miRNA-6236 Regulation of Postischemic Skeletal Muscle Angiogenesis.

Background: Peripheral arterial disease affects >200 million people worldwide and is characterized by impaired blood flow to the lower extremities. There are no effective medical treatments available. Using the mouse hind-limb ischemia model and miRNA sequencing, we identified a novel miRNA, miR-6236, whose expression significantly elevated in ischemic mouse limbs compared with nonischemic limbs.

Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data.

Background: Due to its late stage of diagnosis lung cancer is the commonest cause of death from cancer in the UK. Existing epidemiological risk models in clinical usage, which have Positive Predictive Values (PPV) of less than 10%, do not consider the temporal relations expressed in sequential electronic health record (EHR) data. We aimed to build a model for lung cancer early detection in primary care using machine learning with deep 'transformer' models on EHR data to learn from these complex sequential 'care pathways'.

Standardized wireless deep brain stimulation system for mice.

Deep brain stimulation (DBS) has emerged as a revolutionary technique for accessing and modulating brain circuits. DBS is used to treat dysfunctional neuronal circuits in neurological and psychiatric disorders. Despite over two decades of clinical application, the fundamental mechanisms underlying DBS are still not well understood.

Validation and application of computer vision algorithms for video-based tremor analysis.

Tremor is one of the most common neurological symptoms. Its clinical and neurobiological complexity necessitates novel approaches for granular phenotyping. Instrumented neurophysiological analyses have proven useful, but are highly resource-intensive and lack broad accessibility.

Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning.

Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation.

The neuron mixer and its impact on human brain dynamics.

A signal mixer facilitates rich computation, which has been the building block of modern telecommunication. This frequency mixing produces new signals at the sum and difference frequencies of input signals, enabling powerful operations such as heterodyning and multiplexing. Here, we report that a neuron is a signal mixer.

A unified framework for simplicial Kuramoto models.

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into three main groups: "simple" models, "Hodge-coupled" models, and "order-coupled" (Dirac) models. Our framework is based on topology and discrete differential geometry, as well as gradient systems and frustrations, and permits a systematic analysis of their properties.

Kernel-based joint independence tests for multivariate stationary and non-stationary time series.

Multivariate time-series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate statistical modelling and analysis of such systems. Here, we introduce kernel-based statistical tests of joint independence in multivariate time series by extending the -variable Hilbert-Schmidt independence criterion to encompass both stationary and non-stationary processes, thus allowing broader real-world applications.

Machine versus physician-based programming of deep brain stimulation in isolated dystonia: A feasibility study.

Background: Deep brain stimulation of the internal globus pallidus effectively alleviates dystonia motor symptoms. However, delayed symptom control and a lack of therapeutic biomarkers and a single pallidal sweetspot region complicates optimal programming. Postoperative management is complex, typically requiring multiple, lengthy follow-ups with an experienced physician - an important barrier to widespread adoption in medication-refractory dystonia patients.

Machine Learning in Tremor Analysis: Critique and Directions.

Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy.

Distinct phenotypes of stimulation-induced dysarthria represent different cortical networks in STN-DBS.

Background: Deep brain stimulation of the subthalamic nucleus is an effective treatment of Parkinson's disease, yet it is often associated with a general deterioration of speech intelligibility. Clustering the phenotypes of dysarthria has been proposed as a strategy to tackle these stimulation-induced speech problems.

Methods: In this study, we examine a cohort of 24 patients to test the real-life application of the proposed clustering and attempt to attribute the clusters to specific brain networks with two different approaches of connectivity analysis.

Smartphone video nystagmography using convolutional neural networks: ConVNG.

Background: Eye movement abnormalities are commonplace in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography (VOG) improves diagnostic accuracy, resource intensiveness precludes its broad use.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.

Background: Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.

Relative, local and global dimension in complex networks.

Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhomogeneities, or to intrinsically discrete systems such as networks. To take into account locality, finiteness and discreteness, dynamical processes can be used to probe the space geometry and define its dimension.

Anion-Exchange Membrane Water Electrolyzers.

This Review provides an overview of the emerging concepts of catalysts, membranes, and membrane electrode assemblies (MEAs) for water electrolyzers with anion-exchange membranes (AEMs), also known as zero-gap alkaline water electrolyzers. Much of the recent progress is due to improvements in materials chemistry, MEA designs, and optimized operation conditions. Research on anion-exchange polymers (AEPs) has focused on the cationic head/backbone/side-chain structures and key properties such as ionic conductivity and alkaline stability.

Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service.

The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help.

Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin.

Background: To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making.

Methods: Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT.

Repurposed floxacins targeting RSK4 prevent chemoresistance and metastasis in lung and bladder cancer.

Lung and bladder cancers are mostly incurable because of the early development of drug resistance and metastatic dissemination. Hence, improved therapies that tackle these two processes are urgently needed to improve clinical outcome. We have identified RSK4 as a promoter of drug resistance and metastasis in lung and bladder cancer cells.

HCGA: Highly comparative graph analysis for network phenotyping.

Networks are widely used as mathematical models of complex systems across many scientific disciplines. Decades of work have produced a vast corpus of research characterizing the topological, combinatorial, statistical, and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network.

The evolution of dystonia-like movements in TOR1A rats after transient nerve injury is accompanied by dopaminergic dysregulation and abnormal oscillatory activity of a central motor network.

TOR1A is the most common inherited form of dystonia with still unclear pathophysiology and reduced penetrance of 30-40%. ∆ETorA rats mimic the TOR1A disease by expression of the human TOR1A mutation without presenting a dystonic phenotype. We aimed to induce dystonia-like symptoms in male ∆ETorA rats by peripheral nerve injury and to identify central mechanism of dystonia development.