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Work to build the UK’s first dedicated centre for research into the prevention of stroke and dementia has started in Oxford.
HIV/HBV co-infection remodels the immune landscape and Natural Killer cell ADCC functional responses
Background: HBV and HIV co-infection is a common occurrence globally, with significant morbidity and mortality. Both viruses lead to immune dysregulation including changes in NK cells, a key component of antiviral defense and a promising target for HBV cure strategies. Here we used high-throughput single cell analysis to explore the immune cell landscape in people with HBV mono-infection and HIV/HBV co-infection, on antiviral therapy, with emphasis on identifying the distinctive characteristics of NK cell subsets that can be therapeutically harnessed. Results: Our data show striking differences in the transcriptional programs of NK cells. HIV/HBV co-infection was characterized by an overrepresentation of adaptive, KLRC2 expressing NK cells, including a higher abundance of a chemokine enriched (CCL3/CCL4) adaptive cluster. The NK cell remodeling in HIV/HBV co-infection was reflected in enriched activation pathways, including CD3ζ phosphorylation and ZAP-70 translocation that can mediate stronger ADCC responses and a bias towards chemokine/cytokine signaling. By contrast HBV mono-infection imposed a stronger cytotoxic profile on NK cells and a more prominent signature of ‘exhaustion’ with higher circulating levels of HBsAg. Phenotypic alterations in the NK cell pool in co-infection were consistent with increased ‘adaptiveness’ and better capacity for ADCC compared to HBV mono-infection. Overall, an adaptive NK cell signature correlated inversely with circulating levels of HBsAg and HBV-RNA in our cohort. Conclusions: This study provides new insights into the differential signature and functional profile of NK cells in HBV and HIV/HBV co-infection, highlighting pathways that can be manipulated to tailor NK cell-focused approaches to advance HBV cure strategies in different patient groups.
From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation
Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson’s disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual “forgetting” and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.
MorpheusNet: Resource efficient sleep stage classifier for embedded on-line systems.
Sleep Stage Classification (SSC) is a labor-intensive task, requiring experts to examine hours of electrophysiological recordings for manual classification. This is a limiting factor when it comes to leveraging sleep stages for therapeutic purposes. With increasing affordability and expansion of wearable devices, automating SSC may enable deployment of sleep-based therapies at scale. Deep Learning has gained increasing attention as a potential method to automate this process. Previous research has shown accuracy comparable to manual expert scores. However, previous approaches require sizable amount of memory and computational resources. This constrains the ability to classify in real time and deploy models on the edge. To address this gap, we aim to provide a model capable of predicting sleep stages in real-time, without requiring access to external computational sources (e.g., mobile phone, cloud). The algorithm is power efficient to enable use on embedded battery powered systems. Our compact sleep stage classifier can be deployed on most off-the-shelf microcontrollers (MCU) with constrained hardware settings. This is due to the memory footprint of our approach requiring significantly fewer operations. The model was tested on three publicly available data bases and achieved performance comparable to the state of the art, whilst reducing model complexity by orders of magnitude (up to 280 times smaller compared to state of the art). We further optimized the model with quantization of parameters to 8 bits with only an average drop of 0.95% in accuracy. When implemented in firmware, the quantized model achieves a latency of 1.6 seconds on an Arm Cortex-M4 processor, allowing its use for on-line SSC-based therapies.
Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease.
Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.
Subthalamic stimulation modulates context-dependent effects of beta bursts during fine motor control.
Increasing evidence suggests a considerable role of pre-movement beta bursts for motor control and its impairment in Parkinson's disease. However, whether beta bursts occur during precise and prolonged movements and if they affect fine motor control remains unclear. To investigate the role of within-movement beta bursts for fine motor control, we here combine invasive electrophysiological recordings and clinical deep brain stimulation in the subthalamic nucleus in 19 patients with Parkinson's disease performing a context-varying task that comprised template-guided and free spiral drawing. We determined beta bursts in narrow frequency bands around patient-specific peaks and assessed burst amplitude, duration, and their immediate impact on drawing speed. We reveal that beta bursts occur during the execution of drawing movements with reduced duration and amplitude in comparison to rest. Exclusively when drawing freely, they parallel reductions in acceleration. Deep brain stimulation increases the acceleration around beta bursts in addition to a general increase in drawing velocity and improvements of clinical function. These results provide evidence for a diverse and task-specific role of subthalamic beta bursts for fine motor control in Parkinson's disease; suggesting that pathological beta bursts act in a context dependent manner, which can be targeted by clinical deep brain stimulation.
Dopamine encoding of novelty facilitates efficient uncertainty-driven exploration.
