Burkitt lymphoma (BL) is a highly malignant non-Hodgkin's lymphoma that is closely related to the abnormal expression of genes. Familial acute myelogenous leukemia related factor (FAMLF; GenBank accession No. EF413001.1) is a novel gene that was cloned by our research group, and miR-181b is located in the intron of the FAMLF gene. To verify the role of miR-181b and FAMLF in BL, RNAhybrid software was used to predict target site of miR-181b on FAMLF and real-time quantitative PCR (RQ-PCR) was used to detect expression of miR-181b and FAMLF in BL patients, Raji cells and unaffected individuals. miR-181b was then transfected into Raji and CA46 cell lines and FAMLF expression was examined by RQ-PCR and western blotting. Further, Raji cells viability and proliferation were detected by MTT and clone formation, and Raji cell cycle and apoptosis were detected by flow cytometry. The results showed that miR-181b can bind to bases 21–42 of the FAMLF 5′ untranslated region (UTR), FAMLF was highly expressed and miR-181b was lowly expressed in BL patients compared with unaffected individuals. FAMLF expression was significantly and inversely correlated to miR-181b expression, and miR-181b negatively regulated FAMLF at posttranscriptional and translational levels. A dual-luciferase reporter gene assay identified that the 5′ UTR of FAMLF mRNA contained putative binding sites for miR-181b. Down-regulation of FAMLF by miR-181b arrested cell cycle, inhibited cell viability and proliferation in a BL cell line model. Our findings explain a new mechanism of BL pathogenesis and may also have implications in the therapy of FAMLF-overexpressing BL.

T lymphocytes are important in the pathogenesis of psoriasis, and increasing evidence indicates that B cells also play an important role. The mechanisms of action, however, remain unclear. We evaluated the ratios of CD19+ B cells in peripheral blood mononuclear cells (PBMCs) from 157 patients with psoriasis (65 patients with psoriasis vulgaris, 32 patients with erythrodermic psoriasis, 30 patients with arthropathic psoriasis, and 30 patients with pustular psoriasis) and 35 healthy controls (HCs). Ratios of CD19+ B cells in skin lesions were compared with non-lesions in 7 erythrodermic psoriasis patients. The Psoriasis Area Severity Index (PASI) was used to measure disease severity. CD19+ B cell ratios in PBMCs from psoriasis vulgaris (at both the active and stationary stage) and arthropathic psoriasis patients were higher compared with HCs (P<0.01), but ratios were lower in erythrodermic and pustular psoriasis patients (P<0.01). CD19+ B cell ratios in erythrodermic psoriasis skin lesions were higher than in non-lesion areas (P<0.001). Different subsets of CD19+CD40+, CD19+CD44+, CD19+CD80+, CD19+CD86+, CD19+CD11b+, and CD19+HLA-DR+ B cells in PBMCs were observed in different psoriasis clinical subtypes. PASI scores were positively correlated with CD19+ B cell ratios in psoriasis vulgaris and arthropathic psoriasis cases (r=0.871 and r=0.692, respectively, P<0.01), but were negatively correlated in pustular psoriasis (r=-0.569, P<0.01). The results indicated that similar to T cells, B cells activation may also play important roles in different pathological stages of psoriasis.

The effects of interleukin-10 (IL-10) and glucose on mRNA and protein expression of osteoprotegerin (OPG), and its ligand, receptor activator of nuclear factor-κB ligand (RANKL), were investigated in human periodontal ligament fibroblasts (HPDLFs). Primary HPDLFs were treated with different concentrations of IL-10 (0, 1, 10, 25, 50, and 100 ng/mL) or glucose (0, 5.5, 10, 20, 30, and 40 mmol/L). Changes in mRNA and protein expression were examined using the reverse-transcription polymerase chain reaction (RT-PCR) and Western blot analysis, respectively. After IL-10 treatment, mRNA and protein levels of OPG were increased, while mRNA and protein levels of RANKL were decreased (P<0.05), both in a concentration-dependent manner. Glucose stimulation had the opposite concentration-dependent effect to that of IL-10 on OPG and RANKL expression. IL-10 upregulated OPG expression and downregulated RANKL expression, whereas high glucose upregulated RANKL and downregulated OPG in HDPLFs. Abnormal levels of IL-10 and glucose may contribute to the pathogenesis of periodontal disease.

