31,490 research outputs found

    Previsão automática de AVCs através de imagens de RMN usando Deep Learning

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    Programa de doutoramento em Informática (MAP-i)O Acidente Vascular Cerebral é uma das principais causas de morte, constituindo a segunda causa de morte nos países desenvolvidos. Representa também uma das principais causas de incapacidade funcional a nível mundial, tendo um grande impacto na sociedade. O Acidente Vascular Cerebral pode ser classificado em hemorrágico ou isquémico, sendo este último o subtipo mais frequente. O estudo imagiológico é fundamental na abordagem e planeamento do tratamento, onde a Tomografia Computorizada é o método de imagem mais comummente utilizado devido aos baixos custos de operação e acessibilidade. Contudo, quando disponível, a Ressonância Magnética é o método preferido, dada a sua capacidade na detecção de estadios precoces de isquemia cerebral. Desta forma, o estudo imagiológico permite não só a distinção do tipo de lesão e a sua localização, mas também uma melhor discriminação das áreas com enfarte das áreas de penumbra, onde existe a possibilidade de recuperação do tecido cerebral. Uma rápida ponderação dos riscos e benefícios associados à intervenção é necessária, que tem por base delineações grosseiras da lesão e a experiência clínica, havendo por isso, variabilidade intra- e inter-médico. Assim, ferramentas automáticas, permitem orientar e facilitar o processo de ponderação. Não obstante, o desenvolvimento destas ferramentas não é trivial, dada a variabilidade das lesões, dos fenómenos de perfusão e difusão cerebrais que ocorrem ao longo do tempo, bem como da variabilidade dos aparelhos de aquisição médica e a sua fraca resolução. A Aprendizagem Automática compreende um vasto número de algoritmos, todos eles com o intuito de aprender padrões para realizar um dado objectivo ou tarefa. Uma categoria específica da Aprendizagem Automática é a Aprendizagem de Características, onde os algoritmos têm a capacidade de aprender e extrair automaticamente características através dos dados de entrada. Por sua vez, dentro dos métodos de Aprendizagem de Características, existem algoritmos de Aprendizagem Profunda, onde vários níveis são utilizados para uma maior capacidade de abstracção sobre os dados de entrada e, consequentemente, uma maior discriminação. Assim sendo, foram estudadas e aplicadas Redes Neuronais Convolucionais e Recorrentes, em três diferentes tópicos de investigação. No primeiro tópico, os mapas convencionais usados na prática clínica são combinados com os dados responsáveis por gerar os mapas convencionais. Com esta proposta foi possível demonstrar a vantagem em considerar ambos os tipos de dados em arquitecturas específicas. Uma segunda linha focou-se na conjugação dos dados clínicos do paciente com os dados imagiológicos. Para tal propôs-se uma função de custo, com o intuito de guiar o processo de aprendizagem da rede profunda. Mais ainda, a informação clínica, não imagiológica, foi introduzida como canal de entrada extra, garantido que informação específica de cada paciente é tida em consideração. Por último, explorou-se a aprendizagem não supervisionada, na caracterização da distribuição dos dados que descrevem a capacidade de perfusão e difusão e a hemodinâmica cerebral. Foram ainda validados vários componentes fulcrais da rede, nomeadamente as Redes Neuronais Recorrentes-Fechadas. Ao considerar a etapa de aprendizagem não supervisionada, demonstrou-se a capacidade em obter características representativas das propriedades supra-referidas, alcançando-se resultados estado da arte.Stroke is a leading cause of death worldwide, being the second major cause of death in developed countries. Furthermore, it is also a major cause of disability, having a huge burden in society. World Health Organization predicts that a stroke event occurs at each two seconds. Stroke is categorized either as haemorrhagic or ischaemic, being the latter the most common type of stroke. Neuroimaging acquisitions play an important role during clinical assessment, evaluation and treatment planning. The most commonly used imaging technique is the Computerized Tomography, due to its availability and operational costs. Nonetheless, when available, Magnetic Resonance Imaging is preferred due to its higher capability in characterizing soft tissues, and capacity to detect early levels of ischemia. Onset neuroimaging acquisitions allow the physicians to locate and assess the brain tissue that can be recovered, which plays an important role during the treatment planning and follow-up. However, in a context where time equates to the loss of healthy brain tissue, physicians need to ponder the benefits and risks of performing clinical intervention, based on rough manual delineations and on clinical experience to predict the infarct growth across time. These tasks are time-consuming and prone to intra- and inter-physician variability. Hence, automatic prediction of stroke lesions based on onset neuroimaging acquisitions is needed to help and guide the physicians during the decision making process. The development of automatic methods is however an intricate task, due to the variety of stroke lesions, the underlying brain perfusion and diffusion processes, as well as the variability of Magnetic Resonance scans, their poor resolution and fast acquisitions. Machine Learning comprehends a vast number of algorithms that aim to learn patterns from data, in order to achieve a specific goal or perform a specific task. One category of Machine Learning is the Representation Learning, where algorithms learn how to extract discriminative features directly from the input data. Among these methods, Deep Learning is a group of Representation Learning, which employs several levels of abstraction that characterize the input data. Thus, Convolutional and Recurrent Neural Networks were studied and applied for predicting the final stroke lesion. Three different lines of research were conducted. One research line focus on combining raw imaging data with the standard maps used in clinical practice. We demonstrate the added value of considering both data types in dedicated learning paths. Furthermore, we provide evidence on the impact of performing temporal pre-processing without hindering the performance of our method. A second line of research focused on studying and proposing methods that merge imaging with non-imaging data. To consider the latter clinical data we propose a custom loss function, to guide the learning process of the Deep Learning neural network, as well as an additional input channel, to consider patient-specific data. Lastly, we consider an unsupervised learning approach with the goal of characterizing the underlying distribution of the data. Considering the unsupervised learning block allowed us to demonstrate its discriminative power, and ground-breaking results. Additionally, we demonstrate the added value of considering Gated-Recurrent Neural Networks embedded in a Fully Convolutional Network. All the methods developed during this thesis were trained and evaluated in publicly available datasets. This allows a fair comparison among state of the art proposals, and future comparisons with the different proposals contained in this thesis.Fundação para a Ciência e a Tecnologia (FCT

