Objective The study aimed to explore the relationship between storage methods and pollution degrees of two different storage methods for medical devices which can not be packed promptly after cleaning.Methods 300 pieces of surgical devices were randomly divided into the experimental group and the control group according to random digit table,150 pieces in each group.Each group was washed with two cleaning machines of the same brand and model number using the same cleaning protocol,and after cleaning,the experimental group was stored in the cleaning machine,while the control group was exposed in the packaging area.Ten hours later,each surgical instrument was detected using ATP biological fluorescence detector.Results The percent of pass in the experimental group was higher than that of the control group [98％ (147/150) vs.86％ (129/150)],x2=12.7,P＜ 0.05.Conclusions Medical devices should better be stored in the washing machine than exposure to the air after cleaning if they could not be packaged and sterilized immediately.

Objective: To assess the prognostic value of Charlson weighted index of comorbidities (WIC) combined with sequential organ failure assessment (SOFA) score and procalcitonin (PCT) in sepsis patients in intensive care unit (ICU). Methods: A prospective cohort study was conducted. 118 patients with sepsis admitted to ICU of Sichuan Provincial People's Hospital from July 2015 to June 2018 were enrolled. The clinical data of the patients including gender, age, pathogenic factors, site of infection, underlying diseases and 28-day prognosis were collected, while the WIC score at ICU admission, the acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score and SOFA score within 24 hours after ICU admission, serum PCT level within 1 hour after ICU admission were recorded. The patients were divided into survival group and death group according to 28-day prognosis, and the clinical data of patients with different prognosis were compared. Multivariate Logistic regression model was used to analyze the relationship between WIC score, SOFA score, PCT level and the outcomes of patients. The receiver operating characteristic (ROC) curve was drawn to evaluate the value of WIC score, SOFA score, and PCT level for predicting the prognosis of patients with sepsis. Results: In this study, 118 eligible sepsis patients were enrolled, and 94 patients survived at 28 days, and 24 patients died with a 28-day mortality of 20.3%. Compared with the survival group, the patients in the death group were older and had higher APACHEⅡ score, WIC score, SOFA score, and serum PCT levels. Pathogenic factors analysis showed that the proportion of pulmonary infection in the death group was the highest (62.5%), while in the survival group the main cause was multiple injury (36.2%), followed by pulmonary infection (30.9%). Basic diseases analysis showed that the proportions of tumor, type 2 diabetes, chronic lung disease, cerebrovascular disease, chronic kidney disease, chronic liver disease, and chronic cardiac insufficiency in the death group were significantly higher than those in the survival group. The age [odds ratio (OR) = 1.279, 95% confidence interval (95%CI) was 1.065-1.536], APACHEⅡ score (OR = 1.255, 95%CI was 1.083-1.455), WIC score (OR = 1.429, 95%CI was 1.304-1.568), SOFA score (OR = 1.331, 95%CI was 1.456-1.545), and serum PCT level (OR = 1.497, 95%CI was 1.146-1.547) were related to the 28-day prognosis of patients with sepsis, and were independent predictors of 28-day prognosis in patients with sepsis (all P < 0.01). ROC curve analysis showed that the area under ROC curve (AUC) of WIC score, SOFA score, serum PCT level and combined prediction probability was 0.712 (95%CI was 0.588-0.836), 0.801 (95%CI was 0.695-0.908), 0.889 (95%CI was 0.798-0.979), 0.943 (95%CI was 0.884-1.000), respectively, indicating that the accuracy of combined parameters to predict survival outcome was higher than that of any single parameter with the sensitivity of 91.7% and the specificity of 83.0%. Conclusion WIC score, SOFA score combined with serum PCT level can improve the accuracy of predicting the 28-day prognosis in patients with sepsis.

Objective: To investigate the hearing loss and speech disorders in the elderly, to analyze the risk factors of the elderly deafness, as well as to provide reference for the clinical research of the elderly deafness. Methods: From March 2016 to March 2018, 913 elderly people, who were tested for hearing and speech disorders, were examined by a unified questionnaire to investigate the demographic data of the subjects and the related factors of deafness, and the hearing and speech recognition tests were carried out. According to the hearing loss, the hearing impaired group was divided into the hearing impaired group (500, 1 000, 2 000 and 4 000 Hz, the average hearing threshold>25 dBHL) and the non hearing impaired group (the average hearing threshold of the four frequencies ≤25 dBHL), and then the single factor analysis and the unconditional Logistic regression analysis were used. Finally, the risk factors of senile deafness were analyzed. Results: Of the 913 elderly subjects in the survey, 389 (42.61%, 389/913) had no hearing impaired, 345 (37.79%, 345/913) were mild hearing impaired, and 149 (16.32%, 149/913) had moderate hearing loss. Twenty-six patients were severe hearing loss (2.85%, 26/913); 4 patients had severe hearing loss (0.44%, 4/913). Among the 524 hearing-impaired elderly, there were 244 speech-recognition disorders (46.56%, 244/524), of whom 106 were mild hearing-impaired, accounting for 30.72% (106/345), 108 were moderate hearing loss, accounting for 72.48% (108/149), 26 were severe hearing loss, accounting for 100% (26/26), and 4 were the profound hearing loss, accounting for 100% (4/4). Statistical analysis showed that the age, job status, history of hypertension, history of hyperglycemia, and smoking history were independent risk factors for senile hearing loss (P<0.05). Conclusions High incidences of hearing and speech recognition obstacle are found in health examination for the elderly patients. Noise exposure, age, history of hypertension, high blood sugar, and smoking history are high-risk factors for senile deafness, therefore, prevention and rehabilitation programs are urgent to be developed.