BACKGROUND: To evaluate the efficacy and safety of the first cohort of people in China treated with a responsive neurostimulation system (Epilcure TM , GenLight MedTech, Hangzhou, China) for focal drug-resistant epilepsy in this study. METHODS: This multicenter, before-and-after self-controlled study was conducted across 8 centers from March 2022 to June 2023, involving patients with drug-resistant epilepsy who were undergoing responsive neurostimulation (RNS). The study was based on an ongoing multi-center, single-blind, randomized controlled study. Efficacy was assessed through metrics including median seizure count, seizure frequency reduction (SFR), and response rate. Multivariable linear regression analysis was conducted to explore the relationships of basic clinical factors and intracranial electrophysiological characteristics with SFR. The postoperative quality of life, cognitive function, depression, and anxiety were evaluated as well. RESULTS: The follow-up period for the 19 participants was 10.7 ± 3.4 months. Seizure counts decreased significantly 6 months after device activation, with median SFR of 48% at the 6th month (M6) and 58% at M12 ( P  <0.05). The average response rate after 13 months of treatment was 42%, with 21% ( n  = 4) of the participants achieving seizure freedom. Patients who have previously undergone resective surgery appear to achieve better therapeutic outcomes at M11, M12 and M13 ( β  <0, P  <0.05). No statistically significant differences were observed in patients' scores of quality of life, cognition, depression and anxiety following stimulation when compared to baseline measurements. No serious adverse events related to the devices were observed. CONCLUSIONS: The preliminary findings suggest that Epilcure TM exhibits promising therapeutic potential in reducing the frequency of epileptic seizures. However, to further validate its efficacy, larger-scale randomized controlled trials are required. REGISTRATION Chinese Clinical Trial Registry (No. ChiCTR2200055247).
Humans ; Female ; Male ; Drug Resistant Epilepsy/therapy* ; Adult ; Young Adult ; Middle Aged ; China ; Adolescent ; Treatment Outcome ; Quality of Life ; Single-Blind Method ; Seizures ; Electric Stimulation Therapy/methods*

A system for achieving two-dimensional(2D)visualization and three-dimensional(3D)reconstruction of lung images using CT data is designed.After DICOM image processing,and lung nodule segmentation and labeling,the multi-view and multi-resolution 3D display of lung parenchyma and nodules is realized using techniques such as resampling of CT sequences,3D reconstruction based on surface rendering,and morphological processing.Finally,the interactive interface is designed for implementing the functions of image enhancement,2D visualization,nodule delineation,3D reconstructions of lung parenchyma and nodules,rotation,zooming and switching the viewpoint.Experimental results show that the system provides clear and accurate 2D image visualization and lesion delineation,while reconstructing nodules in the generated 3D images completely and smoothly.Compared with the existing similar medical processing software,the developed system substantially improves the reconstruction and visualization efficiencies,enabling doctors to observe 3D images more quickly and accurately,and assisting in disease diagnosis and surgical planning.

Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = -0.26-0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18-0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.
Humans ; Male ; Female ; Attention Deficit Disorder with Hyperactivity ; Obsessive-Compulsive Disorder ; Adult ; Autism Spectrum Disorder ; Bayes Theorem ; Adolescent ; Young Adult ; Magnetic Resonance Imaging ; Middle Aged ; Child ; Brain/metabolism* ; Cohort Studies ; Comorbidity

Objective    To explore the application of Tsetlin Machine (TM) in heart beat classification. Methods    TM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electro-cardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. Results    The classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. Conclusion    TM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
Humans ; Obsessive-Compulsive Disorder/epidemiology* ; Brain/pathology* ; Neuroimaging/methods* ; Machine Learning ; Comorbidity ; Magnetic Resonance Imaging/methods*

Anti-seizure medications (ASMs) are the main therapy for epilepsy.There are many kinds of ASMs with complex mechanism of action, so it is difficult for pharmacists to examine prescriptions.This paper put forward some suggestions on the indications, dosage forms/routes of administration, appropriateness of usage and dosage, combined medication and drug interaction, long-term prescription review, individual differences in pathophysiology of children, and drug selection when complicated with common epilepsy, for the reference of doctors and pharmacists.