    Data_Sheet_1_RAGE and its ligand amyloid beta promote retinal ganglion cell loss following ischemia-reperfusion injury.docx

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    IntroductionGlaucoma is a progressive neurodegenerative disease associated with age. Accumulation of amyloid-beta (Aß) proteins in the ganglion cell layer (GCL) and subsequent retinal ganglion cell (RGC) loss is an established pathological hallmark of the disease. The mechanism through which Aß provokes RGC loss remains unclear. The receptor for the advanced glycation end product (RAGE), and its ligand Aß, have been shown to mediate neuronal loss via internalizing Aß within the neurons. In this study, we investigated whether the RAGE–Aß axis plays a role in RGC loss in experimental glaucoma.MethodsRetinal ischemia was induced by an acute elevation of intraocular pressure in RAGE–/– and wild-type (WT) control mice. In a subset of animals, oligomeric Aß was injected directly into the vitreous of both strains. RGC loss was assessed using histology and biochemical assays. Baseline and terminal positive scotopic threshold (pSTR) were also recorded.ResultsRetinal ischemia resulted in 1.9-fold higher RGC loss in WT mice compared to RAGE–/– mice (36 ± 3% p –/– mice, 7-days post-injection (55 ± 4% p = 0.008 vs. 24 ± 2%, p = 0.02). We also found a significant decline in the positive scotopic threshold response (pSTR) amplitude of WT mice compared to RAGE–/– (36 ± 3% vs. 16 ± 6%).DiscussionRAGE–/– mice are protected against RGC loss following retinal ischemia. Intravitreal injection of oligomeric Aß accelerated RGC loss in WT mice but not RAGE–/–. A co-localization of RAGE and Aß, suggests that RAGE–Aß binding may contribute to RGC loss.</p

    Phytochemicals targeting lncRNAs: A novel direction for neuroprotection in neurological disorders

