Epstein-Barr virus (EBV), a common pathogen in humans, is suspected as the cause of multiple pregnancy-related pathologies including depression, preeclampsia, and stillbirth. Moreover, transmission of EBV through the placenta has been reported. However, the focus of EBV infection within the placenta has remained unknown to date. In this study, we proved the expression of latent EBV genes in the endometrial glandular epithelial cells of the placenta and investigated the cytological characteristics of these cells. Sixty-eight placentas were obtained from pregnant women. Tissue microarray was constructed. EBV latent genes including EBV-encoding RNA-1 (EBER1), Epstein-Barr virus nuclear antigen 1 (EBNA1), late membrane antigen (LMP1), and RPMS1 were detected with silver in situ hybridization and/or mRNA in situ hybridization. Nuclear features of EBV-positive cells in EBV-infected placenta were compared with those of EBV-negative cells via image analysis. Sixteen placentas (23.5%) showed positive expression of all 4 EBV latent genes; only the glandular epithelial cells of the decidua showed EBV gene expression. EBV infection status was not significantly correlated with maternal, fetal, or placental factors. The nuclei of EBV-positive cells were significantly larger, longer, and round-shaped than those of EBV-negative cells regardless of EBV-infection status of the placenta. For the first time, evidence of EBV gene expression has been shown in placental tissues. Furthermore, we have characterized its cytological features, allowing screening of EBV infection through microscopic examination.
Decidua ; Depression ; Epithelial Cells ; Epstein-Barr Virus Infections ; Female ; Gene Expression ; Herpesvirus 4, Human* ; Humans* ; Image Cytometry ; In Situ Hybridization ; Mass Screening ; Membranes ; Pathology ; Placenta* ; Pre-Eclampsia ; Pregnant Women ; RNA, Messenger ; Silver ; Stillbirth ; Virus Latency

We report on a first case of bone marrow metastasis by dedifferentiated liposarcoma. A 39-year-old male diagnosed with retroperitoneal dedifferentiated liposarcoma underwent surgery and postoperative radiotherapy. In spite of radiotherapy, his whole-body positron emission tomography showed high uptake in multiple bone metastasis. With thrombocytopenia, bone scan suggested bone marrow involvement. After bone marrow biopsy, bone marrow metastasis by dedifferentiated liposarcoma was finally confirmed. He was administered with systemic chemotherapy with doxorubicin. But he died 3 months after chemotherapy due to disease progression. This case revealed that in a patient of unexplained cytopenia with dedifferentiated liposarcoma, bone marrow metastasis should be in consideration.
Adult ; Biopsy ; Bone Marrow* ; Disease Progression ; Doxorubicin ; Drug Therapy ; Humans ; Liposarcoma* ; Male ; Neoplasm Metastasis ; Positron-Emission Tomography ; Radiotherapy ; Thrombocytopenia

General anesthesia is associated with a risk for postoperative pulmonary complications. The risk is even higher in patients with chronic respiratory failure, and postoperative mortality rates are high. Proper perioperative anesthetic management is important in such patients. Therefore, it is essential to optimize the patient’s physical status before anesthesia and to determine the optimal anesthesia technique based on the pre-anesthesia evaluation of the patient’s pulmonary function. We successfully performed abdominal surgery under spinal anesthesia in a patient with severe chronic respiratory failure.

Purpose: This study was conducted to develop a convolutional neural network (CNN) algorithm that can diagnose cervical foraminal stenosis using oblique radiographs and evaluate its accuracy. Materials and Methods: A total of 997 patients who underwent cervical MRI and cervical oblique radiographs within a 3-month interval were included. Oblique radiographs were labeled as “foraminal stenosis” or “no foraminal stenosis” according to whether foraminal stenosis was present in the C2–T1 levels based on MRI evaluation as ground truth. The CNN model involved data augmentation, image preprocessing, and transfer learning using DenseNet161. Visualization of the location of the CNN model was performed using gradient-weight class activation mapping (Grad-CAM). Results: The area under the curve (AUC) of the receiver operating characteristic curve based on DenseNet161 was 0.889 (95% confidence interval, 0.851–0.927). The F1 score, accuracy, precision, and recall were 88.5%, 84.6%, 88.1%, and 88.5%, respectively.The accuracy of the proposed CNN model was significantly higher than that of two orthopedic surgeons (64.0%, p<0.001; 58.0%, p<0.001). Grad-CAM analysis demonstrated that the CNN model most frequently focused on the foramen location for the determination of foraminal stenosis, although disc space was also frequently taken into consideration. Conclusion A CNN algorithm that can detect neural foraminal stenosis in cervical oblique radiographs was developed. The AUC, F1 score, and accuracy were 0.889, 88.5%, and 84.6%, respectively. With the current CNN model, cervical oblique radiography could be a more effective screening tool for neural foraminal stenosis.

Background/Aims: Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database. Methods: Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated. Results: Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively. Conclusions The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model.