Psychotropic medication plays a crucial role in the field of mental illness,and the issues of drug efficacy and safety due to individual differences cannot be ignored.Genetic factors,especially the genetic poly-morphisms related to drug-metabolizing enzymes,drug action targets,and risk,have a significant impact on drug responses.Pharmacogenomics,by detecting genetic polymorphisms,can reveal a patient's inherited tend-encies towards drug efficacy,pharmacokinetic characteristics,and potential toxicity,thereby predicting the therapeutic effects and adverse reactions of drug treatment,and providing guidance for personalized therapy.Therefore,individualized medication based on pharmacogenomics helps to improve cure rates,reduce relapse rates,and decrease medical costs,which is of great significance to clinical medication in mental illness.

Objective To understand the application effect of early screening scale and behavioral intervention effect in children with autism spectrum disorder (ASD). Methods A total of 348 children with suspected ASD were selected and evaluated using the Modified Checklist for Autism in Toddlers (M-CHAT) and Autism Behavior Checklist (ABC). The evaluation results were compared with those from the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). Children enrolled were given Early start Denver model (ESDM) intervention. The evaluation results of Gesell Developmental Scale and Autism Treatment Evaluation Checklist (ATEC) scores were compared before and after intervention. Results The sensitivity, specificity, accuracy and Kappa value of M-CHAT for evaluating ASD in children aged 1-3 years were 89.53%, 90.70%, 89.92% and 0.78. The corresponding values of ABC were 78.49%, 81.40%, 79.46% and 0.56. The sensitivity, specificity, accuracy and Kappa value of M-CHAT for evaluating children aged >3-6 years were 87.30%, 77.78%, 84.44% and 0.64. The corresponding values of ABC were 85.71%, 77.78%, 83.33% and 0.62. The sensitivity and accuracy of M-CHAT were higher than ABC for evaluating ASD in children aged 1-3 years (P<0.05). There were no significant differences in sensitivity, specificity and accuracy between M-CHAT and ABC for evaluating ASD in children aged 3-6 years (P>0.05). After intervention, development quotients (DQ) of personal-social aspects, adaptability, language, gross motor, and fine motor of children with ASD were higher than those before intervention (P<0.05). ATEC scores for language, behavior, sensation, and social contact of children with ASD were lower than those before intervention (P<0.05). Conclusion M-CHAT and ABC both can be used for early screening of ASD in children, especially M-CHAT. Early behavioral intervention can effectively improve the condition and developmental level of children with ASD.

Objective:To establish a practical patient-based internal quality control method for valproic acid drug concentration monitoring.Methods:Observational Study. A PBRTQC model based on the exponentially weighted moving average (EWMA) method was established using Python. All results of a total of 28, 757 valproic acid concentration data from February 1, 2023 to January 31, 2024 were collected and split into training set and validation set at a ratio of 80% and 20% respectively. The truncation limit (TL) was optimized by using the winsorized mean method and the trimmed mean method. Different weighting coefficients λ were set. Different TL and different λ were combined with the EWMA algorithm into different patient-based real-time quality control (PBRTQC) models. The optimized models were verified by introducing simulated constant errors (CE) and proportional errors (PE) respectively. The false positive alarm rate (FAR) was used to evaluate specificity, and the average number of patients before error detection (ANPed) was used to evaluate sensitivity. According to the daily test volume and quality target requirements, we comprehensively judged whether the performance evaluation indicators of FAR and ANPed meet the laboratory requirements. Bias detection curve was used for determination of the best model.Results:The parameters of the best PBRTQC model for valproic acid drug concentration monitoring are: trimmed mean method with 1.5 standard deviations (i.e., truncating data outside 1.5 standard deviations of the data mean), λ=0.01. The performance verification result shows that ANPed of CE and PE of this model are both less than 100. The comparison between the EQA results and the EWMA results show that the EWMA method results are comparable to the EQA results.Conclusion:A PBRTQC model for the valproic acid drug concentration monitoring project based on the EWMA method has been successfully established. It is comparable with both IQC and EQA results, which means PBRTQC may be used as a supplement to the quality control of daily quality control products.

