7,565 research outputs found
Estudo da remodelagem reversa miocárdica através da análise proteómica do miocárdio e do líquido pericárdico
Valve replacement remains as the standard therapeutic option for aortic
stenosis patients, aiming at abolishing pressure overload and triggering
myocardial reverse remodeling. However, despite the instant hemodynamic
benefit, not all patients show complete regression of myocardial hypertrophy,
being at higher risk for adverse outcomes, such as heart failure. The current
comprehension of the biological mechanisms underlying an incomplete reverse
remodeling is far from complete. Furthermore, definitive prognostic tools and
ancillary therapies to improve the outcome of the patients undergoing valve
replacement are missing. To help abridge these gaps, a combined myocardial
(phospho)proteomics and pericardial fluid proteomics approach was followed,
taking advantage of human biopsies and pericardial fluid collected during
surgery and whose origin anticipated a wealth of molecular information
contained therein.
From over 1800 and 750 proteins identified, respectively, in the myocardium
and in the pericardial fluid of aortic stenosis patients, a total of 90 dysregulated
proteins were detected. Gene annotation and pathway enrichment analyses,
together with discriminant analysis, are compatible with a scenario of increased
pro-hypertrophic gene expression and protein synthesis, defective ubiquitinproteasome system activity, proclivity to cell death (potentially fed by
complement activity and other extrinsic factors, such as death receptor
activators), acute-phase response, immune system activation and fibrosis.
Specific validation of some targets through immunoblot techniques and
correlation with clinical data pointed to complement C3 β chain, Muscle Ring
Finger protein 1 (MuRF1) and the dual-specificity Tyr-phosphorylation
regulated kinase 1A (DYRK1A) as potential markers of an incomplete
response. In addition, kinase prediction from phosphoproteome data suggests
that the modulation of casein kinase 2, the family of IκB kinases, glycogen
synthase kinase 3 and DYRK1A may help improve the outcome of patients
undergoing valve replacement. Particularly, functional studies with DYRK1A+/-
cardiomyocytes show that this kinase may be an important target to treat
cardiac dysfunction, provided that mutant cells presented a different response
to stretch and reduced ability to develop force (active tension).
This study opens many avenues in post-aortic valve replacement reverse
remodeling research. In the future, gain-of-function and/or loss-of-function
studies with isolated cardiomyocytes or with animal models of aortic bandingdebanding will help disclose the efficacy of targeting the surrogate therapeutic
targets. Besides, clinical studies in larger cohorts will bring definitive proof of
complement C3, MuRF1 and DYRK1A prognostic value.A substituição da válvula aórtica continua a ser a opção terapêutica de
referência para doentes com estenose aórtica e visa a eliminação da
sobrecarga de pressão, desencadeando a remodelagem reversa miocárdica.
Contudo, apesar do benefício hemodinâmico imediato, nem todos os pacientes
apresentam regressão completa da hipertrofia do miocárdio, ficando com maior
risco de eventos adversos, como a insuficiência cardíaca. Atualmente, os
mecanismos biológicos subjacentes a uma remodelagem reversa incompleta
ainda não são claros. Além disso, não dispomos de ferramentas de
prognóstico definitivos nem de terapias auxiliares para melhorar a condição
dos pacientes indicados para substituição da válvula. Para ajudar a resolver
estas lacunas, uma abordagem combinada de (fosfo)proteómica e proteómica
para a caracterização, respetivamente, do miocárdio e do líquido pericárdico
foi seguida, tomando partido de biópsias e líquidos pericárdicos recolhidos em
ambiente cirúrgico.
Das mais de 1800 e 750 proteínas identificadas, respetivamente, no miocárdio
e no líquido pericárdico dos pacientes com estenose aórtica, um total de 90
proteínas desreguladas foram detetadas. As análises de anotação de genes,
de enriquecimento de vias celulares e discriminativa corroboram um cenário de
aumento da expressão de genes pro-hipertróficos e de síntese proteica, um
sistema ubiquitina-proteassoma ineficiente, uma tendência para morte celular
(potencialmente acelerada pela atividade do complemento e por outros fatores
extrínsecos que ativam death receptors), com ativação da resposta de fase
aguda e do sistema imune, assim como da fibrose.
