631 research outputs found

    Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

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    Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

    East Midlands Research into Ageing Network (EMRAN) Discussion Paper Series

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    Academic geriatric medicine in Leicester . There has never been a better time to consider joining us. We have recently appointed a Professor in Geriatric Medicine, alongside Tom Robinson in stroke and Victoria Haunton, who has just joined as a Senior Lecturer in Geriatric Medicine. We have fantastic opportunities to support students in their academic pursuits through a well-established intercalated BSc programme, and routes on through such as ACF posts, and a successful track-record in delivering higher degrees leading to ACL post. We collaborate strongly with Health Sciences, including academic primary care. See below for more detail on our existing academic set-up. Leicester Academy for the Study of Ageing We are also collaborating on a grander scale, through a joint academic venture focusing on ageing, the ‘Leicester Academy for the Study of Ageing’ (LASA), which involves the local health service providers (acute and community), De Montfort University; University of Leicester; Leicester City Council; Leicestershire County Council and Leicester Age UK. Professors Jayne Brown and Simon Conroy jointly Chair LASA and have recently been joined by two further Chairs, Professors Kay de Vries and Bertha Ochieng. Karen Harrison Dening has also recently been appointed an Honorary Chair. LASA aims to improve outcomes for older people and those that care for them that takes a person-centred, whole system perspective. Our research will take a global perspective, but will seek to maximise benefits for the people of Leicester, Leicestershire and Rutland, including building capacity. We are undertaking applied, translational, interdisciplinary research, focused on older people, which will deliver research outcomes that address domains from: physical/medical; functional ability, cognitive/psychological; social or environmental factors. LASA also seeks to support commissioners and providers alike for advice on how to improve care for older people, whether by research, education or service delivery. Examples of recent research projects include: ‘Local History Café’ project specifically undertaking an evaluation on loneliness and social isolation; ‘Better Visits’ project focused on improving visiting for family members of people with dementia resident in care homes; and a study on health issues for older LGBT people in Leicester. Clinical Geriatric Medicine in Leicester We have developed a service which recognises the complexity of managing frail older people at the interface (acute care, emergency care and links with community services). There are presently 17 consultant geriatricians supported by existing multidisciplinary teams, including the largest complement of Advance Nurse Practitioners in the country. Together we deliver Comprehensive Geriatric Assessment to frail older people with urgent care needs in acute and community settings. The acute and emergency frailty units – Leicester Royal Infirmary This development aims at delivering Comprehensive Geriatric Assessment to frail older people in the acute setting. Patients are screened for frailty in the Emergency Department and then undergo a multidisciplinary assessment including a consultant geriatrician, before being triaged to the most appropriate setting. This might include admission to in-patient care in the acute or community setting, intermediate care (residential or home based), or occasionally other specialist care (e.g. cardiorespiratory). Our new emergency department is the county’s first frail friendly build and includes fantastic facilities aimed at promoting early recovering and reducing the risk of hospital associated harms. There is also a daily liaison service jointly run with the psychogeriatricians (FOPAL); we have been examining geriatric outreach to oncology and surgery as part of an NIHR funded study. We are home to the Acute Frailty Network, and those interested in service developments at the national scale would be welcome to get involved. Orthogeriatrics There are now dedicated hip fracture wards and joint care with anaesthetists, orthopaedic surgeons and geriatricians. There are also consultants in metabolic bone disease that run clinics. Community work Community work will consist of reviewing patients in clinic who have been triaged to return to the community setting following an acute assessment described above. Additionally, primary care colleagues refer to outpatients for sub-acute reviews. You will work closely with local GPs with support from consultants to deliver post-acute, subacute, intermediate and rehabilitation care services. Stroke Medicine 24/7 thrombolysis and TIA services. The latter is considered one of the best in the UK and along with the high standard of vascular surgery locally means one of the best performances regarding carotid intervention

    Unraveling the interplay between daily life fatigue and physical activity after subarachnoid hemorrhage: an ecological momentary assessment and accelerometry study

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    Background Fatigue is one of the most commonly reported symptoms after subarachnoid hemorrhage (SAH) and is indirectly associated with physical activity (PA). Associations between fatigue and PA are primarily examined based on conventional measures (i.e. a single fatigue score or average PA levels), thereby assuming that fatigue and PA do not fluctuate over time. However, levels of fatigue and PA may not be stable and may interrelate dynamically in daily life. Insight in direct relationships between fatigue and PA in daily life, could add to the development of personalized rehabilitation strategies. Therefore we aimed to examine bidirectional relationships between momentary fatigue and PA in people with SAH. Methods People (n = 38) with SAH who suffer from chronic fatigue were included in an observational study using Ecological Momentary Assessment (EMA) and accelerometry. Momentary fatigue was assessed on a scale from 1 to 7 (no to extreme fatigue), assessed with 10–11 prompts per day for 7 consecutive days using EMA with a mobile phone. PA was continuously measured during this 7-day period with a thigh-worn Activ8 accelerometer and expressed as total minutes of standing, walking, running and cycling in a period of 45 min before and after a momentary fatigue prompt. Multilevel mixed model analyses including random effects were conducted. Results Mean age was 53.2 years (SD = 13.4), 58% female, and mean time post SAH onset was 9.5 months (SD = 2.1). Multilevel analyses with only time effects to predict fatigue and PA revealed that fatigue significantly (p < 0.001) increased over the day and PA significantly (p < 0.001) decreased. In addition, more PA was significantly associated with higher subsequent fatigue (ÎČ = 0.004, p < 0.05) and higher fatigue was significantly associated with less subsequent PA (ÎČ=-0.736, p < 0.05). Moreover, these associations significantly differed between participants (p < 0.001). Conclusions By combining EMA measures of fatigue with accelerometer-based PA we found that fatigue and PA are bidirectionally associated. In addition, these associations differ among participants. Given these different bidirectional associations, rehabilitation aimed at reducing fatigue should comprise personalized strategies to improve both fatigue and PA simultaneously, for example by combining exercise therapy with cognitive behavioral and/or energy management therapy

    Cerebral Autoregulation-Based Blood Pressure Management In The Neuroscience Intensive Care Unit: Towards Individualizing Care In Ischemic Stroke And Subarachnoid Hemorrhage

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    The purpose of this thesis is to review the concept of cerebral autoregulation, to establish the feasibility of continuous bedside monitoring of autoregulation, and to examine the impact of impaired autoregulation on functional and clinical outcomes following subarachnoid hemorrhage and ischemic stroke. Autoregulation plays a key role in the regulation of brain blood flow and has been shown to fail in acute brain injury. Disturbed autoregulation may lead to secondary brain injury as well as worse outcomes. Furthermore, there exist several methodologies, both invasive and non-invasive, for the continuous assessment of autoregulation in individual patients. Resultant autoregulatory parameters of brain blood flow can be harnessed to derive optimal cerebral perfusion pressures, which may be targeted to achieve better outcomes. Multiple studies in adults and several in children have highlighted the feasibility of individualizing mean arterial pressure in this fashion. The thesis herein argues for the high degree of translatability of this personalized approach within the neuroscience intensive care unit, while underscoring the clinical import of autoregulation monitoring in critical care patients. In particular, this document recapitulates findings from two separate, prospectively enrolled patient groups with subarachnoid hemorrhage and ischemic stroke, elucidating how deviation from dynamic and personalized blood pressure targets associates with worse outcome in each cohort. While definitive clinical benefits remain elusive (pending randomized controlled trials), autoregulation-guided blood pressure parameters wield great potential for constructing an ideal physiologic environment for the injured brain. The first portion of this thesis discusses basic autoregulatory physiology as well as various tools to interrogate the brain’s pressure reactivity at the bedside. It then reviews the development of the optimal cerebral perfusion pressure as a biological hemodynamic construct. The second chapter pertains to the clinical applications of bedside neuromonitoring in patients with aneurysmal subarachnoid hemorrhage. In this section, the personalized approach to blood pressure monitoring is discussed in greater detail. Finally, in the third chapter, a similar autoregulation-oriented blood pressure algorithm is applied to a larger cohort of patients with ischemic stroke. This section contends that our novel, individualized strategy to hemodynamic management in stroke patients represents a better alternative to the currently endorsed practice of maintaining systolic blood pressures below fixed and static thresholds

    Deep Learning in Cardiology

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    The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

    Precision Monitoring of Antithrombotic Therapy in Cardiovascular Disease

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    Thrombosis, the process of blood clot formation in blood vessels, is an important protective mechanism for avoiding excessive blood spillage when an individual is exposed to trauma. The body has both a thrombosis inhibition and a thrombus removal system, which interact in a balanced manner. If these mechanisms become unbalanced, and too many clots form and block the lumen, thrombosis occurs. Thrombosis is currently the leading cause of death from disease in humans and is one of the most common events leading to many cardiovascular diseases. Antithrombotic drugs are an integral part of the pharmacological treatment regimens, and interventional strategies are currently recommended for thrombotic complications in patients with thrombosis. Despite major advances in these therapies, the high risk associated with thrombosis and bleeding remains, because of the complex interplay among patient comorbidities, drug combinations, multifaceted dose adjustments, and care settings. Detailed assessment of the effects of bleeding and thrombosis is necessary to establish optimal treatment plans for patients with thrombosis. This study retrospectively evaluated methods for assessing the risk of bleeding/ischemia in thrombosis and the individualized use of these methods

    An Integrated Plasmo‐Photoelectronic Nanostructure Biosensor Detects an Infection Biomarker Accompanying Cell Death in Neutrophils

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    Bacterial infections leading to sepsis are a major cause of deaths in the intensive care unit. Unfortunately, no effective methods are available to capture the early onset of infectious sepsis near the patient with both speed and sensitivity required for timely clinical treatment. To fill the gap, the authors develop a highly miniaturized (2.5 × 2.5 ”m2) plasmo‐photoelectronic nanostructure device that detected citrullinated histone H3 (CitH3), a biomarker released to the blood circulatory system by neutrophils. Rapidly detecting CitH3 with high sensitivity has the great potential to prevent infections from developing life‐threatening septic shock. To this end, the author’s device incorporates structurally engineered arrayed hemispherical gold nanoparticles that are functionalized with high‐affinity antibodies. A nanoplasmonic resonance shift induces a photoconduction increase in a few‐layer molybdenum disulfide (MoS2) channel, and it provides the sensor signal. The device achieves label‐free detection of serum CitH3 with a 5‐log dynamic range from 10−4 to 101 ng mL and a sample‐to‐answer time <20 min. Using this biosensor, the authors longitudinally measure the dynamic CitH3 profiles of individual living mice in a sepsis model at high resolution over 12 hours. The developed biosensor may be poised for future translation to personalized management of systemic bacterial infections.The lack of an appropriate biosensing technology to detect the early onset of bacterial infections has prohibited timely clinical treatment of sepitc shock. This article presents a highly miniaturized plasmo‐photoelectronic device incorporating high‐affinity antibody‐conjugated hemispherical gold nanoparticles and a few‐layer molybdenum disulfide (MoS2) photoconductive channel to detect a blood biomarker released by neutrophils with high speed and sensitivity.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152883/1/smll201905611-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152883/2/smll201905611_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152883/3/smll201905611.pd
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