Volume 105 · Issue 31 · 2025

Epilepsy management is experiencing a fundamental paradigm shift, moving beyond seizure control toward precision medicine. Despite continuous innovation in antiseizure medications, approximately one-third of patients still develop drug-resistant epilepsy, highlighting the limitations of traditional symptomatic treatment. In recent years, interdisciplinary convergence has catalyzed a series of breakthrough technologies: cerebral organoid technology demonstrates exceptional capability in modeling pathological features of genetic epilepsy, providing revolutionary tools for personalized drug screening and disease-modifying therapy; multimodal brain network imaging has revealed neural network characteristics of epilepsy, driving the transition of treatment models toward precise neural network-based interventions; the synergistic application of AI-driven drug target prediction, novel nano-drug delivery systems, and gene editing technologies offers innovative strategies for intervening in disease progression. Looking ahead, establishing standardized biomarker systems, conducting real-world studies, and optimizing clinical guidelines will further advance epilepsy treatment from seizure control to disease modification, bringing new therapeutic hope to patients.

Epilepsy is a common chronic neurological disorder, with approximately 30% of patients being drug-resistant epilepsy(DRE), posing significant challenges in clinical management. Advances in neuroimaging techniques and the development of brain network theory have defined epilepsy as a brain network disorder, providing a novel theoretical foundation for neuromodulation therapies. This review systematically summarizes recent progress in epilepsy network modulation, with a focus on the following key scientific issues: (1)precise identification and modulation of epileptic network hubs in focal epilepsy and direct neuromodulation; (2)brain network modulation strategies for generalized epilepsy; (3)current challenges in epilepsy brain network modulation; (4)theory-current status-future perspectives: the path to advancement in epilepsy network modulation. By critically analyzing current research, this article aims to explore new paradigms in epilepsy treatment, and advocate for a shift from traditional "seizure control" to a "network remodeling-functional restoration" approach, thereby facilitating more precise and personalized neuromodulation strategies for patients.

The high prevalence of comorbid psychiatric disorders in epilepsy significantly impaires clinical outcomes prognosis and represents an critical phenotypic manifestation of epilepsy. In recent years, with advancements in neuroimaging, molecular genetics, and precision medicine, research on epilepsy with psychiatric comorbidities has gradually shifted from clinical phenomenology to mechanistic exploration. To date, studies on the underlying mechanisms of these comorbidities remain in their early stages. Future research directions should focus on conducting large-scale, high-quality neuroimaging, genetic, and pathological studies, as well as integrating multimodal data for comprehensive analysis. Strengthening multidisciplinary collaboration, implementing early screening, and standardizing treatment for epilepsy with psychiatric comorbidities constitute the cornerstone in improving patient outcomes.

Epilepsy, as a common neurological disorder, primarily relies on anti-seizure medications (ASM) for treatment, yet most existing drugs fail to prevent core pathological process. Disease-modifying therapies (DMT) for epilepsy, which aim to halt or delay the onset and progression of the disease, have emerged as a critical research direction to overcome the limitations of current therapeutic strategies. Investigating new therapeutic targets and developing effective drugs to prevent or reverse the onset and progression of epilepsy are important goals of clinical and preclinical researches. Based on the current studies, future directions should delve into the multimodal molecular network mechanisms, establish standardized animal models, and address the translational bottlenecks, providing more effective and tailored treatment plans for epilepsy patients from the perspective of disease modification.

To investigate and analyze the profiles of misdiagnosis and risk factors for drug resistance in patients with idiopathic generalized epilepsy (IGE).

The data of 188 patients with IGE treated at the Epilepsy Center of the Second Affiliated Hospital of Zhejiang University School of Medicine from January 2014 to December 2022, who met the latest diagnostic criteria of the International League Against Epilepsy (ILAE) were retrospectively collected. All the patients were followed up for over 12 months. The rates of misdiagnosis and inappropriate medication use were determined. The differences in demographic, clinical and electroencephalography (EEG) characteristics between the drug-resistant and drug-responsive groups were compared. Multivariate logistic regression analysis was employed to identify risk factors for drug resistance in patients with IGE.

