Quantifying the robustness of the neutron reflectometry technique for structural characterization of polymer brushes

Neutron reflectometry is the foremost technique for in situ determination of the volume fraction profiles of polymer brushes at planar interfaces. However, the subtle features in the reflectometry data produced by these diffuse interfaces challenge data interpretation. Historically, data analyses have used least-squares approaches that do not adequately quantify the uncertainty of the modeled profile and ignore the possibility of other structures that also match the collected data (multimodality). Here, a Bayesian statistical approach is used that permits the structural uncertainty and multimodality to be quantified for polymer brush systems. A free-form model is used to describe the volume fraction profile, minimizing assumptions regarding brush structure, while only allowing physically reasonable profiles to be produced. The model allows the total volume of polymer and the profile monotonicity to be constrained. The rigor of the approach is demonstrated via a round-Trip analysis of a simulated system, before it is applied to real data examining the well characterized collapse of a thermoresponsive brush. It is shown that, while failure to constrain the interfacial volume and consider multimodality may result in erroneous structures being derived, carefully constraining the model allows for robust determination of polymer brush compositional profiles. This work highlights that an appropriate combination of flexibility and constraint must be used with polymer brush systems to ensure the veracity of the analysis. The code used in this analysis is provided, enabling the reproduction of the results and the application of the method to similar problems

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Refnx: Neutron and X-ray reflectometry analysis in python

refnx is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform and has been tested on Linux, macOS and Windows. Its graphical user interface is browser based, through a Jupyter notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterized in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule etc.). The model and data are used to create an objective, which is used to calculate the residuals, log-likelihood and log-prior probabilities of the system. Objectives are combined to perform co-refinement of multiple data sets and mixed-area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. The software offers a choice of fitting approaches, including least-squares (global and gradient-based optimizers) and a Bayesian approach using a Markov-chain Monte Carlo algorithm to investigate the posterior distribution of the model parameters. The Bayesian approach is useful for examining parameter covariances, model selection and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in Jupyter notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses