生物医学工程学杂志最新文献

{"title":"[A simulation study on the differences in compressive mechanical properties of Voronoi porous structures with two strut-diameter morphologies].","authors":"Yutao Men, Tonghao Wang, Miaojie Wu, Chunqiu Zhang","doi":"10.7507/1001-5515.202601066","DOIUrl":"10.7507/1001-5515.202601066","url":null,"abstract":"<p><p>Voronoi-based non-periodic porous structures resemble trabecular bone and are widely used in bone scaffold design. To clarify the influence of strut-diameter morphology on compressive mechanical properties, two Voronoi porous structures with identical porosity, namely uniform-strut and non-uniform-strut structures, were constructed. Their quasi-static compressive and compressive-fatigue behaviors were analyzed using the finite element method. The results showed that the uniform-strut structure had a more homogeneous stress distribution and higher stiffness in the linear elastic stage. The non-uniform-strut structure exhibited higher load-bearing capacity and energy absorption during compression failure. Under compressive fatigue loading, the uniform-strut structure generally showed a longer fatigue life. The uniform-strut structure is suitable for implant scaffolds and prostheses under the conditions of long-term cyclic loading and controllable mechanical matching; the non-uniform filament diameter structure is more suitable for applications under the conditions of impact resistance and energy absorption buffering. These results provide a reference for the selection and optimization of Voronoi biomimetic porous structures under different service requirements.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"791-799"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A method for predicting neurological outcomes after cardiac arrest based on time-frequency domain feature fusion].","authors":"Xiaojia Wang, Yunhan Yang, Shijie Wang, Yudong Zhang, Yushun Gong, Chunfeng Yang","doi":"10.7507/1001-5515.202603070","DOIUrl":"10.7507/1001-5515.202603070","url":null,"abstract":"<p><p>Accurate neurological outcome assessment after cardiac arrest is critical for clinical diagnosis and treatment. Existing electroencephalogram (EEG) prediction models suffer from high computational complexity, redundant full-channel data, and insufficient fusion of time-frequency domain features. This study proposes a lightweight deep learning model, WaveConv-SR-RepVGG, based on adaptive time-frequency feature fusion and reparameterized convolution. Taking the reparameterized visual convolutional network (RepVGG) as the baseline framework, the model introduces a shrinkage (SR) mechanism into the time-domain branch to suppress interference from low signal-to-noise ratio EEG signals. A multi-scale wavelet convolution (MS-Conv) module is constructed to extract multi-scale spectral features, and time-frequency features are fused to form joint representations for prognostic classification. Experiments were conducted on the PhysioNet 2023 dataset. Under the 18-channel setting, the model achieved an accuracy of 81.7%, an F1 score of 86.1%, and an AUC of 0.847. Under the 4-channel setting, the accuracy reached 81.2%, the F1 score reached 86.7%, and the AUC reached 0.877, with overall performance outperforming various mainstream deep learning algorithms. The proposed model enables neurological outcome prediction for patients after cardiac arrest and maintains stable and excellent classification performance with fewer EEG acquisition channels.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"711-717"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Classification of systemic lupus erythematosus resting-state functional magnetic resonance imaging data based on the end-to-end model].","authors":"Yunyun Ma, Peng Ding, Yuqi Cheng, Tianwen Li, Lei Zhao, Anmin Gong, Wenya Nan, Yunfa Fu, Jian Xu","doi":"10.7507/1001-5515.202502034","DOIUrl":"10.7507/1001-5515.202502034","url":null,"abstract":"<p><p>Systemic lupus erythematosus (SLE) frequently involves the central nervous system, inducing abnormal alterations in brain functional and structural networks and resulting in cognitive and psychological dysfunction in patients. To identify abnormal brain functional network patterns associated with SLE, this study adopted a dynamic threshold strategy to detect key functional connections and construct sparse brain functional networks. A graph transformation network (GTNet) was utilized to model the optimized networks, capturing local topological features and global dependencies to effectively identify abnormal patterns of SLE-related brain functional networks. Experimental results showed that the proposed model achieved an average classification accuracy of (87.48 ± 6.77)% on the resting-state functional magnetic resonance imaging dataset consisting of 107 SLE patients and 107 healthy controls. Further analysis showed that the difference in small-world properties between the two groups was statistically significant ( <i>t</i> = -2.96, <i>P</i> < 0.01). In conclusion, the model constructed in this study provides a new scheme for the auxiliary diagnosis of SLE, and its automated classification architecture has potential application value.