Chronic inflammatory conditions, which include respiratory diseases and other ailments, are characterized by persistent inflammation and oxidative stress, and represent a significant health burden, often inadequately managed by current therapies which include conventional inhaled bronchodilators and oral or inhaled corticosteroids in the case of respiratory disorders. The present study explores the potential of Vedicinals®9 Advanced, a polyherbal formulation, to mitigate LPS-induced inflammation and oxidative stress in RAW264.7 mouse macrophages.
View Article and Find Full Text PDFAberrant Ras homologous (Rho) GTPase signalling is a major driver of cancer metastasis, and GTPase-activating proteins (GAPs), the negative regulators of RhoGTPases, are considered promising targets for suppressing metastasis, yet drug discovery efforts have remained elusive. Here, we report the identification and characterization of adhibin, a synthetic allosteric inhibitor of RhoGAP class-IX myosins that abrogates ATPase and motor function, suppressing RhoGTPase-mediated modes of cancer cell metastasis. In human and murine adenocarcinoma and melanoma cell models, including three-dimensional spheroid cultures, we reveal anti-migratory and anti-adhesive properties of adhibin that originate from local disturbances in RhoA/ROCK-regulated signalling, affecting actin-dynamics and actomyosin-based cell-contractility.
View Article and Find Full Text PDFIntroduction: Many studies have investigated the impact of congenital heart defects (CHD) on child development. However, because CHD not only affects the child and his or her development but, also the entire family, family functioning after pediatric cardiac surgery is of increasing research interest. This prospective childhood-adolescence case-control study aimed to examine differences and changes in parenting behavior and mother-child relationship quality after early surgical repair of an isolated ventricular septum defect (VSD) compared to non-affected controls.
View Article and Find Full Text PDFBackground: Clinical work-up for suspected acute coronary syndrome (ACS) is resource intensive.
Objectives: This study aimed to develop a machine learning model for digitally phenotyping myocardial injury and infarction and predict 30-day events in suspected ACS patients.
Methods: Training and testing data sets, predominantly derived from electronic health records, included suspected ACS patients presenting to 6 and 26 South Australian hospitals, respectively.