BACKGROUND:Hypertrophic differentiation of chondrocytes is the sign of starting endochondral ossification, and it is also an essential step in endochondral ossification, which is a cascade reaction and difficult to be blocked once started. The end result is the formation of bone structure. RNA interference is a post-transcriptional gene silencing. Relevant studies have shown that the use of RNA interference to block the expression of core binding factorα1 (Cbfα1) can effectively inhibit the formation of heterotopic ossification. OBJECTIVE:To use RNA intereference technology to suppress Cbfα1 expression so as to achieve the purpose of blocking the hypertrophic diferentiation of chondrocytes. METHODs: We constructed an adenovirus containing siRNA against Cbfα1 (Ad-Cbfα1-siRNA). Retinoic acid and interleukin-1α were used to induce hypertrophic differetiation of chondrocytes, and then Ad-Cbfα1-siRNA was utilized to inhibit the hypertrophic differentiation of chondrocytes. Immunohistochemistry method was used to analyze the expression of Cbfα1. RESULTS AND CONCLUSION:After induction with retinoic acid and interleukin-1α, the chondrocytes in the negative control virus group appeared to have hypertrophy and the expression of Cbfα1 was positive. In the Ad-Cbα1-siRNA group, the expression of Cbfα1 was negative. These findings suggest that the inhibition of Cbfα1 by RNA interference can be a powerful way to prevent the hypertrophic differentiation of chondrocytes .

Objective:To analyze the changes of hospitalization rates in different regions, medical institutions and populations in China from 2009 to 2019, so as to provide reference for the country to make relevant decisions.Methods:The data of China′s health statistical yearbook from 2009 to 2019 were obtained, and the changes of hospitalization rate and the number of hospitalizations per 100 outpatient and emergency admissions in different regions and medical institutions, and hospitalization rate of different populations and different diseases were analyzed. Descriptive analysis and frequency analysis were used.Results:The hospitalization rate in China continued to rise, from 9.95% in 2009 to 19.03% in 2019. Among them, the hospitalization rate in the eastern, central and western regions increased from 9.7%, 9.9% and 10.8% to 17.0%, 19.3% and 21.5% respectively, with an average annual growth rate of 5.8%, 6.9% and 7.1% respectively. The number of inpatients in public hospitals increased by 1.2 times and that in private hospitals increased by 4.5 times. The number of inpatients in tertiary, secondary and primary hospitals and primary medical institutions increased by 292.9%, 80.8%, 166.4% and 4.5% respectively.From 2009 to 2018, the number of hospitalizations per 100 outpatient and emergency admissions in the hospital increased from 4.5 to 5.7, and decreased to 5.6 in 2019. The increase of hospitalization rate of urban residents was less than that of rural residents. The hospitalization rate of residents aged 0-4 and ≥55 years increased the fastest. In recent years, the discharge diseases were mainly common diseases, chronic diseases and frequently occurring diseases, including pneumonia, acute upper respiratory tract infection, diabetes, and hypertension.Conclusions:During the ten years of medical reform, the hospitalization rate in China has continued to rise, and the overall trend is reasonable. There are significant differences in the changes of hospitalization rates between urban and rural areas, regions and populations in China.

Objective: To explore the correlation and lag effects of environmental factors on pediatric respiratory disease visits at hospital, so as to provide scientific basis for disease prediction and optimizing clinical diagnosis and treatment. Methods: Data from 503 889 pediatric respiratory disease outpatient and emergency visits a central hospital in Minhang District of Shanghai between 2017 and 2019, along with concurrent meteorological data were collected. A distributed lag non-linear models (DLNM) was constructed to explore the specific relationship between pediatric respiratory disease consultations and various environmental factors and to quantify the cumulative lag effects of environmental factors on respiratory disease consultations. Results: Among the environmental factors, temperature, fine particulate matter (PM 2.5 ), inhalable particulate matter (PM 10 ), nitrogen dioxide (NO 2), and sulfur dioxide (SO 2) were associated with pediatric respiratory disease visits. After adjusting for temperature, PM 2.5 and PM 10 concentrations did not show significant immediate or lag effects. The relative risk (RR) of pediatric respiratory disease visits increased with rising NO 2 concentrations. When NO 2 concentration ≥55 μg/m 3, significant immediate and lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.05, 1.13, 1.17, and 1.21( P <0.05). The RR values showed an inverted “U” shaped relationship with SO 2 concentrations. When SO 2 concentration ≥5 μg/m 3, significant lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.03 , 1.03, and 1.04 ( P <0.05). Conclusion High concentrations of NO 2 and SO 2 increase the risk of pediatric respiratory disease visits, with observable lag effects.

