6,342 research outputs found

    Radiotherapy dosimetry with ultrasound contrast agents

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    Nucleoplasty:A new treatment option for cervical radicular pain due to a disc herniation

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    CANCER TREATMENT BY TARGETING HDAC4 TRANSLOCATION INDUCED BY MICROSECOND PULSED ELECTRIC FIELD EXPOSURE: MECHANISTIC INSIGHTS THROUGH KINASES AND PHOSPHATASES

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    Epigenetic modifications, arising from sub-cellular shifts in histone deacetylase (HDAC) activity and localization, present promising strategies for diverse cancer treatments. HDACs, enzymes responsible for post-translational histone modifications, induce these epigenetic changes by removing acetyl groups from ε-N-acetyl-lysine residues on histones, thereby suppressing gene transcription. Within the HDAC group, class IIa HDACs are notable for their responsiveness to extracellular signals, bridging the gap between external stimuli, plasma membrane, and genome through nuclear-cytoplasmic translocation. This localization offers two significant mechanisms for cancer treatment: nuclear accumulation of HDACs represses oncogenic transcription factors, such as myocyte-specific enhancer factor 2C (MEF2C), triggering various cell death pathways. Conversely, cytoplasmic HDAC accumulation acts similarly to HDAC inhibitors by silencing genes. My dissertation introduces an innovative approach for glioblastoma and breast cancer treatment by investigating the application of microsecond pulsed electric fields. It particularly focuses on HDAC4, a class IIa HDAC overexpressed in these cancers. Beyond demonstrating HDAC4 translocation, my research delves into the intricate roles of kinases and phosphatases, shedding light on the underlying factors governing HDAC4 translocation

    The role of the oral microbiome in the immunobullous diseases pemphigus vulgaris and mucous membrane pemphigoid and oral lichen planus

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    Saliva is formed from contributions of salivary glands and the serum exudates principally from gingival margins or damaged mucosa combined with components derived from the environment, including a community of microorganisms - the microbiome. I postulate that changes in microbial diversity and population structure play key roles in the modulation of host- microbial interactions which influence both the hypersensitive autoimmune responses and inflammation seen in these inflammatory mucocutaneous disorders. For my research, a total of 186 participants were recruited: 48 mucous membrane pemphigoid (MMP), 48 pemphigus vulgaris (PV), 50 oral lichen planus (OLP) patients, and 40 healthy controls. Unstimulated whole saliva, subgingival plaque, serum, and plasma samples were collected from 186 participants. In addition, metadata were collected on the following covariates: age, gender, ethnicity, type of the diet, disease history and therapeutic intervention in the preceding six months. Oral disease severity scores (ODSS) were assessed, and periodontal status was examined using a periodontal six pocket chart. To characterise microbiome profiles, saliva and subgingival plaque were processed for sequencing genomic DNA using the NGS Shotgun metagenomics sequencing technique. Inflammatory cytokines and proteases were investigated in saliva and serum using Human Magnetic Luminex Screening Assay (R&D Systems). Selected cytokines were analysed by enzyme-linked immunosorbent assay (ELISA) technique (R&D Systems) to determine host inflammatory responses in saliva and serum samples. Additionally, saliva and plasma samples were analysed for metabolites by nuclear magnetic resonance (NMR). Significant increases in periodontal score (PISA) in all three groups of disease were identified compared to healthy control group with significant positive correlation between oral disease severity (ODSS) and PISA in OLP and PV groups. All three groups of diseases had significantly higher levels of inflammatory Th2/Th17 cytokines (IL-6, IL-13 and IL-17 in saliva samples), as well as higher levels of MMP-3 matrixins in saliva. In addition, there were positive correlations between ODSS and salivary IL-6, IL-13 and MMP-3 in saliva of OLP, salivary and serum levels of IL-6 and MMP-3 in MMP group, and significant association of salivary IL-6, IL-1β and MMP-3 in PV group. Metabolomic data showed that saliva is a better biofluid for correlation of the metabolomic profile with oral disease severity than plasma. Salivary ethanol was corelated with disease severity in the OLP group, whereas in PV was a strong correlation of ODSS with choline. Finally, a unique microbial community was found in each group of diseases. In the MMP group, ODSS was significantly correlated with L. hofstadii, C. sputigena, N. meningitidis, N. cinerea and P. sacchar0lytica. In PV, a positive correlation was found with F. nucleatum, G. morbillorum, and E. corrodens, G. elegans, H. sapiens and T. vincentii. In OLP, the disease tends to worsen when there was reduced abundance of X. cellulosilytica, Actinomyces ICM 47, S. parasanguinis, S. salivarius, L. mirabilis and O. sinus. Lower microbial diversity was correlated with ODSS in saliva and plaque of the OLP group. In conclusion, this study provides strong evidence of the complex interplay between the oral microbiome, immunological factors, and metabolites in the context of immunobullous diseases and OLP. The findings highlight the integral role of oral bacteria in disease progression, the significance of immune dysregulation, and the potential impact of specific microbial species and metabolic pathways. These insights give the way for further research and clinical applications, offering the promise of personalized approaches for diagnosis, and management of OLP, MMP and PV. Future investigations should focus on discovering the mechanistic details underlying these associations and validating the identified biomarkers in larger patient cohorts, ultimately contributing to a deeper understanding of the pathogenesis of these conditions

    The effect of autologous macrophage therapy in cirrhosis in response to individual immune reparative pathways: developing a novel therapy

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    BACKGROUND: Liver cirrhosis is the end stage of any injury process to the liver. Once established it inevitably progresses to complications such as portal hypertension, cancer and death. There is not cure for liver cirrhosis besides liver transplant. We face an unmet demand for treatment of this condition. The role of macrophages in fibrosis development and resolution in the liver has been extensively investigated. Prof Forbes group invested in the development of autologous macrophage product to promote fibrosis resolution hence cirrhosis regression. This has demonstrated its efficacy and safety in animal models. From these encouraging pre-clinic data a phase 1 first in human clinical trial of autologous activated macrophage product for cirrhotic patients was developed. METHODS: Using an established 3+3 dose escalation model we enrolled a total of 9 subject in the phase 1 trial reaching a maximum achieved and safe dose of 1x10^9 macrophages. In addition to adverse events, dose toxicity and macrophage activation syndrome (MAS) parameter, we evaluated a varied range of circulating cytokines and chemokine pre and post treatment using a commercial kit. Moreover we developed a protocol for P13- magnetic resonance spectrometry (MRS) for the analysis of the metabolically active liver parenchyma. Data from the phase 1 trial were used to improve the autologous cellular produce and phase 2 randomised controlled trial. RESULTS: The autologous activated macrophage produce is demonstrated not to cause any toxicity in this first in human study of cirrhotic population of different aetiology. Cytokine and chemokine analysis supports these findings and specifically demonstrates low levels of IL-8, which represent cardinal feature of MAS. Other interesting cytokine signals may support extra cellular matrix remodelling effect of the autologous macrophage product infusion. In addition we demonstrated a reproducible protocol for MRS in liver disease. DISCUSSION: Autologous activated macrophage infusion did not result in any toxicity in cirrhotic subjects taking part in this study and shows preliminary signs of efficacy in fibrosis resolution both clinically and biochemically. This work places the basis of development of cellular products for treatment of cirrhosis and fibrosis and provides invaluable insight in immune response to cellular treatment

    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

    A Multi-level Analysis on Implementation of Low-Cost IVF in Sub-Saharan Africa: A Case Study of Uganda.

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    Introduction: Globally, infertility is a major reproductive disease that affects an estimated 186 million people worldwide. In Sub-Saharan Africa, the burden of infertility is considerably high, affecting one in every four couples of reproductive age. Furthermore, infertility in this context has severe psychosocial, emotional, economic and health consequences. Absence of affordable fertility services in Sub-Saharan Africa has been justified by overpopulation and limited resources, resulting in inequitable access to infertility treatment compared to developed countries. Therefore, low-cost IVF (LCIVF) initiatives have been developed to simplify IVF-related treatment, reduce costs, and improve access to treatment for individuals in low-resource contexts. However, there is a gap between the development of LCIVF initiatives and their implementation in Sub-Saharan Africa. Uganda is the first country in East and Central Africa to undergo implementation of LCIVF initiatives within its public health system at Mulago Women’s Hospital. Methods: This was an exploratory, qualitative, single, case study conducted at Mulago Women’s Hospital in Kampala, Uganda. The objective of this study was to explore how LCIVF initiatives have been implemented within the public health system of Uganda at the macro-, meso- and micro-level. Primary qualitative data was collected using semi-structured interviews, hospital observations informal conversations, and document review. Using purposive and snowball sampling, a total of twenty-three key informants were interviewed including government officials, clinicians (doctors, nurses, technicians), hospital management, implementers, patient advocacy representatives, private sector practitioners, international organizational representatives, educational institution, and professional medical associations. Sources of secondary data included government and non-government reports, hospital records, organizational briefs, and press outputs. Using a multi-level data analysis approach, this study undertook a hybrid inductive/deductive thematic analysis, with the deductive analysis guided by the Consolidated Framework for Implementation Research (CFIR). Findings: Factors facilitating implementation included international recognition of infertility as a reproductive disease, strong political advocacy and oversight, patient needs & advocacy, government funding, inter-organizational collaboration, tension to change, competition in the private sector, intervention adaptability & trialability, relative priority, motivation &advocacy of fertility providers and specialist training. While barriers included scarcity of embryologists, intervention complexity, insufficient knowledge, evidence strength & quality of intervention, inadequate leadership engagement & hospital autonomy, poor public knowledge, limited engagement with traditional, cultural, and religious leaders, lack of salary incentives and concerns of revenue loss associated with low-cost options. Research contributions: This study contributes to knowledge of factors salient to implementation of LCIVF initiatives in a Sub-Saharan context. Effective implementation of these initiatives requires (1) sustained political support and favourable policy & legislation, (2) public sensitization and engagement of traditional, cultural, and religious leaders (3) strengthening local innovation and capacity building of fertility health workers, in particular embryologists (4) sustained implementor leadership engagement and inter-organizational collaboration and (5) proven clinical evidence and utilization of LCIVF initiatives in innovator countries. It also adds to the literature on the applicability of the CFIR framework in explaining factors that influence successful implementation in developing countries and offer opportunities for comparisons across studies

    Lifelong Learning in the Clinical Open World

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    Despite mounting evidence that data drift causes deep learning models to deteriorate over time, the majority of medical imaging research is developed for - and evaluated on - static close-world environments. There have been exciting advances in the automatic detection and segmentation of diagnostically-relevant findings. Yet the few studies that attempt to validate their performance in actual clinics are met with disappointing results and little utility as perceived by healthcare professionals. This is largely due to the many factors that introduce shifts in medical image data distribution, from changes in the acquisition practices to naturally occurring variations in the patient population and disease manifestation. If we truly wish to leverage deep learning technologies to alleviate the workload of clinicians and drive forward the democratization of health care, we must move away from close-world assumptions and start designing systems for the dynamic open world. This entails, first, the establishment of reliable quality assurance mechanisms with methods from the fields of uncertainty estimation, out-of-distribution detection, and domain-aware prediction appraisal. Part I of the thesis summarizes my contributions to this area. I first propose two approaches that identify outliers by monitoring a self-supervised objective or by quantifying the distance to training samples in a low-dimensional latent space. I then explore how to maximize the diversity among members of a deep ensemble for improved calibration and robustness; and present a lightweight method to detect low-quality lung lesion segmentation masks using domain knowledge. Of course, detecting failures is only the first step. We ideally want to train models that are reliable in the open world for a large portion of the data. Out-of-distribution generalization and domain adaptation may increase robustness, but only to a certain extent. As time goes on, models can only maintain acceptable performance if they continue learning with newly acquired cases that reflect changes in the data distribution. The goal of continual learning is to adapt to changes in the environment without forgetting previous knowledge. One practical strategy to approach this is expansion, whereby multiple parametrizations of the model are trained and the most appropriate one is selected during inference. In the second part of the thesis, I present two expansion-based methods that do not rely on information regarding when or how the data distribution changes. Even when appropriate mechanisms are in place to fail safely and accumulate knowledge over time, this will only translate to clinical usage insofar as the regulatory framework allows it. Current regulations in the USA and European Union only authorize locked systems that do not learn post-deployment. Fortunately, regulatory bodies are noting the need for a modern lifecycle regulatory approach. I review these efforts, along with other practical aspects of developing systems that learn through their lifecycle, in the third part of the thesis. We are finally at a stage where healthcare professionals and regulators are embracing deep learning. The number of commercially available diagnostic radiology systems is also quickly rising. This opens up our chance - and responsibility - to show that these systems can be safe and effective throughout their lifespan
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