A survey of UK General Practitioners about depression, antidepressants and withdrawal: Implementing the 2019 Public Health England Report

Background: In 2019 a literature review indicated that more than half of people who try to come off antidepressants experience withdrawal effects. Both NICE guidelines and the Royal College of Psychiatrists updated their positions in line with that review, and Public Health England published a 152-page report called Dependence and withdrawal associated with some prescribed medicines: an evidence review. The report made several recommendations relevant to GP practice. Method: In order to facilitate implementation of these recommendations an online survey was designed to explore UK GPs’ experiences, opinions, knowledge and needs in relation to depression, antidepressants, and withdrawal. 66 GPs had completed the survey when COVID-19 occurred. Results: In keeping with previous findings, this small sample of GPs had a predominantly psycho-social perspective on the causes of, and treatments for, depression. They broadly considered antidepressants effective for moderate/severe depression and ineffective for minimal/mild depression, for which they preferred psychological therapies and social prescribing. There was a marked lack of consistency in GPs’ knowledge about the incidence and duration of withdrawal effects. Only a minority (29%) felt their knowledge about withdrawal was ‘adequate’ and fewer (17%) believed this about their ‘Ability to distinguish between withdrawal effects and return of the original problem (eg depression)’. Two thirds (68%) would like more training on these matters. Conclusion: It is hoped that even this small sample will be helpful when designing, and seeking funding for, GP training programmes, and when implementing the PHE recommendations for support services, based in the primary care system, for the millions of people contemplating or initiating withdrawal from antidepressants every year in the UK

OSPEN: an open source platform for emulating neuromorphic hardware

This paper demonstrates a framework that entails a bottom-up approach to accelerate research, development, and verification of neuro-inspired sensing devices for real-life applications. Previous work in neuromorphic engineering mostly considered application-specific designs which is a strong limitation for researchers to develop novel applications and emulate the true behaviour of neuro-inspired systems. Hence to enable the fully parallel brain-like computations, this paper proposes a methodology where a spiking neuron model was emulated in software and electronic circuits were then implemented and characterized. The proposed approach offers a unique perspective whereby experimental measurements taken from a fabricated device allowing empirical models to be developed. This technique acts as a bridge between the theoretical and practical aspects of neuro-inspired devices. It is shown through software simulations and empirical modelling that the proposed technique is capable of replicating neural dynamics and post-synaptic potentials. Retrospectively, the proposed framework offers a first step towards open-source neuro-inspired hardware for a range of applications such as healthcare, applied machine learning and the internet of things (IoT)

Fan-Slicer: A Pycuda Package for Fast Reslicing of Ultrasound Shaped Planes

Fan-Slicer (https://github.com/UCL/fan-slicer) is a Python package that enables the fast sampling (slicing) of 2D ultrasound-shaped images from a 3D volume. To increase sampling speed, CUDA kernel functions are used in conjunction with the Pycuda package. The main features include functions to generate images from both 3D surface models and 3D volumes. Additionally, the package also allows for the sampling of images from curvilinear (fan shaped planes) and linear (rectangle shaped planes) ultrasound transducers. Potential uses of Fan-slicer include the generation of large datasets of 2D images from 3D volumes and the simulation of intra-operative data among others

Large scale simulation of labeled intraoperative scenes in unity

PURPOSE: The use of synthetic or simulated data has the potential to greatly improve the availability and volume of training data for image guided surgery and other medical applications, where access to real-life training data is limited. METHODS: By using the Unity game engine, complex intraoperative scenes can be simulated. The Unity Perception package allows for randomisation of paremeters within the scene, and automatic labelling, to make simulating large data sets a trivial operation. In this work, the approach has been prototyped for liver segmentation from laparoscopic video images. 50,000 simulated images were used to train a U-Net, without the need for any manual labelling. The use of simulated data was compared against a model trained with 950 manually labelled laparoscopic images. RESULTS: When evaluated on data from 10 separate patients, synthetic data outperformed real data in 4 out of 10 cases. Average DICE scores across the 10 cases were 0.59 (synthetic data), 0.64 (real data) and 0.75 (both synthetic and real data). CONCLUSION: Synthetic data generated using this method is able to make valid inferences on real data, with average performance slightly below models trained on real data. The use of the simulated data for pre-training boosts model performance, when compared with training on real data only

