660 research outputs found

    Pathophysiology of Spinal Cord Injury (SCI)

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    Spinal cord injury (SCI) leads to paralysis, sensory, and autonomic nervous system dysfunctions. However, the pathophysiology of SCI is complex, and not limited to the nervous system. Indeed, several other organs and tissue are also affected by the injury, directly or not, acutely or chronically, which induces numerous health complications. Although a lot of research has been performed to repair motor and sensory functions, SCI-induced health issues are less studied, although they represent a major concern among patients. There is a gap of knowledge in pre-clinical models studying these SCI-induced health complications that limits translational applications in humans. This reprint describes several aspects of the pathophysiology of spinal cord injuries. This includes, but is not limited to, the impact of SCI on cardiovascular and respiratory functions, bladder and bowel function, autonomic dysreflexia, liver pathology, metabolic syndrome, bones and muscles loss, and cognitive functions

    Accidental awareness during general anaesthesia in obstetric surgery

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    Accidental awareness during general anaesthesia (AAGA) occurs when a patient becomes unintentionally conscious during general anaesthesia, which may involve unpleasant memories of experiences during surgery. Contributory factors that may increase risk of AAGA coincide in pregnant women undergoing general anaesthesia for childbirth related surgery. Whilst obstetric general anaesthesia has largely been substituted by spinal and epidural (termed neuraxial) anaesthetic techniques, in which a mother can be awake and pain free during childbirth, general anaesthesia is still necessary to facilitate surgery rapidly in emergency situations or for mothers with certain medical conditions. In this thesis I investigate the distinct characteristics of general anaesthesia for pregnant women undergoing surgery for childbirth, whether these characteristics increase risk of AAGA, and changes to obstetric anaesthetic technique occurring in the context of wider anaesthetic developments over time. I provide evidence on the incidence, experiences, risk factors and psychological consequences of AAGA in peripartum women. Challenges to large scale clinical study of AAGA are explored and addressed in the design of a multi-centre, prospective, cross-sectional cohort study of women receiving general anaesthesia for obstetric surgery in 72 hospitals in England. A four-stage process for screening patients using direct questioning, verifying with corroborative detail, adjudicating and classifying descriptions of experiences is described. The interactional nature of research interviews, statistical modelling, psychological factors and the neurophysiology of memory are considered during development of study methodology. Psychological morbidity was assessed for 12 months after surgery. As part of an embedded study, descriptive epidemiology of obstetric patients and general anaesthesia techniques were identified, alongside risk factors for airway complications. A total of 3,115 patients were recruited, 12 of whom had certain/probable or possible AAGA: a prevalence of 0.39% or 1 in 256 (95%CI 149–500) for all obstetric surgery. Distressing experiences were reported by seven (0.22%) patients, paralysis by five (0.16%) and paralysis with pain by two (0.06%). Associations were identified between AAGA and patient risk factors (abnormal body mass index), organisational factors (out-of-hours surgery) and pharmacological factors (use of thiopental during induction of anaesthesia). Contextual factors relating anaesthesia for obstetric patients with AAGA and other anaesthesia complications, including difficult airway management, were evaluated. My study methodology and it’s context, in English public sector hospitals, identified a higher risk of AAGA in obstetric patients than previously detected using other methods and locations. These results have implications for healthcare policy of obstetric anaesthesia, informed consent of patients receiving general anaesthesia and post-natal screening care. I conclude on recommendations to minimise awareness risk for future patients and address the challenge of implementing systemic improvements in obstetric general anaesthesia care and patient safety

    Source reconstruction of the neural correlates of ongoing pain using magnetoencephalography

