工程技术最新文献

{"title":"Efficient system for classifying cyclic alternating pattern phases in sleep.","authors":"Megha Agarwal, Amit Singhal","doi":"10.1007/s11571-025-10261-x","DOIUrl":"10.1007/s11571-025-10261-x","url":null,"abstract":"<p><p>Electroencephalogram (EEG) signals are a popular tool to analyze sleep patterns. Cyclic alternating patterns (CAP) can be observed in EEG signals during unconscious periods of sleep. Detailed study of CAP can help in early diagnosis of many sleep disorders. Firstly, the CAP cycles need to be segregated into their constituents, phase A and phase B periods. In this work, we develop an accurate and easy-to-implement system to distinguish between the two CAP phases. The EEG signals are denoised and divided into smaller segments for an easier processing. These segments are decomposed into different frequency sub-bands using zero-phase filtering. Thereafter, statistical features are extracted from the sub-band components, and significant features are selected using the Kruskal-Wallis test. We consider four different algorithms for classification, namely, k-nearest neighbour (kNN), support vector machine (SVM), bagged tree (BT) and neural network (NN). The classification results are compiled for the datasets that include healthy subjects and those suffering from insomnia. The BT classifier produces the best results for the combined balanced dataset, with 83.29% accuracy and 83.58% F-1 score. The proposed method is more accurate and efficient than the existing schemes and can be considered for widespread deployments in real-world scenarios.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"79"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119118","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}

{"title":"Deep brain stimulation-induced two manners to eliminate bursting for Parkinson's diseases: synaptic current and bifurcation mechanisms.","authors":"Hui Zhou, Xianjun Wang, Huaguang Gu, Yanbing Jia","doi":"10.1007/s11571-025-10267-5","DOIUrl":"10.1007/s11571-025-10267-5","url":null,"abstract":"<p><p>Although deep brain stimulation (DBS) is effective in treating Parkinson's disease (PD) related to bursting, the underlying mechanisms remain unclear. In the present paper, the dynamical and synaptic mechanisms are studied in a basal ganglia-thalamus model. Firstly, slow and large oscillations of synaptic gating variables/currents are identified as the cause of the irregular and non-synchronous bursting for PD, indicating that interruption of these slow modulations may be a feasible measure to treat PD. Secondly, strong DBS with high frequency applied to subthalamic nucleus (STN) can induce fast synchronous spiking in both STN and external globus pallidus (GPe), then interrupt the slow gating variables, thereby eliminating the irregular bursting. Meanwhile, the gating variables of the excitatory and inhibitory synapses respectively from STN and GPe to the internal globus pallidus (GPi) become fast. Finally, competition between these two opposite synapses can induce two manners to eliminate the bursting of GPi and restore the normal state, appearing in vast majority of parameter space composed of multiple synaptic conductances. One is the synchronous silence of GPi, and the other the synchronous regular fast spiking, which occurs for large conductance of the inhibitory and excitatory synapse, respectively. Both result in regular spiking of thalamus, via interrupting slow gating variables of synapse projected to thalamus. In addition, as the two conductances approach each other, the synaptic current to GPi oscillates around zero slowly, resulting in irregular firings of GPi and thalamus for PD in a narrow parameter space. Furthermore, the bursting observed in PD before DBS and three types of electrical activities of GPi during DBS are explained, using a saddle-node bifurcation of limit cycles and oscillation patterns of synaptic current. The distinction from the post inhibitory rebound bursting reported in previous studies is discussed. The results present the mechanisms for DBS to treat PD via eliminating bursting in wide parameter region.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"78"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119115","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}