Objective To investigate the predictive value of PCSK9 gene rs562556 polymorphism for major adverse cardiovascular events(MACE)after percutaneous coronary intervention(PCI)in patients with type 2 diabetes mellitus(T2DM)complicated by acute myocardial infarction(AMI).Methods A total of 97 patients were involved in this study with T2DM complicated by AMI,who underwent PCI at The Third Affiliated Hospital of Qiqihar Medical University between January 2019 and December 2021.Based on MACE occurrence during a 2-year follow-up period,patients were divided into non-MACE group and MACE group(n=57 and 40,respectively).Clinical biochemical parameters,including blood glucose and lipid levels,were recorded.Plasma PCSK9 levels were assessed using enzyme-linked immunosorbent assay.Plasma PCSK9 gene rs562556 polymorphism was detected through sequencing.Kaplan-Meier curve analysis was performed to assess how rs562556 polymorphism impacts MACE incidence post-PCI.Multivariate logistic regression was applied to identify independent MACE-associated risk factors.ROC curve analysis was performed to evaluate the predictive value of rs562556 poly-morphism and key clinical variables for MACE occurrence post-PCI.Results Compared to the non-MACE group,patients in the MACE group exhibited significantly higher age,heart rate,creatinine,NT-proBNP,LDL-C,and plasma PCSK9 levels,along with higher hyper-tension and coronary atherosclerotic heart disease prevalence,and lower diastolic blood pressure(all P＜0.05).In patients with T2DM and AMI,the rs562556 genotype AA of the PCSK9 gene positively correlated with plasma PSCK9 levels(r=0.61,P＜0.000 1).The frequen-cies of the rs562556 genotype AA and allele A were significantly higher in the MACE compared to the non-MACE group(P＜0.05).The AA genotype of the PCSK9 gene rs562556 was associated with an increased risk of MACE during follow-up in patients with T2DM and AMI(P＜0.05).After adjusting for other confounding variables,advanced age,increased NT-proBNP and PCSK9 levels,and the rs562556 AA genotype were identified as independent risk factors for MACE post-PCI in this patient population.Combined analysis of these factors demonstrated superior predictive value for MACE occurrence compared to individual markers.Conclusion The PCSK9 gene rs562556 genotype AA is associated with a significantly increased risk of MACE within two years post-PCI in patients with T2DM and AMI,sug-gesting that it could serve as a promising predictive biomarker for post-PCI MACE in the given population.

Objective To investigate the predictive value of PCSK9 gene rs562556 polymorphism for major adverse cardiovascular events(MACE)after percutaneous coronary intervention(PCI)in patients with type 2 diabetes mellitus(T2DM)complicated by acute myocardial infarction(AMI).Methods A total of 97 patients were involved in this study with T2DM complicated by AMI,who underwent PCI at The Third Affiliated Hospital of Qiqihar Medical University between January 2019 and December 2021.Based on MACE occurrence during a 2-year follow-up period,patients were divided into non-MACE group and MACE group(n=57 and 40,respectively).Clinical biochemical parameters,including blood glucose and lipid levels,were recorded.Plasma PCSK9 levels were assessed using enzyme-linked immunosorbent assay.Plasma PCSK9 gene rs562556 polymorphism was detected through sequencing.Kaplan-Meier curve analysis was performed to assess how rs562556 polymorphism impacts MACE incidence post-PCI.Multivariate logistic regression was applied to identify independent MACE-associated risk factors.ROC curve analysis was performed to evaluate the predictive value of rs562556 poly-morphism and key clinical variables for MACE occurrence post-PCI.Results Compared to the non-MACE group,patients in the MACE group exhibited significantly higher age,heart rate,creatinine,NT-proBNP,LDL-C,and plasma PCSK9 levels,along with higher hyper-tension and coronary atherosclerotic heart disease prevalence,and lower diastolic blood pressure(all P＜0.05).In patients with T2DM and AMI,the rs562556 genotype AA of the PCSK9 gene positively correlated with plasma PSCK9 levels(r=0.61,P＜0.000 1).The frequen-cies of the rs562556 genotype AA and allele A were significantly higher in the MACE compared to the non-MACE group(P＜0.05).The AA genotype of the PCSK9 gene rs562556 was associated with an increased risk of MACE during follow-up in patients with T2DM and AMI(P＜0.05).After adjusting for other confounding variables,advanced age,increased NT-proBNP and PCSK9 levels,and the rs562556 AA genotype were identified as independent risk factors for MACE post-PCI in this patient population.Combined analysis of these factors demonstrated superior predictive value for MACE occurrence compared to individual markers.Conclusion The PCSK9 gene rs562556 genotype AA is associated with a significantly increased risk of MACE within two years post-PCI in patients with T2DM and AMI,sug-gesting that it could serve as a promising predictive biomarker for post-PCI MACE in the given population.

Objective:To establish a practical patient-based internal quality control method for valproic acid drug concentration monitoring.Methods:Observational Study. A PBRTQC model based on the exponentially weighted moving average (EWMA) method was established using Python. All results of a total of 28, 757 valproic acid concentration data from February 1, 2023 to January 31, 2024 were collected and split into training set and validation set at a ratio of 80% and 20% respectively. The truncation limit (TL) was optimized by using the winsorized mean method and the trimmed mean method. Different weighting coefficients λ were set. Different TL and different λ were combined with the EWMA algorithm into different patient-based real-time quality control (PBRTQC) models. The optimized models were verified by introducing simulated constant errors (CE) and proportional errors (PE) respectively. The false positive alarm rate (FAR) was used to evaluate specificity, and the average number of patients before error detection (ANPed) was used to evaluate sensitivity. According to the daily test volume and quality target requirements, we comprehensively judged whether the performance evaluation indicators of FAR and ANPed meet the laboratory requirements. Bias detection curve was used for determination of the best model.Results:The parameters of the best PBRTQC model for valproic acid drug concentration monitoring are: trimmed mean method with 1.5 standard deviations (i.e., truncating data outside 1.5 standard deviations of the data mean), λ=0.01. The performance verification result shows that ANPed of CE and PE of this model are both less than 100. The comparison between the EQA results and the EWMA results show that the EWMA method results are comparable to the EQA results.Conclusion:A PBRTQC model for the valproic acid drug concentration monitoring project based on the EWMA method has been successfully established. It is comparable with both IQC and EQA results, which means PBRTQC may be used as a supplement to the quality control of daily quality control products.

