Objective To explore an epileptic seizure prediction method for patients with refractory epilepsy to improve the classification and prediction efficiency of epileptic electroencephalogram(EEG)signals.Methods The study used the long-term EEG database of patients with intractable epilepsy from Children's Hospital Boston(CHB-MIT).The EEG features of epileptic seizures and preictal periods were extracted from multiple dimensions such as EEG synchronization,complexity,and energy distribution,and then these features were input into the artificial neural network model for classification and identification,thereby achieving accurate prediction of epilepsy.The performance were optimized by adjusting the model parameters,and a comparative evaluation was conducted with existing deep learning models.Results The model proposed in this study showed an accuracy rate of 99.29％,a precision of 91.44％,a sensitivity of 96.46％,and a specificity of 99.46％.Compared with current epilepsy seizure prediction studies based on machine learning or deep learning frameworks,the model in this study improved its classification prediction capabilities and demonstrated higher prediction accuracy.Conclusion An effective prediction of epileptic seizures was achieved by manually extracting epileptic EEG features and constructing an artificial neural network model.The model demonstrated high accuracy and stability,providing reliable technique to support clinical treatment and prevention of epilepsy.

Objective To explore an epileptic seizure prediction method for patients with refractory epilepsy to improve the classification and prediction efficiency of epileptic electroencephalogram(EEG)signals.Methods The study used the long-term EEG database of patients with intractable epilepsy from Children's Hospital Boston(CHB-MIT).The EEG features of epileptic seizures and preictal periods were extracted from multiple dimensions such as EEG synchronization,complexity,and energy distribution,and then these features were input into the artificial neural network model for classification and identification,thereby achieving accurate prediction of epilepsy.The performance were optimized by adjusting the model parameters,and a comparative evaluation was conducted with existing deep learning models.Results The model proposed in this study showed an accuracy rate of 99.29％,a precision of 91.44％,a sensitivity of 96.46％,and a specificity of 99.46％.Compared with current epilepsy seizure prediction studies based on machine learning or deep learning frameworks,the model in this study improved its classification prediction capabilities and demonstrated higher prediction accuracy.Conclusion An effective prediction of epileptic seizures was achieved by manually extracting epileptic EEG features and constructing an artificial neural network model.The model demonstrated high accuracy and stability,providing reliable technique to support clinical treatment and prevention of epilepsy.

Objective To explore prescription and syndrome law of TCM in the treatment of non-alcoholic fatty liver disease(NAFLD);To provide reference for clinical medication.Methods The relevant literature on the treatment of NAFLD with TCM was retrieved from CNKI,VIP,Wanfang Data and CBM from the establishment of the databases to October 31,2023.Excel 2019,Lantern 5.0 and SPSS Modeler 18.0 software were used to analyze the latent structure model,association rules and frequency statistics of high-frequency drugs(≥3%)to explore the prescription and syndrome law of TCM in the treatment of NAFLD.Results A total of 453 prescriptions were included,involving 260 kinds of Chinese materia medica,with a cumulative frequency of 4 910 times.The high-frequency drugs were Crataegi Fructus,Salviea Miltiorrhizae Radix et Rhizoma,Alismatis Rhizoma,Bupleuri Radix,Poria and Atractylodis Macrocephalae Rhizoma,etc.The efficacy categories were mainly tonic medicine,diuretic dampness medicine,blood circulation-activating and stasis-resolving medicine,heat-clearing medicine and qi-regulating medicine.The latent structure model obtained 12 latent variables,24 latent classes,and 7 comprehensive clustering models.The commonly used prescriptions were Erchen Decoction,Yinchenhao Decoction,Danggui Shaoyao Powder,Sini Powder,Sijunzi Decoction,Weiling Decoction,Zhuyu Decoction and Dihuang Decoction categorized formula.Conclusion NAFLD is the syndrome of deficiency in root and excess in superficiality.Spleen deficiency is the root cause,phlegm,dampness,heat and blood stasis are the symptoms.In clinical practice,it is mainly based on tonifying qi and spleen,cooperating with the methods of resolving phlegm,eliminating dampness,clearing heat and activating blood circulation.