{"title":"A multilayer deep neural network framework for hemodynamic assessment of cognitive load management during problem-solving tasks.","authors":"Priyanka Paul, Shaoni Banerjee, Apurba Nandi, Avik Kumar Das, Arijeet Ghosh","doi":"10.1007/s11571-025-10292-4","DOIUrl":"10.1007/s11571-025-10292-4","url":null,"abstract":"<p><p>Cognitive load refers to the mental effort required to process information and perform tasks, significantly influencing learning and performance outcomes. This paper presents a novel approach for cognitive load classification using a hybrid model that integrates Long Short-Term Memory (LSTM) networks with the Block Attention Module (BAM). Leveraging functional Near-Infrared Spectroscopy (fNIRS), we investigate the relationship between cognitive load and brain activity in a controlled experimental setting. Our methodology encompasses data collection from 50 participants engaged in various problem-solving tasks, with cognitive load categorized as high, medium, or low. The acquired fNIRS data underwent a rigorous preprocessing pipeline, including normalization and wavelet transform for feature extraction, enabling a comprehensive analysis of hemodynamic responses. The proposed model employs BAM to enhance feature representation by refining the importance of spatial and channel dimensions, thus improving the LSTM's ability to capture temporal dependencies in the data. The experimental results demonstrate significant performance improvements in cognitive load classification, showcasing the efficacy of the integrated LSTM-BAM architecture. This work not only contributes to the understanding of cognitive load dynamics but also highlights the potential of fNIRS as a non-invasive tool for real-time monitoring of cognitive performance, paving the way for advancements in instructional design and cognitive research.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"104"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552509","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}

{"title":"Adaptive cholinergic feedback network oscillations: insights into striatal beta oscillations and circuit dynamics.","authors":"Ziling Wang, Dandan Qian, Songting Li, Wei Lu, Douglas Zhou","doi":"10.1007/s11571-025-10301-6","DOIUrl":"10.1007/s11571-025-10301-6","url":null,"abstract":"<p><p>Enhanced beta oscillations (12-25 Hz) within the cortico-basal ganglia-thalamic network are significantly associated with motor deficits and are a prominent characteristic of the neural dynamic pathology in Parkinson's disease. Although the striatum has been proposed as a promising origin for enhanced beta oscillations, the precise mechanism through which distinct striatal neurons collaborate to orchestrate beta oscillations remains elusive. This study constructs a biophysical neural network model of the striatum based on experimental constraints. The model faithfully reproduces various experimental observations, including dopamine-dependent beta oscillations and phase-locked firing patterns. Through both theoretical and numerical analysis, our analysis reveals that striatal beta oscillations emerge from interactions within the cellular architecture, particularly the somatostatin-expressing interneurons (SOM) driven choline acetyltransferase-expressing interneurons (ChAT)-indirect pathway striatal projection neurons (iSPN) loop. Our results underscore the critical role of ChATs in enhancing beta oscillations. ChATs, instead of passively providing excitatory drive, actively amplify beta oscillations by enhancing their excitation efficacy through a phase-locked mode. Additionally, the inhibitory interactions among iSPNs, with robust and slow inhibitory recovery dynamics within iSPNs, potentially result in beta oscillations. The slow inhibitory recovery is likely attributed to the slow dynamics of the KCNQ current. SOMs further modulate the beta oscillations by affecting their downstream ChAT-iSPN loop. These results provide novel insights into the mechanism underlying striatal beta oscillations, shedding light on the processes involved in beta oscillations generation during pathological states.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"117"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728382","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}