ObjectiveTo explore the establishment of performance assessment indicators for the classification and grading of public health staff in district-level Centers for Disease Control and Prevention (CDCs), and to provide a basis for such evaluations. MethodsThrough literature review and group interviews, performance evaluation indicators were developed based on competency evaluation. Experts were invited to evaluate the weight of performance evaluation indicators for public health staff from different categories, with the average value used to represent the weight of each indicator. ResultsTwenty-nine experts from universities in Shanghai, municipal CDCs, and district CDCs participated, yielding an expert authority coefficient of 0.86. The performance evaluation indicators for department managers were categorized into three levels, with 4 indicators at the primary level, 16 indicators at the secondary level, and 42 indicators at the tertiary level, while those for general staff included 4 primary indicators, 15 secondary indicators, and 36 tertiary indicators. Significant differences were observed in the weight coefficients of the primary indicators (internal operations, professional work, and learning and growth) between department managers and general staff. The top three secondary indicators for department managers were department management, monitoring and prevention, and level of expertise. For mid-level and senior staff, the top three secondary indicators were monitoring and prevention, level of expertise, and research work. The top three secondary indicators for junior staff were monitoring and prevention, professional expertise, and professional attitude. No significant statistical differences were found among tertiary indicators. ConclusionThe developed performance evaluation indicators are reliable. Staff at different levels and classifications should be evaluated using different performance evaluation standards to accurately reflect individual performance and contributions.

ObjectiveTo understand the stress level of people seeking psychological counseling under the coronavirus disease 2019 （COVID⁃19） pandemic and to explore its related factors. MethodsAn online survey was conducted on 1 194 people who sought psychological counseling in Shanghai through the “health cloud” psychological counseling service platform. The questionnaire included demographic information，lifestyle and stress during the COVID-19 pandemic. ResultsParticipants with low，medium，high and very high stress levels accounted for 33.1% （395/1 194），34.6% （413/1 194），25.4% （303/1 194） and 7.0% （83/1 194），respectively. Women and participants aged 18 to 30 years had higher stress levels（Z=-5.368，P<0.001； Z=35.822，P<0.001） compared with other groups. Factors contributing to the rise in stress included reading too much information about COVID-19 （OR=2.057，95%CI：1.012‒4.181），large changes in sleep state （OR=3.496，95%CI：1.669‒7.325），lack of hobbies and interests （OR=2.852，95%CI：1.252‒6.500），and prone to anxiety/irritability/sadness （OR=4.098，95%CI：1.772‒9.480）. Conclusionpeople who sought psychological counseling show high levels of psychological stress during the COVID-19 pandemic. We should pay more attention to the vulnerable groups with the following characteristics： women，18‒30 years old， residents who pay too much attention to the pandemic information，sleep less， and almost lose interest in hobbies， and easily become anxious/irritable/sad.

