IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10190-1

K G Shanthi, A Mary Joy Kinol, S Rukmani Devi, K Kannan

{"title":"Cognitive neurodynamic approaches to adaptive signal processing in wireless sensor networks.","authors":"K G Shanthi, A Mary Joy Kinol, S Rukmani Devi, K Kannan","doi":"10.1007/s11571-024-10190-1","DOIUrl":"10.1007/s11571-024-10190-1","url":null,"abstract":"In recent years, Wireless Sensor Networks (WSN) have become vital because of their versatility in numerous applications. Nevertheless, the attain problems like inherent noise, and limited node computation capabilities, result in reduced sensor node lifespan as well as enhanced power consumption. To tackle such problems, this study develops a Modified-Distributed Arithmetic-Offset Binary Coding-based Adaptive Finite Impulse Response (MDA-OBC based AFIR) framework. By leveraging Modified Distributed Arithmetic (MDA) which optimizes arithmetic operations by replacing the multipliers with lookup tables (LUT) hence minimizing energy consumption as well as computational complexity. Offset Binary Coding (OBC) enhanced the efficiency of data transmission by minimizing the data representation overhead. In addition to this, the adaptive strategy is incorporated with the Adaptive Finite Impulse Response (AFIR) framework permitting the filters to dynamically adjust to varying signal characteristics, thus offering high noise suppression and low distortion rates. Comprehensive simulations and comparative analysis validate the effectiveness of the proposed MDA-OBC-based AFIR method. The proposed method attained a lower energy consumption of 1.5 J and 130 W power consumption than the traditional implementations, resulting in significant energy efficiency and data transmission in signal preprocessing and noise suppression in WSNs.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"11"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study. 经皮耳迷走神经刺激（taVNS）对运动规划的影响：一项多模态信号研究。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-24 DOI: 10.1007/s11571-025-10220-6

Long Chen, Yihao Hu, Zhongpeng Wang, Lei Zhang, Chuxiang Jian, Shengcui Cheng, Dong Ming

{"title":"Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study.","authors":"Long Chen, Yihao Hu, Zhongpeng Wang, Lei Zhang, Chuxiang Jian, Shengcui Cheng, Dong Ming","doi":"10.1007/s11571-025-10220-6","DOIUrl":"10.1007/s11571-025-10220-6","url":null,"abstract":"Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG). To investigate the effect of taVNS on motor planning, a total of 21 subjects were recruited for this study and were divided into two groups: active group (n = 10) and sham group (n = 11). Each subject was required to be involved in a single-blind, sham-controlled, between-subject end-state comfort (ESC) experiment. The study compared behavioral indicators and electrophysiological features before and following taVNS. The results indicated a notable reduction in reaction time and an appreciable increase in the proportion of end-state comfort among the participants following taVNS, accompanied by notable alterations in motor-related cortical potential (MRCP) amplitude, low-frequency power of HRV (LF), and cortico-cardiac coherence, particularly in the parietal and occipital regions. These findings show that taVNS may impact the brain and heart, potentially enhancing their interaction, and improve participants' ability of motor planning.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"35"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BISNN: bio-information-fused spiking neural networks for enhanced EEG-based emotion recognition. BISNN：用于增强基于脑电图的情感识别的生物信息融合尖峰神经网络。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-03-22 DOI: 10.1007/s11571-025-10239-9

Hongze Sun, Shifeng Mao, Wuque Cai, Yan Cui, Duo Chen, Dezhong Yao, Daqing Guo

{"title":"BISNN: bio-information-fused spiking neural networks for enhanced EEG-based emotion recognition.","authors":"Hongze Sun, Shifeng Mao, Wuque Cai, Yan Cui, Duo Chen, Dezhong Yao, Daqing Guo","doi":"10.1007/s11571-025-10239-9","DOIUrl":"10.1007/s11571-025-10239-9","url":null,"abstract":"Spiking neural networks (SNNs), known for their rich spatio-temporal dynamics, have recently gained considerable attention in EEG-based emotion recognition. However, conventional model training approaches often fail to fully exploit the capabilities of SNNs, posing challenges for effective EEG data analysis. In this work, we propose a novel bio-information-fused SNN (BISNN) model to enhance EEG-based emotion recognition. The BISNN model incorporates biologically plausible intrinsic parameters into spiking neurons and is initialized with a structurally equivalent pre-trained ANN model. By constructing a bio-information-fused loss function, the BISNN model enables simultaneous training under dual constraints. Extensive experiments on benchmark EEG-based emotion datasets demonstrate that the BISNN model achieves competitive performance compared to state-of-the-art methods. Additionally, ablation studies investigating various components further elucidate the mechanisms underlying the model's effectiveness and evolution, aligning well with previous findings.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"52"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shap-driven explainable AI with simulated annealing for optimized seizure detection using multichannel EEG signal. 形状驱动的可解释人工智能与模拟退火优化癫痫检测使用多通道脑电图信号。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-05 DOI: 10.1007/s11571-025-10269-3