EIT-MESHER – Segmented FEM Mesh Generation and Refinement

EIT-MESHER (https://github.com/EIT-team/Mesher) is C++ software, based on the CGAL library, which generates high quality Finite Element Model tetrahedral meshes from binary masks of 3D volume segmentations. Originally developed for biomedical applications in Electrical Impedance Tomography (EIT) to address the need for custom, non-linear refinement in certain areas (e.g. around electrodes), EIT-MESHER can also be used in other fields where custom FEM refinement is required, such as Diffuse Optical Tomography (DOT)

CMakeCatchTemplate: A C++ template project

CMakeCatchTemplate (https://github.com/MattClarkson/CMakeCatchTemplate) is a project to provide a starting structure for C++ projects configured with CMake, that can be customised to work in a variety of scenarios, allowing developers to deploy new algorithms to users in a shorter timeframe. Main features include a SuperBuild to build optional dependencies; unit tests using Catch; support for CUDA, OpenMP and MPI; examples of command line and GUI applications; Doxygen integration; Continuous Integration templates and support for building/deploying Python modules

What do we want to get out of this?:a critical interpretive synthesis of the value of process evaluations, with a practical planning framework

BACKGROUND: Process evaluations aim to understand how complex interventions bring about outcomes by examining intervention mechanisms, implementation, and context. While much attention has been paid to the methodology of process evaluations in health research, the value of process evaluations has received less critical attention. We aimed to unpack how value is conceptualised in process evaluations by identifying and critically analysing 1) how process evaluations may create value and 2) what kind of value they may create. METHODS: We systematically searched for and identified published literature on process evaluation, including guidance, opinion pieces, primary research, reviews, and discussion of methodological and practical issues. We conducted a critical interpretive synthesis and developed a practical planning framework. RESULTS: We identified and included 147 literature items. From these we determined three ways in which process evaluations may create value or negative consequences: 1) through the socio-technical processes of ‘doing’ the process evaluation, 2) through the features/qualities of process evaluation knowledge, and 3) through using process evaluation knowledge. We identified 15 value themes. We also found that value varies according to the characteristics of individual process evaluations, and is subjective and context dependent. CONCLUSION: The concept of value in process evaluations is complex and multi-faceted. Stakeholders in different contexts may have very different expectations of process evaluations and the value that can and should be obtained from them. We propose a planning framework to support an open and transparent process to plan and create value from process evaluations and negotiate trade-offs. This will support the development of joint solutions and, ultimately, generate more value from process evaluations to all. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01767-7

Comparison of total variation algorithms for electrical impedance tomography

The applications of total variation (TV) algorithms for electrical impedance tomography (EIT) have been investigated. The use of the TV regularisation technique helps to preserve discontinuities in reconstruction, such as the boundaries of perturbations and sharp changes in conductivity, which are unintentionally smoothed by traditional l2 norm regularisation. However, the non-differentiability of TV regularisation has led to the use of different algorithms. Recent advances in TV algorithms such as the primal dual interior point method (PDIPM), the linearised alternating direction method of multipliers (LADMM) and the spilt Bregman (SB) method have all been demonstrated successful EIT applications, but no direct comparison of the techniques has been made. Their noise performance, spatial resolution and convergence rate applied to time difference EIT were studied in simulations on 2D cylindrical meshes with different noise levels, 2D cylindrical tank and 3D anatomically head-shaped phantoms containing vegetable material with complex conductivity. LADMM had the fastest calculation speed but worst resolution due to the exclusion of the second-derivative; PDIPM reconstructed the sharpest change in conductivity but with lower contrast than SB; SB had a faster convergence rate than PDIPM and the lowest image errors