{"title":"DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.","authors":"Liang Chang, Banghua Yang, Jiayang Zhang, Tie Li, Juntao Feng, Wendong Xu","doi":"10.1007/s11571-025-10296-0","DOIUrl":"10.1007/s11571-025-10296-0","url":null,"abstract":"<p><p>Accurate decoding and strong feature interpretability of Motor Imagery (MI) are expected to drive MI applications in stroke rehabilitation. However, the inherent nonstationarity and high intra-class variability of MI-EEG pose significant challenges in extracting reliable spatio-temporal features. We proposed the Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net), which combines DSTA and the Spatio-Temporal Convolution (STC) modules. In DSTA module, multi-scale temporal convolutional kernels tailored to the α and β frequency bands of MI neurophysiological characteristics, while raw EEG serve as a baseline feature layer to retain original information. Next, Grouped Spatial Convolutions extract multi-level spatial features, combined with weight constraints to prevent overfitting. Spatial convolution kernels map EEG channel information into a new spatial domain, enabling further feature extraction through dimensional transformation. And STC module further extracts features and conducts classification. We evaluated DSTA-Net on three public datasets and applied it to a self-collected stroke dataset. In tenfold cross-validation, DSTA-Net achieved average accuracy improvements of 6.29% (<i>p</i> < 0.01), 3.05% (<i>p</i> < 0.01), 5.26% (<i>p</i> < 0.01), and 2.25% over the ShallowConvNet on the BCI-IV-2a, OpenBMI, CASIA, and stroke dataset, respectively. In hold-out validation, DSTA-Net achieved average accuracy improvements of 3.99% (<i>p</i> < 0.01) and 4.2% (<i>p</i> < 0.01) over the ShallowConvNet on the OpenBMI and CASIA datasets, respectively. Finally, we applied DeepLIFT, Common Spatial Pattern, and t-SNE to analyze the contributions of individual EEG channels, extract spatial patterns, and visualize features. The superiority of DSTA-Net offers new insights for further research and application in MI. The code is available in https://github.com/CL-Cloud-BCI/DSTANet-code.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"118"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728383","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}

{"title":"Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.","authors":"Shan Huang, Yixin Liu, Jingyu Zhang, Yiming Wang","doi":"10.1080/21691401.2025.2506591","DOIUrl":"https://doi.org/10.1080/21691401.2025.2506591","url":null,"abstract":"<p><p>The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multimodal and multi-expression data. However, traditional association analysis methods face challenges in handling nonlinear, multimodal and multi-expression data. Therefore, a multimodal attention fusion deep self-restructuring presentation (MAFDSRP) model is proposed to solve the above problem. First, multimodal brain imaging data are processed through a novel histogram-matching multiple attention mechanisms to dynamically adjust the weight of each input brain image data. Simultaneous, the genetic data are preprocessed to remove low-quality samples. Subsequently, the genetic data and fused neuroimaging data are separately input into the self-reconstruction network to learn the nonlinear relationships and perform subspace clustering at the top layer of the network. Finally, the learned genetic data and fused neuroimaging data are analysed through expression association analysis to identify AD-related biomarkers. The identified biomarkers underwent systematic multi-level analysis, revealing biomarker roles at molecular, tissue and functional levels, highlighting processes like inflammation, lipid metabolism, memory and emotional processing linked to AD. The experimental results show that MAFDSRP achieved 0.58 in association analysis, demonstrating its great potential in accurately identifying AD-related biomarkers.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"231-243"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135996","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}

{"title":"A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.","authors":"Ilknur Sercek, Niranjana Sampathila, Irem Tasci, Tuba Ekmekyapar, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Ru-San Tan, U R Acharya","doi":"10.1007/s11571-025-10249-7","DOIUrl":"https://doi.org/10.1007/s11571-025-10249-7","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"71"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981499","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}

