Computational Modelling in the Management of Patients with Aortic Valve Stenosis

Background Stenosis of the aortic valve causes increased left ventricular pressure leading to adverse clinical outcomes. The selection and timing of intervention (surgical replacement or transcatheter implantation) is often unclear and is based upon limited data. Hypothesis A comprehensive and integrated personalised approach, including recognition of cardiac energetics parameters extracted from a personalised mathematical model, mapped to patient activity, has the potential to improve diagnosis and the planning and timing of interventions. Aims This project seeks to implement a simple, personalised, mathematical model of patients with aortic stenosis (AS), which can ‘measure’ cardiac work and power parameters that provide an effective characterisation of the demand on the heart in both rest and exercise conditions and can predict the changes of these parameters following an intervention. The specific aims of this project are: • to critically review current diagnostic methods • to evaluate the potential role of pre- and post-procedural measured patient activity • to implement a simple, personalised, mathematical model of patients with AS • to evaluate the potential role of a clinical decision support system Methods Twenty-two patients with severe AS according to ESC criteria were recruited. Relevant clinical, imaging, activity monitoring, six-minute walk test, and patient reported data were collected, before and early and after treatment. Novel imaging techniques were developed to help in the diagnosis of AS. A computational model was developed and executed using the data collected to create non-invasive pressure volume loops and study the global haemodynamic burden on the left ventricle. Simulations were run to predict the haemodynamic parameters both during exercise and following intervention. Modelled parameters were validated against clinically measured values. This information was then correlated with symptoms and activity data. A clinical decision support tool was created and populated with data obtained and its clinical utility evaluated. Outcomes The results of this project suggest that the combination of imaging and activity data with computational modelling provides a novel, patient-specific insight into patients’ haemodynamics and may help guide clinical decision making in patients with AS

Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable

In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable measurements, they use considerably more energy than accelerometers and have a major impact on battery life of wearable devices. By using energy-efficient accelerometers to predict heart rate, significant energy savings can be made. Further, we are interested in understanding patient recovery after a heart rate intervention, where we expect a variation in heart rate over time. Therefore, we propose an online approach to tackle the concept as time passes. We evaluate the methods on approximately 4 weeks of free living data from three patients over a number of months. We show that our approach can achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples).Comment: MLMH 2018: 2018 KDD Workshop on Machine Learning for Medicine and Healthcar

Vesta:A Digital Health Analytics Platform for a Smart Home in a Box

© 2020 This paper presents Vesta, a digital health platform composed of a smart home in a box for data collection and a machine learning based analytic system for deriving health indicators using activity recognition, sleep analysis and indoor localization. This system has been deployed in the homes of 40 patients undergoing a heart valve intervention in the United Kingdom (UK) as part of the EurValve project, measuring patients health and well-being before and after their operation. In this work a cohort of 20 patients are analyzed, and 2 patients are analyzed in detail as example case studies. A quantitative evaluation of the platform is provided using patient collected data, as well as a comparison using standardized Patient Reported Outcome Measures (PROMs) which are commonly used in hospitals, and a custom survey. It is shown how the ubiquitous in-home Vesta platform can increase clinical confidence in self-reported patient feedback. Demonstrating its suitability for digital health studies, Vesta provides deeper insight into the health, well-being and recovery of patients within their home

Flow field visualization of entangled polybutadiene solutions under nonlinear viscoelastic flow conditions

Using self-designed particle visualization instrumentation, startup shear and step-strain tests were conducted under a series of systematically varied rheological and geometrical conditions, and the velocity profiles in three different well-entangled polybutadiene/oligomer solutions were obtained. For startup shear tests, in the regime of entanglement densities up to 89 and nominal reptation Weissenberg numbers up to 18.6, we generally observe either wall slip and a linear velocity/strain profile or simply the linear profile with no wall slip unless a massive edge fracture or instability has occurred in the sample. Meanwhile, step-strain tests conducted at similar and higher step Weissenberg numbers revealed little particle motion upon cessation. These results lead us to a conclusion that there is no compelling evidence of shear banding or nonquiescent relaxation in the range of entanglement density and Wi investigated; we interpret the results to imply that any observed banding probably correlates with edge effects.National Science Foundation (U.S.) (Grant DMR-0934305