A validação de alguns alvos específicos através de immunoblot e correlação
com dados clínicos apontou para a cadeia β do complemento C3, a Muscle
Ring Finger protein 1 (MuRF1) e a dual-specificity Tyr-phosphoylation
regulated kinase 1A (DYRK1A) como potenciais marcadores de uma resposta
incompleta. Por outro lado, a predição de cinases a partir do fosfoproteoma,
sugere que a modulação da caseína cinase 2, a família de cinases do IκB, a
glicogénio sintase cinase 3 e da DYRK1A pode ajudar a melhorar a condição
dos pacientes indicados para intervenção. Em particular, a avaliação funcional
de cardiomiócitos DYRK1A+/- mostraram que esta cinase pode ser um alvo
importante para tratar a disfunção cardíaca, uma vez que os miócitos mutantes
responderam de forma diferente ao estiramento e mostraram uma menor
capacidade para desenvolver força (tensão ativa).
Este estudo levanta várias hipóteses na investigação da remodelagem reversa.
No futuro, estudos de ganho e/ou perda de função realizados em
cardiomiócitos isolados ou em modelos animais de banding-debanding da
aorta ajudarão a testar a eficácia de modular os potenciais alvos terapêuticos
encontrados. Além disso, estudos clínicos em coortes de maior dimensão
trarão conclusões definitivas quanto ao valor de prognóstico do complemento
C3, MuRF1 e DYRK1A.Programa Doutoral em Biomedicin
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
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
Epilepsy Mortality: Leading Causes of Death, Co-morbidities, Cardiovascular Risk and Prevention
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 ..
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
Socio-endocrinology revisited: New tools to tackle old questions
Animals’ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)
Examining the Impact of Personal Social Media Use at Work on Workplace Outcomes
A noticable shift is underway in today’s multi-generational workforce. As younger employees propel digital workforce transformation and embrace technology adoption in the workplace, organisations need to show they are forward-thinking in their digital transformation strategies, and the emergent integration of social media in organisations is reshaping internal communication strategies, in a bid to improve corporate reputations and foster employee engagement. However, the impact of personal social media use on psychological and behavioural workplace outcomes is still debatebale with contrasting results in the literature identifying both positive and negative effects on workplace outcomes among organisational employees.
This study seeks to examine this debate through the lens of social capital theory and study personal social media use at work using distinct variables of social use, cognitive use, and hedonic use. A quantitative analysis of data from 419 organisational employees in Jordan using SEM-PLS reveals that personal social media use at work is a double-edged sword as its impact differs by usage types. First, the social use of personal social media at work reduces job burnout, turnover intention, presenteeism, and absenteeism; it also increases job involvement and organisational citizen behaviour. Second, the cognitive use of personal social media at work increases job involvement, organisational citizen behaviour, employee adaptability, and decreases presenteeism and absenteeism; it also increases job burnout and turnover intention. Finally, the hedonic use of personal social media at work carries only negative effects by increasing job burnout and turnover intention.
This study contributes to managerial understanding by showing the impact of different types of personal social media usage and recommends that organisations not limit employee access to personal social media within work time, but rather focus on raising awareness of the negative effects of excessive usage on employee well-being and encourage low to moderate use of personal social media at work and other personal and work-related online interaction associated with positive workplace outcomes. It also clarifies the need for further research in regions such as the Middle East with distinct cultural and socio-economic contexts
Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods
Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection.
In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application.
The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings.
Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection
Developing automated meta-research approaches in the preclinical Alzheimer's disease literature
Alzheimer’s disease is a devastating neurodegenerative disorder for which there is no cure. A crucial part of the drug development pipeline involves testing therapeutic interventions in animal disease models. However, promising findings in preclinical experiments have not translated into clinical trial success. Reproducibility has often been cited as a major issue affecting biomedical research, where experimental results in one laboratory cannot be replicated in another. By using meta-research (research on research) approaches such as systematic reviews, researchers aim to identify and summarise all available evidence relating to a specific research question. By conducting a meta-analysis, researchers can also combine the results from different experiments statistically to understand the overall effect of an intervention and to explore reasons for variations seen across different publications. Systematic reviews of the preclinical Alzheimer’s disease literature could inform decision making, encourage research improvement, and identify gaps in the literature to guide future research. However, due to the vast amount of potentially useful evidence from animal models of Alzheimer’s disease, it remains difficult to make sense of and utilise this data effectively. Systematic reviews are common practice within evidence based medicine, yet their application to preclinical research is often limited by the time and resources required. In this thesis, I develop, build-upon, and implement automated meta-research approaches to collect, curate, and evaluate the preclinical Alzheimer’s literature. I searched several biomedical databases to obtain all research relevant to Alzheimer’s disease. I developed a novel deduplication tool to automatically identify and remove duplicate publications identified across different databases with minimal human effort. I trained a crowd of reviewers to annotate a subset of the publications identified and used this data to train a machine learning algorithm to screen through the remaining publications for relevance. I developed text-mining tools to extract model, intervention, and treatment information from publications and I improved existing automated tools to extract reported measures to reduce the risk of bias. Using these tools, I created a categorised database of research in transgenic Alzheimer’s disease animal models and created a visual summary of this dataset on an interactive, openly accessible online platform. Using the techniques described, I also identified relevant publications within the categorised dataset to perform systematic reviews of two key outcomes of interest in transgenic Alzheimer’s disease models: (1) synaptic plasticity and transmission in hippocampal slices and (2) motor activity in the open field test.