Indu Dokare, Sudha Gupta

{"title":"Shap-driven explainable AI with simulated annealing for optimized seizure detection using multichannel EEG signal.","authors":"Indu Dokare, Sudha Gupta","doi":"10.1007/s11571-025-10269-3","DOIUrl":"10.1007/s11571-025-10269-3","url":null,"abstract":"The aim of this research is to combine Explainable AI (XAI) with advanced optimization techniques to provide a unique framework for seizure detection. This proposed work investigates how to enhance patient-specific and patient-non-specific seizure detection models by combining multiband feature extraction, SHAP-based feature selection, SMOTE, and a metaheuristic algorithm for hyperparameter tuning.The discrete wavelet transform (DWT) is used to decompose EEG signals to retrieve entropy-based and statistical information. Simulated Annealing (SA) is employed to optimize the Random Forest (RF) classifier's hyperparameters, and SHAP (SHapley Additive exPlanations) values are utilized for feature selection. Furthermore, a novel technique SHAP-RELFR has been demonstrated to select patient-non-specific features. Additionally, SMOTE is employed to handle imbalanced data. The proposed methodology is evaluated on the CHB-MIT and Siena datasets using both patient-specific and patient-non-specific feature selection approaches. Experimental findings demonstrate that the proposed methodology significantly improves the performance of seizure detection. The average accuracy, precision, sensitivity, specificity, F1-score, and AUC obtained for a patient-non-specific case are 96.58%, 95.19%, 94.52%, 98.02%, 94.72%, and 0.9452, respectively, using the CHB-MIT dataset. For the Seina dataset, the average accuracy, precision, sensitivity, specificity, F1-score, and AUC obtained for a patient-non-specific case are 94.81%, 94.51%, 94.04%, 96.87%, 94.28%, and 0.9400, respectively. Explainable AI combined with SMOTE and a metaheuristic optimization algorithm facilitates an enhanced seizure detection. The novel SHAP-RELFR method provides an effective patient-non-specific feature selection, enabling this approach to be applicable across diverse patients. This proposed framework offers a step toward enhancing clinical decision-making by providing interpretable and versatile seizure detection models.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"85"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of transcranial magneto-acoustical stimulation on excitatory and inhibitory neuronal discharge patterns. 经颅磁声刺激对兴奋性和抑制性神经元放电模式的影响。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-30 DOI: 10.1007/s11571-025-10290-6

Yiming Li, Haoyu Qiu, Haijun Zhu

{"title":"Effects of transcranial magneto-acoustical stimulation on excitatory and inhibitory neuronal discharge patterns.","authors":"Yiming Li, Haoyu Qiu, Haijun Zhu","doi":"10.1007/s11571-025-10290-6","DOIUrl":"10.1007/s11571-025-10290-6","url":null,"abstract":"Transcranial Magneto-Acoustical Stimulation (TMAS) represents an innovative, highly efficacious, and non-invasive modality for brain stimulation. Neurons, as integral components of neural networks, are crucial for the transmission of information. Nevertheless, the impact of TMAS on the discharge patterns of both excitatory and inhibitory neurons is not yet fully understood. To address this gap, the Hodgkin-Huxley neuronal model is analyzed using the improved Euler method. The neuronal discharge patterns are comprehensively examined by systematically adjusting ion channel parameters and stimulation parameters. The results indicate that ultrasound frequency exerted minimal influence on the properties of neuronal action potentials. Conversely, as the static magnetic field strength and ultrasound power are augmented, the excitability of both types of neurons progressively enhances. However, the changes in the electrical properties of action potentials are less pronounced in inhibitory neurons compared to excitatory neurons. Furthermore, alterations in ion channel parameters significantly influence the firing characteristics of both types of neurons. The present study elucidates that TMAS has a significant effect on the firing patterns of excitatory and inhibitory neurons. Excitatory neurons showed stronger regular discharges in response to static magnetic fields and increased ultrasound power, whereas inhibitory neurons did not respond to low-intensity static magnetic fields. In addition, our systematic analysis revealed synergistic effects between ion channel parameters and TMAS stimulation parameters. These findings shed light on how neuron type specificity and ion channel dynamics work together to shape the efficacy of TMAS, thus advancing previous studies.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"108"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of dendritic properties on the correlations in ionic channels emerging from firing rate homeostasis: a two-compartment modeling study. 树突性质对放电速率稳态中出现的离子通道相关性的影响：一项双室模型研究。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-30 DOI: 10.1007/s11571-025-10297-z