Objective:To explore the expression of caveolin-1 (Cav-1) and Chitinase-40 (YKL-40) in acute cerebral infarction and the value of combined detection in prognosis evaluation.Methods:118 patients with acute cerebral infarction admitted to the First Affiliated Hospital of Hebei Northern University from January 2016 to June 2019 were selected as the research objects. According to the cerebral infarction volume, the patients were divided into small infarction group (<5 cm 3), middle infarction group (5-10 cm 3) and large infarction group (>10 cm 3). 108 healthy people were selected as the healthy control group. The serum levels of Cav-1 and YKL-40 were compared in the 3 groups, and the correlation between the degree of cerebral infarction and serum levels of Cav-1 and YKL-40 was analyzed. Receiver operating characteristic curve (ROC) was used to analyze the diagnostic value of the expression levels of Cav-1 and YKL-40 in patients with acute cerebral infarction; the patients were followed up for one year and the prognosis was evaluated by modified Rankin Scale (mRS); the correlation between serum Cav-1 and YKL-40 and prognosis was analyzed. Results:The expression levels of serum Cav-1 and YKL-40 in patients with acute cerebral infarction were significantly higher than those in healthy group ( P<0.001). The serum levels of Cav-1 and YKL-40 were positively correlated with the infarct volume of acute cerebral infarction ( r=0.854, P=0.004; r=0.867, P=0.002). ROC curve analysis showed that the sensitivity, Youden index and area under ROC curve of Cav-1 (21.78 μg/L) combined with YKL-40 (158.69 ng/ml) in the diagnosis of acute cerebral infarction were 85.59%, 0.532 and 0.896 (95% CI: 0.741-0.932), respectively, which were significantly higher than those of single index ( P<0.05). At 8 and 12 months of follow-up, the proportion of death and mRS score in the positive group were significantly higher than those in the negative group ( P<0.05). Conclusions:The serum Cav-1 and YKL-40 levels are significantly higher in patients with acute cerebral infarction. The combined examination of Cav-1 and YKL-40 can improve the diagnostic efficiency and has potential application value for early diagnosis and prognosis prediction of patients.

Objective: To investigate the surgical complications in the treatment of stage Ⅰ endometrial cancer by robotic-assisted laparoscopy, the risk degree of Clavein-Dindo complications and the main risk factors affecting the occurrence of surgical complications. Methods: A retrospective case-control study was conducted in the First Affiliated Hospital of Zhengzhou University from October 2014 to June 2019. The patients were divided into robotic-assisted laparoscopy group and traditional laparoscopy group according to the operation mode, including 131 cases in robot group and 290 cases in traditional laparoscopy group. To compare the complications during and after operation and the risk degree of complications between the two groups by Clavein-Dindo classification standard, the age, body mass index (BMI), comorbidities, past history of pelvic surgery, American Society of Anesthesiologists (ASA) grade, preoperative anemia, number of pelvic lymph node resection, number of abdominal aortic lymph node resection, the total number of lymph node resection, operation time, surgical methods (robot surgery or traditional laparoscopic surgery) and other clinicopathological data were analyzed by logistic regression analysis. Results: (1) Complications of operation: the incidence of operative complications (including intraoperative and postoperative complications) in robot group was significantly lower than that in traditional laparoscopy group [(20.6%, 27/131) vs (34.8%, 101/290); χ²=8.620, P=0.003)]. The incidence of intraoperative complications in robot group was lower than that in traditional laparoscopy group [1.5% (2/131) vs 6.2% (18/290); χ²=4.368, P=0.037]. The incidence of intraoperative vascular injury in robot group was significantly lower than that in traditional laparoscopy group [0.8% (1/131) vs 5.2% (15/290); χ²=4.798, P=0.022]. The incidence of postoperative complications in robot group was also lower than that in traditional laparoscopy group [19.1% (25/131) vs 28.6% (83/290); χ²=4.303, P=0.038], but the incidence of postoperative lymphatic leakage in robot group was higher than that in traditional laparoscopy group [10.7% (14/131) vs 5.2% (15/290); χ²=4.279, P=0.039]. (2) Clavein-Dindo classification: the incidence of Clavein-Dindo Ⅰ, Ⅲ, Ⅲ, Ⅳ and Ⅴ grade between two groups were respectively 3.8% (5/131) vs 11.0% (32/290), 13.7% (18/131) vs 14.5% (42/290), 3.1% (4/131) vs 8.6% (25/290), 0 (0/131) vs 0.3% (1/290), 0 (0/131) vs 0.3% (1/290), and the incidence of grade Ⅰ (χ²=5.684, P=0.015) and Ⅲ (χ²=4.361, P=0.037) complications were statistically significant. The incidence of severe complications in robot group (grade Ⅲ and above) was lower than that in traditional laparoscopy group [3.1% (4/131) vs 9.3% (27/290); χ²=5.179, P=0.023]. (3) Analysis of influencing factors of surgical complications: univariate analysis showed that BMI (χ²=15.801, P=0.000), preoperative anemia (χ²=14.299, P=0.000), total number of lymph node resection (χ²=10.425, P=0.001), surgical methods (χ²=8.620, P=0.003) were related to the occurrence of surgical complications of endometrial carcinoma. Multivariate analysis showed that BMI (OR=0.289, 95%CI: 0.097-0.864, P=0.026), preoperative anemia (OR=0.309, 95%CI: 0.129-0.740, P=0.008), the total number of lymph node resection (OR=0.624, 95%CI: 0.403-0.966, P=0.034) and surgical methods (OR=3.491, 95%CI: 1.030-11.840, P=0.045) were independent risk factors for surgical complications of endometrial carcinoma. Conclusions Compared with traditional laparoscopic surgery, robot-assisted laparoscopic surgery has fewer complications and lower incidence of severe complications. BMI, preoperative anemia, the total number of lymph node resection and surgical methods are independent risk factors for the occurrence of surgical complications of stage Ⅰ endometrial cancer.