Bioengineering最新文献_第3页

Optimized Signal Acquisition and Advanced AI for Robust 1D EMG Classification: A Comparative Study of Machine Learning, Deep Learning, and Reinforcement Learning. 优化信号采集和先进的人工智能鲁棒1D肌电图分类：机器学习，深度学习和强化学习的比较研究。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-15 DOI: 10.3390/bioengineering13040463

Anagha Shinde, Virendra Shete, Ninad Mehendale

{"title":"Optimized Signal Acquisition and Advanced AI for Robust 1D EMG Classification: A Comparative Study of Machine Learning, Deep Learning, and Reinforcement Learning.","authors":"Anagha Shinde, Virendra Shete, Ninad Mehendale","doi":"10.3390/bioengineering13040463","DOIUrl":"10.3390/bioengineering13040463","url":null,"abstract":"Electromyography (EMG) signals are critical for prosthetic control, rehabilitation, and human-machine interaction, yet their classification remains challenging due to noise, non-stationarity, and inter-subject variability. This study presents a comprehensive comparative analysis of machine learning (ML), deep learning (DL), and reinforcement learning (RL) approaches for 1D EMG signal classification, with a systematic evaluation of signal acquisition parameters. Using both synthetic and real-world EMG datasets, we demonstrate that 8-10 bit quantization and a 2000 Hz sampling rate provide optimal signal fidelity while maintaining data efficiency. Among the evaluated models, ensemble methods (Gradient Boosting, Voting Ensemble) and advanced DL architectures (LSTM, Transformer) achieved superior performance on real EMG data, with accuracies reaching 100% and 96.3%, respectively. Notably, reinforcement learning agents (Deep Q-Networks) demonstrated 100% accuracy on multiclass synthetic data, revealing their potential for learning complex bio-signal representations. Our findings establish that meticulous optimization of preprocessing pipelines, combined with robust AI models, significantly enhances EMG classification accuracy. This work provides empirical guidance for selecting optimal acquisition parameters and AI architectures for practical EMG analysis systems, with direct implications for prosthetic control and rehabilitation technologies.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterization of Multilayer Structure-Graded Dental Zirconias. 多层结构分级牙科氧化锆的表征。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-14 DOI: 10.3390/bioengineering13040462

Ragai-Edward Matta, Renan Belli, Katrin Hurle, Arulraj Sangarapillai, Oleksandr Sednyev, Manfred Wichmann, Lara Berger

{"title":"Characterization of Multilayer Structure-Graded Dental Zirconias.","authors":"Ragai-Edward Matta, Renan Belli, Katrin Hurle, Arulraj Sangarapillai, Oleksandr Sednyev, Manfred Wichmann, Lara Berger","doi":"10.3390/bioengineering13040462","DOIUrl":"10.3390/bioengineering13040462","url":null,"abstract":"Multilayer zirconias have recently been introduced as dental biomaterials to combine improved translucency with sufficient mechanical reliability by implementing yttria-driven gradients in phase composition. Such materials can be considered functionally graded ceramics, where local phase stabilization influences strength and crack resistance. However, manufacturer-specific gradient profiles and their structure-property relationships remain insufficiently characterized. This study investigated two commercially available multilayer zirconias with distinct gradient concepts: IPS e.max® ZirCAD Prime (continuous gradient) and KATANA™ Zirconia YML (stepwise gradient). Ten equidistant sections along the blank height were analyzed using quantitative X-ray diffraction and Rietveld refinement to quantify zirconia phase fractions and estimate local Y2O3 content. Mechanical behavior was evaluated by biaxial flexural strength testing (ball-on-three-balls method) and fracture toughness testing using the chevron-notched beam technique. Both materials exhibited pronounced yttria- and phase-dependent gradients consistent with their reported layer designs. Regions with increased yttria content showed higher t″ fractions and reduced fracture toughness and strength, whereas deeper regions displayed increased mechanical performance associated with higher fractions of transformable tetragonal phase. These findings emphasize that multilayer zirconias exhibit spatially dependent mechanical properties, which should be considered in biomaterial selection and restoration design, particularly when balancing aesthetic demands and fracture resistance.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In Vitro Experimental Study of Biofiligree^® Osteosynthesis in Calcaneus Fracture Fixation. 生物纤维®骨合成在跟骨骨折固定中的体外实验研究。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-14 DOI: 10.3390/bioengineering13040460

