Behavioral Assessment of Sensory, Motor, Emotion, and Cognition in Rodent Models of Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) is the second most common type of stroke and has one of the highest fatality rates of any disease. There are many clinical signs and symptoms after ICH due to brain cell injury and network disruption resulted from the rupture of a tiny artery and activation of inflammatory cells, such as motor dysfunction, sensory impairment, cognitive impairment, and emotional disturbance, etc. Thus, researchers have established many tests to evaluate behavioral changes in rodent ICH models, in order to achieve a better understanding and thus improvements in the prognosis for the clinical treatment of stroke. This review summarizes existing protocols that have been applied to assess neurologic function outcomes in the rodent ICH models such as pain, motor, cognition, and emotion tests. Pain tests include mechanical, hot, and cold pain tests; motor tests include the following 12 types: neurologic deficit scale test, staircase test, rotarod test, cylinder test, grid walk test, forelimb placing test, wire hanging test, modified neurologic severity score, beam walking test, horizontal ladder test, and adhesive removal test; learning and memory tests include Morris water maze, Y-maze, and novel object recognition test; emotion tests include elevated plus maze, sucrose preference test, tail suspension test, open field test, and forced swim test. This review discusses these assessments by examining their rationale, setup, duration, baseline, procedures as well as comparing their pros and cons, thus guiding researchers to select the most appropriate behavioral tests for preclinical ICH research.

Agrin expression is correlated with tumor development and poor prognosis in cholangiocarcinoma.

OBJECTIVE: This study examined the role of agrin in the development of cholangiocarcinoma (CCA). METHODS: Western blotting was performed to detect the expression of target genes. The correlation between agrin expression and prognosis was analyzed using the Kaplan-Meier method. Proliferation, migration, invasion, and tumorigenesis were examined in CCA cells and tissues using the Cell Counting Kit-8 assay, cell cycle analysis, transwell migration assay, and nude mouse tumorigenicity assay in vivo, respectively. RESULTS: Agrin expression was significantly upregulated in CCA tissues compared with that in adjacent non-tumor tissues, and agrin expression was correlated with poorer tumor characteristics such as portal vein tumor thrombus, intrahepatic metastasis, and worse survival. Forced agrin expression in CCA cells apparently promoted proliferation, colony formation, migration, invasion, and cell cycle progression, but agrin depletion had the opposite effects. Furthermore, agrin-depleted CCA cells developed fewer and smaller tumors than control cells in vivo. Mechanistic analyses indicated that agrin activated the Hippo signaling pathway and induced the translocation of YAP to the nucleus. CONCLUSIONS: Agrin promoted CCA progression by activating the Hippo signaling pathway, suggesting its promise as a target for CCA therapy.

Highly sensitive and specific detection of tumor cells based on a split aptamer-triggered dual hybridization chain reaction.

Highly sensitive and specific detection of rare tumor cells is urgently needed for early tumor diagnosis. Herein, a split aptamer-based dual hybridization chain reaction (dual-HCR) strategy with flow cytometry analysis was developed to meet this purpose. With the split aptamer pair as the recognition unit and HCR as the signal amplification technique, this strategy achieved an improved detection limit as low as 20 cells in 200 µL binding buffer. Meanwhile, this method was highly specific with distinct recognition of the target cells from the control cell and mixed cell samples. Furthermore, we succeeded in the specific detection of the target cells in 50% human serum, demonstrating that this method has great potential in clinical applications. In theory, this strategy can be used to detect different target cells by using different split aptamers. Therefore, this general, sensitive and specific tumor cell detection method may be helpful for early clinical diagnosis and cancer research.

Bioinformatics analysis of molecular genetic targets and key pathways for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the second leading cause of death among cancers worldwide. In this study, we aimed to identify the molecular target genes and detect the key mechanisms of HCC. Three gene expression profiles (GSE84006, GSE14323, GSE14811) and two miRNA expression profiles (GSE40744, GSE36915) were analyzed to determine the molecular target genes, microRNAs (miRNAs) and the potential molecular mechanisms in HCC. METHODS: All profiles were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs) was analyzed by the GEO2R method. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment analysis performed database for Integrated Discovery, Visualization and Annotation. The miRNA-gene network and protein-protein interaction (PPI) network were correlated by the Cytoscape software. The key target genes were identified by the CytoHubba plugin, Molecular Complex Detection (MCODE) plugin and miRNA-gene network. The identified hub genes were testified for survival curve using the Kaplan-Meier plotter database. RESULTS: Expression profiles had 592 overlapped DEGs. The majority of the DEGs were enriched in membrane-bounded organelles and intracellular membrane-bounded organelles. These DEGs were significantly enriched in metabolic, protein processing in the endoplasmic reticulum and thyroid cancer pathways. PPI network analysis showed these genes were mostly involved in the pathogenic Escherichia coli infection and the regulation of actin cytoskeleton pathways. Combining these results, we identified 10 key genes involving in the progression of HCC. Finally, PLK1, PRCC, PRPF4 and PSMA7 exhibited higher expression levels in HCC patients with poor prognosis than those for lower expression via Kaplan-Meier plotter database. CONCLUSION: PLK1, PRCC, PRPF4 and PSMA7 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, protein processing in the endoplasmic reticulum and the thyroid cancer pathway may play vital roles in the progression of HCC.