1.Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.
Yiwei GONG ; Zheng ZHANG ; Yuanzhi YANG ; Shuo ZHANG ; Ruifeng ZHENG ; Xin LI ; Xiaoyun QIU ; Yang ZHENG ; Shuang WANG ; Wenyu LIU ; Fan FEI ; Heming CHENG ; Yi WANG ; Dong ZHOU ; Kejie HUANG ; Zhong CHEN ; Cenglin XU
Neuroscience Bulletin 2025;41(5):790-804
Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis*
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Animals
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Drug Resistant Epilepsy/drug therapy*
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Electroencephalography/methods*
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Rats
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Anticonvulsants/pharmacology*
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Neural Networks, Computer
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Male
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Humans
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Phenytoin/pharmacology*
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Adult
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Disease Models, Animal
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Female
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Rats, Sprague-Dawley
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Young Adult
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Convolutional Neural Networks
2.Comprehensive application of multiple methods in determination of half-sibling relationship
Hongyan WU ; Lin ZHANG ; Kejie WANG ; Aiying FAN
Chinese Journal of Forensic Medicine 2018;33(1):55-57,61
Objective To identify the half-sibling relationship by comprehensive using three different methods. Methods STR genotype was performed on A, A's mother, B and B's mother by using PowerPlex21 kit, AGCU Expressmarker 21+1 kit, Microreader 23sp-B kit and AGCU X-19 STR kit respectively. Based on the results of STR genotype and X-STR, we determined half-sibling relationship by ITO, discriminant function analysis and IBS. Results HIS was between 1.36×102and 2.09×105in ITO which indicated that A and B had the same father. The IBS and discriminant function analysis also had the same conclusion. Conclusion Comprehensive using multiple methods can obtain reliable result to identify the half-sibling relationship from the same father.
3.Analysis of mutation of 20 autosomal short tandem repeat loci in Henan Han population
Lin ZHANG ; Kejie WANG ; Hongyan WU ; Aiying FAN ; Xupeng SUN ; Zhendong ZHU
Chinese Journal of Forensic Medicine 2017;32(1):33-35
Objective To investigate the mutation phenomenon of 20 autosomal STR loci in Henan Han population. Methods A total of 3011 parentage confirmed cases were collected to screen mutation events, ascertain the source of mutation, calculate mutation rate, analyze mutation rules and compare with the mutation condition of populations in different regions. Results 76 mutation events were observed in 19 STR loci, the average and accumulative mutation rate reached to 0.08% and 1.6629%, respectively. The ration of paternal versus maternal mutation was 8:1. Mutation rates of Penta E and D12S391 loci in Henan Han population were lower than the Han population of northern China(P<0.05); the mutation rate of CSF1PO locus were lower than Guangdong population and Yunan Han population(P<0.05); the mutation rates of D6S1043 and D12S391 loci were lower than Guangdong population(P<0.05). Conclusion STR mutation events were common in paternity testing. Region differences among mutation rate were significant.
4.Extracorporeal shock wave therapy combined with autologous bone marrow stem cells transplantation for treating early-stage osteonecrosis of the femoral head
Hongjun ZHANG ; Shuai WANG ; Kejie FAN ; Shaohui WANG ; Yanzhao ZHANG
Chinese Journal of Physical Medicine and Rehabilitation 2015;37(4):287-290
Objective To observe the therapeutic effects of extracorporeal shock wave therapy (ESWT) combined with autologous bone marrow stem cells (BMSC) transplantation in treating early-stage osteonecrosis of the femoral head (ONFH).Methods Forty patients diagnosed as stage Ⅰ or Ⅱ according to the classification of osteonecrosis of the femoral head put forward by the Association Research Circulation Osseous were randomly divided into a treatment group and a control group with 20 cases in each group.Both groups were treated with BMSC transplantation,while the treatment group was further treated with ESWT the next day.Harris scores were assigned before the treatment and 3,6,12 and 24 months after the treatment to evaluate hip function,the degree of pain and the effects of the treatment.Frog-bit X-rays of the anteroposterior pelvis were taken to assess the stages.Results There were significant differences in the Harris scores and pain degree of the two groups before and at the different time points after the treatment.After 6,12 and 24 months,significant differences were observed in the two values between the two groups.After 12 months the number of hips rated excellent,good and medium level were 8,18 and 1 in the treatment group and 5,13 and 8 in the control group,showing significant differences.Conclusion Extracorporeal shock wave therapy along with autologous bone marrow stem cell transplantation can be used to treat patients with early stage osteonecrosis of the femoral head,relieving their pain and improving their joint function.

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