Among the 188 patients with IGE, there were 105 males and 83 females, with a median onset age of 14 years (Interquartile range: 12.0-16.0). Thirty-five patients (18.6%) were misdiagnosed with focal epilepsy, and 33 (28.0%) patients with juvenile absence or myoclonic epilepsy had ever received inappropriate medication. With a median follow-up duration of 3.8 years (1.4, 6.8), 25 patients(15.4%) were diagnosed with drug-resistant epilepsy at the end of follow-up. Risk factors associated with drug resistance included catamenial cycle-related epilepsy (OR=26.93, 95%CI: 3.44-211.04), presence of≥2 seizure types (OR=5.20, 95%CI: 1.15-23.46), coexistence of generalized and focal discharges on interictal EEG (OR=39.25, 95%CI: 1.76-873.31), and spike and slow wave complex burden grade ≥3 (OR=6.26, 95%CI: 1.59-24.64).

Misdiagnosis and inappropriate medication use in IGE are not uncommon. Approximately 15% of patients develop drug-resistant epilepsy, while catamenial cycle-related epilepsy, multiple seizure types, and interictal epileptiform discharge characteristics may be risk factors for drug resistance.

To investigate the value of fluorine-18 fluorodeoxyglucose simultaneous positron emission tomography/magnetic resonance (¹⁸F-FDG PET/MR) in detecting intracranial tubers in tuberous sclerosis complex (TSC) patients with epilepsy, and analyze the imaging-related factors associated with the presence of intracranial tubers in the epileptogenic zone.

Five TSC patients with epilepsy who visited Zhongshan hospital affiliated to Fudan University from 2018 to 2025 were included in this study. All of them underwent integrated ¹⁸F-FDG PET/MR. The MR sequences, including the T₁ weighted imaging (T₁WI), T₂ fluid attenuated inversion recovery (T₂-FLAIR) and double inversion recovery (DIR), were performed simultaneously with PET. The number of tubers detected by each MR and ¹⁸F-FDG PET/MR sequence using the Friedman test was compared. The tubers were categorized based on the patient′s clinical symptoms, interictal or ictal EEG findings, and the nodules′ location and characteristics. Tubers were classified as either in the epileptogenic zones or the non-epileptogenic zones. The multivariate logistic regression model was conducted to identify the imaging-related factors associated with the presence of intracranial tubers in the epileptogenic zone.

A total of five patients aged (20.80±2.95) years were enrolled, including three males and two females. There were significant differences in the number of tubers detected among the six sets of images from MR sequences and ¹⁸F-FDG PET/MR sequences in TSC patients with epilepsy (P<0.001), with the highest average number detected by ¹⁸F-FDG PET/T₂-FLAIR [M(Q₁, Q₃): 13(12, 18)]. ¹⁸F-FDG PET/T₂-FLAIR detected a total of 70 nodules in five patients, among which 17 nodules were located in the epileptogenic zone and the remaining 53 nodules were in the non-epileptogenic zone. The proportion of tubers [(64.71%, 11/17) vs (5.66%, 3/53)] (P<0.001) and DIR signal value [(366±110) vs (258±74)] in the epileptogenic zone with adjacent hypometabolic cortex were higher than those in the non-epileptogenic zone, while PET standard uptake value (SUV) [1.95 (1.66, 2.21) vs 3.05 (2.41, 3.88)] and T₁WI signal intensity [(182±31) vs (207±47)] were lower than that those in the non-epileptogenic zone (all P<0.05). Low PET SUV values (OR=0.066, 95%CI: 0.008-0.583) and the presence of adjacent hypometabolic cortex (OR=9.541, 95%CI: 1.277-71.263) were identified as two imaging-related factors associated with intracranial sclerotic tubers located in the epileptogenic zone.

¹⁸F-FDG PET/T₂-FLAIR is more effective in detecting intracranial tubers in patients with TSC and epilepsy. Low SUV values of intracranial tubers on PET images and the presence of adjacent hypometabolic cortex are the imaging-related factors for determining whether intracranial tubers are located in the epileptogenic zone.

To identify brain metabolic network features for temporal lobe epilepsy (TLE) subtype classification and surgical prognosis prediction using machine learning algorithms, thereby supporting clinical decision-making for TLE subtyping and outcome assessment.

¹⁸F-FDG PET images from 137 patients with drug-resistant TLE treated at Xiangya Hospital′s Comprehensive Epilepsy Center from January 2016 to June 2021 were retrospectively analyzed as the training cohort. Network connectivity data were derived using Kullback-Leibler divergence similarity estimation (KLSE), yielding 6 902 network attributes alongside relevant demographic and clinical features. Eight machine learning models (including decision tree and random forest) were trained. The resulting models classified TLE subtypes and were validated using ¹⁸F-FDG PET metabolic network data from an independent cohort of 92 drug-resistant TLE patients (from July 2021 to August 2023). Decision curve analysis was used to select the most clinically practical model for predicting the surgical prognosis of 138 temporal lobe epilepsy patients, including 105 with mesial TLE (76 in the training group and 29 in the independent test group) and 33 with neocortical TLE (23 in the training group and 10 in the independent test group).

There were 84 males and 53 females in the training group, with an age of (22.0±8.0) years; in the independent test group, there were 45 males and 47 females, with an age of (24.2±12.8) years. The area under the receiver operating characteristic curve(AUC) of the 8 machine learning models in the training group ranged from 0.904 to 0.985; the AUC in the independent test group ranged from 0.859 to 0.946. According to the comparison of the performance of the above models, it was found that the prediction result of the random forest model was the most accurate and stable [AUC 0.985 (95%CI: 0.985-0.986), accuracy 0.998(95%CI: 0.995-1.000), sensitivity 0.950 (95%CI: 0.898-1.000), specificity 1.000 (95%CI: 1.000-1.000)]. For patients with mesial temporal lobe epilepsy who underwent surgery, the AUC for predicting surgical prognosis in the training group was 0.838 (95%: 0.753-0.923), and the accuracy was 0.838 (95%CI: 0.836-0.841); the AUC in the independent test group reached 0.783(95%CI: 0.549-1.000), with an accuracy of 0.793 (95%CI: 0.782-0.804). For patients with neocortical temporal lobe epilepsy who underwent surgery, the AUC for predicting surgical prognosis in the training group was 0.962(95%CI: 0.881-1.000), and the accuracy was 0.957 (95%CI: 0.953-0.960); while the AUC in the independent test group also reached 0.800 (95%CI: 0.408-1.000), with an accuracy of 0.900 (95%CI: 0.882-0.918).

Machine learning models incorporating metabolic network features extracted from ¹⁸F-FDG PET data effectively support TLE subtype classification and surgical prognosis assessment.

To establish and validate an automated detection model for interictal epileptiform discharges (IED) through a multi-task learning algorithm that integrates sleep features, providing more precise electroencephalogram (EEG) interpretation support for clinical practice.

Based on convolutional neural networks, a multi-task learning model Siamese-ES that integrates sleep feature was developed. The dataset comprised EEG recordings from 150 patients at Peking Union Medical College Hospital Epilepsy Center from March 2019 to April 2023, of which 140 cases were diagnosed with epilepsy, and the other 10 cases were non-epileptic patients without IED. There were 79 male and 71 female patients, with an age of 27 (3-87) years. After EEG data preprocessing and time-frequency conversion, EEG features were put into two networks, Twins-Electron and Twins-Sleep, to extract the IED features and deep sleep features respectively. Then the features were fused for IED detection. Siamese-ES and two classic single-task IED detection models were trained on the same dataset EpiSet-260K for model comparisons. Additionally, ablation experiments of sleep features and multi-task learning modes were set up to verify the effectiveness.

The EpiSet-260K dataset contained 265, 551 samples. The multi-task learning Siamese-ES model integrated with sleep features showed better precision (71.18%), specificity(98.46%), F1 value(76.26%) and area under the curve [0.978 (95%CI:0.977-0.980)]. Moreover, the ablation experiments confirmed that integration of sleep features through a multi-task learning algorithm achieved better evaluation markers. At 80.00% sensitivity, sleep features can improve precision by 1.19%, and multi-task learning mode can improve precision by 2.18%.

Our study demonstrates that the Siamese-ES model effectively improves the performance of IED detection model through sleep features and multi-task learning algorithm. The results provide future research directions for IED detection models in different real clinical scenarios.

To evaluate the efficacy and safety of eravacycline in the treatment of complicated intra-abdominal infections (cIAI) and common infections.

Literature search was conducted across PubMed, EMBASE, Cochrane Library, Web of Science, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang Data, and VIP Database to retrieve relevant literature on the treatment of bacterial infections with eravacycline. The search period spanned from the inception of each database to July 9, 2024. Two independent researchers screened the literature, extracted data and assessed the quality of the studies, based on predefined inclusion and exclusion criteria. The R 4.3.3 software was used to conducting the meta-analysis. The relative risk (RR) was used as the effect measure. Heterogeneity was assessed, and publication bias was evaluated. The analysis focused on randomized controlled trials (RCT) to compare the differences in clinical cure rates and risks of adverse events between the eravacycline group and the control group across four populations: the modified intent-to-treat population, the clinically evaluable population, the microbiologically evaluable population, and the microbiologically intent-to-treat population. Additionally, the clinical efficacy rate, microbiological clearance rate, 30 day all-cause mortality rate, and incidence of adverse events associated with eravacycline in non-RCT was analysed.

A total of 34 articles were included based on the inclusion and exclusion criteria, encompassing 31 studies involving 2 958 patients. The meta-analysis results from 4 RCT studies showed no statistically significant differences in clinical cure rates for cIAI between the eravacycline group and the control group across various populations (all P>0.05, all I²<50%). The risk of adverse events occurring during treatment was higher in the eravacycline group compared to the control group (RR=1.23, 95%CI: 1.02-1.47, I²=43%), whereas no statistically significant difference was observed in the risk of serious adverse events between the two groups (RR=1.03, 95%CI: 0.65-1.63, I²=0). The meta-analysis results from 27 non-RCT studies indicated that the clinical efficacy rate of eravacycline against multi-site resistant bacterial infections was 71.8%(95%CI: 65.9%-77.7%), I²=86%, and the microbiological clearance rate was 84.1%(95%CI: 72.0%-96.3%), I²=58%. The 30 d all-cause mortality rate was 21.2%(95%CI: 15.7%-27.9%), I²=83%, the overall incidence of adverse events was 7.1%(95%CI: 1.6%-12.5%), I²=70%, and the incidence of treatment-related adverse events was 5.8%(95%CI: 2.2%-10.6%), I²=55%.

Based on existing evidence from both RCT and non-RCT studies, eravacycline has demonstrated favorable efficacy and safety in the treatment of cIAI and common infections.

This article reports a case of a 21-year-old young man who visited Peking Union Medical College Hospital on December 3, 2024 due to "intermittent abdominal pain for 2 weeks and blood in the stool for 1 week". During the course of the disease, there were 3 major gastrointestinal hemorrhage (>1 000 ml each time) and hemorrhagic shock (minimum hemoglobin 31 g/L). Digital subtraction angiography (DSA) and multiple CT angiography (CTA) showed no overflow of active contrast medium. Gastric and colonoscopic examinations considered that the bleeding originated from the small intestine. Capsule endoscopy and enteroscopy found multiple small round ulcers in upper jejunum and terminal ileum. During the course of the disease, Crohn's disease and intestinal Behcet's disease were suspected, and drugs and endoscopic hemostasis were ineffective. Finally, ileal Meckel's diverticulum with gastric mucosal ectopia was confirmed by emergency laparotomy, and it was cured after diverticulectomy. The patient was followed up for a total of 6 months (follow-up until June 2, 2025), and the patient gradually transitioned to normal diet without complaints of discomfort.

The 41-year-old female patient was seen in the Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine on March 15, 2018 due to "abdominal pain for half a year, and the colon was found to occupy space for 3 days". The diagnosis of sigmoid colon cancer with peritoneal, liver and adnexal metastasis was achieved after multidisciplinary discussion, combined with surgery and multi-line comprehensive anti-tumor treatment, and the efficacy of imaging without tumor residue was achieved. For patients with advanced colorectal cancer, it is necessary to formulate an individualized treatment plan according to the specific condition, physical condition and economic situation. More localized peritoneal and abdominopelvic organ metastases may benefit from local treatment.

With the development of evidence-based medicine and implementation science, as well as the advocacy and implementation of the "patient-centered" concept at home and abroad, the application of qualitative research in the medical field is increasing day by day, which has important practical value for realizing patient-centered and promoting high-quality medical development. Traditional qualitative research data analysis has a heavy workload and time-consuming, which is difficult to meet the demand of providing timely and efficient basis for subsequent decision-making or research. Therefore, rapid qualitative research has emerged as an efficient and practical qualitative research method, which is suitable for research scenarios with limited time or resources. At present, it is widely used in implementation science, and can also be used to solve other focused research problems in the medical field that require rapid decision-making in a short time. However, how to ensure the rigor of rapid qualitative research in design, implementation, analysis and reporting is still an important issue for researchers. This paper is based on the rapid qualitative analysis framework published by international scholars recently, combined with literature cases to interpret, in order to improve the standardization and rigor of relevant domestic researchers to carry out such research.

Epilepsy is a highly heterogeneous chronic disease of the nervous system with a complex pathogenesis involving abnormal alterations in multiple cell types and molecular pathways. Traditional transcriptome analysis methods limit the accurate analysis of pathogenic mechanisms due to cell mixture. The rise of single-cell transcriptome sequencing (scRNA-seq) technology provides a new research dimension for studying cell type-specific transcriptional changes during the development of epilepsy. This paper reviews the development history of scRNA-seq technology and various recent technology types, and systematically summarizes the key application progress of scRNA-seq technology in epilepsy research, including: revealing the expression profile of epilepsy-related genes at the single cell level, identifying neuronal subtypes and functional changes, characterizing the abnormal activation state of glial and immune cells; The key role of this technique in revealing the association between neuron electrophysiological activity and transcriptome expression, characterizing the pathological evolution process and the pathogenesis of epilepsy was discussed; The technical challenges and data interpretation difficulties faced by current single-cell research in the field of epilepsy are analyzed, and the future development directions such as multi-omics integrated analysis, database construction and individualized therapeutic target screening are prospected. As a powerful tool to analyze the pathogenesis of epilepsy, scRNA-seq is constantly promoting the process of transformation from basic research to clinical.

The unique physiological characteristics, social prejudice and stigma of female epilepsy patients are the main factors that affect the prognosis of the disease, and also the important factors that cause depression, anxiety and other emotional abnormalities. Localization of epileptic lesions and medication for epilepsy can cause reproductive endocrine abnormalities by affecting hormone levels in women with epilepsy, and affect the quality of life at different stages. Clinicians need to carefully evaluate the patient's condition and psychological needs, and carefully adjust medication. In addition, raising public awareness of epilepsy and providing positive measures is an effective way to enhance social support for female patients and improve their mental health.

Interferon-inducible protein (IFIT) 3 gene with tetrapeptide repeats is a member of IFIT family and belongs to interferon-stimulated genes (ISGs). In recent years, it has been found that IFIT3 not only has complex functions in antiviral immunity and tumor immunity, but also plays a key role in the occurrence and development of various autoimmune diseases. In this paper, the molecular structure, expression regulation mechanism and functional characteristics of IFIT3 in viral infection and tumor immunity were systematically reviewed. The expression characteristics and potential mechanism of IFIT3 in autoimmune diseases such as systemic lupus erythematosus, primary Sjogren's syndrome and primary biliary cholangitis were discussed. This article also reviews that IFIT3 plays a complex regulatory role in cell cycle and inflammatory response by participating in cGAS-STING, JAK-STAT and other pathways, and may become a diagnostic marker and key therapeutic target of autoimmune diseases.

Although Asia is home to more than half of the world's population, regional epidemiological data on pulmonary hypertension (PAH) are lacking for a long time. To fill this gap, this study systematically analyzed the incidence, prevalence, mortality and disability-adjusted life years (DALYs) of PAH in Asia from 1990 to 2021 based on GBD 2021 database, covering five subregions and major countries, and explored its relationship with sociodemographic index (SDI) and universal health coverage index (UHC).

Research data from the World Health Organization show that pneumonia and other lower respiratory tract infections are still the deadliest infectious diseases, and the number of deaths caused by them far exceeds that of other infectious diseases, including tuberculosis, malaria and HIV/AIDS. In addition to causing local damage to the respiratory system, pneumonia also involves systemic changes such as systemic inflammation. In addition, the occurrence and development of pneumonia are closely related to the function of the body's immune system. Adopting a healthy lifestyle can reduce inflammation levels and improve immune function. Although the educational message of pneumonia prevention will also emphasize adopting a healthy lifestyle to prevent the occurrence of pneumonia, the lack of sufficient scientific evidence supports this measure, which is considered "one-size-fits-all" and lacks specificity, so it has not received enough attention. By studying the relationship between lifestyle and the occurrence and development of pneumonia, we can help everyone better understand the role of adhering to lifestyle in preventing the occurrence of pneumonia and improving the prognosis of pneumonia patients.