The p38 mitogen-activated protein kinase (MAPK) plays an evolutionarily conserved role in the cellular response to microbial infection and environmental stress. Activation of p38 is mediated through phosphorylation by upstream MAPKK, which in turn is activated by MAPKKK. In the Caenorhabditis elegans, the p38 MAPK (also called PMK-1) signaling pathway has been shown to be required in its resistance to bacterial infection. However, how different upstream MAP2Ks and MAP3Ks specifically contribute to the activation of PMK-1 in response to bacterial infection still is not clearly understood. By using double-stranded RNA-mediated interference (RNAi) and genetic mutants of C. elegans, we demonstrate that C. elegans MOM-4, a mammalian TAK1 homolog, is required for the resistance of C. elegans to a P. aeruginosa infection. We have also found that the MKK-4 of C. elegans is required for P. aeruginosa resistance, but not through the regulation of DLK-1. In summary, our results indicate that different upstream MAPKKKs or MAPKKs regulate the activation of PMK-1 in response to P. Aeruginosa.
Animals ; Caenorhabditis elegans ; enzymology ; genetics ; immunology ; microbiology ; Caenorhabditis elegans Proteins ; genetics ; metabolism ; Disease Resistance ; Enzyme Activation ; MAP Kinase Kinase 1 ; metabolism ; MAP Kinase Signaling System ; Membrane Proteins ; deficiency ; genetics ; metabolism ; Mutation ; Pseudomonas Infections ; enzymology ; Pseudomonas aeruginosa ; physiology ; RNA Interference ; p38 Mitogen-Activated Protein Kinases ; metabolism

Objective To evaluate antimicrobial effect and mechanism of meropenem in the model of PA infection by C.elegans.Methods To evaluate drug effects of PA infection with caenorhabditis elegans by different concentrations of culture medium, determinate the lethal rate of C.elegans.Western blot detected mitogen activated protein kinase ( Mitogen-activated protein kinase MAPK ) activity change, and PCR detected antimicrobial peptide genes expression in C.elegans after PA infection,the effect of meropenem on MAPK activity change and antimicrobial peptide genes expression.Results Compared with the control group (OP-50), the death rate of C.elegans in PA infection group changed significantly (P<0.01). Meropenem showed protective effect after C.elegans infection ( P <0.01 ) .Detection of MAPK kinase activity showed that PA infection caused PMK-1 kinase activation, further study showed that antibiotics meropenem did not affect the activation of PMK-1 kinase (no significant difference).C.elegans antimicrobial peptide gene Lys-1, clec-85, F55G11.7, K08D8.5 activity increased in PA infection (P<0.01).Meropenem promoted the expression of the antimicrobial peptide gene increased (P<0.01),with synergistic effects.Conclusion Our results show that a C.elegans pathogenicity model can be applied screening drug susceptible to pathogens infection quickly and easily.

Objective To explore the effect of human leukocyte antigen B* genotype and age on serum homocysteine (Hcy) levels in children with seizures or epilepsy. Methods Fifteen children with seizures or epilepsy in whom HLA-B*15:02 genotype was detected during October 2015 to June 2016 were included. The plasma Hcy concentration in children with different genotypes was compared. The association of Hcy concentration and age was performed by linear-regression analysis. Results The mean concentration of Hcy was 8.38±4.23 μmol/L in children not carrying HLA-B*15:02 gene, which was obviously higher than that in children carrying HLA-B*15:02 gene 13.03±0.97 μmol/L (P<0.05). The Hcy concentration increased with the age (r2 =0.29, P<0.05). Conclusions Elder children with seizures or epilepsy carrying HLA-B*15:02 gene tend to have higher Hcy concentration and increased potential risk of disease. HLA-B*15:02 gene type and age can predict the changes of Hcy concentration in children with convulsions.

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients' physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.
Bone Density ; Deep Learning ; Diabetes Mellitus/epidemiology* ; Female ; Fractures, Bone/etiology* ; Humans ; Osteoporosis/complications* ; Risk Factors