Over 400,000 publications were identified across biomedical research databases, with 230,203 unique publications. In a performance evaluation across different preclinical datasets, the automated deduplication tool I developed could identify over 97% of duplicate citations and a had an error rate similar to that of human performance. When evaluated on a test set of publications, the machine learning classifier trained to identify relevant research in transgenic models performed was highly sensitive (captured 96.5% of relevant publications) and excluded 87.8% of irrelevant publications. Tools to identify the model(s) and outcome measure(s) within the full-text of publications may reduce the burden on reviewers and were found to be more sensitive than searching only the title and abstract of citations. Automated tools to assess risk of bias reporting were highly sensitive and could have the potential to monitor research improvement over time. The final dataset of categorised Alzheimer’s disease research contained 22,375 publications which were then visualised in the interactive web application. Within the application, users can see how many publications report measures to reduce the risk of bias and how many have been classified as using each transgenic model, testing each intervention, and measuring each outcome. Users can also filter to obtain curated lists of relevant research, allowing them to perform systematic reviews at an accelerated pace with reduced effort required to search across databases, and a reduced number of publications to screen for relevance. Both systematic reviews and meta-analyses highlighted failures to report key methodological information within publications. Poor transparency of reporting limited the statistical power I had to understand the sources of between-study variation. However, some variables were found to explain a significant proportion of the heterogeneity. Transgenic animal model had a significant impact on results in both reviews. For certain open field test outcomes, wall colour of the open field arena and the reporting of measures to reduce the risk of bias were found to impact results. For in vitro electrophysiology experiments measuring synaptic plasticity, several electrophysiology parameters, including magnesium concentration of the recording solution, were found to explain a significant proportion of the heterogeneity. Automated meta-research approaches and curated web platforms summarising preclinical research could have the potential to accelerate the conduct of systematic reviews and maximise the potential of existing evidence to inform translation
Central Control Of Pain And Inflammation Through A Hunger Circuit
Homeostasis is established through bidirectional communication between the periphery and the central nervous system. To maintain homeostasis, some biological drives can become prioritized over others. This changing balance between biological drives encourages peak performance and survival. However, when homeostasis is disturbed, chronic inflammatory diseases such as obesity, chronic pain, and arthritis can arise. We became interested in understanding if competing biological drives could be leveraged for therapeutic purposes. Food restriction inhibits inflammation; therefore, we explored how hunger and feeding neural circuits affect responses to noxious agents. Our first study investigated the role of hunger to alleviate pain behavior. We found that hunger significantly reduces time spent licking during the inflammatory phase of a formalin pain assay but leaves intact pain responses to acute threats. We next evaluated if hypothalamic hunger neurons are involved in this behavioral change. Stimulation of agouti-related protein expressing (AgRP) neurons significantly reduced formalin pain behavior. To determine the central nodes that mediate this effect, we systematically screened AgRP neuron projections for their ability to suppress pain. Only AgRP neurons projecting to the hindbrain parabrachial nucleus was able to reduce inflammatory pain behavior. Our second study investigated the role of hunger to influence inflammatory responses of an injury site. Using two models of localized inflammation, we found that food deprivation robustly reduces inflammation, pro-inflammatory cytokine levels, and associated temperature increases induced by injection of noxious stimuli [complete Freund’s adjuvant (CFA) or formalin]. Activation of AgRP neurons recapitulated the effect of food deprivation on inflammation. We then evaluated the role of each AgRP axonal target structure to reduce inflammation. Interestingly, stimulation of AgRP neurons that project to the paraventricular nucleus of the hypothalamus or the parabrachial nucleus were sufficient to reduce CFA-induced inflammation. Finally, we identified the vagus nerve as a key pathway for the anti-inflammatory effect of hunger. We propose that hunger, through AgRP neurons, inhibits pro-inflammatory responses from the central nervous system and changes the output of efferent vagal fibers. This body of work reveals a central node for the reduction of pain and inflammation, highlighting a novel role for hypothalamic circuits to influence injury responses
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