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Immunopathogenesis of Post-Infectious Hydrocephalus
No full textHydrocephalus is characterized by the abnormal accumulation of cerebrospinal fluid (CSF) within the brain ventricles. In post-infectious hydrocephalus (PIH) cases, the condition presents challenges in understanding the immune response. PIH is a complex condition, often persisting after the initial infection is treated and thus requiring a deeper understanding of the immune mechanisms involved in its development. This thesis will explore the immunopathogenesis of PIH, elucidating the relationship between the immune response and neurological complications that would succeed infection. The immune response of PIH includes a series of events, beginning with the activation of immune cells and finishing with the release of inflammatory mediators and molecular signaling pathways. Resident immune cells of the central nervous system (CNS), microglia and astrocytes, are important in producing neuroinflammation and contributing to the pathology of PIH. Invading immune cells (T-cells and B-cells), as well as cytokines and chemokines, contribute to inflammation within the brain, worsening impairment of CSF flow and disrupting the blood-brain barrier (BBB). Understanding the progression of infectious disease in a stem cell niche and the ventricular-subventricular zone (V-SVZ) is important for conceptualizing the regulatory components of the immune response and ventricular stability. By having a strong foundation of the immunopathogenesis of PIH, of not only its etiology but the importance of limiting neuroinflammation and improving immune responses to infection, allows for a deeper understanding of PIH development
Higher Education Collaboration for Digital Transformation in Pandemic Panamá
No full textSpecial Issu
Acculturative Stress of Asian International Students before and during the COVID-19 Pandemic
No full textDigital Turn in Higher Education: An Examination of Enablers and Inhibitors in the Turkish Context
No full textCuba y la Pandemia de 2020: El Rol de la Educación Superior Cubana Respecto al Cumplimiento de los Objetivos del Desarrollo Sostenible
No full textTransformation of Korean Higher Education in the Digital Era: Achievements and Challenges
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Understanding the Internationalization of Higher Education in the Context of the War in Ukraine: Critical Conversations from Kazakhstan
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Analyzing Information Cascades through Machine Learning and Data Analytics
No full textIn today\u27s digital age, social media platforms have become pivotal in influencing public opinion and behavior, with information spreading being both beneficial and detrimental. This rapid spread is typically called an information cascade, and they are important in further understanding social influence, managing misinformation, and even predicting potential trends of public responses. With social media, people are connected so easily to one another like a network, wherein it becomes possible for them to influence each other’s behavior and decisions. Utilizing a dataset from Weibo that spans critical periods of the COVID-19 outbreak, this study integrates machine learning and data analytics to analyze the spread of posts and their impact on public behavior and decision-making. Central to the thesis is the development and application of a Graph Convolutional Network (GCN) model, designed to predict and classify posts as part of significant information cascades. By constructing a graph where nodes represent individual posts and edges depict repost relationships, the model captures how information—both true and false—flows through social networks. This approach allows for a nuanced analysis of connectivity and influence among posts, highlighting how certain information gains prominence
