6,446 research outputs found

    Clinical, immunological and genetic features of histiocytic disorders

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

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    The Photosensitizer Temoporfin (mTHPC) – Chemical, Pre‐clinical and Clinical Developments in the Last Decade†‡

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    This review follows the research, development and clinical applications of the photosensitizer 5,10,15,20‐tetra(m‐hydroxyphenyl)chlorin (mTHPC, temoporfin) in photodynamic (cancer) therapy (PDT) and other medical applications. Temoporfin is the active substance in the medicinal product Foscan¼ authorized in the EU for the palliative treatment of head and neck cancer. Chemistry, biochemistry and pharmacology, as well as clinical and other applications of temoporfin are addressed, including the extensive work that has been done on formulation development including liposomal formulations. The literature has been covered from 2009 to early 2022, thereby connecting it to the previous extensive review on this photosensitizer published in this journal [Senge, M. O. and J. C. Brandt (2011) Photochem. Photobiol. 87, 1240–1296] which followed its way from initial development to approval and clinical application

    Dialogue without barriers. A comprehensive approach to dealing with stuttering

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    Orvosképzés 2023

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    Deep Learning Models For Biomedical Data Analysis

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    The field of biomedical data analysis is a vibrant area of research dedicated to extracting valuable insights from a wide range of biomedical data sources, including biomedical images and genomics data. The emergence of deep learning, an artificial intelligence approach, presents significant prospects for enhancing biomedical data analysis and knowledge discovery. This dissertation focused on exploring innovative deep-learning methods for biomedical image processing and gene data analysis. During the COVID-19 pandemic, biomedical imaging data, including CT scans and chest x-rays, played a pivotal role in identifying COVID-19 cases by categorizing patient chest x-ray outcomes as COVID-19-positive or negative. While supervised deep learning methods have effectively recognized COVID-19 patterns in chest x-ray datasets, the availability of annotated training data remains limited. To address this challenge, the thesis introduced a semi-supervised deep learning model named ssResNet, built upon the Residual Neural Network (ResNet) architecture. The model combines supervised and unsupervised paths, incorporating a weighted supervised loss function to manage data imbalance. The strategies to diminish prediction uncertainty in deep learning models for critical applications like medical image processing is explore. It achieves this through an ensemble deep learning model, integrating bagging deep learning and model calibration techniques. This ensemble model not only boosts biomedical image segmentation accuracy but also reduces prediction uncertainty, as validated on a comprehensive chest x-ray image segmentation dataset. Furthermore, the thesis introduced an ensemble model integrating Proformer and ensemble learning methodologies. This model constructs multiple independent Proformers for predicting gene expression, their predictions are combined through weighted averaging to generate final predictions. Experimental outcomes underscore the efficacy of this ensemble model in enhancing prediction performance across various metrics. In conclusion, this dissertation advances biomedical data analysis by harnessing the potential of deep learning techniques. It devises innovative approaches for processing biomedical images and gene data. By leveraging deep learning\u27s capabilities, this work paves the way for further progress in biomedical data analytics and its applications within clinical contexts. Index Terms- biomedical data analysis, COVID-19, deep learning, ensemble learning, gene data analytics, medical image segmentation, prediction uncertainty, Proformer, Residual Neural Network (ResNet), semi-supervised learning

    2023-2024 Undergraduate Catalog

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    2023-2024 undergraduate catalog for Morehead State University

    On Making Fiction: Frankenstein and the Life of Stories

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    Fiction is generally understood to be a fascinating, yet somehow deficient affair, merely derivative of reality. What if we could, instead, come up with an affirmative approach that takes stories seriously in their capacity to bring forth a substance of their own? Iconic texts such as Mary Shelley's Frankenstein and its numerous adaptations stubbornly resist our attempts to classify them as mere representations of reality. The author shows how these texts insist that we take them seriously as agents and interlocutors in our world- and culture-making activities. Drawing on this analysis, she develops a theory of narrative fiction as a generative practice

    Positron emission tomography imaging biomarkers of frontotemporal dementia

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    There are currently no disease modifying treatments available for frontotemporal dementia (FTD). Pathological heterogeneity within and between FTD phenotypes and genotypes makes accurate diagnosis challenging. Biomarkers that can aid diagnosis and monitor disease progression will be critical for clinical trials of potential treatments. Positron emission tomography (PET) imaging provides insights into molecular changes in the brain during life that are otherwise only directly quantifiable at postmortem. In this thesis I aimed to identify potential biomarkers of FTD using PET imaging. In Chapter 3 I use PET imaging of glucose metabolism to identify early neuronal dysfunction in presymptomatic genetic FTD, revealing specific involvement of the anterior cingulate in a subgroup of mutation carriers. In Chapter 4 I evaluate the utility of a PET tracer of tau protein deposition in genetic FTD against volumetric imaging, which appears to provide a more sensitive biomarker of disease than this tau PET tracer in FTD. In Chapter 5 I investigate neuroinflammation via PET imaging and identify different areas of neuroinflammation in different FTD genotypes, suggesting an association between neuroinflammation and protein deposition and that PET imaging of neuroinflammation might provide a sensitive biomarker in MAPT-related FTD. In Chapter 6 I investigate synaptic and mitochondrial dysfunction via PET imaging in FTD, the latter of which has been previously unexplored. I reveal marked differences in both markers in FTD versus controls which suggests both might provide sensitive biomarkers of disease. Furthermore, in Chapter 7 I evaluate the same biomarkers at longitudinal follow up where I find continued reductions in mitochondrial function over time suggesting mitochondrial PET imaging may provide a biomarker of disease progression in FTD. Future replication of the findings in this thesis in larger cohorts might facilitate the advancement of clinical trials in FTD
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