Background: and Purpose The accurate prediction of functional outcomes in patients with acute ischemic stroke (AIS) is crucial for informed clinical decision-making and optimal resource utilization. As such, this study aimed to construct an ensemble deep learning model that integrates multimodal imaging and clinical data to predict the 90-day functional outcomes after AIS. Methods: We used data from the Korean Stroke Neuroimaging Initiative database, a prospective multicenter stroke registry to construct an ensemble model integrated individual 3D convolutional neural networks for diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR), along with a deep neural network for clinical data, to predict 90-day functional independence after AIS using a modified Rankin Scale (mRS) of 3–6. To evaluate the performance of the ensemble model, we compared the area under the curve (AUC) of the proposed method with that of individual models trained on each modality to identify patients with AIS with an mRS score of 3–6. Results: Of the 2,606 patients with AIS, 993 (38.1%) achieved an mRS score of 3–6 at 90 days post-stroke. Our model achieved AUC values of 0.830 (standard cross-validation [CV]) and 0.779 (time-based CV), which significantly outperformed the other models relying on single modalities: b-value of 1,000 s/mm2 (P<0.001), apparent diffusion coefficient map (P<0.001), FLAIR (P<0.001), and clinical data (P=0.004). Conclusion The integration of multimodal imaging and clinical data resulted in superior prediction of the 90-day functional outcomes in AIS patients compared to the use of a single data modality.

Background: This study aimed to investigate the recent prevalence, management, and comorbidities of diabetes among Korean adults aged ≥30 years by analyzing nationally representative data. Methods: This study used data from the Korea National Health and Nutrition Examination Survey from 2016 to 2018, and the percentage and total number of people ≥30 years of age with diabetes and impaired fasting glucose (IFG) were estimated. Results: In 2018, 13.8% of Korean adults aged ≥30 years had diabetes, and adults aged ≥65 years showed a prevalence rate of 28%. The prevalence of IFG was 26.9% in adults aged ≥30 years. From 2016 to 2018, 35% of the subjects with diabetes were not aware of their condition. Regarding comorbidities, 53.2% and 61.3% were obese and hypertensive, respectively, and 72% had hypercholesterolemia as defined by low-density lipoprotein cholesterol (LDL-C) ≥100 mg/dL in people with diabetes. Of the subjects with diabetes, 43.7% had both hypertension and hypercholesterolemia. With regard to glycemic control, only 28.3% reached the target level of <6.5%. Moreover, only 11.5% of subjects with diabetes met all three targets of glycosylated hemoglobin, blood pressure, and LDL-C. The percentage of energy intake from carbohydrates was higher in diabetes patients than in those without diabetes, while that from protein and fat was lower in subjects with diabetes. Conclusion The high prevalence and low control rate of diabetes and its comorbidities in Korean adults were confirmed. More stringent efforts are needed to improve the comprehensive management of diabetes to reduce diabetes-related morbidity and mortality.

Since the human papillomavirus (HPV) vaccine guidelines were developed by the Korean Society of Gynecologic Oncology (KSGO) in 2011, 2016, and 2019, several recent studies on the efficacy and safety of HPV vaccines in middle-aged women and men have been reported. Furthermore, there has been an ongoing debate regarding the efficacy of the HPV vaccine in women with prior HPV infection or who have undergone conization for cervical intraepithelial neoplasia (CIN). We searched and reviewed studies on the efficacy and safety of the HPV vaccine in middle-aged women and men and the efficacy of the HPV vaccine in patients infected with HPV and those who underwent conization for CIN. The KSGO updated their guidelines based on the results of the studies included in this review.

Purpose: This study aimed to develop a bedside nursing shift report protocol and evaluate the effect of the protocol in a tertiary hospital in South Korea. Methods: The bedside nursing handoff protocol with patient engagement was developed based on the literature review and the validation of an expert group. The effect of the protocol on clinical implication was tested in three medical-surgical units in a tertiary hospital. Outcomes were assessed by patient perception, nurse perception, and reporting time. Data collected from June to August in 2018 and analyzed with descriptive statistics and One-way ANOVA using SPSS version 25.0. Results: The bedside nursing shift report protocol with patient engagement consisted of two steps: nurse to nurse report and bedside report with patients. Nurse’s perception with patient engagement was significantly increased after applying protocol (F=17.85, p<.001). Patient’s perception was significantly improved in the areas of discharge plan (F=7.86, p<.001), health information privacy (F=4.46, p=.012) and identify attending nurse (F=3.19, p=.042). There were no differences in reporting time between the bedside nursing shift report and a traditional shift report (F=0.61, p=.054). Conclusion Patient perception was significantly increased, while nurse perception was not different after applying this protocol. For the change in the perception of nurses, education may be preceded to improve nurses' competence for the bedside shift report. Furthermore, the support in enough nurse staffing should be needed for encouraging the bedside shift report. The bedside shift report may enhance patient engagement. Therefore it may improve patient safety and health outcome in clinics.