Guosheng Yi, Jiayi Cui, Ruifeng Bai

{"title":"Effects of dendritic properties on the correlations in ionic channels emerging from firing rate homeostasis: a two-compartment modeling study.","authors":"Guosheng Yi, Jiayi Cui, Ruifeng Bai","doi":"10.1007/s11571-025-10297-z","DOIUrl":"10.1007/s11571-025-10297-z","url":null,"abstract":"Homeostatic regulation of firing rate is an important feature of neural excitability, which is achieved through feedback control of diverse ionic channel expression levels. The output firing rate is controlled by the active currents and passive properties of the dendrites. The objective of this study is to determine how dendritic properties affect the homeostatic regulation of somatic firing rate. We used a two-compartment Pinsky-Rinzel model to simulate action potentials in a pyramidal neuron in response to external inputs. We applied a feedback framework to determine the maximum ionic conductances during homeostatic regulation and examined the pairwise correlations among these conductances. We find that the effective regulation of somatic firing rate could be achieved through controlling both somatic and dendritic ionic channels. The correlations among these channels are lower than those emerging from the regulation through the control of somatic or dendritic channels. It is also shown that increasing the number of adjustable channels alters ionic channel correlations when the additional channel has a strong compensatory relationship with other channels. Compared to the coupling conductance between two compartments, varying the proportion of area occupied by the dendrite produces a greater effect on firing rate dynamics and expression correlations between adjustable channels in both dendrite and soma. The results reveal that dendritic ionic channels, morphological feature and dendritic-somatic coupling are all factors that influence the correlations in ionic channel expression. These findings provide a biophysical basis for the relationship between dendritic properties and neuronal information processing.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10297-z.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"107"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of deep learning for multi-scale behavioral analysis in SNCA E46K Parkinson's disease drosophila. 深度学习在SNCA E46K帕金森病果蝇多尺度行为分析中的应用

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-30 DOI: 10.1007/s11571-025-10294-2

Keyi Dong, April Burch, Kang Huang

{"title":"Application of deep learning for multi-scale behavioral analysis in SNCA E46K Parkinson's disease drosophila.","authors":"Keyi Dong, April Burch, Kang Huang","doi":"10.1007/s11571-025-10294-2","DOIUrl":"10.1007/s11571-025-10294-2","url":null,"abstract":"Drosophila melanogaster is widely used as a model organism in Parkinson's disease research. However, due to the complexity of motion capture and the challenges of quantitatively assessing spontaneous behavior in Drosophila melanogaster, it remains technically difficult to identify symptoms of Parkinson's disease within Drosophila based on objective spontaneous behavioral characteristics. Here, we present an automated multi-scale behavioral phenotyping pipeline that classifies phenotypes related to Parkinson's disease using motion features extracted from pose estimation data of wild-type and Synuclein Alpha E46K mutant Drosophila melanogaster. Locomotor activity was recorded in a custom-designed 3D-printed behavioral trap, and body kinematics were analyzed using a markerless pose estimation tool to extract numerical features such as movement speed, tremor-like oscillations, and limb motion patterns. Beyond kinematic analysis, we applied unsupervised clustering to the pose-derived trajectories to extract recurrent movement subtypes that characterize spontaneous behavioral sequences. We found that kinematic features alone were insufficient to distinguish mutant flies from normal individuals, whereas behavioral sequence patterns captured through unsupervised clustering enabled robust group separation. Combining both feature types further enhanced classification accuracy, with the best model achieving 85%. This system provides an objective and scalable approach for analyzing behavior related to Parkinson's disease in Drosophila melanogaster, with potential applications in monitoring disease progression and screening pharmaceutical compounds.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"105"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of mitophagy-related biomarkers in human rheumatoid arthritis using machine learning models. 利用机器学习模型鉴定类风湿关节炎中与线粒体自噬相关的生物标志物。

IF 4.5 3区生物学

Artificial Cells, Nanomedicine, and Biotechnology Pub Date : 2025-12-01 Epub Date: 2025-07-16 DOI: 10.1080/21691401.2025.2533361