Meta-Mass Shift Chemical (MeMSChem) profiling of metabolomes from coral reefs

Untargeted metabolomics of environmental samples routinely detects thousands of small molecules, the vast majority of which cannot be identified. Meta-mass shift chemical (MeMSChem) profiling was developed to identify mass differences between related molecules using molecular networks. This approach illuminates metabolome-wide relationships between molecules and the putative chemical groups that differentiate them (e.g., H2, CH2, COCH2). MeMSChem profiling was used to analyze a publicly available metabolomic dataset of coral, algal, and fungal mat holobionts (i.e., the host and its associated microbes and viruses) sampled from some of Earth's most remote and pristine coral reefs. Each type of holobiont had distinct mass shift profiles, even when the analysis was restricted to molecules found in all samples. This result suggests that holobionts modify the same molecules in different ways and offers insights into the generation of molecular diversity. Three genera of stony corals had distinct patterns of molecular relatedness despite their high degree of taxonomic relatedness. MeMSChem profiles also partially differentiated between individuals, suggesting that every coral reef holobiont is a potential source of novel chemical diversity

Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease

Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision-making in heart disease, it remains unknown to which extent wearables can help to determine such functional capacity measures. In clinical settings, the 6-minute walk test has become a standardized diagnostic and prognostic marker. We aimed to explore, whether 6-minute walk distances can be predicted by wrist-worn devices in patients with different stages of mitral and aortic valve disease. A total of n = 107 sensor datasets with 1,019,748 min of recordings were analysed. Based on heart rate recordings and literature information, activity levels were determined and compared to results from a 6-minute walk test. The percentage of time spent in moderate activity was a predictor for the achievement of gender, age and body mass index-specific 6-minute walk distances (p < 0.001; R2 = 0.48). The uncertainty of these predictions is demonstrated

Laboratory transmission potential of British mosquitoes for equine arboviruses

Background: There has been no evidence of transmission of mosquito-borne arboviruses of equine or human health concern to date in the UK. However, in recent years there have been a number of outbreaks of viral diseases spread by vectors in Europe. These events, in conjunction with increasing rates of globalisation and climate change, have led to concern over the future risk of mosquito-borne viral disease outbreaks in northern Europe and have highlighted the importance of being prepared for potential disease outbreaks. Here we assess several UK mosquito species for their potential to transmit arboviruses important for both equine and human health, as measured by the presence of viral RNA in saliva at diferent time points after taking an infective blood meal. Results: The following wild-caught British mosquitoes were evaluated for their potential as vectors of zoonotic equine arboviruses: Ochlerotatus detritus for Venezuelan equine encephalitis virus (VEEV) and Ross River virus (RRV), and Culiseta annulata and Culex pipiens for Japanese encephalitis virus (JEV). Production of RNA in saliva was demonstrated at varying efciencies for all mosquito-virus pairs. Ochlerotatus detritus was more permissive for production of RRV RNA in saliva than VEEV RNA. For RRV, 27.3% of mosquitoes expectorated viral RNA at 7 days post-infection when incubated at 21 °C and 50% at 24 °C. Strikingly, 72% of Cx. pipiens produced JEV RNA in saliva after 21 days at 18 °C. For some mosquito-virus pairs, infection and salivary RNA titres reduced over time, suggesting unstable infection dynamics. Conclusions: This study adds to the number of Palaearctic mosquito species that demonstrate expectoration of viral RNA, for arboviruses of importance to human and equine health. This work adds to evidence that native mosquito species should be investigated further for their potential to vector zoonotic mosquito-borne arboviral disease of equines in northern Europe. The evidence that Cx. pipiens is potentially an efcient laboratory vector of JEV at temperatures as low as 18 °C warrants further investigation, as this mosquito is abundant in cooler regions of Europe and is considered an important vector for West Nile Virus, which has a comparable transmission ecolog

Mitral regurgitation quantification by cardiac magnetic resonance imaging (MRI) remains reproducible between software solutions [version 3; peer review: 1 approved, 1 approved with reservations]