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    Neurological disorders with various etiologies impacting the nervous system are prevalent in clinical practice. Long non-coding RNA (lncRNA) molecules are functional RNA molecules exceeding 200 nucleotides in length that do not encode proteins, but participate in essential activities. Research indicates that lncRNAs may contribute to the pathogenesis of neurological disorders, and may be potential targets for their treatment. Phytochemicals in traditional Chinese herbal medicine (CHM) have been found to exert neuroprotective effects by targeting lncRNAs and regulating gene expression and various signaling pathways. We aim to establish the development status and neuroprotective mechanism of phytochemicals that target lncRNAs through a thorough literature review. A total of 369 articles were retrieved through manual and electronic searches of PubMed, Web of Science, Scopus and CNKI databases from inception to September 2022. The search utilized combinations of natural products, lncRNAs, neurological disorders, and neuroprotective effects as keywords. The included studies, a total of 31 preclinical trials, were critically reviewed to present the current situation and the progress in phytochemical-targeted lncRNAs in neuroprotection. Phytochemicals have demonstrated neuroprotective effects in preclinical studies of various neurological disorders by regulating lncRNAs. These disorders include arteriosclerotic ischemia-reperfusion injury, ischemic/hemorrhagic stroke, Alzheimer's disease, Parkinson's disease, glioma, peripheral nerve injury, post-stroke depression, and depression. Several phytochemicals exert neuroprotective roles through mechanisms such as anti-inflammatory, antioxidant, anti-apoptosis, autophagy regulation, and antagonism of Aβ-induced neurotoxicity. Some phytochemicals targeted lncRNAs and served a neuroprotective role by regulating microRNA and mRNA expression. The emergence of lncRNAs as pathological regulators provides a novel direction for the study of phytochemicals in CHM. Elucidating the mechanism of phytochemicals regulating lncRNAs will help to identify new therapeutic targets and promote their application in precision medicine

    RNA pull-down-confocal nanoscanning (RP-CONA), a novel method for studying RNA/protein interactions in cell extracts that detected potential drugs for Parkinson’s disease targeting RNA/HuR complexes

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    MicroRNAs (miRNAs, miRs) are a class of small non-coding RNAs that regulate gene expression through specific base-pair targeting. The functional mature miRNAs usually undergo a two-step cleavage from primary miRNAs (pri-miRs), then precursor miRNAs (pre-miRs). The biogenesis of miRNAs is tightly controlled by different RNA-binding proteins (RBPs). The dysregulation of miRNAs is closely related to a plethora of diseases. Targeting miRNA biogenesis is becoming a promising therapeutic strategy. HuR and MSI2 are both RBPs. MiR-7 is post-transcriptionally inhibited by the HuR/MSI2 complex, through a direct interaction between HuR and the conserved terminal loop (CTL) of pri-miR-7-1. Small molecules dissociating pri-miR-7/HuR interaction may induce miR-7 production. Importantly, the miR-7 levels are negatively correlated with Parkinson’s disease (PD). PD is a common, incurable neurodegenerative disease causing serious motor deficits. A hallmark of PD is the presence of Lewy bodies in the human brain, which are inclusion bodies mainly composed of an aberrantly aggregated protein named α-synuclein (α-syn). Decreasing α-syn levels or preventing α-syn aggregation are under investigation as PD treatments. Notably, α-syn is negatively regulated by several miRNAs, including miR-7, miR-153, miR-133b and others. One hypothesis is that elevating these miRNA levels can inhibit α-syn expression and ameliorate PD pathologies. In this project, we identified miR-7 as the most effective α-syn inhibitor, among the miRNAs that are downregulated in PD, and with α-syn targeting potentials. We also observed potential post-transcriptional inhibition on miR-153 biogenesis in neuroblastoma, which may help to uncover novel therapeutic targets towards PD. To identify miR-7 inducers that benefit PD treatment by repressing α-syn expression, we developed a novel technique RNA Pull-down Confocal Nanoscaning (RP-CONA) to monitor the binding events between pri-miR-7 and HuR. By attaching FITC-pri-miR-7-1-CTL-biotin to streptavidin-coated agarose beads and incubating them in human cultured cell lysates containing overexpressed mCherry-HuR, the bound RNA and protein can be visualised as quantifiable fluorescent rings in corresponding channels in a confocal high-content image system. A pri-miR-7/HuR inhibitor can decrease the relative mCherry/FITC intensity ratio in RP-CONA. With this technique, we performed several small-scale screenings and identified that a bioflavonoid, quercetin can largely dissociate the pri-miR-7/HuR interaction. Further studies proved that quercetin was an effective miR-7 inducer as well as α-syn inhibitor in HeLa cells. To understand the mechanism of quercetin mediated α-syn inhibition, we tested the effects of quercetin treatment with miR-7-1 and HuR knockout HeLa cells. We found that HuR was essential in this pathway, while miR-7 hardly contributed to the α-syn inhibition. HuR can directly bind an AU-rich element (ARE) at the 3’ untranslated region (3’-UTR) of α-syn mRNA and promote translation. We believe quercetin mainly disrupts the ARE/HuR interaction and disables the HuR-induced α-syn expression. In conclusion, we developed and optimised RP-CONA, an on-bead, lysate-based technique detecting RNA/protein interactions, as well as identifying RNA/protein modulators. With RP-CONA, we found quercetin inducing miR-7 biogenesis, and inhibiting α-syn expression. With these beneficial effects, quercetin has great potential to be applied in the clinic of PD treatment. Finally, RP-CONA can be used in many other RNA/protein interactions studies