Jiayi Chen, Zuhai Huang, Chengyu Qin, Zixiang Pang, Yuanming Chen

{"title":"Identification of mitophagy-related biomarkers in human rheumatoid arthritis using machine learning models.","authors":"Jiayi Chen, Zuhai Huang, Chengyu Qin, Zixiang Pang, Yuanming Chen","doi":"10.1080/21691401.2025.2533361","DOIUrl":"https://doi.org/10.1080/21691401.2025.2533361","url":null,"abstract":"Rheumatoid arthritis (RA) is a systemic immune-mediated disease characterized by synovitis and joint cartilage destruction. Although many studies have shown that mitophagy is crucial in the development of bone metabolism disorders, its exact function in rheumatoid arthritis (RA) is still not well understood. This study analysed the GSE77298 dataset from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) between rheumatoid arthritis (RA) patients and healthy controls. Mitophagy-related genes (MRGs) were extracted from the literature and screened using bioinformatics techniques, resulting in differentially expressed MRGs (DE-MRGs). The diagnostic value of these genes was assessed using receiver operating characteristic (ROC) curves, and an ANN model was constructed. In the GSE77298 dataset, 267 differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) identified 2191 key module genes, leading to 63 DE-MRGs. Two MRGs, TMEM45A and ZBTB25, were identified as hub genes with areas under the curve (AUC) of 0.991 and 0.911, respectively. The nomogram model demonstrated high diagnostic value. Mitophagy plays a critical role in the progression of rheumatoid arthritis (RA). Identifying two genes associated with mitophagy may aid in the early diagnosis, mechanistic understanding, and treatment of RA.","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"287-303"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144648359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reward masks the learning of cognitive control demand. 奖励掩盖了认知控制需求的学习。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-07-18 DOI: 10.1007/s11571-025-10307-0

Bettina Bustos, Jiefeng Jiang, Wouter Kool

{"title":"Reward masks the learning of cognitive control demand.","authors":"Bettina Bustos, Jiefeng Jiang, Wouter Kool","doi":"10.1007/s11571-025-10307-0","DOIUrl":"https://doi.org/10.1007/s11571-025-10307-0","url":null,"abstract":"Cognitive control refers to a set of cognitive functions that modulate other cognitive processes to align with internal goals. Recent research has shown that cognitive control can flexibly adapt to internal and external factors such as reward, effort, and environmental demands. This suggests that learning processes track changes in these factors and drive an optimization process to determine how cognitive control should be applied in changing situations. In real life, multiple factors often simultaneously affect how cognitive control is deployed. However, previous studies mainly concern how cognitive control adjusts to changes in a single factor. Here, we investigate how cognitive control learns to adjust to two concurrently changing factors: statistical regularity in cognitive control demand and performance-contingent reward. We consider two competing hypotheses: reward promotes cognitive control to adjust to cognitive control demand, and the processing of reward information obstructs the adaptation to cognitive control demand. In our experiment, statistical regularity in cognitive control demand is manipulated within subjects such that some stimuli require higher levels of cognitive control than others. Reward is manipulated across subjects. Using a computational model that captures temporal changes in cognitive control, we find that in the absence of reward, participants can adjust to different levels of cognitive control demand. Importantly, when performance-contingent reward is available, participants fail to adapt to changes in cognitive control demand. The findings support the hypothesis that reward blocks the learning of cognitive control.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10307-0.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"114"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revealing the impact of gut microbiota-derived metabolites on depression through network pharmacology. 通过网络药理学揭示肠道微生物衍生代谢物对抑郁症的影响。

IF 4.5 3区生物学

Artificial Cells, Nanomedicine, and Biotechnology Pub Date : 2025-12-01 Epub Date: 2025-07-24 DOI: 10.1080/21691401.2025.2531752

Si Su, Tengarile A, Ruhan A, Riga Wu, Lisi Wei, La Ta, Wenfeng Huo, Lijun Tong, Jing Zhang, Rilebagen Hu, Li Li

{"title":"Revealing the impact of gut microbiota-derived metabolites on depression through network pharmacology.","authors":"Si Su, Tengarile A, Ruhan A, Riga Wu, Lisi Wei, La Ta, Wenfeng Huo, Lijun Tong, Jing Zhang, Rilebagen Hu, Li Li","doi":"10.1080/21691401.2025.2531752","DOIUrl":"https://doi.org/10.1080/21691401.2025.2531752","url":null,"abstract":"A total of 208 metabolites and 223 targets were initially extracted from the gutMGene v1.0 database. In addition, 1,630 and 1,321 targets were identified using the Similarity Ensemble Approach and Swiss Target Prediction databases, respectively, resulting in 921 overlapping targets. By integrating data from gutMGenev1.0, 13 core targets were finally identified. A microbiota-metabolite-target-signal pathway-disease network was constructed using Cytoscape 3.9.1, revealing 15 metabolites associated with the IL-17, TLR, and PI3K-Akt signalling pathways. Among these, five metabolites exhibited favourable drug similarity and acceptable toxicological profiles. Molecular docking confirmed the stable binding of two key metabolites-succinate and phenylacetylglutamine-to their respective targets. The results showed that succinate and phenylacetylglutamine demonstrated strong binding affinities to IL-1β and GSK3B, both involved in the IL-17, TLR, and PI3K-Akt signalling pathways. IL-17 and TLR4, as important pro-inflammatory cytokines, are closely associated with the development of depression, while the PI3K/AKT signalling pathway plays a key role in its pathogenesis. The present study reveals the potential mechanisms by which gut microbiota influence MDD and provides a scientific basis and a comprehensive research framework for future investigations.","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"327-342"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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