BACKGROUND: The reproducibility of mitral regurgitation (MR) quantification by cardiovascular magnetic resonance (CMR) imaging using different software solutions remains unclear. This research aimed to investigate the reproducibility of MR quantification between two software solutions: MASS (version 2019 EXP, LUMC, Netherlands) and CAAS (version 5.2, Pie Medical Imaging). METHODS: CMR data of 35 patients with MR (12 primary MR, 13 mitral valve repair/replacement, and ten secondary MR) was used. Four methods of MR volume quantification were studied, including two 4D-flow CMR methods (MRMVAV and MRJet) and two non-4D-flow techniques (MRStandard and MRLVRV). We conducted within-software and inter-software correlation and agreement analyses. RESULTS: All methods demonstrated significant correlation between the two software solutions: MRStandard (r=0.92, p<0.001), MRLVRV (r=0.95, p<0.001), MRJet (r=0.86, p<0.001), and MRMVAV (r=0.91, p<0.001). Between CAAS and MASS, MRJet and MRMVAV, compared to each of the four methods, were the only methods not to be associated with significant bias. CONCLUSIONS: We conclude that 4D-flow CMR methods demonstrate equivalent reproducibility to non-4D-flow methods but greater levels of agreement between software solutions

Successful awake proning is associated with improved clinical outcomes in patients with COVID-19: single-centre high-dependency unit experience

The SARS-CoV-2 can lead to severe illness with COVID-19. Outcomes of patients requiring mechanical ventilation are poor. Awake proning in COVID-19 improves oxygenation, but on data clinical outcomes is limited. This single-centre retrospective study aimed to assess whether successful awake proning of patients with COVID-19, requiring respiratory support (continuous positive airways pressure (CPAP) or high-flow nasal oxygen (HFNO)) on a respiratory high-dependency unit (HDU), is associated with improved outcomes. HDU care included awake proning by respiratory physiotherapists. Of 565 patients admitted with COVID-19, 71 (12.6%) were managed on the respiratory HDU, with 48 of these (67.6%) requiring respiratory support. Patients managed with CPAP alone 22/48 (45.8%) were significantly less likely to die than patients who required transfer onto HFNO 26/48 (54.2%): CPAP mortality 36.4%; HFNO mortality 69.2%, (p=0.023); however, multivariate analysis demonstrated that increasing age and the inability to awake prone were the only independent predictors of COVID-19 mortality. The mortality of patients with COVID-19 requiring respiratory support is considerable. Data from our cohort managed on HDU show that CPAP and awake proning are possible in a selected population of COVID-19, and may be useful. Further prospective studies are required

ATM Regulates Differentiation of Myofibroblastic Cancer-Associated Fibroblasts and Can Be Targeted to Overcome Immunotherapy Resistance

Myofibroblastic cancer-associated fibroblast (myoCAF)-rich tumors generally contain few T cells and respond poorly to immune-checkpoint blockade. Although myoCAFs are associated with poor outcome in most solid tumors, the molecular mechanisms regulating myoCAF accumulation remain unclear, limiting the potential for therapeutic intervention. Here, we identify ataxia-telangiectasia mutated (ATM) as a central regulator of the myoCAF phenotype. Differentiating myofibroblasts in vitro and myoCAFs cultured ex vivo display activated ATM signaling, and targeting ATM genetically or pharmacologically could suppress and reverse differentiation. ATM activation was regulated by the reactive oxygen species-producing enzyme NOX4, both through DNA damage and increased oxidative stress. Targeting fibroblast ATM in vivo suppressed myoCAF-rich tumor growth, promoted intratumoral CD8 T-cell infiltration, and potentiated the response to anti-PD-1 blockade and antitumor vaccination. This work identifies a novel pathway regulating myoCAF differentiation and provides a rationale for using ATM inhibitors to overcome CAF-mediated immunotherapy resistance.SignificanceATM signaling supports the differentiation of myoCAFs to suppress T-cell infiltration and antitumor immunity, supporting the potential clinical use of ATM inhibitors in combination with checkpoint inhibition in myoCAF-rich, immune-cold tumors