Purpose: Aster tataricus (AT) is one of the Asteraceae perennial herbs used in traditional Chinese medicine. The herb contains various bioactive substances, such as flavonoids, isoflavonoids, and phenolic compounds in the roots, and exhibits a range of effects including anti-bacterial, anti-oxidant, and anti-inflammatory activities. This study compared the immunomodulatory effects of ethanol and water extracts of whole AT, except the roots, and analyzed the molecular mechanisms for the regulatory effects on cytokine secretion from THP-1 cells. Methods: The effects of AT extract on the cell viability and proliferation of THP-1 cells were analyzed using the Cell Counting Kit-8 method. The concentrations of interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) in the cell culture supernatant of the AT-treated THP-1 cells were measured using an enzyme-linked immunosorbent assay. The protein levels of cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), inhibitor of nuclear factor kappa B (IκBα), and mitogen-activated protein kinase (MAPK) phosphorylation in the cell lysates were determined by western blotting. Results: The water extract and the ethanol extract of AT did not affect the cell viability, and increased the proliferation of THP-1 cells significantly compared to the vehicle. The water extract increased the secretion of IL-1β from THP-1 cells in a dose-dependent manner, but the ethanol extract had no effect. The expression of COX-2 and iNOS protein and the phosphorylation of MAPK and Akt were induced in AT-treated cells. In addition, IκBα was degraded by AT in a concentration-dependent manner. IL-1β secretion by AT was reduced by extracellular-signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) inhibitors, while TNF-α secretion was decreased by inhibitors of ERK, p38 MAPK, and JNK.Interestingly, the p38 MAPK inhibitor increased the production of IL-1β by AT further. Conclusion The water extract of the above-ground parts of AT contains immunomodulatory bioactive substances that stimulate immune cells through the MAPK signaling pathway.

The first edition of the ‘Standardized Pathology Report for Colorectal Cancer,’ which was developed by the Gastrointestinal Pathology Study Group (GIP) of the Korean Society of Pathologists, was published 13 years ago. Meanwhile, there have been many changes in the pathologic diagnosis of colorectal cancer (CRC), pathologic findings included in the pathology report, and immunohistochemical and molecular pathology required for the diagnosis and treatment of colorectal cancer. In order to reflect these changes, we (GIP) decided to make the second edition of the report. The purpose of this standardized pathology report is to provide a practical protocol for Korean pathologists, which could help diagnose and treat CRC patients. This report consists of “standard data elements” and “conditional data elements.” Basic pathologic findings and parts necessary for prognostication of CRC patients are classified as “standard data elements,” while other prognostic factors and factors related to adjuvant therapy are classified as “conditional data elements” so that each institution could select the contents according to the characteristics of the institution. The Korean version is also provided separately so that Korean pathologists can easily understand and use this report. We hope that this report will be helpful in the daily practice of CRC diagnosis.

Purpose: The most recent 2017 World Health Organization (WHO) classification of pancreatic neuroendocrine neoplasms (PanNENs) has refined the three-tiered 2010 scheme by separating grade 3 pancreatic neuroendocrine tumors (G3 PanNETs) from poorly differentiated pancreatic neuroendocrine carcinomas (PanNECs). However, differentiating between G3 Pan- NETs and PanNECs is difficult in clinical practice. Materials and Methods: Eighty-two surgically resected PanNENs were collected from 16 institutions and reclassified according to the 2017 WHO classification based on the histological features and proliferation index (mitosis and Ki-67). Immunohistochemical stains for ATRX, DAXX, retinoblastoma, p53, Smad4, p16, and MUC1 were performed for 15 high-grade PanNENs. Results: Re-classification resulted in 20 G1 PanNETs (24%), 47 G2 PanNETs (57%), eight G3 well-differentiated PanNETs (10%), and seven poorly differentiated PanNECs (9%). PanNECs showed more frequent diffuse nuclear atypia, solid growth patterns and apoptosis, less frequent organoid growth and regular vascular patterns, and absence of low-grade PanNET components than PanNETs. The Ki-67 index was significantly higher in PanNEC (58.2%± 15.1%) compared to G3 PanNET (22.6%±6.1%, p < 0.001). Abnormal expression of any two of p53, p16, MUC1, and Smad4 could discriminate PanNECs from G3 PanNETs with 100% specificity and 87.5% sensitivity. Conclusion Histological features supporting the diagnosis of PanNECs over G3 PanNETs were the absence of a low-grade PanNET component in the tumor, the presence of diffuse marked nuclear atypia, solid growth pattern, frequent apoptosis and markedly increased proliferative activity with homogeneous Ki-67 labeling. Immunohistochemical stains for p53, p16, MUC1, and Smad4 may be helpful in distinguishing PanNECs from G3 PanNETs in histologically ambiguous cases, especially in diagnostic practice when only small biopsied tissues are available.

OBJECTIVE: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. MATERIALS AND METHODS: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6–10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50–100, > 100), time intervals to DWI, and DWI protocols. RESULTS: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). CONCLUSION: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.
Brain Ischemia ; Consensus ; Diffusion ; Ethics Committees, Research ; Humans ; Retrospective Studies