Pereiramunk8543

Down syndrome (DS), caused by the trisomy of chromosome 21, is one of the common chromosomal disorders, the main clinical manifestations of which are delayed nervous development and intellectual disability. Long non-coding RNAs (lncRNAs) have critical roles in various biological processes, including cell growth, cell cycle regulation and differentiation. The roles of abnormally expressed lncRNAs have been previously reported; however, the biological functions and regulatory patterns of lncRNAs in DS have remained largely elusive. The aim of the present study was to perform a whole-genome-wide identification of lncRNAs and mRNAs associated with DS. In addition, global expression profiling analysis of DS-induced pluripotent stem cells was performed and differentially expressed (DE) lncRNAs and mRNAs were screened. Furthermore, the target genes and functions of the DE lncRNAs were predicted using Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analysis. The results revealed that the majority of the lncRNAs exerted functions in DS via cis-acting target genes. In addition, the results of the enrichment analysis indicated that these target genes were mainly involved in nervous and muscle development in DS. In conclusion, this integrative analysis using lncRNA and mRNA profiling provided novel insight into the pathogenesis of DS and it may promote the diagnosis and development of novel therapeutics for this disease.Age-related macular degeneration (AMD) is the most common cause of visual impairment in developed countries. Inflammation serves a critical role in the pathogenesis of AMD. Gardenia jasminoides is found in several regions of China and is traditionally used as an organic yellow dye but has also been widely used as a therapeutic agent in numerous diseases, including inflammation, depression, hepatic and vascular disorders, which may reflect the variability of functional compounds that are present in Gardenia jasminoides extracts (GJE). To investigate the therapeutic potential of GJE for AMD, ARPE-19 cells were treated with lipopolysaccharide (LPS) or LPS plus GJE. GJE significantly decreased LPS-induced expression of proinflammatory cytokines, including IL-1β, IL-6 and TNF-α. In the in vivo study, GJE inhibited CuSO4-induced migration of primitive macrophages to the lateral line in zebrafish embryos. MSAB GJE also attenuated expression of cytokines (IL-1β, IL-6 and TNF-α), NFKB activating protein (nkap) and TLR4 in ARPE-19 cells. The results of the present study demonstrated the anti-inflammatory potential of GJE in vitro and in vivo, and suggested GJE as a therapeutic candidate for AMD.Inhibitor of growth 3 (ING3) has been identified as a potential cancer drug target, but little is known about its role in breast cancer. Thus, the present study aimed to investigate ING3 expression in breast cancer, its clinical value, and how ING3 influences the migration and invasion of breast cancer cells. The Cancer Genome Atlas and UALCAN databases were used to analyze ING3 expression in cancer tissues and normal tissues. Survival analysis was performed using the UALCAN, UCSC Xena and KM-plot databases. In addition, reverse transcription-quantitative PCR and western blot analyses were performed to detect ING3 mRNA and protein expression levels. ING3 was overexpressed via lentiviral vector transfection, while the Transwell and wound healing assays were performed to assess the cell migratory and invasive abilities. Protein interaction and pathway analyses were performed using the GeneMANIA and Kyoto Encyclopedia of Genes and Genomes databases, respectively. The results demonstrated that ING3 expression wass regulated by ING3. Notably, overexpression of ING3 inhibited migration and invasion in vitro. However, further studies are required to determine whether ING3 regulates the biological behavior of breast cancer via tumor-related pathways.Peripheral nerve injuries (PNIs) continue to present both diagnostic and treatment challenges. While nerve transections are typically a straightforward diagnosis, other types of PNIs, such as chronic or traumatic nerve compression, may be more difficult to evaluate due to their varied presentation and limitations of current diagnostic tools. As a result, diagnosis may be delayed, and these patients may go on to develop progressive symptoms, impeding normal activity. In the past, PNIs were diagnosed by history and clinical examination alone or techniques that raised concerns regarding accuracy, invasiveness, or operator dependency. Magnetic resonance neurography (MRN) has been increasingly utilized in clinical settings due to its ability to visualize complex nerve structures along their entire pathway and distinguish nerves from surrounding vasculature and tissue in a noninvasive manner. In this review, we discuss the clinical applications of MRN in the diagnosis, as well as pre- and postsurgical assessments of patients with peripheral neuropathies.Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.The automatic diagnosis of Alzheimer's disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer's disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer's symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer's severity. The relationship between Alzheimer's patients' functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories.