Background: The pathogenesis of Alzheimer’s disease (AD) remains to be elucidated. This study aimed to identify the hub genes in AD pathogenesis and determine their functions and pathways.Methods: A co-expression network for an AD gene dataset with 401 samples was constructed, and the AD status-related genes were screened. The hub genes of the network were identified and validated by an independent cohort. The functional pathways of hub genes were analyzed.Results: The co-expression network revealed a module that related to the AD status, and 101 status-related genes were screened from the trait-related module. Gene enrichment analysis indicated that these status-related genes are involved in synaptic processes and pathways. Four hub genes (ENO2, ELAVL4, SNAP91, and NEFM) were identified from the module, and these hub genes all participated in AD-related pathways, but the associations of each gene with clinical features were variable. An independent dataset confirmed the different expression of hub genes between AD and controls.Conclusions: Four novel genes associated with AD pathogenesis were identified and validated, which provided novel therapeutic targets for AD.
A new method for parameter optimization of pharmacokinetics based on an artificial immune network named PKAIN is proposed. To improve local searching ability of the artificial immune network, a partition-based concurrent simplex mutation is developed. By means of evolution of network cells in the PKAIN artificial immune network, an optimal set of parameters of a given pharmacokinetic model is obtained. The Laplace transform is applied to the pharmacokinetic differential equations of remifentanil and its major metabolite, remifentanil acid. The PKAIN method is used to optimize parameters of the derived compartment models. Experimental results show that the twocompartment model is sufficient for the pharmacokinetic study of remifentanil acid for patients with mild degree of renal impairment.
The PKAIN algorithm is an artificial immune network, which has been designed to optimize parameters of linear pharmacokinetic models in our previous work. In this paper, the algorithm is modified to optimise parameters of nonlinear pharmacokinetic models. To evaluate parameters, the numerical inverse Laplace method is adopted to calculate drug concentrations of the dynamic system. The initial solutions of pharmacokinetic parameters are generated randomly by the PKAIN algorithm in a given solution space. Memory cells to be used in the search of global optimal parameters are generated. The optimal mechanism of the algorithm is based on artificial immune network principles and simplex mutation. In addition, a distributed version of the PKAIN algorithm is proposed to improve its efficiency.
In this paper, the resistance and the gene homology of Acinetobacter calcoaceticus-baumannii strains which were isolated clinically from the Fourth and the Third People's Hospital of Wuxi, China was studied. The prevalence and distribution was observed and recorded. The susceptibility testing was performed with agar dilution technique and compared with the genotype was investigated with randomly amplified polymorphic DNA (RAPD) technique. To these strains, the resistant rates of Ampicillin, Piperacillin, Cefazolin, Nitrofurantoin were 100%, those of Imipenem, Meropenem and Cefoperazone/Sulbactam were 27%, 15% and 12%, respectively. The resistant rates of other β-lactams and quinolones were higher than 60%. The resistant rates of aminoglycosides were higher than 75%. The type A and D included 35 strains and 22 strains, respectively. The same genotype separated from the same and the different endemic areas. It is sensitive to Imipenem, Meropenem and Cefoperazone/Sulbactam among the 100 strains of Acinetobacter calcoaceticus –baumannii. Maybe the 2 strains are Acinetobacter calcoaceticus and 98 strains are Acinetobacter baumannii. The type A and D are the major types, which show cross-transmission in two hospitals in Wuxi.
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