Medicine
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Observational Study
Effect of Yiqihuoxue Formula for the treatment of ischemic stroke: A retrospective study.
This retrospective study assessed the feasible effect of Yiqihuoxue Formula (YQHXF) for the treatment of patients with ischemic stroke (IS). A total of 66 patients with IS were included in this retrospective study. All patients received routine treatment, and were divided into two groups: a treatment group (n = 33) and a control group (n = 33). ⋯ After treatment, patients in the treatment group showed better improvements in NIHSS scale (P = .01), mRS (P < .01), BIS (P = .04), and SS-QOL scale (P = .04), than patients in the control group. No treatment-associated adverse events were recorded in this study. The results of this study indicated that YQHXF may benefit for patients with IS.
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This study was carried out to identify potential key genes associated with the pathogenesis and prognosis of breast cancer (BC). ⋯ The findings of this research would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of BC, which could be used as a new biomarker for diagnosis and to guide the combination medicine of BC.
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To explore the gene modules and key genes of head and neck squamous cell carcinoma (HNSCC), a bioinformatics algorithm based on the gene co-expression network analysis was proposed in this study. Firstly, differentially expressed genes (DEGs) were identified and a gene co-expression network (i-GCN) was constructed with Pearson correlation analysis. Then, the gene modules were identified with 5 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. ⋯ Lastly, 34 key genes were identified, such as CDKN2A, HOXA1, CDC7, PPL, EVPL, PXN, PDGFRB, CALD1, and NUSAP1. Among them, HOXA1, PXN, and NUSAP1 were negatively correlated with the survival prognosis. HOXA1, PXN, and NUSAP1 might play important roles in the progression of HNSCC and severed as potential biomarkers for future diagnosis.
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Case Reports
Primary pure large cell neuroendocrine carcinoma of the ovary: A rare case report and review of literature.
Ovarian large cell neuroendocrine carcinoma (LCNEC), or ovarian non-small cell neuroendocrine carcinoma, which is a newly described tumour in the classification of primary ovarian neoplasms by the World Health Organization, is a rare entity that is frequently associated with a surface epithelial and germ cell neoplasm component. Few cases have been reported in the literature, and only 18 primary pure ovarian LCNEC cases have been reported so far, including our 1 case. Ovarian LCNEC is a highly aggressive tumor with a poor prognosis even at an early stage. ⋯ This case is 1 of the ovarian LCNEC which is a rare and extremely malignant tumor. Diagnosis requires histopathology and immunohistochemistry. The treatment includes primary cytoreductive surgery followed by chemotherapy.
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Two-dimensional gel electrophoresis (2D-GE) is an indispensable technique for the study of proteomes of biological systems, providing an assessment of changes in protein abundance under various experimental conditions. However, due to the complexity of 2D-GE gels, there is no systematic, automatic, and reproducible protocol for image analysis and specific implementations are required for each context. In addition, practically all available solutions are commercial, which implies high cost and little flexibility to modulate the parameters of the algorithms. ⋯ In a second pipeline with the same program, differential identification of spots was addressed when comparing pairs of protein profiles. Due to the characteristics of the programs used, our workflow can automatically analyze a large number of images and it is parallelizable, which is an advantage with respect to other implementations. Finally, we compared six experimental conditions of bacterial strain in the presence or absence of antibiotics, determining protein profiles relationships by applying clustering algorithms PCA (Principal Components Analysis) and HC (Hierarchical Clustering).