Objective To investigate the factors affecting the protective effects of morphine preconditioning on murine hippocampal neurons against anoxia/reoxygenation (A/R) injury and the underlying mechanisms.Methods Hippocampal slices (400 μm thick) were prepared using hippocampi isolated from decapitated mice. A/R injury was simulated in vitro using artificial cerebral spinal fluid (ACSF) deprived of O_2 and glucose for 20 min followed by reoxygenation and glucose supply for 2 h. The experiment was performed in 4 parts: I .The slices were incubated with 5 different concentrations of morphine (0.1, 0.3, 0.5, 1.0, 3.0, 10.0 /μmol/L) for 30 min at 30 min before A/R; Ⅱ. The slices were incubated with morphine 3.0 /μmol/L for 5 different periods of time (5, 15, 30, 45, 60 min) at 30 min before A/R; Ⅲ. The slices were incubated with morphine 3.0 μmol/L for 30 min followed by A/R at 6 different intervals (0, 5, 15,30,60, 120 min); Ⅳ. The slices were incubated with (a) chelerythrine (a non-selective PKC antagonist) 10 /μmol/L or (b) εVl-2 (a selective nPKCe isoform antagonist) 2 μmol/L or (c) AIP 2 μmol/L (a selective CaMK Ⅱ antagonist) or (d) MK-801 10 μmol/L (a non-competitive NMDA receptor blocker) for 30 min and then for another 30 min together with morphine 3.0 μmol/L before A/R at 30 min interval. The survival rates of the hippocampal neurons were assessed by TTC staining. Results Neuronal survival rates were significantly higher in morphine preconditioning groups which preconditioned with morphine (0.5-10.0 μmol/L) for 15-60 min at an interval of 0-60 min before A/R than in A/R group. Increase in neuronal survival rate induced by morphine preconditioning was partially blocked by chelerythrine or εV1-2 or AIP or MK-801. Conclusion Preconditioning with appropriate concentrations of morphine (0.5-10.0 μmol/L) for appropriate period of time (15-60 min) at appropriate interval (within 60 min) before A/R can protect hippocampal neurons against A/R injury through activation of nPKCε, NMDA receptor and CaMKⅡ.

Objective Co-stimulation cells is a kind of natual killer (NK)-like T cells, which can kill tumor cells.Previous studies show that lupeol , an natural plant extracts , can change the growth of NK cells ,γδT cells and their effects on tumor cells .This study aimed to investigate the effects of lupeol on human co-stimulation cells and colonic cancer cell lines SW 480 . Methods The peripheral blood mononuclear cells ( PBMC) from healthy volunteers were induced in vitro by different cytokines and transferred into Co-simulation cells.After SW480 and Co-stimulation cells were incubated with different concentrations of lupeol for different durations , methyl thiazolyl tetrazolium (MTT) was used to detect the effects of lupeol on co-stimulation cells and colonic cancer cell lines SW480. Lactate dehydrogenase was used to determine the cell-killing activity of Co-simulation cells to colonic cancer lines SW 480 . Results The concentration of lupeol in 0.1-200.0 mg/L promoted the growth of Co-stimulation cells and inhibited the colonic cancer cell lines SW480.When the concentration of lupeol is at 12.5 mg/L, the cell-killing activity of Co-simulation cells to colonic cancer lines SW 480 was enhanced significantly compared with the controls (76%vs 40%, P<0.05). Conclusion Lupeol could promote the prolifera-tion of Co-stimulation cells, inhibit the growth of cancer lines SW480, and strengthen the cytotoxicity of Co-stimulation cells against co-lonic cell lines SW480.

OBJECTIVETo investigate the effects of D-galactose (D-gal) on aging of rat marrow mesenchymal stem cells (MSCs) and its mechanism.

METHODSMSCs isolated from young (7 d) SD rats were randomly divided into four groups:control group, 1g/L, 10g/L and 50g/L D-gal treatment groups. In control group MSCs were cultured in DMEM containing 10% FBS for 48 h. In the D-gal treatment groups, MSCs were cultured in DMEM containing 10% FBS with 1g/L, 10g/L or 50g/L D-gal for 48 h. The senescence-associated changes were examined with SA-β-galactosidase (SA-β-gal) staining, the expressions of p53, p21 and p16 were detected by Western blot. The living and apoptotic cells were determined by AO/EB staining. Cell proliferation was detected by MTT assay. SOD activity was measured by xanthine oxidase method, and the MDA content was estimated with thiobarbituric acid (TBA) method.

RESULTSCompared to control group, the number of SA-β-gal positive cells and the expression of p53, p21 and p16 were significantly increased in the 10g/L and 50g/L D-gal treatment groups. The apoptosis rate in 50g/L D-gal group was significantly higher than that in control group (P<0.01). The proliferation of MSCs was decreased in the 10g/L and 50g/L D-gal groups compared to control group (P<0.05). After 10g/L and 50g/L D-gal treatment, SOD activity was significantly decreased (P<0.01), and MDA level was increased (P<0.01).

CONCLUSIONThe aging of MSCs can be induced by 10g/L and 50g/L D-gal, which may be associated with the elevated levels of oxidative stress.

