Background The diagnosis and treatment of attention deficit hyperactivity disorder(ADHD)comorbid with epilepsy have been insufficiently addressed in China.We conducted a study in China to investigate the current statu...Background The diagnosis and treatment of attention deficit hyperactivity disorder(ADHD)comorbid with epilepsy have been insufficiently addressed in China.We conducted a study in China to investigate the current status,diagnosis,and treatment of ADHD in children to further our understanding of ADHD comorbid with epilepsy,strengthen its management,and improve patients’quality of life.Methods We carried out a multicenter cross-sectional survey of children with epilepsy across China between March 2022 and August 2022.We screened all patients for ADHD and compared various demographic and clinical factors between children with and without ADHD,including gender,age,age at epilepsy onset,duration of epilepsy,seizure types,seizure frequency,presence of epileptiform discharges,and treatment status.Our objective was to explore any possible associations between these characteristics and the prevalence of ADHD.Results Overall,395 epilepsy patients aged 6–18 years were enrolled.The age at seizure onset and duration of epilepsy ranged from 0.1–18 to 0.5–15 years,respectively.Focal onset seizures were observed in 212(53.6%)patients,while 293(76.3%)patients had epileptiform interictal electroencephalogram(EEG)abnormalities.Among the 370 patients treated with anti-seizure medications,200(54.1%)had monotherapy.Although 189(47.8%)patients had ADHD,only 31 received treatment for it,with the inattentive subtype being the most common.ADHD was more common in children undergoing polytherapy compared to those on monotherapy.Additionally,poor seizure control and the presence of epileptiform interictal EEG abnormalities may be associated with a higher prevalence of ADHD.Conclusions While the prevalence of ADHD was higher in children with epilepsy than in normal children,the treatment rate was notably low.This highlights the need to give more importance to the diagnosis and treatment of ADHD in children with epilepsy.展开更多
Mining from simulation data of the golden model in hardware design verification is an effective solution to assertion generation.While the simulation data is inherently incomplete,it is necessary to evaluate the truth...Mining from simulation data of the golden model in hardware design verification is an effective solution to assertion generation.While the simulation data is inherently incomplete,it is necessary to evaluate the truth values of the mined assertions.This paper presents an approach to evaluating and constraining hardware assertions with absent scenarios.A Belief-fail Rate metric is proposed to predict the truth/falseness of generated assertions.By considering both the occurrences of free variable assignments and the conflicts of absent scenarios,we use the metric to sort true assertions in higher ranking and false assertions in lower ranking.Our Belief-failRate guided assertion constraining method leverages the quality of generated assertions.The experimental results show that the Belief-failRate framework performs better than the existing methods.In addition,the assertion evaluating and constraining procedure can find more assertions that cover new design functionality in comparison with the previous methods.展开更多
文摘Background The diagnosis and treatment of attention deficit hyperactivity disorder(ADHD)comorbid with epilepsy have been insufficiently addressed in China.We conducted a study in China to investigate the current status,diagnosis,and treatment of ADHD in children to further our understanding of ADHD comorbid with epilepsy,strengthen its management,and improve patients’quality of life.Methods We carried out a multicenter cross-sectional survey of children with epilepsy across China between March 2022 and August 2022.We screened all patients for ADHD and compared various demographic and clinical factors between children with and without ADHD,including gender,age,age at epilepsy onset,duration of epilepsy,seizure types,seizure frequency,presence of epileptiform discharges,and treatment status.Our objective was to explore any possible associations between these characteristics and the prevalence of ADHD.Results Overall,395 epilepsy patients aged 6–18 years were enrolled.The age at seizure onset and duration of epilepsy ranged from 0.1–18 to 0.5–15 years,respectively.Focal onset seizures were observed in 212(53.6%)patients,while 293(76.3%)patients had epileptiform interictal electroencephalogram(EEG)abnormalities.Among the 370 patients treated with anti-seizure medications,200(54.1%)had monotherapy.Although 189(47.8%)patients had ADHD,only 31 received treatment for it,with the inattentive subtype being the most common.ADHD was more common in children undergoing polytherapy compared to those on monotherapy.Additionally,poor seizure control and the presence of epileptiform interictal EEG abnormalities may be associated with a higher prevalence of ADHD.Conclusions While the prevalence of ADHD was higher in children with epilepsy than in normal children,the treatment rate was notably low.This highlights the need to give more importance to the diagnosis and treatment of ADHD in children with epilepsy.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(No.LQ14C180001)the Fundamental Research Funds for the Central Universities(No.2014QNA6026),China
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61876173,61432017,and 61532017.
文摘Mining from simulation data of the golden model in hardware design verification is an effective solution to assertion generation.While the simulation data is inherently incomplete,it is necessary to evaluate the truth values of the mined assertions.This paper presents an approach to evaluating and constraining hardware assertions with absent scenarios.A Belief-fail Rate metric is proposed to predict the truth/falseness of generated assertions.By considering both the occurrences of free variable assignments and the conflicts of absent scenarios,we use the metric to sort true assertions in higher ranking and false assertions in lower ranking.Our Belief-failRate guided assertion constraining method leverages the quality of generated assertions.The experimental results show that the Belief-failRate framework performs better than the existing methods.In addition,the assertion evaluating and constraining procedure can find more assertions that cover new design functionality in comparison with the previous methods.