António Ramos, Olga Noronha, Orlando Simões, José Noronha, José Simões

{"title":"In Vitro Experimental Study of Biofiligree® Osteosynthesis in Calcaneus Fracture Fixation.","authors":"António Ramos, Olga Noronha, Orlando Simões, José Noronha, José Simões","doi":"10.3390/bioengineering13040460","DOIUrl":"10.3390/bioengineering13040460","url":null,"abstract":"Surgical fixation techniques for bone fracture healing are well established and effective; however, opportunities remain to improve both functional outcomes and the patient experience. The Biofiligree® concept integrates medicine, engineering, and design by reimagining conventional osteosynthesis plates as both therapeutic and aesthetic devices. Inspired by traditional Portuguese filigree, these plates allow patient participation through personalized geometries, patterns, or engravings and may later be transformed into wearable jewellery after removal, preserving them as symbolic artefacts of recovery. This study introduces and biomechanically evaluates a novel calcaneal fixation plate incorporating the biofiligree geometry concept. A biofiligree plate was designed for calcaneus fracture fixation and manufactured in stainless steel 306L. Experimental testing was conducted on synthetic composite calcaneus bone models to simulate anatomical conditions and compare the new design with a standard commercial plate. The biofiligree plate, 2 mm thick, was fixed using five screws and two percutaneous screws positioned at 45° to compress the fracture line. Results demonstrated comparable biomechanical performance between both systems, with similar strain distributions and fracture stabilization. The biofiligree plate showed stresses around 430 MPa and fracture displacement below 0.7 mm. Fixation stiffness values were 1445 N/mm for intact calcaneus, 1065 N/mm for the commercial plate, and 725 N/mm for the biofiligree plate, indicating adequate support for bone healing.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bibliometric Analysis of Artificial Intelligence in Pediatric Radiology and Medical Imaging: A Focus on Deep Learning Applications. 人工智能在儿科放射学和医学影像学中的文献计量分析：重点关注深度学习应用。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-14 DOI: 10.3390/bioengineering13040461

Ahmad Tijjani Garba, Aminu Bashir Suleiman, Wenze Du, Ahmed Ibrahim Mahmud, Harisu Abdullahi Shehu, Huseyin Kusetogullari, Md Haidar Sharif

{"title":"Bibliometric Analysis of Artificial Intelligence in Pediatric Radiology and Medical Imaging: A Focus on Deep Learning Applications.","authors":"Ahmad Tijjani Garba, Aminu Bashir Suleiman, Wenze Du, Ahmed Ibrahim Mahmud, Harisu Abdullahi Shehu, Huseyin Kusetogullari, Md Haidar Sharif","doi":"10.3390/bioengineering13040461","DOIUrl":"10.3390/bioengineering13040461","url":null,"abstract":"This study presents the first dedicated bibliometric analysis of artificial intelligence (AI) and deep learning applications in pediatric radiology and medical imaging, mapping the intellectual structure of a rapidly evolving field. A total of 2688 articles and conference proceedings published between 2005 and 2025 were retrieved from the Web of Science Core Collection and analyzed using Bibliometrix R and VOSviewer. The findings reveal exponential growth in publications, from 7 papers in 2005 to 559 in 2025, with journal articles dominating the corpus (85.9%). The most-cited contributions, led by Kermany et al. (2018) with 2886 citations, are predominantly technical feasibility studies rather than clinical outcome trials, indicating a field that has advanced methodologically but remains in early stages of clinical translation. Thematic mapping identifies convolutional neural networks, pneumonia, and transfer learning as Motor Themes representing methodological maturity in chest imaging, while neuroimaging and image segmentation clusters occupy Niche Themes, reflecting insular development with limited cross-field connectivity. Geographic analysis reveals concentrated co-authorship along US-China and US-Europe corridors, with African, Latin American, and Southeast Asian institutions largely absent from knowledge production networks. Eight of the ten most productive affiliations are North American, highlighting structural inequities that risk producing AI tools optimized for high-resource settings rather than the global pediatric population. This analysis provides an empirical foundation for reorienting the field toward clinical validation, geographic inclusion, and methodological integration across isolated research communities.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ureteral Orifice Detection in Ureteroscopic Images Based on Large-Kernel Convolutional Neural Networks and Attention-Based Feature Fusion. 基于大核卷积神经网络和基于注意力的特征融合的输尿管镜图像输尿管口检测。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-14 DOI: 10.3390/bioengineering13040459