The familial acute myeloid leukemia related factor gene (FAMLF) was previously identified from a familial AML subtractive cDNA library and shown to undergo alternative splicing. This study used real-time quantitative PCR to investigate the expression of the FAMLF alternative-splicing transcript consensus sequence (FAMLF-CS) in peripheral blood mononuclear cells (PBMCs) from 119 patients with de novo acute leukemia (AL) and 104 healthy controls, as well as in CD34+ cells from 12 AL patients and 10 healthy donors. A 429-bp fragment from a novel splicing variant of FAMLF was obtained, and a 363-bp consensus sequence was targeted to quantify total FAMLF expression. Kruskal-Wallis, Nemenyi, Spearman's correlation, and Mann-Whitney U-tests were used to analyze the data. FAMLF-CS expression in PBMCs from AL patients and CD34+ cells from AL patients and controls was significantly higher than in control PBMCs (P<0.0001). Moreover, FAMLF-CS expression in PBMCs from the AML group was positively correlated with red blood cell count (rs =0.317, P=0.006), hemoglobin levels (rs =0.210, P=0.049), and percentage of peripheral blood blasts (rs =0.256, P=0.027), but inversely correlated with hemoglobin levels in the control group (rs =–0.391, P<0.0001). AML patients with high CD34+ expression showed significantly higher FAMLF-CS expression than those with low CD34+ expression (P=0.041). Our results showed that FAMLF is highly expressed in both normal and malignant immature hematopoietic cells, but that expression is lower in normal mature PBMCs.

The present study investigated the effect of silibinin, the principal potential anti-inflammatory flavonoid contained in silymarin, a mixture of flavonolignans extracted from Silybum marianum seeds, on palmitate-induced insulin resistance in C2C12 myotubes and its potential molecular mechanisms. Silibinin prevented the decrease of insulin-stimulated 2-NBDG (2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino]-2-deoxy-D-glucose) uptake and the downregulation of glutamate transporter type 4 (GLUT4) translocation in C2C12 myotubes induced by palmitate. Meanwhile, silibinin suppressed the palmitate-induced decrease of insulin-stimulated Akt Ser473 phosphorylation, which was reversed by wortmannin, a specific inhibitor of phosphatidylinositol-3-kinase (PI3K). We also found that palmitate downregulated insulin-stimulated Tyr632 phosphorylation of insulin receptor substrate 1 (IRS-1) and up-regulated IRS-1 Ser307 phosphorylation. These effects were rebalanced by silibinin. Considering several serine/threonine kinases reported to phosphorylate IRS-1 at Ser307, treatment with silibinin downregulated the phosphorylation of both c-Jun N-terminal kinase (JNK) and nuclear factor-κB kinase β (IKKβ), which was increased by palmitate in C2C12 myotubes mediating inflammatory status, whereas the phosphorylation of PKC-θ was not significantly modulated by silibinin. Collectively, the results indicated that silibinin prevented inhibition of the IRS-1/PI3K/Akt pathway, thus ameliorating palmitate-induced insulin resistance in C2C12 myotubes.

To effectively control and treat river water pollution, it is very critical to establish a water quality prediction system. Combined Principal Component Analysis (PCA), Genetic Algorithm (GA) and Back Propagation Neural Network (BPNN), a hybrid intelligent algorithm is designed to predict river water quality. Firstly, PCA is used to reduce data dimensionality. 23 water quality index factors can be compressed into 15 aggregative indices. PCA improved effectively the training speed of follow-up algorithms. Then, GA optimizes the parameters of BPNN. The average prediction rates of non-polluted and polluted water quality are 88.9% and 93.1% respectively, the global prediction rate is approximately 91%. The water quality prediction system based on the combination of Neural Networks and Genetic Algorithms can accurately predict water quality and provide useful support for realtime early warning systems.