Epicardial adipose tissue(EAT)is a type of fat tissue that is closely adjacent to the coronary arteries and myocardium,and it caused physiological and pathological changes to the body through the secretion of autocrine and paracrine active factors.EAT is regarded as a diagnostic marker and a potential therapeutic target for cardiovascular diseases,and it is of great significance to segment and quantify EAT.This article introduced the evolution of the EAT segmentation and quantification methods from the aspects of traditional imaging,atlas,and artificial intelligence.Furthermore,it reviewed the research progresses on automatically quantified EAT indices in the diagnosis and treatment of cardiovascular diseases.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

Objective:To investigate the association between anti-rituximab antibodies (ARA) and relapse after rituximab (RTX) therapy in children with frequently relapsing or steroid-dependent nephrotic syndrome (FRNS or SDNS).Methods:A retrospective cohort study was conducted. Clinical and laboratory data were collected from 48 FRNS or SDNS children treated with RTX in the Department of Pediatrics, General Hospital of Eastern Theater Command, between April 2024 and October 2024. Data included RTX dosing frequency, relapse events, peripheral CD20? B-cell counts, and ARA levels. With a 6-month observation period after the last RTX therapy, the children were divided into an ARA-positive group and an ARA-negative group based on ARA test results. Chi-square test, independent sample t-test, or Mann-Whitney U test were used to compare relapse rates and laboratory indicators between the two groups. The predictive value of ARA levels for relapse was evaluated using univariate receiver operating characteristic (ROC) curve analysis. Results:Among the 48 children (36 males, 12 females), the age of disease onset was 3.5 (2.0, 6.0) years, the ages at the first and last RTX treatments were 7.0 (5.0, 12.0) years and 9.5 (7.0, 13.0) years, respectively. The overall ARA positive rate was 29% (14/48). The relapse rate in the ARA-positive group was significantly higher than that in the negative group ( P<0.05). The ARA level was 0.01 (0.01, 5.88) μg/L, and all 12 children with ARA levels >5.88 μg/L relapsed. ROC curve analysis showed that ARA levels predicted relapse after RTX treatment in FRNS or SDNS children with an area under the curve (AUC) of 0.73, sensitivity of 0.50, specificity of 1.00, and an optimal cut-off value of 5.02 μg/L. All children received single-dose RTX therapy, with no significant difference in treatment frequency between the two groups ( P>0.05). At 3 months after the last rituximab therapy, CD20? B cell counts were significantly higher in the ARA-positive group ( P<0.05). During follow-up, 15% (7/48) of the children experienced infusion-related adverse reactions, with no significant difference in incidence between the two groups ( P>0.05). Conclusion:ARA is significantly associated with relapse in FRNS or SDNS children after RTX therapy.

Objective To explore an epileptic seizure prediction method for patients with refractory epilepsy to improve the classification and prediction efficiency of epileptic electroencephalogram(EEG)signals.Methods The study used the long-term EEG database of patients with intractable epilepsy from Children's Hospital Boston(CHB-MIT).The EEG features of epileptic seizures and preictal periods were extracted from multiple dimensions such as EEG synchronization,complexity,and energy distribution,and then these features were input into the artificial neural network model for classification and identification,thereby achieving accurate prediction of epilepsy.The performance were optimized by adjusting the model parameters,and a comparative evaluation was conducted with existing deep learning models.Results The model proposed in this study showed an accuracy rate of 99.29％,a precision of 91.44％,a sensitivity of 96.46％,and a specificity of 99.46％.Compared with current epilepsy seizure prediction studies based on machine learning or deep learning frameworks,the model in this study improved its classification prediction capabilities and demonstrated higher prediction accuracy.Conclusion An effective prediction of epileptic seizures was achieved by manually extracting epileptic EEG features and constructing an artificial neural network model.The model demonstrated high accuracy and stability,providing reliable technique to support clinical treatment and prevention of epilepsy.

The ability to localize sound sources rapidly allows human beings to efficiently understand the surrounding environment. Previous studies have suggested that there is an auditory "where" pathway in the cortex for processing sound locations. The neural activation in regions along this pathway encodes sound locations by opponent hemifield coding, in which each unilateral region is activated by sounds coming from the contralateral hemifield. However, it is still unclear how these regions interact with each other to form a unified representation of the auditory space. In the present study, we investigated whether functional connectivity in the auditory "where" pathway encoded sound locations during passive listening. Participants underwent functional magnetic resonance imaging while passively listening to sounds from five distinct horizontal locations (-90°, -45°, 0°, 45°, 90°). We were able to decode sound locations from the functional connectivity patterns of the "where" pathway. Furthermore, we found that such neural representation of sound locations was primarily based on the coding of sound lateralization angles to the frontal midline. In addition, whole-brain analysis indicated that functional connectivity between occipital regions and the primary auditory cortex also encoded sound locations by lateralization angles. Overall, our results reveal a lateralization-angle-based representation of sound locations encoded by functional connectivity patterns, which could add on the activation-based opponent hemifield coding to provide a more precise representation of the auditory space.
Humans ; Sound Localization/physiology* ; Male ; Female ; Magnetic Resonance Imaging ; Young Adult ; Functional Laterality/physiology* ; Adult ; Brain Mapping ; Auditory Cortex/physiology* ; Acoustic Stimulation ; Auditory Pathways/physiology* ; Brain/physiology*

