Electroencephalography (EEG) is a powerful tool for investigating brain electrophysiology with high temporal resolution. In recent years, advancements in EEG signal analyses have expanded our research capabilities. Among these, microstate analysis has emerged as a promising approach, providing insights into the topographical distribution of EEG signals and capturing millisecond-scale brain network dynamics. This technique offers a window into the intrinsic activity of large-scale neural networks. A hallmark of disorders of consciousness (DoC) is the disruption of brain activity dynamics. Research shows that EEG microstate analysis can deeply assess the consciousness level and neural mechanism during recovery process in DoC patients, demonstrating significant value in diagnosis and prognosis evaluation of DoC. This article reviews the principles and parameters of microstate analysis, the correlation between microstates and brain networks, and the parameter changes under different states of consciousness, and explores its application value in the diagnosis, classification and prognosis assessment of DoC, with the aim of providing new ideas for DoC precise diagnosis and treatment.

Objective:To analyze the gene mutation spectrum of children with T-acute lymphoblastic leukemia (T-ALL) using next generation sequencing technology and to evaluate the value of gene mutations in prognosis stratification.Methods:A case series analysis was made.The clinical data of newly diagnosed pediatric T-ALL patients in the First Affiliated Hospital of Zhengzhou University from January 1, 2019 to February 29, 2024 were analyzed retrospectively.T-ALL gene mutations were analyzed.The relationships of gene mutations with clinical features and induction of responses to therapy were studied.The effects of gene mutations on overall survival (OS) and event-free survival (EFS) were examined by the Kaplan-Meier method and COX regression model.Results:A total of 80 newly diagnosed pediatric T-ALL patients were enrolled in the study, with a male-to-female ratio of 3.4∶1.0 and a median age of 8 (range, 2-17) years.A total of 57 mutations were detected in 74 patients, 46.2% (37/74) of whom showed 3 or more gene mutations.The coexistence of mutated genes was obvious. PTEN mutations were more prevalent in male patients ( P=0.018).Initial leukocyte counts were higher in patients with PTEN mutations ( P=0.038) and lower in patients with JAK3 mutations ( P=0.002).Patients with NOTCH1 mutations had a higher positive rate of fusion genes ( P=0.043).Patients with PTEN mutations had a higher rate of minimal residual disease(MRD) remission after 15/19 d of treatment with induction therapy, respectively ( P=0.013).The rate of MRD remission after 33/46 d of treatment with induction therapy was higher in patients with the FBXW7 mutation ( P=0.004) and lower in patients with JAK3 mutations ( P=0.003).Multifactorial COX regression analysis showed that IL7R mutation and three or more gene mutations were independent risk factors for OS and EFS in T-ALL patients(OS: HR=3.252, 7.357, 95% CI: 1.020-10.372, 1.646-32.882; EFS: HR=3.372, 3.009, 95% CI: 1.234-9.214, 1.174-7.708; all P<0.05). Conclusions:Gene mutations are prevalent in T-ALL children and correlate with clinical manifestations and prognosis.The coexistence of mutated genes is obvious.Pediatric T-ALL patients with IL7R mutations and three or more gene mutations have a poorer prognosis.

Objective:To analyze the gene mutation spectrum of children with T-acute lymphoblastic leukemia (T-ALL) using next generation sequencing technology and to evaluate the value of gene mutations in prognosis stratification.Methods:A case series analysis was made.The clinical data of newly diagnosed pediatric T-ALL patients in the First Affiliated Hospital of Zhengzhou University from January 1, 2019 to February 29, 2024 were analyzed retrospectively.T-ALL gene mutations were analyzed.The relationships of gene mutations with clinical features and induction of responses to therapy were studied.The effects of gene mutations on overall survival (OS) and event-free survival (EFS) were examined by the Kaplan-Meier method and COX regression model.Results:A total of 80 newly diagnosed pediatric T-ALL patients were enrolled in the study, with a male-to-female ratio of 3.4∶1.0 and a median age of 8 (range, 2-17) years.A total of 57 mutations were detected in 74 patients, 46.2% (37/74) of whom showed 3 or more gene mutations.The coexistence of mutated genes was obvious. PTEN mutations were more prevalent in male patients ( P=0.018).Initial leukocyte counts were higher in patients with PTEN mutations ( P=0.038) and lower in patients with JAK3 mutations ( P=0.002).Patients with NOTCH1 mutations had a higher positive rate of fusion genes ( P=0.043).Patients with PTEN mutations had a higher rate of minimal residual disease(MRD) remission after 15/19 d of treatment with induction therapy, respectively ( P=0.013).The rate of MRD remission after 33/46 d of treatment with induction therapy was higher in patients with the FBXW7 mutation ( P=0.004) and lower in patients with JAK3 mutations ( P=0.003).Multifactorial COX regression analysis showed that IL7R mutation and three or more gene mutations were independent risk factors for OS and EFS in T-ALL patients(OS: HR=3.252, 7.357, 95% CI: 1.020-10.372, 1.646-32.882; EFS: HR=3.372, 3.009, 95% CI: 1.234-9.214, 1.174-7.708; all P<0.05). Conclusions:Gene mutations are prevalent in T-ALL children and correlate with clinical manifestations and prognosis.The coexistence of mutated genes is obvious.Pediatric T-ALL patients with IL7R mutations and three or more gene mutations have a poorer prognosis.

Electroencephalography (EEG) is a powerful tool for investigating brain electrophysiology with high temporal resolution. In recent years, advancements in EEG signal analyses have expanded our research capabilities. Among these, microstate analysis has emerged as a promising approach, providing insights into the topographical distribution of EEG signals and capturing millisecond-scale brain network dynamics. This technique offers a window into the intrinsic activity of large-scale neural networks. A hallmark of disorders of consciousness (DoC) is the disruption of brain activity dynamics. Research shows that EEG microstate analysis can deeply assess the consciousness level and neural mechanism during recovery process in DoC patients, demonstrating significant value in diagnosis and prognosis evaluation of DoC. This article reviews the principles and parameters of microstate analysis, the correlation between microstates and brain networks, and the parameter changes under different states of consciousness, and explores its application value in the diagnosis, classification and prognosis assessment of DoC, with the aim of providing new ideas for DoC precise diagnosis and treatment.