The matrix metalloproteinases (MMP) play an important role in tumor invasion, angiogenesis and inflammatory tissue destruction. Increased expression of MMP was observed in benign tissue hyperplasia and in atherosclerotic lesions. Invasive cancer cells utilize MMP to degrade the extracellular matrix and vascular basement membrane during metastasis, where MMP-2 has been implicated in the development and dissemination of malignancies. The present study attempted to examine the antiangiogenic activity of the medicinal herbs of Aspergillus usamii var. shirousamii-transformed Angelicae Gigantis Radix and Zizyphus jujube (tAgR and tZj) with respect to MMP-2 production and endothelial motility in phorbol 12-myristate 13-acetate (PMA)- or VEGF-exposed human umbilical vein endothelial cells (HUVEC). Nontoxic tAgR and tZj substantially suppressed PMA-induced MMP-2 secretion. In addition, 25 microg/mL tAgR and tZj prevented vascular endothelial growth factor-stimulated endothelial cell transmigration and tube formation. The results reveal that tAgR and tZj dampened endothelial MMP-2 production leading to endothelial transmigration and tube formation. tAgR and tZj-mediated inhibition of endothelial MMP may boost a therapeutic efficacy during vascular angiogenesis.
Angelica ; Aspergillus ; Basement Membrane ; Endothelial Cells ; Extracellular Matrix ; Human Umbilical Vein Endothelial Cells ; Hyperplasia ; Matrix Metalloproteinases ; Neoplasm Metastasis ; Phorbols ; Plants, Medicinal ; Transendothelial and Transepithelial Migration ; Ziziphus

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Introduction: Benzodiazepines (BZDs) are commonly prescribed to psychiatric patients. However, there have been few studies evaluating BZD refusal among patients with psychiatric disorders during the dispensing process. Thus, this study aimed to determine 1) the factors associated with BZD refusal during the dispensing process and to determine 2) the association between BZD refusal and psychiatric medication adherence among patients diagnosed with depression or schizophrenia. Method: This study was conducted at the Specialist Clinic Pharmacy, Jerantut Hospital, Malaysia, from May 2018 to June 2018. BZD refusal status was determined after the dispensing process, and general information on BZD was determined using a questionnaire developed by the researchers. Medication adherence was assessed using the Malaysian Medication Adherence Scale (MALMAS). Results: Overall, 75 patients with psychiatric disorders participate in the study. Participants had been on BZD treatment for a mean of 32.8± 21.6 months. The BZD refusal rate was 38.7%. BZD refusal was significantly associated with several factors. A one-year increase in age resulted in increased odds of BZD refusal by 1.16 times (95% CI 1.05-1.27). Other factors were male gender (OR,9.14; 95% CI, 1.17-71.27), being single (OR,15.07; 95% CI, 1.12-184.28), the diagnosis of schizophrenia (OR,13.45; 95% CI, 1.75-10.33) and not having history of illicit drug use (OR,20.63; 95% CI, 2.49-171.0). Medication adherence was not associated with BZD refusal. Conclusion: BZD refusal was significantly associated with demographic factors such as increased age, male gender and being single and diagnosis factors namely schizophrenia diagnosis and not having history of illicit substance use. Thus, the need for BZD in these groups of patients should be reviewed regularly.