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Brain Edema: From Molecular Mechanisms to Clinical Practice brings together the most widely recognized experts in experimental and clinical brain edema research to review the current knowledge gathered on the molecular and cellular pathophysiology and clinical management of brain edema. This timely book also discusses future directions of research and treatment. Brain edema is an integral and acutely life-threatening part of the pathophysiology of multiple cerebral and non-cerebral disorders, including traumatic brain injury, cerebral ischemia, brain tumors, cardiac arrest, altitude sickness and liver failure. Affecting millions worldwide, research over the past few years has shown that a plethora of complex molecular and cellular mechanisms contribute to this pathological accumulation of water in the brain parenchyma. In parallel, the development of new neuroimaging tools has provided a new way to examine how edema develops longitudinally and in real time, both in pre-clinical models and in patients. Despite intense research over the past few decades, therapeutic options are still limited and sometimes not effective. - Presents a comprehensive understanding of the molecular mechanisms involved in edema formation and resolution - Discusses the specific role of edema development in several pathologies, including traumatic brain injury, stroke, brain tumors, cardiac arrest, and liver failure - Proposes a new classification of edema based on molecular processes - Discusses clinical management of new clinical trials coming from pre-clinical studies - Addresses the possible link between edema formation, other molecular and cellular processes, including inflammation and neuroinflammation
Traumatic brain injury (TBI) remains a significant source of death and permanent disability, contributing to nearly one-third of all injury related deaths in the United States and exacting a profound personal and economic toll. Despite the increased resources that have recently been brought to bear to improve our understanding of TBI, the developme
With the contribution from more than one hundred CNS neurotrauma experts, this book provides a comprehensive and up-to-date account on the latest developments in the area of neurotrauma including biomarker studies, experimental models, diagnostic methods, and neurotherapeutic intervention strategies in brain injury research. It discusses neurotrauma mechanisms, biomarker discovery, and neurocognitive and neurobehavioral deficits. Also included are medical interventions and recent neurotherapeutics used in the area of brain injury that have been translated to the area of rehabilitation research. In addition, a section is devoted to models of milder CNS injury, including sports injuries.
The successful treatment of acute stroke remains one of the major challenges in clinical medicine. Over the last decades, the understanding of stroke pathophysiology has greatly improved, while the therapeutic options in stroke therapy remain very limited. Today, hyperacute mechanisms of damage, such as excitotoxicity, can be discriminated from delayed ones, such as inflammation and apoptosis. Targeting of inflammation has already been successfully applied in various stroke models, but translation into a clinically efficacious strategy has not been achieved so far. In this book, leading experts in basic cerebrovascular research as well as stroke treatment review the current evidence for and against an important role for inflammation in stroke, and explore the potential of treating or modulating inflammation in stroke therapy.
As the cost of high-throughput sequencing goes down, huge volumes of biological and medical data have been produced from various sequencing platforms at multiple molecular levels including genome, transcriptome, proteome, epigenome, metabolome, and so on. For a long time, data analysis on single molecular levels has paved the way to answer many important research questions. However, many Aging-Related Neuronal Diseases (ARNDs) and Central Nervous System (CNS) aging involve interactions of molecules from multiple molecular levels, in which conclusions based on single molecular levels are usually incomplete and sometimes misleading. In these scenarios, multi-omics data analysis has unprecedentedly helped capture much more useful information for the diagnosis, treatment, prognosis, and drug discovery of ARNDs. The first step towards a multi-omics analysis is to establish reliable and robust multi-omics datasets. In the past years, a few important ARNDs-associated multi-omics databases like Allen Brain have been constructed, which raised immediate needs like data curation, normalization, interpretation, and visualization for integrative multi-omics explorations. Though there have been several well-established multi-omics databases for ARNDs like Alzheimer’s disease, similar databases for other ARNDs are still in urgent need. After the databases establish, many computational tools and experiential strategies should be developed specifically for them. First, the multi-omics data are usually extremely noisy, complex, heterogeneous and in high dimension, which presents the need for appropriate denoising and dimension reduction methods. Second, since the multi-omics and non-omics data like pathological and clinical data are usually in different data spaces, a useful algorithm to mapping them into the same data space and integrate them is nontrivial. In the multi-omics era, there are numerous data-centric tools for the integration of multi-omics datasets, which could be generally divided into three categories: unsupervised, supervised, and semi-supervised methods. Commonly used algorithms include but not limited to Bayesian-based methods, Network-based methods, multi-step analysis methods, and multiple kernel learning methods. Third, methods are needed in studying and verifying the association between two or more levels of multi-omics data and non-omics data. For example, expression quantitative trait loci (eQTL) analysis is widely used to infer the association between a single nucleotide polymorphism (SNP) and the expression of a gene. Recently, the association between omics data and more complex data like pathological and clinical imaging data has been a hot research topic. The outcomes may reveal the underlying molecular mechanism and promote de novo drug design as well as drug repurposing for ARNDs. Here, we welcome investigators to share their Original Research, Review, Mini Review, Hypothesis and Theory, Perspective, Conceptual Analysis, Data Report, Brief Research Report, Code related to multi-omics studies of ARNDs, which can be applied for better diagnosis, treatment, prognosis and drug discovery of human diseases in the future era of precision medicine. Potential contents include but are not limited to the following: ▪ Methods for integrating, interpreting, or visualizing two or more omics data. ▪ Methods for identifying interactions between different data modalities. ▪ Methods for disease subtyping, biomarker prediction. ▪ Machine learning or deep learning methods on dimensional reduction and feature selection for big noisy data. ▪ Methods for studying the association among different omics data or between omics and non-omics data like clinical, pathological, and imaging data. ▪ Review of multi-omics resource about ARNDs and/or CNS aging. ▪ Experimental validation of biomarkers identified from multi-omics data analysis. ▪ Disease diagnosis and prognosis prediction from imaging and non-imaging data analysis, or both. ▪ Clinical applications or validations of findings from multi-omics data analysis.