Enhance Categorisation Of Multilevel High-Sensitivity Cardiovascular Biomarkers From Lateral Flow Immunoassay Images Via Neural Networks And Dynamic Time Warping

The 27th IEEE International Conference on Image Processing, Abu Dhabi, United Arab Emirates (held online due to coronavirus outbreak), 25-28 October 2020Lateral Flow Immunoassays (LFA) are low cost, rapid and highly efficacious Point-of-Care devices. Traditional LFA testing faces challenges to detect high-sensitivity biomarkers due to low sensitivity. Unlike most approaches based on averaging image intensity from a region-of-interest (ROI), this paper presents a novel system that considers each row of an LFA image as a time series signal and, consequently, does not require the detection of ROI. Long Short-Term Memory (LSTM) networks are used to classify LFA data obtained from multilevel high-sensitivity cardiovascular biomarkers. Dynamic Time Warping (DTW) was incorporated with LSTM to align the LFA data from different concentration levels to a common reference before feeding the distance maps into an LSTM network. The LSTM network outperforms other classifiers with or without DTW. Furthermore, performance of all classifiers is improved after incorporating DTW. The positive outcomes suggest the potential of the proposed methods for early risk assessment of cardiovascular diseases.Science Foundation IrelandInsight Research Centre2020-10-06 JG: PDF replaced with correct versio

Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40

Predicting trajectories of symptom change during and following treatment in adolescents with Unipolar Major Depression.

Objective: Definitions of treatment response used in randomised controlled trials for unipolar major depression are non-standardised and arbitrary. Proportion of non-responders has been estimated as ranging from 20%-40% across such trials. I aimed to classify depressed adolescents recruited to the UK IMPACT trial into different trajectories of depression symptom response using a longitudinal data-driven approach: growth mixture modelling (GMM) and investigate potential predictors of trajectory classes in this cohort. Method: 465 depressed adolescents received manualised psychological therapies in the IMPACT trial. GMM was used to plot the trajectories of self-reported depressive symptoms measured at 6 nominal time points over 86 weeks from randomisation, and categorise patients into their most likely trajectory class. Chapters 2-4 investigated the prognostic value of a number of variables. Chapter 2 investigated a battery of demographic and clinical variables including subclinical psychotic symptoms. Chapter 3 focused on a subsample of patients: 109 of the 465 with structural magnetic resonance imaging (MRI) data. FreeSurfer was used to extract cortical thickness (CT) and surface area (SA) measures from 4 regions of interest (ROI; rostral anterior cingulate, dorsolateral prefrontal cortex, orbitofrontal cortex, and insular cortex). Chapter 4 focused on another subsample of patients: 166 of the 465 with salivary basal cortisol data at both waking and evening. Logistic regressions were used in Chapters 2-4 to investigate whether these variables were associated with increased likelihood of membership to a certain GMM class. Results: A piecewise GMM categorised patients into two classes with initially similar and subsequently distinct trajectories. Both groups had a significant decline in depressive symptoms over the first 18 weeks. Eighty-four per cent of patients were classed as “continued-improvers” through reporting an improvement in symptoms over the full duration of the study. A further class of 15.9% of patients were termed “halted-improvers” who had higher depression scores at baseline, faster recovery but no further improvement after 18 weeks. This data-driven method of classification showed only moderate agreement with a priori classification methods, and suggested misclassification rate could be as great as 31%. Co-morbid psychiatric disorders at baseline moderately increased the liability of being a member of the halted-improvers class (OR = 1.40, CI 1.00-1.96). No other clinical, neurological or cortisol variable reached statistical significance for predicting trajectory class. Conclusion: A fast reduction in depressive symptoms in the first few weeks of treatment may not indicate a good prognosis. Further, halted-improvement may not be apparent until after 18 weeks of treatment. Capitalizing on repeated symptom assessments with longitudinal data driven modelling may improve the precision of revealing patient groups with differential responses to treatment. Further work should seek to validate these trajectories in a separate sample of adolescents.The IMPACT study was funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme (project number 06/05/01). MR-IMPACT was funded by the Medical Research Council (grant: G0802226) and IMPACT-GH was funded by the Evelyn Trust. My doctoral studentship was awarded by the Neuroscience in Psychiatry (NSPN.Org) Consortium itself funded by a strategic award from the Wellcome Trust (095844/Z/11/Z) awarded to Professor Ian Goodyer and Professor Peter Fonagy

Immunohistochemical and electrophysiological investigation of E/I balance alterations in animal models of frontotemporal dementia

Behavioural variant frontotemporal dementia (bvFTD) is a neurodegenerative disease characterised by changes in behaviour. Apathy, behavioural disinhibition and stereotyped behaviours are the first symptoms to appear and all have a basis in reward and pleasure deficits. The ventral striatum and ventral regions of the globus pallidus are involved in reward and pleasure. It is therefore reasonable to suggest alterations in these regions may underpin bvFTD. One postulated contributory factor is alteration in E/I balance in striatal regions. GABAergic interneurons play a role in E/I balance, acting as local inhibitory brakes, they are therefore a rational target for research investigating early biological predictors of bvFTD. To investigate this, we will carry out immunohistochemical staining for GABAergic interneurons (parvalbumin and neuronal nitric oxide synthase) in striatal regions of brains taken from CHMP2B mice, a validated animal model of bvFTD. We hypothesise that there will be fewer GABAergic interneurons in the striatum which may lead to ‘reward-seeking’ behaviour in bvFTD. This will also enable us to investigate any preclinical alterations in interneuron expression within this region. Results will be analysed using a mixed ANOVA and if significant, post hoc t-tests will be used. The second part of our study will involve extracellular recordings from CHMP2B mouse brains using a multi-electrode array (MEA). This will enable us to determine if there are alterations in local field potentials (LFP) in preclinical and symptomatic animals. We will also be able to see if neuromodulators such as serotonin and dopamine effect LFPs after bath application. We will develop slice preparations to preserve pathways between the ventral tegmental area and the ventral pallidum, an output structure of the striatum, and the dorsal raphe nucleus and the VP. Using the MEA we will stimulate an endogenous release of dopamine and serotonin using the slice preparations as described above. This will enable us to see if there are any changes in LFPs after endogenous release of neuromodulators. We hypothesise there will be an increase in LFPs due to loss of GABAergic interneurons

FAIR and bias-free network modules for mechanism-based disease redefinitions

Even though chronic diseases are the cause of 60% of all deaths around the world, the underlying causes for most of them are not fully understood. Hence, diseases are defined based on organs and symptoms, and therapies largely focus on mitigating symptoms rather than cure. This is also reflected in the most commonly used disease classifications. The complex nature of diseases, however, can be better defined in terms of networks of molecular interactions. This research applies the approaches of network medicine – a field that uses network science for identifying and treating diseases – to multiple diseases with highly unmet medical need such as stroke and hypertension. The results show the success of this approach to analyse complex disease networks and predict drug targets for different conditions, which are validated through preclinical experiments and are currently in human clinical trials