Objective To establish and verify a mature and stable glioblastoma(GBM)organoid model,so as to provide an accurate and personalized preclinical model for the research and treatment of GBM.Methods Fresh GBM tissues obtained through surgical procedures were initially processed,and then GBM stem cells(GSCs)were isolated using stem cell culture medium and were identified.Subsequently,GSCs were cultured in organoid culture medium for 3D cultivation,and GBM organoids were successfully obtained.The histological morphology of GBM organoids was observed by hematoxylin-eosin(H-E)staining;the stemness and similarity to the parental tumor were identified by immunofluorescence staining;and the in vivo tumorigenic ability of GBM organoids was identified by orthotopic tumorigenesis experiments in nude mice.Results A total of 7 GBM organoids were constructed from 9 human GBM samples,with a morphology resembling"neurosphere",and the average duration for organoid formation was 1 week.H-E staining results showed that the histological morphology of GBM organoids under high-power microscope was very similar to that of GBM tumor tissues;immunofluorescence staining results indicated that the GBM organoids possessed stemness characteristics and histological cellular similarity;and GBM organoids had a stronger tumorigenic ability compared to ordinary GBM cells in nude mice.Conclusion This study presents a stable and reliable method for constructing GBM organoids retaining the histological characteristics of the original GBM tissue,which providing new insights for future GBM research and clinical practice.

Objective To explore an epileptic seizure prediction method for patients with refractory epilepsy to improve the classification and prediction efficiency of epileptic electroencephalogram(EEG)signals.Methods The study used the long-term EEG database of patients with intractable epilepsy from Children's Hospital Boston(CHB-MIT).The EEG features of epileptic seizures and preictal periods were extracted from multiple dimensions such as EEG synchronization,complexity,and energy distribution,and then these features were input into the artificial neural network model for classification and identification,thereby achieving accurate prediction of epilepsy.The performance were optimized by adjusting the model parameters,and a comparative evaluation was conducted with existing deep learning models.Results The model proposed in this study showed an accuracy rate of 99.29％,a precision of 91.44％,a sensitivity of 96.46％,and a specificity of 99.46％.Compared with current epilepsy seizure prediction studies based on machine learning or deep learning frameworks,the model in this study improved its classification prediction capabilities and demonstrated higher prediction accuracy.Conclusion An effective prediction of epileptic seizures was achieved by manually extracting epileptic EEG features and constructing an artificial neural network model.The model demonstrated high accuracy and stability,providing reliable technique to support clinical treatment and prevention of epilepsy.

Epicardial adipose tissue(EAT)is a type of fat tissue that is closely adjacent to the coronary arteries and myocardium,and it caused physiological and pathological changes to the body through the secretion of autocrine and paracrine active factors.EAT is regarded as a diagnostic marker and a potential therapeutic target for cardiovascular diseases,and it is of great significance to segment and quantify EAT.This article introduced the evolution of the EAT segmentation and quantification methods from the aspects of traditional imaging,atlas,and artificial intelligence.Furthermore,it reviewed the research progresses on automatically quantified EAT indices in the diagnosis and treatment of cardiovascular diseases.

Objective:To investigate the association between anti-rituximab antibodies (ARA) and relapse after rituximab (RTX) therapy in children with frequently relapsing or steroid-dependent nephrotic syndrome (FRNS or SDNS).Methods:A retrospective cohort study was conducted. Clinical and laboratory data were collected from 48 FRNS or SDNS children treated with RTX in the Department of Pediatrics, General Hospital of Eastern Theater Command, between April 2024 and October 2024. Data included RTX dosing frequency, relapse events, peripheral CD20? B-cell counts, and ARA levels. With a 6-month observation period after the last RTX therapy, the children were divided into an ARA-positive group and an ARA-negative group based on ARA test results. Chi-square test, independent sample t-test, or Mann-Whitney U test were used to compare relapse rates and laboratory indicators between the two groups. The predictive value of ARA levels for relapse was evaluated using univariate receiver operating characteristic (ROC) curve analysis. Results:Among the 48 children (36 males, 12 females), the age of disease onset was 3.5 (2.0, 6.0) years, the ages at the first and last RTX treatments were 7.0 (5.0, 12.0) years and 9.5 (7.0, 13.0) years, respectively. The overall ARA positive rate was 29% (14/48). The relapse rate in the ARA-positive group was significantly higher than that in the negative group ( P<0.05). The ARA level was 0.01 (0.01, 5.88) μg/L, and all 12 children with ARA levels >5.88 μg/L relapsed. ROC curve analysis showed that ARA levels predicted relapse after RTX treatment in FRNS or SDNS children with an area under the curve (AUC) of 0.73, sensitivity of 0.50, specificity of 1.00, and an optimal cut-off value of 5.02 μg/L. All children received single-dose RTX therapy, with no significant difference in treatment frequency between the two groups ( P>0.05). At 3 months after the last rituximab therapy, CD20? B cell counts were significantly higher in the ARA-positive group ( P<0.05). During follow-up, 15% (7/48) of the children experienced infusion-related adverse reactions, with no significant difference in incidence between the two groups ( P>0.05). Conclusion:ARA is significantly associated with relapse in FRNS or SDNS children after RTX therapy.