ObjectiveTo investigate the incidence of nosocomial infection in acute and serious schizophrenic inpatients and its risk factors. MethodsBetween January 1st and December 31st, 2012, we investigated 1 621 schizophrenic patients on the status of nosocomial infections according to the hospital standard of nosocomial infection diagnosis. They were divided into infected group and uninfected group according to the survey results. The risk factors were analyzed by logistic regression method. ResultsTwenty-nine infected patients were found among the 1 621 patients, and the incidence rate was 1.79%. Among the nosocomial infections, the most common one was respiratory infection (79.31%), followed by gastrointestinal infection and urinary infection (6.90%). There were significant differences between the two groups of patients in age, hospital stay, positive and negative syndrome scale (PASS), combined somatopathy, the time of protective constraint, modified electraconvulsive therapy (MECT), using two or more antipsychotics drugs, using antibiotics and side effects of drugs (P<0.05). However, there were no statistical differences in gender, age classes, the course of disease, frequency of hospitalization and seasonal incidence of hospital infection (P>0.05). The results of multivariate analysis showed that hospital stay, positive symptom score, negative symptom score, the time of protective constraint, MECT, using two or more antipsychotics drugs and side effects of drugs were the main risk factors for nosocomial infection of inpatients with psychopathy (P<0.05). ConclusionBased on the different traits and treatments of acute and serious schizophrenia, a screening table of infections should be set. For the high risk group of nosocomial infection, effective measures should be taken to prevent and control the nosocomial infection of patients with schizophrenia.
To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.