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"845-852"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Functional requirements and research advances of antimicrobial hydrogels for combat trauma wound repair].","authors":"Bianmei Wu, Gejin Wei","doi":"10.7507/1001-5515.202510036","DOIUrl":"10.7507/1001-5515.202510036","url":null,"abstract":"<p><p>Battlefield trauma wounds, characterized by complex injury mechanisms and extremely high infection risks, impose stringent multifunctional requirements on wound repair materials-specifically rapid hemostasis, potent antimicrobial activity, dynamic adhesion, and promotion of tissue regeneration. Antimicrobial hydrogels demonstrate significant advantages in infection control and tissue regeneration owing to their three-dimensional network structure, high water content, and tunable physicochemical properties. Based on the distinct requirements of each stage of the battlefield trauma echelon care system (encompassing emergency field care, medical evacuation, and definitive hospital treatment), and while fully accounting for battlefield environmental constraints, this review systematically evaluates the applicability, technical bottlenecks, and clinical translation prospects of various antimicrobial hydrogels. The analysis aims to provide a theoretical foundation and targeted research directions for the design of next-generation intelligent hydrogels optimized for battlefield application.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"879-885"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148832517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Research on electrooculography artifact removal from electroencephalography signals based on frequency slice wavelet transform].","authors":"Haoyu Fu, Mingfu Zhao, Lurui Wang, Bin Tang, Chen Xue, Yuanyuan Chen","doi":"10.7507/1001-5515.202602026","DOIUrl":"10.7507/1001-5515.202602026","url":null,"abstract":"<p><p>To address the strong contamination of single-channel electroencephalography (EEG) signals by electrooculography (EOG) artifacts during acquisition, this paper proposes an EOG artifact suppression method based on an improved frequency slice wavelet transform (FSWT). First, the EEG signal is adaptively decomposed into frequency-slice components using FSWT. Based on the low-frequency energy bursts induced by eye blinks, a robust median absolute deviation (MAD)-based threshold is used to localize artifact-contaminated intervals. Subsequently, only within the detected artifact-contaminated intervals, the dominant low-frequency range of eye-blink artifacts (0.8～3.5 Hz) is used as a prior reference, and adaptive local time-frequency masking is applied to the corresponding frequency components to reduce excessive removal of low-frequency components in normal EEG signals. Finally, the cleaned EEG signal is reconstructed via inverse FSWT, forming a three-stage artifact-removal framework of \"detection-masking-reconstruction\". Experiments were conducted on the publicly available semi-simulated Klados dataset using signal-to-noise ratio improvement (ΔSNR) and band energy retention rate as evaluation metrics. In addition, the applicability of the proposed method was validated using self-collected single-channel EEG data acquired under real-world conditions. Results showed that the proposed method achieved mean ΔSNR values of (2.81 ± 2.80) dB for all contaminated samples ( <i>n</i> = 265) and (5.69 ± 1.88) dB for strongly contaminated samples ( <i>n</i> = 101) on the Klados dataset. These values were numerically higher than those obtained using three single-channel methods: empirical wavelet transform (EWT), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and wavelet thresholding. The theta-band energy retention rate was 0.889, indicating that the method retained a relatively high proportion of theta-band energy while suppressing EOG artifacts. Results from the self-collected single-channel EEG data further showed that the proposed method could reduce the high-amplitude fluctuations caused by eye blinks while preserving the main EEG rhythm structures under real-world acquisition conditions. In summary, the proposed improved FSWT method can be used for EOG artifact suppression in single-channel EEG signals without relying on multichannel spatial redundancy.