeth and missing permanent teeth both existed, extra teeth existed only, missing permanent teeth existed only, and normal teeth number, to calculate Kappa values. Results:The sensitivity, specificity, positive predictive value and negative predictive value were 98.0%, 98.3%, 99.0% and 96.7% in the internal test set, and 97.1%, 98.4%, 91.9% and 99.5% in the external test set respectively, according to whether there was abnormality of tooth number. While images were divided into four types, the Kappa value obtained in the internal test set was 0.886, and that in the external test set was 0.912. Conclusions:In this study, a deep learning-based model for identifying abnormal tooth number of children was developed, which could identify the position of additional teeth and output the position of missing permanent teeth on the basis of identifying normal deciduous and permanent teeth and permanent tooth germs on panoramic radiographs, so as to assist in diagnosing tooth number abnormalities.