    An investigation of the relationship between perioperative characteristics and perioperative anaesthesia on the postoperative systemic inflammatory response and clinical outcome in patients undergoing surgery for colorectal cancer

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    In UK, colorectal cancer (CRC) is the fourth most common cancer and the second most common cause of cancer death. Until now, surgical resection remains the cornerstone for the management of CRC in all stages, however, stress response elicit from surgery may cause different changes through multiple systems in human body including neural, endocrine, metabolic, inflammatory, and immunological changes. In addition, other perioperative factors such as volatile anaesthetic and opioids may induce the immunosuppression. There is a proportional correlation between the stress response and the magnitude of the inflammatory immune response, invasiveness, and duration of surgery. The pre-operative and post-operative status of patients are important when considering the prognosis. The systemic inflammatory response (SIR) has been recognised to correlate with tumour progression and the prognosis of CRC. An exaggerated postoperative SIR is associated with postoperative infective complications and poor survival. Several predictive markers of the SIR have been used, such as the neutrophil to lymphocyte ratio (NLR), serum C-reactive protein (CRP) level, and Glasgow prognostic score (GPS). Some evidence reported that general anaesthesia (GA) combined with regional anaesthesia (RA) are better than the single use of general anaesthesia in reducing the post-operative immuno-suppression in some degrees. Furthermore, the peri-operative inflammatory process may be affected by the choice of anaesthetic technique, with propofol reported to have anti-inflammatory effect by targeting neutrophil activity. Up to now, there is insufficient evidence to recommend any specific anaesthetic or analgesic technique for patients undergoing surgery for tumour resection based on inflammatory response, recurrence, and metastasis. The work presented in this thesis further examines the relationship between the perioperative characteristics, perioperative anaesthesia, and the postoperative systemic inflammatory response following surgery for colorectal cancer. Several preoperative medications along with anaesthesia might influence the postoperative systemic inflammatory response but the question is whether the post-operative systemic inflammatory response affected by the administration of different types of anaesthesia or not following surgery for colorectal cancer. Chapter 1 discusses the epidemiology, aetiology, carcinogenesis, risk factors of colorectal cancer, pro-carcinogenic factors, anti-carcinogenic agents, inflammation and cancer, the post-operative systemic inflammatory response, tumour staging, screening, and diagnosis of colorectal cancer. Chapter 2 discusses the treatment of colorectal cancer. Chapter 3 discusses different anaesthetic techniques and agents. Chapter 4 provides summary and aims of the thesis. Chapter 5 represents findings from a systematic review and meta-analysis about the effect of anaesthesia on the postoperative systemic inflammatory response in patients undergoing surgery. The results conclude that there was some evidence that anaesthetic regimens may reduce the magnitude of the post-operative SIR. However, the studies identified in this systematic review were heterogeneous and generally of low quality. Chapter 6 represents a retrospective cohort study about the relationship between anaesthetic technique, clinicopathological characteristics and the magnitude of the postoperative systemic inflammatory response in patients undergoing elective surgery for colon cancer. The results show that the type of anaesthesia varied over time and appears to influence the magnitude of the postoperative SIR on post-operative day 2 for those patients who underwent for open surgery but not laparoscopic surgery. Chapter 7 represents a prospective cohort study about the effect of anaesthesia on the magnitude of the postoperative systemic inflammatory response in patients undergoing elective surgery for colorectal cancer in the context of an enhanced recovery pathway. The results show that there was a modest but an independent association between regional anaesthesia (RA) and a lower magnitude of the postoperative SIR. Chapter 8 represents the relationship between pre-operative medications, the type of anaesthesia and post-operative sequelae in patients undergoing surgery for colorectal cancer. The results show that there was no association between the preoperative administration of aspirin, statins and ACE inhibitors and anaesthesia. Chapter 9 represents the relationship between nutritional status, anaesthetic approach, and peri-operative characteristics of patients undergoing surgery for colorectal cancer. The results show that there was no significant association between measures of nutritional status and anaesthetic approach. Chapter 10 represents the relationship between opioid administration, type of anaesthesia and clinicopathological characteristics in patients undergoing surgery for colorectal cancer. The results show that opioid administration was independently associated with both anaesthetic and operative factors. Chapter 11 represents the main findings of the thesis and some recommendation for a future work

