目的研究干扰素诱导蛋白10(interferon-inducible protein 10,IP-10)水平在结核性脑膜炎诊断中的作用。方法用ELISA检测32例结核性脑膜炎(结脑组)和32例非结核性脑膜炎患者(非结脑组)脑脊液中IP-10水平绘制ROC曲线(receiver operating c...目的研究干扰素诱导蛋白10(interferon-inducible protein 10,IP-10)水平在结核性脑膜炎诊断中的作用。方法用ELISA检测32例结核性脑膜炎(结脑组)和32例非结核性脑膜炎患者(非结脑组)脑脊液中IP-10水平绘制ROC曲线(receiver operating characteristic curve)确定其诊断结核性脑膜炎的临界值,并评价其敏感性、特异性和诊断效能。结果结核性脑膜炎组IP-10含量为(1164.06±450.23)pg/ml,显著高于非结核性脑膜炎组(237.02±161.37)pg/ml(P<0.001)。IP-10诊断结核性脑膜炎的临界值为605.63pg/ml,敏感性和特异性分别为87.5%和96.9%;IP-10的诊断效能与γ-干扰素(IFN-γ)相近。结论脑脊液中IP-10水平测定有助于结核性脑膜炎的诊断。展开更多
Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss...Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss minimization and the shortest project delay time are considered as optimization goals. Firstly, mathematical modelling of the problem is carried out, and the multi-objective optimization problem is transformed into a single-objective optimization problem by means of a weighted solution. In the second step, the traditional pigeon-inspired optimization(PIO) algorithm is discretized, and an adaptive parameter strategy is adopted to improve the shortcomings of the algorithm itself. Finally, by comparing the simulation results with the original algorithm and the genetic algorithm in the optimization of human resource allocation in multiple projects, the feasibility and superiority of the proposed algorithm in the optimization of human resource allocation in multi-scientific research projects is verified.展开更多
基金supported by the Fundamental Research Funds for the Central Scientific Research Institutes (Grant No. 20200306)。
文摘Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss minimization and the shortest project delay time are considered as optimization goals. Firstly, mathematical modelling of the problem is carried out, and the multi-objective optimization problem is transformed into a single-objective optimization problem by means of a weighted solution. In the second step, the traditional pigeon-inspired optimization(PIO) algorithm is discretized, and an adaptive parameter strategy is adopted to improve the shortcomings of the algorithm itself. Finally, by comparing the simulation results with the original algorithm and the genetic algorithm in the optimization of human resource allocation in multiple projects, the feasibility and superiority of the proposed algorithm in the optimization of human resource allocation in multi-scientific research projects is verified.