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"741-750"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148832857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Advances in deep learning multimodal fusion for early diagnosis of knee osteoarthritis].","authors":"Jinglin Tian, Jianxiong Ma, Jie Zhao, Xinlong Ma","doi":"10.7507/1001-5515.202601026","DOIUrl":"10.7507/1001-5515.202601026","url":null,"abstract":"<p><p>Knee osteoarthritis (KOA) is a common chronic degenerative disease that causes pain, functional limitation, and reduced quality of life in middle-aged and elderly populations. Early KOA is characterized by relatively subtle symptomatic, structural, and functional changes, including knee pain or stiffness, cartilage matrix alterations, meniscal degeneration, bone marrow lesions, and mild abnormalities in load-related function, while radiographic findings may remain atypical. A single imaging modality or clinical indicator is therefore insufficient to characterize the early pathological status of KOA. Deep learning (DL)-based multimodal fusion can integrate X-ray, magnetic resonance imaging, clinical variables, gait/biomechanical data, and potential molecular biomarkers, supporting early identification and risk stratification from the perspectives of bony structure, soft-tissue change, symptom burden, and functional loading. This review summarizes multimodal data types, preprocessing and feature extraction methods, early/intermediate/late fusion strategies, representative DL architectures, and current application evidence for early KOA diagnosis. We further discuss key translational issues, including cost-effectiveness, label consistency, center effects, missing modalities, and multimodal interpretability. Current evidence suggests that multimodal fusion may provide incremental value over unimodal approaches, but its clinical utility requires further confirmation through unified early-stage definitions, standardized acquisition, multi-reader consensus annotation, and multicenter external validation.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"863-871"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Development and validation of an explainable machine learning-based model for predicting mortality risk in polytrauma patients].","authors":"Yupeng Jiang, Jinlong Mao, Lin Li, Chunyang Liang, Hongjun Kang, Zhilong Jia, Xiaoli Liu, Zhengbo Zhang","doi":"10.7507/1001-5515.202605025","DOIUrl":"10.7507/1001-5515.202605025","url":null,"abstract":"<p><p>Polytrauma is commonly defined as multisystem trauma involving at least two body regions with an Abbreviated Injury Scale (AIS) score ≥ 3, characterized by complex pathophysiological interactions and extreme clinical heterogeneity. This study retrospectively analyzed data from 171 274 polytrauma patients in the National Trauma Data Bank (NTDB) from 2018 to 2021. Key features were selected using the eXtreme Gradient Boosting (XGBoost) algorithm. Prediction models were then developed using XGBoost, logistic regression, random forest, naive Bayes, and support vector machine, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Subgroup standardized mortality ratio (SMR) validation and global and local interpretability analysis based on SHapley Additive exPlanations (SHAP) theory were also conducted. The results demonstrated that the XGBoost model incorporating Glasgow Coma Scale (GCS), Injury Severity Score (ISS), massive transfusion within 4 hours, respiratory support, unplanned intubation, prehospital care-limiting directives, unplanned intensive care unit (ICU) admission, sepsis, ventilator-associated pneumonia, and liver cirrhosis achieved optimal performance, with an area under the ROC curve (AUC) of 0.90. Its calibration curve closely aligned with the ideal diagonal, and decision curve analysis indicated the highest clinical net benefit across a wide range of threshold probabilities. Subgroup SMRs ranged from 1.07 to 1.18, all close to 1.0. In conclusion, the developed in-hospital mortality risk prediction model for polytrauma patients exhibits favorable discrimination and clinical net benefit, and can assist clinicians in identifying high-risk populations while providing evidence to support individualized treatment decisions.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"769-776"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Inductance calculation method for transcranial magnetic stimulation figure-8 coils Accounting for spatial mutual inductance].","authors":"Yuansheng Fan, Nianshuang Wu, Jiawei Li, Weiyu Meng, Zhen Wu, Penghao Wang, Cheng Zhang, Changzhe Wu, Xiaolin Huo, Guanghao Zhang","doi":"10.7507/1001-5515.202601070","DOIUrl":"10.7507/1001-5515.202601070","url":null,"abstract":"<p><p>Transcranial magnetic stimulation (TMS) is widely used in the treatment of neuropsychiatric disorders, and the stimulation coil constitutes a critical component of TMS devices. Existing inductance calculations for figure-8 coils suffer from three notable shortcomings: they are mostly limited to single-layer geometries, they still rely on elliptic integrals, and they systematically overlook spatial mutual coupling among windings. To address these issues, we propose a high-precision analytical method that accommodates both flat spiral and multi-layer stacked coil configurations. Instead of adopting a conventional lumped-equivalent model, the proposed approach decoupled the total inductance into four independent contributions: self-inductance, coaxial layer-to-layer mutual inductance, coplanar mutual inductance, and non-coplanar mutual inductance. The self-inductance term was computed through a modified magnetic circuit procedure. The coaxial mutual inductance was obtained from parametric curves that incorporated the wire cross-section, built upon the geometric mean distance theory. The coplanar and non-coplanar mutual inductances were derived by combining the Maxwell-Wien series expansion with geometric perturbation theory. The overall coil inductance was then determined by analyzing the circuit connection. Finite element simulations and experimental measurements verified the accuracy and robustness of the method at both the individual-component level and the system level, laying a theoretical foundation for the optimal design of high-performance TMS coils.