    Epilepsy Mortality: Leading Causes of Death, Co-morbidities, Cardiovascular Risk and Prevention

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    a reuptake inhibitor selectively prevents seizure-induced sudden death in the DBA/1 mouse model of sudden unexpected ... Bilateral lesions of the fastigial nucleus prevent the recovery of blood pressure following hypotension induced by&nbsp;..

    Does dual tasking affect the ability to generate anticipatory postural adjustments?

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    Introduction: To date, little is known about the impact of additional cognitive tasks on perturbed balance and whether different types of cognitive tasks can generate different balance mechanisms. The aim of the study was to investigate how two different cognitive tasks (Stroop test and counting backwards task) would influence young adults’ ability to generate appropriate postural responses. Methods: Twenty young adults (25.95 ± 2.97 years) were asked to stand eyes open, bare feet shoulder-width apart on a moving platform which was translated in the anterior-posterior direction at three different frequencies (0.1, 0.25, 0.5 Hz) and perform either a counting backwards task, a Stroop task, or no cognitive task. Tonic activity and muscle onset latencies of the Rectus Femoris, Bicep Femoris, Tibialis Anterior and Gastrocnemius Medialis muscles were measured through surface electromyography (1000 Hz), and the number of cognitive errors was recorded. Results: Results showed no significant differences in muscle onset latencies and tonic activity between dual tasking and single tasking conditions, nor between the two dual tasking conditions. More cognitive errors were made in the counting backwards task (238 total cognitive errors across all frequencies) compared to the Stroop task where no errors were recorded. A frequency effect was identified with participants, regardless of condition, showing greater tonic activity in the Rectus Femoris (p= 0.012, M= 177% baseline, SD= 79.2), the Gastrocnemius Medialis (p= 0.016, M= 274.8% baseline, SD= 201.4) and the Bicep Femoris (p= 0.043, M= 291%, SD= 3.5) at 0.5 Hz, as well as earlier muscle activation in the Tibialis Anterior (p< 0.001, M= -2.7, SD= 8.1% half cycle), the Gastrocnemius Medialis (p< 0.001, M= -9.54, SD= 3.3% half cycle) and the Bicep Femoris (p< 0.001, M= -1.34, SD= 3.9% half cycle) at 0.5 Hz compared to the other frequencies. Transition and steady state muscle onset latencies were only significantly different for the Gastrocnemius Medialis at 0.25 Hz (p= 0.001), possibly because the 0.1 Hz frequency was too easy to require adaptation and the 0.5 Hz frequency was large enough to trigger earlier muscle activation from transition state which was then carried to steady state. Dual tasking did not seem to influence anticipatory postural adjustments, however perturbation intensities did. Discussion: It is assumed that due to the ‘threatening’ nature of the 0.5 Hz perturbation, a stiffer position was adopted as seen by the increased tonic activity, and anticipatory mechanisms were triggered sooner than the other frequencies, as seen by earlier muscle activation. Since posture was unchanged between single and dual tasking, it is suggested that participants’ postural control was automated and the cognitive errors in the two mental tasks could reflect their difficulty level. Future research should explore body kinematics to identify the balance strategies adopted, as well as take into account the reaction time of the cognitive task to better understand participants’ allocation of attention during perturbed balance dual tasking
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