ObjectiveThis study aimed to evaluate the clinical efficacy of Ruyi Zhenbaowan（RYZBW）in the treatment of initial and early knee osteoarthritis （KOA） through a prospective multicenter，randomized，double-blind，and placebo-parallel controlled trial. MethodFrom October 13^th， 2021 to December 25^th， 2021， 240 KOA subjects meeting the acceptance criteria were enrolled in 15 sub-centers including Wangjing Hospital， Chinese Academy of Chinese Medical Sciences， and they were randomly divided into observation group and control group， with 120 cases in each group. The intervention measures for the observation group were RYZBW + health education， and the intervention measures for the control group were RYZBW placebo + health education. The intervention period in both groups was four weeks， and they were followed up for four weeks after the intervention. The primary outcome measure was the total score of Western Ontario and McMaster University Osteoarthritis Index score （WOMAC score）， and the secondary outcome measures were the response rate of visual scale （VAS） pain score， WOMAC sub item scores （joint pain， joint stiffness， and joint function）， quality of life （SF-12） score， and traditional Chinese medicine （TCM） syndrome score. Result（1） Efficacy evaluation. The marginal model results showed that the observation group was better than the control group in improving the WOMAC total score and WOMAC pain score in the treatment of KOA with RYZBW， and the difference was statistically significant （P<0.05）. There was no significant difference between the two groups in improving VAS score response rate， WOMAC function score， WOMAC stiffness score， SF12-PCS （quality of life-physical health） score， SF12-MCS （quality of life-mental health） score， and TCM syndrome score. （2） Subgroup analysis. ① In terms of VAS score response rate， the response rate of the observation group was higher than that of the control group for subjects with baseline VAS score of （4， 5］， and the difference was statistically significant （P<0.05）. ② In terms of TCM syndrome score， for subjects aged ［56， 60］ and ［61， 65］， the decrease in total TCM syndrome score in the observation group was better than that in the control group， and the difference was statistically significant （P<0.05）. ConclusionTibetan medicine RYZBW has good clinical efficacy in improving WOMAC total score， VAS score response rate， WOMAC pain score， WOMAC function score， and TCM syndrome score for patients with initial and early KOA， which can fill the lack of Tibetan medicine RYZBW in the treatment of KOA and make a demonstration study for the inheritance and development of ethnic medicine.

Objective:To re-evaluate the systematic reviews of the effectiveness of virtual reality technology in pain management in children.Methods:Using computers to search for systematic reviews of the effectiveness of virtual reality technology in pain management for pediatric patients in China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP, China Biology Medicine disc, Cochrane Library, PubMed, Embase, CINAHL and Web of Science. The retrieval period was from the establishment of the databases to November 4, 2022. Literature screening and data extraction were conducted independently by two researchers, methodological quality evaluation was performed for the included studies using the A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) checklist, and the evidence quality rating was evaluated using Grades of Recommendations Assessment, Development and Evaluation (GRADE) system.Results:A total of 14 systematic reviews were included. AMSTAR 2 evaluation results showed that the methodological quality of 2 systematic reviews was low, and 12 were extremely low. GRADE was used to evaluate the quality of evidence of the outcome indicators, and the results showed that among the 26 outcome indicators included, 23.08% (6/26) were medium evidence, 76.92% (20/26) were low evidence, and the quality of no evidence was high.Conclusions:There is a lack of high-quality evidence to support the conclusion that virtual reality technology can relieve pain in children. The methodological quality and evidence quality of systematic reviews are low, and more standardized and rigorous studies need to be carried out in the future to further verify the application effect of virtual reality technology in pain management of children.

Objective: To analyze the clinical characteristics of SARS-CoV-2 Omicron variant infected children in convalescence in Tianjin. Methods: A total of 104 pediatric patients infected by SARS-CoV-2 Omicron variant Tianjin First Central Hospital (designated hospital for SARS-CoV-2 infection in Tianjin) for convalescent treatment from January 22^nd, 2022 to February 24^th were included for a retrospective study.Clinical data including clinical typing, SARS-CoV-2 IgG and IgM test and 2019-nCoV nucleic acid test were collected.The cases were divided into 2-dose group and zero-dose group based on the doses of inactivated SARS-CoV-2 vaccine. The children were divided into repositive group and negative group, according to the nucleic acid test during hospitalization. Chi-square test was used for the comparison between the groups. Results: The age of these 104 children was 10.0 (0.3, 14.0) years on admission, 53 children were males and 51 were females, 92 cases (88.5%) had mild symptoms, 12 cases (11.5%) had common symptoms.The age and SARS-CoV-2 IgG level of zero-dose group was lower (2.0 (0.3, 10.2) vs. 10.0 (3.2, 14.0) years, 10 (2, 17) vs. 193 (157, 215), χ²=-5.57, Z=-48.76,both P<0.001) than that of 2-dose group. The zero-dose group had a high rate of transmission among family members and a high level of SARS-CoV-2 IgM level (13/14 vs. 62.2% (56/90), 0.4 (0.2, 0.8) vs. 0.4 (0.2, 1.1),χ²=5.09, Z=-48.95, both P<0.05) than the 2-dose group. Repositive group had a high rate of underlying diseases and SARS-CoV-2 IgM level was higher (2/13) vs. 1.1% (1/91), (0.6 (0.2, 1.0) vs. 0.3 (0.2, 0.7), χ²=8.29, Z=2.70, both P<0.05) than negative group. The SARS-CoV-2 IgG level of repositive group was lower than that of negative group (160 (78, 197) vs. 213 (186, 231), χ²=-3.20, P<0.05). Conclusions: Children infected with SARS-CoV-2 Omicron variant in Tianjin were mainly transmitted by family members, and most of them had mild symptoms. Two-dose group had higher IgG levels and lower IgM levels than zero-dose group.The probability of SARS-CoV-2 nucleic acid test repositivity increased in children with underlying diseases and lower IgG levels.
Antibodies, Viral ; COVID-19/therapy* ; COVID-19 Vaccines ; Child ; Female ; Humans ; Immunoglobulin G ; Immunoglobulin M ; Male ; Nucleic Acids ; Retrospective Studies ; SARS-CoV-2