2,135 research outputs found

    Genomic investigation of antimicrobial resistant enterococci

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    Enterococcus faecium and Enterococcus faecalis are important causes of healthcare-associated infections in immunocompromised patients. Enterococci thrive in modern healthcare settings, being able to resist killing by a range of antimicrobial agents, persist in the environment, and adapt to changing circumstances. In Scotland, rates of vancomycin resistant E. faecium (VREfm) have risen almost 150% in recent years leaving few treatment options and challenging healthcare delivery. Resistance to the last line agent linezolid has also been detected in E. faecalis. Whole genome sequencing (WGS) allows investigation of the population structure and transmission of microorganisms, and identification of antimicrobial resistance mechanisms. The aim of this thesis was to use WGS to understand the molecular epidemiology of antimicrobial resistant enterococci from human healthcare settings in Scotland. Analysis of some of the earliest identified Scottish linezolid-resistant E. faecalis showed the resistance mechanism, optrA, was present in unrelated lineages and in different genetic elements, suggesting multiple introductions from a larger reservoir. To inform transmission investigations, within-patient diversity of VREfm was explored showing ~30% of patients carried multiple lineages and identifying a within-patient diversity threshold for transmission studies. WGS was then applied to a large nosocomial outbreak of VREfm, highlighting a complex network of related variants across multiple wards. Having examined within-hospital transmission, the role of regional relationships was investigated which showed that VREfm in Scotland is driven by multiple clones transmitted within individual Health Boards with occasional spread between regions. The most common lineage in the national collection (ST203) was estimated to have been present in Scotland since around 2005, highlighting its persistence in the face of increasing infection prevention and control measures. This thesis provides a starting point for genomic surveillance of enterococci in Scotland, and a basis for interventional studies aiming to reduce the burden of enterococcal infections."This work was supported by the Chief Scientist Office (Scotland) [grant number SIRN/10]; the Wellcome Trust [grant numbers 105621/Z/14/Z, 206194]; and the BBSRC [grant number BB/S019669/1]."—Fundin

    Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning

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    No explorer reached a pole without a map, no chef served a meal without tasting, and no surgeon implants untested devices. Higher accuracy maps, more sensitive taste buds, and more rigorous tests increase confidence in positive outcomes. Biomedical manufacturing necessitates rigour, whether developing drugs or creating bioengineered tissues [1]–[4]. By designing a dynamic environment that supports mammalian cells during experiments within a Raman spectroscope, this project provides a platform that more closely replicates in vivo conditions. The platform also adds the opportunity to automate the adaptation of the cell culture environment, alongside spectral monitoring of cells with machine learning and three-dimensional Raman mapping, called volumetric Raman mapping (VRM). Previous research highlighted key areas for refinement, like a structured approach for shading Raman maps [5], [6], and the collection of VRM [7]. Refining VRM shading and collection was the initial focus, k-means directed shading for vibrational spectroscopy map shading was developed in Chapter 3 and exploration of depth distortion and VRM calibration (Chapter 4). “Cage” scaffolds, designed using the findings from Chapter 4 were then utilised to influence cell behaviour by varying the number of cage beams to change the scaffold porosity. Altering the porosity facilitated spectroscopy investigation into previously observed changes in cell biology alteration in response to porous scaffolds [8]. VRM visualised changed single human keratinocyte (HaCaT) cell morphology, providing a complementary technique for machine learning classification. Increased technical rigour justified progression onto in-situ flow chamber for Raman spectroscopy development in Chapter 6, using a Psoriasis (dithranol-HaCaT) model on unfixed cells. K-means-directed shading and principal component analysis (PCA) revealed HaCaT cell adaptations aligning with previous publications [5] and earlier thesis sections. The k-means-directed Raman maps and PCA score plots verified the drug-supplying capacity of the flow chamber, justifying future investigation into VRM and machine learning for monitoring single cells within the flow chamber

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

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    Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

    Combined Nutrition and Exercise Interventions in Community Groups

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    Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Exploring the impact of integrated COPD care in general practice

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    Integrated care is an umbrella term used to describe collaboration across differing healthcare sectors. Integrated care interventions directed towards patients with Chronic Obstructive Pulmonary Disease (COPD) in primary care have been shown to improve patient outcomes, such as quality of life. However, the utilisation of integrated care interventions to improve guideline adherence and reduce the prevalence of COPD misdiagnosis in primary care has not been explored previously. The mixed methods systematic review demonstrated that misdiagnosis of COPD does occur in primary care and is predominantly due to difficulties utilising spirometry and differentiating COPD from asthma. Integrated care interventions utilising specialist led spirometry were shown to be able to identify misdiagnosed patients and were perceived to be able to reduce the prevalence of COPD misdiagnosis in primary care. The impact of integrating COPD specialists into GP practices was evaluated through a pragmatic cluster randomised controlled trial (INTEGR COPD). The integration of COPD specialists led to significant improvements in the delivery of guideline adherent care, which was shown to correlate with improvements in quality of life. Integrating COPD specialists into GP practice also led to misdiagnosed patients being identified and having their diagnosis and treatment corrected. The integration of COPD specialists into GP practices was found to be acceptable to patients and healthcare professionals. The reluctance to challenge historic diagnoses was thought to be the underlying cause of patients remaining misdiagnosed in primary, within this cohort. Specialist involvement was deemed to have a positive impact in reducing the extent of COPD misdiagnosis in primary care. The findings from this thesis suggest that integrated COPD care has a positive impact on the delivery of optimal patient care as well as the prevalence of COPD misdiagnosis in GP practices

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
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