Objective: To study the role of rs 12145833 polymorphism of SDCCAG 8 gene in the intervention of childhood obesity, so as to provide scientific basis for formulating personalized intervention measures based on genetic background in children with obesity. Methods: From September 2018 to June 2019, a total of 393 children aged 8-10 years in Beijing were enrolled in a cluster randomized controlled trial. Eight schools were randomly allocated into intervention group and control group at a ratio of 1∶1. Saliva DNA samples were collected to detect rs 12145833 polymorphism of SDCCAG 8 gene. The intervention group received a comprehensive intervention, while the control group received usual practice. Intervention measures included diet improvement, sports, school amd family sport. The obesity related indicators were measured at baseline and after the end of intervention 1 academic year. Multiple linear regression and Logistic regression were used to analyze the interaction between genes and intervention on obesity indicators. Results: In the intervention group, children with TT genotype of rs 12145833 of the SDCCAG 8 gene had less increase in systolic( β=4.56, 95%CI=1.84-7.28, P <0.01) and diastolic blood pressure( β=2.59, 95%CI=0.45-4.73, P <0.05) than those with GT and GG genotypes. In the control group, the systolic blood pressure of children with TT genotype increased more than those with GT and GG genotype( β=-2.86, 95%CI=-5.63--0.83, P <0.05). There was an interaction between rs 12145833 polymorphism of SDCCAG 8 gene and intervention on systolic blood pressure, diastolic blood pressure and body fat percentage in children( P <0.05). Conclusion Children with TT genotype of rs 12145833 in the SDCCAG 8 gene are more sensitive to obesity intervention than those with GG and GT genotypes, especially in the improvement of systolic blood pressure, diastolic blood pressure and body fat percentage. Further trials to study the role of rs 12145833 polymorphism of SDCCAG 8 gene in the intervention of childhood obesity among different ethnic populations are needed.

Objective:To know the clinical characteristics of liver injury related to levetiracetam (LEV).Methods:The relevant databases at home and abroad (up to August 31st, 2021) were searched and the case reports on LEV-associated liver injury were collected. Clinical information including patients′ basic characteristics, LEV application, concomitant medication, and occurrence, treatment, and outcome of liver injury, etc. were collected and analyzed by descriptive statistical method.Results:A total of 17 patients were enrolled in the study, including 9 males and 8 females, aged from 1 month to 76 years with an average age of 35 years. The primary disease was idiopathic epilepsy in 7 patients and secondary epilepsy in 10 patients. Five cases had comorbidities. Thirteen patients had drug dosage records, all of which were within the range recommended in the labels; 13 patients had concomitant medication. The time from LEV treatment to the occurrence of liver injury ranged from several hours to 5 months in 17 patients and it was ≤2 months in 14 patients. The classification of liver injury was hepatocellular type in 7 patients, cholestasis type in 1 patient, mixed type in 1 patient, and unable to be determined due to lack of relevant data in 8 patients. Clinical symptoms were recorded in 10 patients, including yellowish skin and sclera in 5 cases, fever in 4 cases, nausea in 2 cases, vomiting in 2 cases, and biliuria in 2 cases. LEV was discontinued in 14 patients, 4 of whom did not received other interventions and the liver function was improved or returned to normal 2 to 20 days after drug withdrawal; LEV was replaced with other antiepileptic drugs and/or symptomatic treatments in 10 patients, 9 patients′ liver function were improved or returned to normal (the recovery time was 5-37 days in 5 patients and not recorded in 4 patients), 1 patient had normal liver function after liver transplantation, but the liver injury recurred after LEV use again and was improved after drug withdrawal. Two patients did not stop LEV, one underwent liver transplantation due to liver failure and hepatic encephalopathy, and the prognosis was unknown; the other one developed fulminant liver failure and died. One patient had no record of whether or not stopping LEV, and the liver function returned to normal after artificial liver support treatment.Conclusions:LEV-related liver injury mostly occurred within 2 months after drug administration. The clinical manifestations were similar to the liver injury caused by other drugs. Liver function usually was improved or returned to normal after the drug withdrawal. The patients who did not stop LEV had poor prognosis, and severe cases could lead to liver failure or death.

Objective:To know the clinical characteristics of liver injury related to levetiracetam (LEV).Methods:The relevant databases at home and abroad (up to August 31st, 2021) were searched and the case reports on LEV-associated liver injury were collected. Clinical information including patients′ basic characteristics, LEV application, concomitant medication, and occurrence, treatment, and outcome of liver injury, etc. were collected and analyzed by descriptive statistical method.Results:A total of 17 patients were enrolled in the study, including 9 males and 8 females, aged from 1 month to 76 years with an average age of 35 years. The primary disease was idiopathic epilepsy in 7 patients and secondary epilepsy in 10 patients. Five cases had comorbidities. Thirteen patients had drug dosage records, all of which were within the range recommended in the labels; 13 patients had concomitant medication. The time from LEV treatment to the occurrence of liver injury ranged from several hours to 5 months in 17 patients and it was ≤2 months in 14 patients. The classification of liver injury was hepatocellular type in 7 patients, cholestasis type in 1 patient, mixed type in 1 patient, and unable to be determined due to lack of relevant data in 8 patients. Clinical symptoms were recorded in 10 patients, including yellowish skin and sclera in 5 cases, fever in 4 cases, nausea in 2 cases, vomiting in 2 cases, and biliuria in 2 cases. LEV was discontinued in 14 patients, 4 of whom did not received other interventions and the liver function was improved or returned to normal 2 to 20 days after drug withdrawal; LEV was replaced with other antiepileptic drugs and/or symptomatic treatments in 10 patients, 9 patients′ liver function were improved or returned to normal (the recovery time was 5-37 days in 5 patients and not recorded in 4 patients), 1 patient had normal liver function after liver transplantation, but the liver injury recurred after LEV use again and was improved after drug withdrawal. Two patients did not stop LEV, one underwent liver transplantation due to liver failure and hepatic encephalopathy, and the prognosis was unknown; the other one developed fulminant liver failure and died. One patient had no record of whether or not stopping LEV, and the liver function returned to normal after artificial liver support treatment.Conclusions:LEV-related liver injury mostly occurred within 2 months after drug administration. The clinical manifestations were similar to the liver injury caused by other drugs. Liver function usually was improved or returned to normal after the drug withdrawal. The patients who did not stop LEV had poor prognosis, and severe cases could lead to liver failure or death.

This paper studied the correlation between traditional Chinese medicine (TCM) prescription and disease based on machine learning.This paper selected TCM literature abstract data in the TCM category of the China National Knowledge Infrastructure (CNKI) database by crawler technology.After data cleaning,lexicon building,word segmentation and other related basic pre-treatment work,it uses natural language processing technique to extract the feature of the web text data,constructs the Support Vector Machine (SVM) classification model,and extracts the relation between TCM prescription and disease.The results showed that among 1073581 abstracts,204780 sentences,which included both TCM prescription and the disease according to dictionaries,were filtered.The SVM classification model whose feature is constructed by constituency parser is in a better accuracy,which achieved 87％.Applying the SVM model in filtered sentences,this study obtained the relation triples between TCM prescription and the disease.It was concluded that by using the method of machine learning to extract relation on abstract data from the CNKI database,the extracted relation triples of TCM prescription and disease will take a positive effect on the research of disease treatment by TCM prescription.