Functional recovery in penetrating neurological injury is hampered by a lack of clinical regenerative therapies. Biomaterial therapies show promise as medical materials for neural repair through immunomodulation, structural support, and delivery of therapeutic biomolecules. However, a lack of facile and pathology-mimetic models for therapeutic testing is a bottleneck in neural tissue engineering research.
View Article and Find Full Text PDFBackground: Venous thromboembolism after colorectal cancer resection is common and highly morbid. Extended pharmacologic venous thromboembolism prophylaxis after cancer surgery lowers venous thromboembolism risk and is recommended by major professional societies. Adherence is low in contemporary local and regional studies.
View Article and Find Full Text PDFBackground: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic review aimed to synthesise evidence from ML studies predicting CMDs, evaluate their performance, and establish the potential benefit of incorporating lifestyle data in ML models alongside biological and/or demographic-environmental factors.
Methods: This systematic review adheres to the PRISMA statement (Prospero CRD42023401194).
Background: After the risk of drug-induced liver injury was detected during tolvaptan clinical development for the treatment of autosomal dominant polycystic kidney disease (ADPKD), a post-marketing pharmacovigilance study was required for European Union regulatory approval.
Methods: This is an interim analysis from a prospective, observational study enrolling patients prescribed tolvaptan for ADPKD in routine clinical practice. Data were obtained through physician records collected during regular care.