Technical note: Optimizing the deliverability of binary collimation-based SRS treatment for multiple metastases with multiple prescriptions.

intra-arc binary collimation (iABC) is a novel treatment technique in which dynamic conformal arcs are periodically interrupted with binary collimation. It has demonstrated its utility through planning studies for the treatment of multiple metastases. However, the binary collimation approach is idealized in the planning system, while the treatment deliveries must adhere to the physical limitations of the mechanical systems involved [e.

Design optimization of a 1-D array of stemless plastic scintillation detectors.

Background: A stemless plastic scintillation detector (SPSD) is composed of an organic plastic scintillator coupled to an organic photodiode. Previous research has shown that SPSDs are ideally suited to challenging dosimetry measurements such as output factors and profiles in small fields. Lacking from the current literature is a systematic effort to optimize the performance of the photodiode component of the detector.

Serial and parallel organ-at-risk-specific noncoplanar arc optimization for small versus large target volumes in liver SBRT.

Noncoplanar arc optimization has been shown to reduce OAR doses in SRS/SRT and has the potential to reduce doses to OARs in SBRT. Extracranial targets have additional considerations, including large OARs and, in the case of the liver, volume constraints on the healthy liver. Considering pathlengths through OARs that encompass target volumes may lead to specific dose reductions as in the encompassing healthy liver tissue.

Computational drug discovery approaches identify mebendazole as a candidate treatment for autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is a rare genetic disorder characterised by numerous renal cysts, the progressive expansion of which can impact kidney function and lead eventually to renal failure. Tolvaptan is the only disease-modifying drug approved for the treatment of ADPKD, however its poor side effect and safety profile necessitates the need for the development of new therapeutics in this area. Using a combination of transcriptomic and machine learning computational drug discovery tools, we predicted that a number of existing drugs could have utility in the treatment of ADPKD, and subsequently validated several of these drug predictions in established models of disease.

Neurological outcome following out of hospital cardiac arrest: Evaluation of performance of existing risk prediction models in a UK cohort.

Introduction: Out of hospital cardiac arrest (OHCA) is a common problem. Rates of survival are low and a proportion of survivors are left with an unfavourable neurological outcome. Four models have been developed to predict risk of unfavourable outcome at the time of critical care admission - the Cardiac Arrest Hospital Prognosis (CAHP), MIRACLE, Out of Hospital Cardiac Arrest (OHCA), and Targeted Temperature Management (TTM) models.