When facing an unfamiliar environment, animals need to explore to gain new knowledge about which actions provide reward, but also put the newly acquired knowledge to use as quickly as possible. Optimal reinforcement learning strategies should therefore assess the uncertainties of these action-reward associations and utilise them to inform decision making. We propose a novel model whereby direct and indirect striatal pathways act together to estimate both the mean and variance of reward distributions, and mesolimbic dopaminergic neurons provide transient novelty signals, facilitating effective uncertainty-driven exploration. We utilised electrophysiological recording data to verify our model of the basal ganglia, and we fitted exploration strategies derived from the neural model to data from behavioural experiments. We also compared the performance of directed exploration strategies inspired by our basal ganglia model with other exploration algorithms including classic variants of upper confidence bound (UCB) strategy in simulation. The exploration strategies inspired by the basal ganglia model can achieve overall superior performance in simulation, and we found qualitatively similar results in fitting model to behavioural data compared with the fitting of more idealised normative models with less implementation level detail. Overall, our results suggest that transient dopamine levels in the basal ganglia that encode novelty could contribute to an uncertainty representation which efficiently drives exploration in reinforcement learning.
A Feasibility Study of Parenting for Lifelong Health for Adolescents in China
Purpose: This study examined the feasibility, acceptability, and preliminary effectiveness of Parenting for Lifelong Health for Parents and Adolescents program in reducing the risk of child maltreatment in Chinese families with adolescents. Methods: A pre-post single-arm pilot trial was conducted in July and August, 2023, and involved 16 parents and 13 teenagers with a mixed-methods design to collect both quantitative and qualitative data. Results: Parents reported enhanced positive parenting and reduced child behavioral problems. Adolescents reported decreased general child maltreatment and emotional maltreatment, along with perceived improvements in positive parenting and parent-child communication. Thematic analyses suggested of tangible benefits for participants, as well as for their family dynamics. Discussion: The program demonstrated promising feasibility, and it was significantly associated with reduced adolescent maltreatment. Further rigorous randomized controlled trials are needed.
Test-Retest Reproducibility of Reduced-Field-of-View Density-Weighted CRT MRSI at 3T.
Quantifying an imaging modality's ability to reproduce results is important for establishing its utility. In magnetic resonance spectroscopic imaging (MRSI), new acquisition protocols are regularly introduced which improve upon their precursors with respect to signal-to-noise ratio (SNR), total acquisition duration, and nominal voxel resolution. This study has quantified the within-subject and between-subject reproducibility of one such new protocol (reduced-field-of-view density-weighted concentric ring trajectory (rFOV-DW-CRT) MRSI) by calculating the coefficient of variance of data acquired from a test-retest experiment. The posterior cingulate cortex (PCC) and the right superior corona radiata (SCR) were selected as the regions of interest (ROIs) for grey matter (GM) and white matter (WM), respectively. CVs for between-subject and within-subject were consistently around or below 15% for Glx, tCho, and Myo-Ins, and below 5% for tNAA and tCr.
Methods to estimate body temperature and energy expenditure dynamics in fed and fasted laboratory mice: effects of sleep deprivation and light exposure.
Monitoring body temperature and energy expenditure in freely-moving laboratory mice remains a powerful methodology used widely across a variety of disciplines-including circadian biology, sleep research, metabolic phenotyping, and the study of body temperature regulation. Some of the most pronounced changes in body temperature are observed when small heterothermic species reduce their body temperature during daily torpor. Daily torpor is an energy saving strategy characterized by dramatic reductions in body temperature employed by mice and other species when challenged to meet energetic demands. Typical measurements used to describe daily torpor are the measurement of core body temperature and energy expenditure. These approaches can have drawbacks and developing alternatives for these techniques provides options that can be beneficial both from an animal-welfare and study-complexity perspective. First, this paper presents and assesses a method to estimate core body temperature based on measurements of subcutaneous body temperature, and second, a separate approach to better estimate energy expenditure during daily torpor based on core body temperature. Third, the effects of light exposure during the habitual dark phase and sleep deprivation during the light period on body temperature dynamics were tested preliminary in fed and fasted mice. Together, the here-published approaches and datasets can be used in the future to assess body temperature and metabolism in freely-moving laboratory mice.
A role for vessel‐associated extracellular matrix proteins in multiple sclerosis pathology
AbstractMultiple sclerosis (MS) is unsurpassed for its clinical and pathological hetherogeneity, but the biological determinants of this variability are unknown. HLA‐DRB1*15, the main genetic risk factor for MS, influences the severity and distribution of MS pathology. This study set out to unravel the molecular determinants of the heterogeneity of MS pathology in relation to HLA‐DRB1*15 status. Shotgun proteomics from a discovery cohort of MS spinal cord samples segregated by HLA‐DRB*15 status revealed overexpression of the extracellular matrix (ECM) proteins, biglycan, decorin, and prolargin in HLA‐DRB*15‐positive cases, adding to established literature on a role of ECM proteins in MS pathology that has heretofore lacked systematic pathological validation. These findings informed a neuropathological characterisation of these proteins in a large autopsy cohort of 41 MS cases (18 HLA‐DRB1*15‐positive and 23 HLA‐DRB1*15‐negative), and seven non‐neurological controls on motor cortical, cervical and lumbar spinal cord tissue. Biglycan and decorin demonstrate a striking perivascular expression pattern in controls that is reduced in MS (−36.5%, p = 0.036 and − 24.7%, p = 0.039; respectively) in lesional and non‐lesional areas. A concomitant increase in diffuse parenchymal accumulation of biglycan and decorin is seen in MS (p = 0.015 and p = 0.001, respectively), particularly in HLA‐DRB1*15‐positive cases (p = 0.007 and p = 0.046, respectively). Prolargin shows a faint parenchymal pattern in controls that is markedly increased in MS cases where a perivascular deposition pattern is observed (motor cortex +97.5%, p = 0.001; cervical cord +49.1%, p = 0.016). Our findings point to ECM proteins and the vascular interface playing a central role in MS pathology within and outside the plaque area. As ECM proteins are known potent pro‐inflammatory molecules, their parenchymal accumulation may contribute to disease severity. This study brings to light novel factors that may contribute to the heterogeneity of the topographical variation of MS pathology.
Pathophysiological Mechanisms in Long COVID: A Mixed Method Systematic Review
Introduction: Long COVID (LC) is a global public health crisis affecting more than 70 million people. There is emerging evidence of different pathophysiological mechanisms driving the wide array of symptoms in LC. Understanding the relationships between mechanisms and symptoms helps in guiding clinical management and identifying potential treatment targets. Methods: This was a mixed-methods systematic review with two stages: Stage one (Review 1) included only existing systematic reviews (meta-review) and Stage two (Review 2) was a review of all primary studies. The search strategy involved Medline, Embase, Emcare, and CINAHL databases to identify studies that described symptoms and pathophysiological mechanisms with statistical analysis and/or discussion of plausible causal relationships between mechanisms and symptoms. Only studies that included a control arm for comparison were included. Studies were assessed for quality using the National Heart, Lung, and Blood Institute quality assessment tools. Results: 19 systematic reviews were included in Review 1 and 46 primary studies in Review 2. Overall, the quality of reporting across the studies included in this second review was moderate to poor. The pathophysiological mechanisms with strong evidence were immune system dysregulation, cerebral hypoperfusion, and impaired gas transfer in the lungs. Other mechanisms with moderate to weak evidence were endothelial damage and hypercoagulation, mast cell activation, and auto-immunity to vascular receptors. Conclusions: LC is a complex condition affecting multiple organs with diverse clinical presentations (or traits) underpinned by multiple pathophysiological mechanisms. A ‘treatable trait’ approach may help identify certain groups and target specific interventions. Future research must include understanding the response to intervention based on these mechanism-based traits.
Effects of cognitive behavioural therapy and bright light therapy for insomnia in youths with eveningness: study protocol for a randomised controlled trial.
BACKGROUND: Insomnia and eveningness are common and often comorbid conditions in youths. While cognitive behavioural therapy for insomnia (CBT-I) has been suggested as a promising intervention, it remains unclear whether it is sufficient to also address circadian issues in youths. In addition, despite that light has been shown to be effective in phase-shifting one's circadian rhythm, there has been limited data on the effects of bright light therapy and its combination with CBT-I on sleep and circadian outcomes in youths. The current protocol outlines a randomised controlled trial that examines the efficacy of CBT-I and CBT-I plus bright light therapy (BLT) in reducing insomnia severity, improving mood symptoms and daytime functioning (e.g. sleepiness, fatigue, cognitive function), and improving subjective and objective sleep and circadian measures compared to a waitlist control group. METHODS: We will carry out a randomised controlled trial (RCT) with 150 youths aged 12-24 who meet the criteria of insomnia and eveningness. Participants will be randomised into one of three groups: CBT-I with bright light therapy, CBT-I with placebo light, and waitlist control. Six sessions of CBT-I will be delivered in a group format, while participants will be currently asked to use a portable light device for 30 min daily immediately after awakening throughout the intervention period for bright light therapy. The CBT-I with light therapy group will receive bright constant green light (506 lx) while the CBT-I with placebo light group will receive the modified light device with the LEDs emitting less than 10 lx. All participants will be assessed at baseline and post-treatment, while the two active treatment groups will be additionally followed up at 1 month and 6 months post-intervention. The primary outcome will be insomnia severity, as measured by the Insomnia Severity Index. Secondary outcomes include self-reported mood, circadian, daytime functioning, and quality of life measures, as well as sleep parameters derived from actigraphy and sleep diary and neurocognitive assessments. Objective measures of the circadian phase using dim-light melatonin onset assessment and sleep parameters using polysomnography will also be included as the secondary outcomes. DISCUSSION: This study will be the first RCT to directly compare the effects of CBT-I and BLT in youths with insomnia and eveningness. Findings from the study will provide evidence to inform the clinical management of insomnia problems and eveningness in youths. TRIAL REGISTRATION: ClinicalTrials.gov NCT04256915. Registered on 5 February 2020.