Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0040822 (tremor)
 18,428 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Neurodegenerative disorders such as Alzheimer disease (AD), frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), Parkinson disease (PD), Huntington's disease (HD), and multiple sclerosis (MS) affect different neuronal cells, and have a variable age of onset, clinical symptoms, and pathological features. Despite the great progress in understanding the etiology of these disorders, the underlying mechanisms remain largely unclear. Among the processes affected in neurodegenerative diseases, alteration in RNA metabolism is emerging as a crucial player. RNA-binding proteins (RBPs) are involved at all stages of RNA metabolism and display a broad range of functions, including modulation of mRNA transcription, splicing, editing, export, stability, translation and localization and miRNA biogenesis, thus enormously impacting regulation of gene expression. On the other hand, aberrant regulation of RBP expression or activity can contribute to disease onset and progression. Recent reports identified mutations causative of neurological disorders in the genes encoding a family of RBPs named FET (FUS/TLS, EWS and TAF15). This review summarizes recent works documenting the involvement of FET proteins in the pathology of ALS, FTLD, essential tremor (ET) and other neurodegenerative diseases. Moreover, clinical implications of recent advances in FET research are critically discussed.
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PMID:Role of FET proteins in neurodegenerative disorders. 2741 68

Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLSE) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLSE method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.
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PMID:Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution. 3159 2