ObjectiveTo determine the factors associated with aggressive behaviors of patients with schizophrenia by gender in communities in Shanghai， and further develop and validate the prediction model. MethodsA total of 7 955 community patients with schizophrenia were investigated in Xuhui District， Hongkou District and Jiading District of Shanghai. Baseline information was collected from April 2018 and follow-up was conducted every 3 months for 6 months. Multivariate logistic regression was used to calculate the odd ratio （OR） and 95% CI， and determine the factors associated with aggressive behaviors of patients. The risk score for each patient was developed based on the β coefficient， and the best cut-off value was determined by the Youden index. For the models， predictive ability was determined using area under the curve （AUC） of receiver operator characteristic curve （ROC curve）， and internal validation ability was evaluated by the ten-fold cross validation method. ResultsThere were 3 563 males in this study with an average age of （54.83±13.72） years old， and the incidence of aggressive behaviors was 2.55%.There were 4 392 females with an average age of （57.20±14.98） years， and the incidence of aggressive behaviors was 2.64%. For male patients with schizophrenia， single/divorced status （OR=2.04， 95%CI： 1.15‒3.61）， low economic status （OR=2.79， 95%CI： 1.71‒4.54）， irregular medication （OR=4.35， 95%CI：2.23‒8.47）， no medication （OR=1.83， 95%CI：1.03‒3.26）， incomplete/no insight （OR=1.97， 95%CI：0.99‒3.94）， adverse drug reaction （OR=2.61， 95%CI：1.27‒5.37）， psychiatric symptoms involving violence （OR=2.06， 95%CI：1.01‒4.18）， history of aggression （OR=5.29， 95%CI：2.33‒11.98） and recent stress events （OR=8.36， 95%CI：4.13‒16.92） were associated with aggressive behaviors. In contrast， for female patients， age less than 60 years （50‒59 years， OR=2.09， 95%CI： 1.13‒3.87； 40‒49 years， OR=2.74， 95%CI： 1.46‒5.17； 30‒39 years，OR=2.88， 95%CI： 1.48‒5.60； 18‒29 years， OR=5.71， 95%CI： 2.44‒13.37）， educational level of high school and above （senior high school， OR=3.30， 95%CI： 1.46‒7.49； college and university， OR=2.88， 95%CI： 1.21‒6.82）， unemployed status （OR=1.81， 95%CI=1.17‒2.82）， irregular medication （OR=7.87， 95%CI：4.75‒13.05）， no medication （OR=2.11， 95%CI：1.24‒3.62）， adverse drug reaction （OR=2.75， 95%CI：1.50‒5.04）， psychiatric symptoms involving violence （OR=3.08， 95%CI：1.77‒5.37）， social function （OR=3.51， 95%CI：2.07‒5.94） and recent stress events （OR=5.92， 95%CI： 2.82‒12.44） were risk factors. In both male and female， the prediction models for aggressive behaviors of community patients with schizophrenia had strong predictive ability （AUC=0.779， 95%CI： 0.725‒0.834； AUC=0.822， 95%CI： 0.780‒0.863）. ConclusionThis study suggests that diverse risk factors should be considered for community patients with schizophrenia by gender to prevent the aggressive behaviors.

ObjectiveTo explore the relapse status based on the positive and negative syndrome scale （PANSS Scale） and related factors of schizophrenics in Shanghai communities， and to analyze the association between socio demographic characteristics， lifestyles， clinical characteristics and relapse. MethodsA dynamic cohort prospective study design was used in this study. From March 2018 to February 2019， a total of 189 schizophrenics in Xuhui， Hongkou， Changning， Jiading， Songjiang and Baoshan districts were enrolled successively. Baseline questionnaires were conducted through face-to-face interviews at baseline， which contained social demographic information， lifestyle information and clinical information. A follow-up was conducted every 2 weeks for a measurement of PANSS Scale for a total of 6 months. Relapse was assessed by a PANSS score increase of ≥25% from baseline （or an increase of 10 points or more if the baseline score was ≤40 points）. Univariate and multivariate Cox regression models were used to analyze the associations between relapse status （assessed by PANSS Scale） and socio demographic characteristics， lifestyles， and clinical characteristics， respectively. ResultsA total of 165 community schizophrenics completed baseline and follow-up surveys， with a loss to follow-up rate of about 12.7%. After exclusion of sociodemographic and clinical information deficits， 132 patients were included in the analysis totally， with an average age of 48.18±12.67 years， among whom 41.67% were male. Totally 33 patients relapsed during the 6-month follow-up period， with a relapse rate of 25.0%. After adjusting for gender， family history， age， employment， education， marital status， smoking， drinking， exercise frequency， medication compliance， insight， social function， violence history， stress recent events， adverse drug reactions and baseline scores of PANSS Scale， risk factors of relapse included the following four factors： age below 40 years （HR=4.47， 95%CI： 1.15-17.40）， primary school or below （HR=7.11， 95%CI： 1.54-32.83）， unemployed （HR=8.34， 95%CI： 1.78-38.98）， and adverse drug reactions （HR=5.02， 95%CI： 1.75-14.37）. ConclusionWe should pay attention to the risk factors such as age， education， employment and adverse drug reactions， in order to identify high-risk patients and to conduct timely interventions during the relapse management of schizophrenics in Shanghai community.