Animals ; Apoptosis ; drug effects ; Cells, Cultured ; Cellular Senescence ; drug effects ; Galactose ; pharmacology ; Male ; Mesenchymal Stromal Cells ; drug effects ; physiology ; Oxidative Stress ; Rats ; Rats, Sprague-Dawley

OBJECTIVE: To explore the correlations of echocardiographic parameters in patients with gout. METHODS: The hospitalization data and medical records of patients with gout between January, 2012 and June, 2019 were retrieved from the database of Anhui Provincial Hospital of Traditional Chinese Medicine, and the echocardiographic parameters and clinical laboratory test results of the inflammatory, immunological and metabolic indicators were analyzed. SPSS 22.0, SPSS Clementine 11.1 Aprior and other statistical software were used to determine the association rules and carry out correlation analysis, heat map analysis and multi-factor logistic regression analysis of the indicators. RESULTS: Heat map analysis showed that the expressions of EF and SV were the most significant, followed by AODd, LADs, LVDd and FS. Cluster analysis showed that AODd, EF, FS, LADs, LVDd, and SV were all in cluster 1, and IVSTd, LVPWTd, MPAD, Pmax, and RVDd were in cluster 2. Correlation analysis showed that in the 383 patients, EF was negatively correlated with LVDd ( < 0.05) and positively correlated with FS and SV ( < 0.05); AODd was positively correlated with IVSTd, LADs, LVDd, LVPWTd, RVDd, SV, and ESR ( < 0.05); FS was positively correlated with EF and SV ( < 0.05) and negatively correlated with LVDd ( < 0.05);IVSTd was positively correlated with AODd, LADs, LVPWTd, and complement C4 ( < 0.05); LADs were positively correlated with AODd, IVSTd, MPAD, RVDd, and SV ( < 0.05); LVDd was positively correlated with AODd, IVSTd ( < 0.05), and negatively correlated with LVDd and complement C3 ( < 0.05); MPAD and LADs, HDLC and TC were positively correlated ( < 0.05)and negatively correlated with Pmax ( < 0.05); Pmax was positively correlated with LVDd, RVDd and SV ( < 0.05)and negatively correlated with FS and MPAD ( < 0.05); RVDd was positively correlated with AODd, LADs, LVDd, Pmax, SV ( < 0.05); SV was positively correlated with AODd, EF, LADs, LVDd, Pmax, and RVDd ( < 0.05); complement C3 was positively correlated with complement C4 and CRP ( < 0.05), and negatively correlated with LVPWTd ( < 0.05); complement C4 was positively correlated with IVSTd, complement C3, CRP, and ESR ( < 0.05); CRP was positively correlated with complement C3, complement C4, IgA, IgG ( < 0.05), and negatively correlated with TC, HDLC, and TG ( < 0.05); TG was positively correlated with HDLC, IgM, and TC ( < 0.05), and negatively correlated with CRP ( < 0.05); HDLC was positively correlated with MPAD, HDLC and TC ( < 0.05) and negatively correlated with CRP ( < 0.05); IgA was positively correlated with CRP, IgG and IgM ( < 0.05); IgG was positively correlated with CRP, IgA and IgM ( < 0.05); IgM is positively correlated with TG, IgA, IgG, UA ( < 0.05) and negatively correlated with CRP ( < 0.05); UA was positively correlated with IgM ( < 0.05); ESR was positively correlated with AODd and complement C4 ( < 0.05); HCY was negatively correlated with RVDd ( < 0.05); TC was positively correlated with MPAD and TG ( < 0.05), and negatively correlated with CRP ( < 0.05). The increase of Pmax was significantly associated with the increase of LDL-C, UA, complement C4, TG, HCY, HDL-C, IgG, ESR, CRP, and complement C3; the increase of SV was associated with the elevations of UA, LDL-C, complement C4, HDL-C, CRP, IgG, HCY, TC, ESR, TG, and complement C3. Multivariate logistic regression analysis indicated that FS was positively correlated with LDL-C ( < 0.05), Pmax was negatively correlated with IgM ( < 0.05), and SV was negatively correlated with ESR ( < 0.05). CONCLUSIONS The changes of echocardiographic parameters in patients with gout are correlated with the increase in inflammation, immunity, and metabolic indexes. Patients with a history of smoking and drinking do not show obvious changes in cardiac function. The changes in metabolic indexes are risk factors for changes in echocardiographic parameters.
Echocardiography ; Gout ; Humans ; Inflammation ; Retrospective Studies ; Risk Factors

OBJECTIVE: To detect the differentially expressed inflammatory proteins in acute gouty arthritis (AGA) with protein chip. METHODS: The Raybiotech cytokine antibody chip was used to screen the proteomic expression in serum samples of 10 AGA patients and 10 healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were applied to determine the biological function annotation of differentially expressed proteins and the enrichment of signal pathways. ELISA method was used to verify the differential protein expression in 60 AGA patients and 60 healthy subjects. The ROC curve was employed to evaluate the diagnostic value of differential proteins in AGA patients. RESULTS: According to|log CONCLUSIONS Proteomics can be applied to identify the biomarkers of AGA, which may be used for risk prediction and diagnosis of AGA patients.
Arthritis, Gouty/diagnosis* ; Cytokines/genetics* ; Gene Expression Profiling ; Gene Expression Regulation ; Humans ; Inflammation ; Protein Array Analysis ; Proteomics