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    Pain is a pervasive, complex, and subjective phenomenon that can be described by many features and researched using many paradigms; chronic pain has a significant impact on the quality of life of patients experiencing it and constitutes a large burden on the National Health Service. Discovering neural biomarkers for ongoing pain and pain sensitivity has the potential to elucidate underlying mechanisms, evaluate therapy effectiveness, and identify regions of interest within the brain for further study or intervention; something that is possible with functional imaging of brain activity. Magnetoencephalography (MEG) is a non-invasive technique that records brain activity through magnetic fields unobstructed by tissue of the head. This thesis utilises modern source reconstruction of MEG data to explore brain activity that characterises tonic pain conditions, and explores the future of tonic pain research by evaluating the utility of the PATHWAY Contact Heat Evoked Potentials Stimulator (CHEPS) – a tool used both as an experimental pain stimulus, and a clinical evaluation method in chronic pain – in current and future MEG research. A systematic review of studies exploring the CHEPS and MEG, which highlights the paucity of the literature combining the two despite the potential benefits of each, is presented within. Study one investigates the brain activity changes resulting from paraesthesia-based Spinal Cord Stimulation for chronic pain: significant enhancements in synchrony for theta and delta frequency bands during SCS-on resting-state are demonstrated, and a significant reduction in Somatosensory Evoked Potential (SSEP) power spectra in the SCS-on condition – providing evidence that conventional SCS influences resting and ascending processing in the brain, but does not necessarily suppress the field strength of SSEPs. Study two compared the neural activity of participants with high and low pain sensitivity during the Cold Pressor Test, and identifies regions of interest for future study. Study three is a methodological chapter which attempts to mitigate the methodological challenges involved in utilising the PATHWAY CHEPS in MEG research: The thorough exploration of independent component analysis, signal space separation and beamforming parameters demonstrates that it is possible to suppress the artefacts generated by the non-fMRI compatible CHEPS’ thermode with the application of signal attenuation techniques, but only in an empty room dataset; the implications of this for future research are discussed

    CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

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    This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

    Haptics: Science, Technology, Applications

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    This open access book constitutes the proceedings of the 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022, held in Hamburg, Germany, in May 2022. The 36 regular papers included in this book were carefully reviewed and selected from 129 submissions. They were organized in topical sections as follows: haptic science; haptic technology; and haptic applications

    Prediction and causal inference in the transition from acute to chronic low back pain

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    The overarching aim of this thesis was to enhance our understanding of the neurobiological risk factors associated with the transition from acute to chronic Low back pain (LBP). To achieve this aim, the Understanding persistent Pain Where it ResiDes (UPWaRD) study was conducted. In this thesis, six chapters describe the background, methods, and results of the UPWaRD study. Chapter 2 describes the protocol, published ‘a priori’ for developing a multivariable prediction model, including candidate predictors selected from the neurobiological (e.g. sensorimotor cortical excitability assessed by sensory and motor evoked potentials, Brain Derived Neurotrophic Factor [BDNF] genotype), psychological (e.g. depression and anxiety), symptom-related (e.g. LBP history) and demographic domains. Chapter 3 builds on the study protocol in the form of a cohort profile, describing baseline characteristics of 120 people experiencing an acute LBP episode and 57 pain-free control participants that form the UPWaRD cohort. Chapter 4 reports the results of the multivariable prediction model developed in 120 people experiencing acute LBP. To further understand the importance of these prognostic factors we developed a causal model of chronic LBP using directed acyclic graphs. The methodology and statistical analysis plan for drawing causal inferences, thus transparently reporting our causal assumptions, are reported in Chapter 5. Chapter 6 then provides the first evidence that low sensory cortex excitability during an acute LBP episode is a causal mechanism underpinning the development of chronic LBP. Finally, in Chapter 7, we report the results of a proteomic analysis, using hydrophobic interaction chromatography and electrospray ionization tandem mass spectrometry. Taken together this thesis makes an extensive and original contribution to our understanding of neurobiological risk factors involved in the transition from acute to chronic LBP. Not only is the inclusion of neurobiological prognostic factors in multivariable clinical prediction models a promising direction for future research that aims to identify people at high risk of poor outcome, but low sensory cortex excitability during acute LBP may be a promising causal mechanism that future treatments could target during acute LBP in the hope of expediting recovery and preventing the development of chronic LBP. Further, this thesis provides some of the earliest evidence to suggest sex-specific differential expression of proteins, measured from human serum, contributes to recovery status at three-month follow-up. This work provides foundational evidence for future research exploring strategies targeting distinct immune system processes in males and females that may interfere with the transition from acute to chronic LBP

    Intelligent Biosignal Processing in Wearable and Implantable Sensors

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    This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine
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