{"title":"MicroRNA-214-3p Delivered by Bone Marrow Mesenchymal Stem Cells-Secreted Exosomes Affects Oxidative Stress in Alzheimer's Disease Rats by Targeting CD151.","authors":"Luzy Zhang","doi":"10.1080/15476278.2025.2489673","DOIUrl":"https://doi.org/10.1080/15476278.2025.2489673","url":null,"abstract":"<p><strong>Objective: </strong>This study probed the effect of targeted regulation of CD151 by microRNA-214-3p (miR-214-3p) delivered by bone marrow mesenchymal stem cells-secreted exosomes (BMSCs-exo) on oxidative stress and apoptosis of neurons in Alzheimer's disease (AD).</p><p><strong>Methods: </strong>Rat BMSCs were isolated, from which MSCs-exo were extracted and identified. The AD rat model was established and injected with MSC-exo suspension. Meanwhile, miR-214-3p and CD151 interfering lentivirus were transfected in MSCs. After injection, learning and cognitive ability of the rats were assessed, as well as neuronal apoptosis and oxidative stress injury. miR-214-3p and CD151 levels were determined, and their relationship was explored.</p><p><strong>Results: </strong>AD rats had prolonged escape latency, weakened learning and cognitive ability, increased neuronal apoptosis in the hippocampal CA3 region, and aggravated oxidative stress. After MSC-exo injection, these changes in AD rats were partially rescued. CD151 was targeted by miR-214-3p, and MSC-exo improved AD in rats through the miR-214-3p/CD151 axis.</p><p><strong>Conclusion: </strong>MSC-exo down-regulates CD151 by targeting miR-214-3p to enhance antioxidant capacity, thereby improving the pathological injury of AD rats.</p>","PeriodicalId":19596,"journal":{"name":"Organogenesis","volume":"21 1","pages":"2489673"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12036478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143991800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Individualized Nursing Improves Recovery and Reduces Complications in ICU Patients with Severe Pneumonia.","authors":"Linjuan Wu, Jingchuan Lin","doi":"10.1080/15476278.2025.2489670","DOIUrl":"10.1080/15476278.2025.2489670","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluates the effectiveness of optimized individualized nursing interventions on clinical outcomes in intensive care unit (ICU) patients with severe pneumonia.</p><p><strong>Methods: </strong>In this randomized controlled trial, 76 patients with severe pneumonia were randomized into a control group and an experimental group. Both groups received routine nursing care. On this basis, the experimental group received optimized individualized nursing. After the nursing intervention, clinical outcomes, respiratory function, coagulation function, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and St. George's Respiratory Problems Questionnaire (SGRQ) score were assessed, and the complication and mortality rates were counted.</p><p><strong>Results: </strong>After the intervention, compared with the control group, the experimental group exhibited shorter times of fever reduction, white blood cell count recovery, and off-boarding and ICU stay, higher oxygenation index, lower rapid shallow breathing index, respiratory rate, activated partial thromboplastin time, prothrombin time, fibrinogen, and D-Dimer levels, lower APACHE II scores and SGRQ scores (<i>p</i> < 0.05). Additionally, the experimental group possessed a lower complication rate and mortality rate than the control group (<i>p</i> < 0.05).</p><p><strong>Conclusion: </strong>Implementing optimized individualized nursing can significantly enhance recovery and reduce complications in ICU patients with severe pneumonia.</p>","PeriodicalId":19596,"journal":{"name":"Organogenesis","volume":"21 1","pages":"2489670"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11980446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-patient seizure prediction via continuous domain adaptation and similar sample replay.","authors":"Ziye Zhang, Aiping Liu, Yikai Gao, Ruobing Qian, Xun Chen","doi":"10.1007/s11571-024-10216-8","DOIUrl":"10.1007/s11571-024-10216-8","url":null,"abstract":"<p><p>Seizure prediction based on electroencephalogram (EEG) for people with epilepsy, a common brain disorder worldwide, has great potential for life quality improvement. To alleviate the high degree of heterogeneity among patients, several works have attempted to learn common seizure feature distributions based on the idea of domain adaptation to enhance the generalization ability of the model. However, existing methods ignore the inherent inter-patient discrepancy within the source patients, resulting in disjointed distributions that impede effective domain alignment. To eliminate this effect, we introduce the concept of multi-source domain adaptation (MSDA), considering each source patient as a separate domain. To avoid additional model complexity from MSDA, we propose a continuous domain adaptation approach for seizure prediction based on the convolutional neural network (CNN), which performs sequential training on multiple source domains. To relieve the model catastrophic forgetting during sequential training, we replay similar samples from each source domain, while learning common feature representations based on subdomain alignment. Evaluated on a publicly available epilepsy dataset, our proposed method attains a sensitivity of 85.0% and a false alarm rate (FPR) of 0.224/h. Compared to the prevailing domain adaptation paradigm and existing domain adaptation works in the field, the proposed method can efficiently capture the knowledge of different patients, extract better common seizure representations, and achieve state-of-the-art performance.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"26"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001017","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}