Objective To investigate the feasibility of constructing the risk index of Echinococcus infection based on the classification of echinococcosis lesions, so as to provide insights into the management of echinococcosis. Methods The imaging data of echinococcosis cases were collected from epidemiological surveys of echinococcosis in China from 2012 to 2016, and the detection of incident echinococcosis cases was captured from the annual echinococcosis prevention and control reports across provinces (autonomous regions) and Xinjiang Production and Construction Corps in China from 2017 to 2022. After echinococcosis lesions were classified, a risk index of Echinococcus infection was constructed based on the principle of discrete distribution marginal probability and multi-group classification data tests. The correlation between the risk index of Echinococcus infection and the detection of incident echinococcosis cases was evaluated in the provinces (autonomous regions and corps) from 2017 to 2022, and the correlations between the short and medium-term risk indices and between the medium and long-term risk indices of Echinococcus infection were examined using a univariate linear regression model. Results A total of 4 014 echinococcosis cases in China from 2012 to 2016 were included in this study. The short-, medium- and long-term risk indices of E. granulosus infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ² = 4.12 to 708.65, all P values < 0.05), with high short- (0.058), medium- (0.137) and long-term risk indices (0.104) in Tibet Autonomous Region, and the short-, medium- and long-term risk indices of E. multilocularis infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ² = 6.74 to 122.60, all P values < 0.05), with a high short-term risk index in Sichuan Province (0.016) and high medium- (0.009) and long-term risk indices in Qinghai Province (0.018). There were no significant correlations between the risk index of E. granulosus infection and the detection of incident cystic echinococcosis cases during the study period (t = −0.518 to 2.265, all P values > 0.05), and strong correlations were found between the risk indices of E. multilocularis infection and the detection of incident alveolar echinococcosis cases (including mixed type) in 2018, 2020, 2021, 2022, during the period from 2017 through 2020, from 2017 through 2021, from 2017 through 2022 (all r values > 0.7, t = 2.521 to 3.692, all P values < 0.05). Linear regression models were established between the risk index of E. multilocular infection and the detection of alveolar echinococcosis cases (including mixed type), and the models were all statistically significant (b = 0.214 to 2.168, t = 2.458 to 3.692, F = 6.044 to 13.629, all P values < 0.05). The regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. granulosus infection were 2.339 and 0.765, and the regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. multilocular infection were 0.280 and 1.842, with statistical significance seen in both the regression coefficients and regression models (t = 16.479 to 197.304, F = 271.570 to 38 928.860, all P values < 0.05). Conclusions The risk index of Echinococcus infection has been successfully established based on the classification of echinococcosis lesions, which may provide insights into the prevention and control, prediction, diagnosis and treatment, and classified management of echinococcosis.

Objective: To understand the characteristics and changes of the incidence of amoebic dysentery in China during 2015-2018, explore the causes of high incidence in some areas and provide a data base for the development of national prevention and control strategies and measures. Methods: Data were collected from the infectious disease reporting management information system from Chinese Disease Control and Prevention. To understand the seasonal, population and area distributions of amoebic dysentery, descriptive epidemiological method and software SPSS 16.0 were used to analyze the amoebic dysentery data. Results: A total of 4 366 amoebic dysentery cases were reported without death in China during 2015-2018. The reported average annual incidence was 0.08/100 000, and the overall proportion of laboratory confirmed cases was 68.23%(2 979/4 366). Amoeba dysentery mainly occurred during May to October. One seasonal peak was observed in 2015 and 2017 (July and June, respectively), and two seasonal peaks were observed in 2016 and 2018 (June and October). The patients were mainly children aged under 5 years (42.28%, 1 846/4 366), and the incidence rate decreased with age in children aged under 10 years. Of these, children under 1 years of age had the highest incidence rate (1.28/100 000). The number of cumulative reported cases in Guangxi, Henan, Guangdong, Heilongjiang and Jiangxi provinces ranked top five from 2015-2018, accounting for 64.50% (2 816/4 366) of the total. The cumulative cases in Dongxing county, Guangxi, in Suixian county, Henan and in Ranghulu district, Heilongjiang, respectively accounted for more than 50.00% of the total number of cases in their provinces. Conclusions The incidence rate of amoebic dysentery reported in China during 2015-2018 showed a decreasing trend, with a higher incidence in children under 5 years old and a higher number of cases in some areas. It is suggested to further investigate and analyze the diagnosis and reporting of amoeba dysentery in key areas and promote the update of the diagnostic standards for amoeba dysentery.