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"678-685"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148832496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Preparation of HLYVSPW peptide-displaying outer membrane vesicles and validation of their in vitro targeting effects on triple-negative breast cancer cells].","authors":"Qing Wang, Shuo Zhao, Huilin Wang, Xiaorui Li, Weihong Ren","doi":"10.7507/1001-5515.202603076","DOIUrl":"10.7507/1001-5515.202603076","url":null,"abstract":"<p><p>Bacterial outer membrane vesicles (OMVs) are readily amenable to engineering but naturally lack active targeting ability. In this study, the HLYVSPW peptide was displayed on the surface of OMVs using cytolysin A (ClyA) to construct Toll-like receptor 2 (TLR2)-targeted engineered OMVs (HLYV-OMVs), and their <i>in vitro</i> targeted uptake by triple-negative breast cancer cells was evaluated. HLYV-OMVs were prepared by ultracentrifugation and characterized by transmission electron microscopy, dynamic light scattering, zeta potential analysis, and proteinase K protection assays. TLR2-high-expressing MDA-MB-231 cells, TLR2-knockdown MDA-MB-231 cells (231-siTLR2), and MCF-10A cells were used to evaluate cell viability, cellular uptake, and batch-to-batch reproducibility. HLYV-OMVs exhibited a typical bilayer vesicular morphology with particle sizes of approximately 56-64 nm, and the HLYVSPW peptide was successfully displayed on the vesicle surface. Within the tested dose and exposure ranges, HLYV-OMVs did not cause an obvious decrease in cell viability. Their uptake by MDA-MB-231 cells was higher than that by TLR2-knockdown cells and MCF-10A cells and increased in a dose-related manner. Particle size and cellular uptake results showed a certain degree of reproducibility across batches. These findings indicate that HLYV-OMVs exhibit a TLR2 expression-associated uptake advantage <i>in vitro</i> and may provide an experimental basis for developing targeted delivery platforms for triple-negative breast cancer.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"853-862"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148832851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A study on the propagation characteristics of epileptic spike signals based on simultaneous intracranial and scalp electroencephalography].","authors":"Mingming Jiang, Dandan Gao, Chunsheng Li","doi":"10.7507/1001-5515.202602025","DOIUrl":"10.7507/1001-5515.202602025","url":null,"abstract":"<p><p>Spikes, as a key electrophysiological marker of abnormal synchronized neuronal discharges, can be identified and detected by scalp electroencephalography (EEG), and are of great value for localizing epileptic foci and investigating the mechanisms of epilepsy. However, spike propagation is strongly influenced by the skull, making it difficult to infer the precise intracranial origin of spikes from scalp EEG alone. Therefore, based on simultaneous intracranial and scalp EEG recordings, this study investigated the propagation characteristics of spikes from the intracranial space to the scalp. The study mainly focused on four aspects: the characteristics of simultaneous intracranial and scalp spikes, the scalp visibility of intracranial spikes, the spatial distribution of scalp spikes, and the influence of epileptogenic and non-epileptogenic regions on spike propagation. The results showed that spikes detectable on the scalp differed significantly in energy-related features from those not detectable on the scalp ［ <i>MD</i> = 2.6 × 10 ^-6 (2.5 × 10 ^-6, 2.6 × 10 ^-6), <i>P</i> < 0.01］. When the intracranial spike activation area was less than 10 cm ², the scalp detection rate was 18.9%; when the area reached 30～40 cm ², the detection rate increased to 62.5%, showing an increasing trend. After spikes propagated to the scalp, the detection rate decreased by 63.5% when the horizontal distance of their spatial distribution exceeded 4 cm. In addition, there was no statistically significant difference in scalp detection rate between spikes originating from epileptogenic and non-epileptogenic regions ( <i>P</i> > 0.05). In summary, these findings are important for elucidating the propagation characteristics of spikes and may provide quantitative evidence for electrophysiological analysis and focus localization in epilepsy.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"704-710"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519822/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}