Monte Carlo modelling of a prototype small-body portable graphite calorimeter for ultra-high dose rate proton beams.

Background And Purpose: Accurate dosimetry in Ultra-High Dose Rate (UHDR) beams is challenging because high levels of ion recombination occur within ionisation chambers used as reference dosimeters. A Small-body Portable Graphite Calorimeter (SPGC) exhibiting a dose-rate independent response was built to offer reduced uncertainty on secondary standard dosimetry in UHDR regimes. The aim of this study was to quantify the effect of the geometry and material properties of the device on the dose measurement.

Absolute dosimetry for FLASH proton pencil beam scanning radiotherapy.

A paradigm shift is occurring in clinical oncology exploiting the recent discovery that short pulses of ultra-high dose rate (UHDR) radiation-FLASH radiotherapy-can significantly spare healthy tissues whilst still being at least as effective in curing cancer as radiotherapy at conventional dose rates. These properties promise reduced post-treatment complications, whilst improving patient access to proton beam radiotherapy and reducing costs. However, accurate dosimetry at UHDR is extremely complicated.

The heparin-von Willebrand factor interaction and conventional tests of haemostasis - the challenges in predicting bleeding in cardiopulmonary bypass.

Bleeding is a significant complication of cardiopulmonary bypass (CPB), despite routine anticoagulation monitoring. This is likely to be multifactorial. In this prospective, single-centre cohort study of 30 patients undergoing CPB surgery, our aim was to characterise the changes in von Willebrand factor (VWF) function, platelet interaction and the global coagulation changes during and after CPB surgery and to determine whether bleeding can be predicted.

Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools.

There are various types of hepatic steatosis of which non-alcoholic fatty liver disease, which may be caused by exposure to chemicals and environmental pollutants is the most prevalent, representing a potential major health risk. QSAR modelling has the potential to provide a rapid and cost-effective method to identify compounds which may trigger steatosis. Although models exist to predict key molecular initiating events of steatosis such as nuclear receptor binding, we are aware of no models to predict the apical effect steatosis.