Liang Li, Chen-Yi Jiang, Xing-Jie Wang, Yuan-Jun Wang, Jian Zhuo

{"title":"Ureteral Orifice Detection in Ureteroscopic Images Based on Large-Kernel Convolutional Neural Networks and Attention-Based Feature Fusion.","authors":"Liang Li, Chen-Yi Jiang, Xing-Jie Wang, Yuan-Jun Wang, Jian Zhuo","doi":"10.3390/bioengineering13040459","DOIUrl":"10.3390/bioengineering13040459","url":null,"abstract":"Objective: To enhance the information modeling capacity of large-kernel convolutional neural networks and to build a ureteral orifice detection framework for ureteroscopic imaging. Methods: A retrospective dataset of ureteroscopic images from 222 patients was collected. The patients were randomly divided into training and testing sets at a ratio of 7:3. Initially, video files were converted into image frames, and feature-relevant images were manually labeled by physicians. Subsequently, a ConvNeXt-based backbone augmented with squeeze-and-excitation (SE) modules was employed to extract diverse deep features. SCConv modules were incorporated across stages to strengthen the network's feature extraction performance. Lastly, enhanced spatial excitation attention mechanisms were cascaded to achieve superior feature fusion and detection accuracy. Comparative experiments were conducted against baseline models, including ConvNeXt, assessing accuracy, computational overhead, and inference latency. Results: On a test set of 491 ureteroscopic images, all models achieved mAP@50 values above 0.75, whereas the proposed network achieved 0.890, markedly exceeding baseline performance. The model operated at 20 ms per frame, achieving a frame rate of 50 FPS. Conclusions: We developed an improved deep learning framework based on large-kernel convolutional networks for real-time ureteral orifice detection in endoscopic scenarios. This system achieves a favorable balance between detection accuracy and real-time efficiency. The method demonstrates significant potential as a training and feedback tool for residents and junior urologists in clinical environments.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prospects and Limitations of Bioprinting in Studying Human Cells' Responses to Extreme Environments. 生物打印技术在人类细胞对极端环境反应研究中的前景与局限。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-14 DOI: 10.3390/bioengineering13040458

Taieba Tuba Rahman, Zhijian Pei, Hongmin Qin, Hamid R Parsaei

{"title":"Prospects and Limitations of Bioprinting in Studying Human Cells' Responses to Extreme Environments.","authors":"Taieba Tuba Rahman, Zhijian Pei, Hongmin Qin, Hamid R Parsaei","doi":"10.3390/bioengineering13040458","DOIUrl":"10.3390/bioengineering13040458","url":null,"abstract":"Understanding human's responses to extreme environments holds significant importance for space exploration, deep-sea research, and environmental adaptation. Traditionally, human subjects were used to study humans' responses to extreme environments. The main limitations of this approach include the inability to independently investigate specific cellular mechanisms, ethical and safety constraints, limited experimental controllability, and inter-individual variability that complicates mechanistic interpretation. Another approach is to study humans' responses at the cellular level using 2D culture. This approach often exhibits limited reproducibility due to its inability to recapitulate physiologically relevant microenvironments. Bioprinting can enable studies on human's responses at the cellular level and within 3D environments. One way is to study human cells' responses to localized and transient extreme environments created during printing. Another way is to expose 3D printed samples (embedded with human cells) to extreme environments. However, the literature does not contain comprehensive review papers to discuss the prospects and limitations of bioprinting for investigating human cells' responses to extreme environments. This review paper aims to fill this gap in the literature. It begins with a brief description of the effects of extreme environments on human health and summarizes reported studies on cells' responses to extreme environments. Afterward, it discusses the prospects and limitations of the two ways of using bioprinting to investigate cells' responses to extreme environments. Finally, it concludes with identifying knowledge gaps and proposing research directions in the application of bioprinting to study human cells' responses to extreme environments.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings. 在缺乏服务的临床环境中，深度学习辅助从皮肤镜图像中早期检测皮肤癌。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-13 DOI: 10.3390/bioengineering13040456

Anchal Kumari, Punam Rattan, Anand Kumar Shukla, Sita Rani, Aman Kataria, Hong Min, Taeho Kim

{"title":"Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings.","authors":"Anchal Kumari, Punam Rattan, Anand Kumar Shukla, Sita Rani, Aman Kataria, Hong Min, Taeho Kim","doi":"10.3390/bioengineering13040456","DOIUrl":"10.3390/bioengineering13040456","url":null,"abstract":"Skin cancer is caused by aberrant cells that proliferate uncontrollably after unrepaired DNA damage results in mutations in the epidermis. The majority of skin cancer is caused by high UV exposure from the sun, tanning beds, or sunlamps. Due to sociocultural hurdles, limited access to specialized dermatological care, and low public knowledge, many nations, including India, have higher mortality rates and late-stage presentations. The unequal distribution of specialized dermatological treatments, particularly in rural and underdeveloped areas, makes detection and treatment more difficult. For skin cancer, one of the most prevalent malignancies with a high death rate, early detection is crucial. This study gathered 1200 dermoscopic images from two clinics in Himachal Pradesh in order to solve these problems. In order to automatically classify dermoscopic clinical images into melanoma and non-melanoma skin cancer categories, this study compares VGG16 with ResNet-50. Preprocessing, lesion segmentation, and classification are all part of the suggested approach. A collection of 1200 dermoscopic images with clinical annotations was used to improve the models. ResNet-50 outperformed VGG16 in tests, with 93% accuracy and 96% AUC-ROC as opposed to 89% and 94%, respectively. These results emphasize how crucial model selection and preprocessing are to diagnostic performance. Ensemble methods, multi-class classification, explainability integration, and clinical validation will be investigated in order to facilitate the implementation of AI-assisted dermatological diagnostic tools.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Controller Logics for Automating Vasopressor Administration Using a Hardware-in-Loop Test Platform. 使用硬件在环测试平台自动给药血管加压器的控制器逻辑比较。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-13 DOI: 10.3390/bioengineering13040454

Michael D Lopez, Jonathan Marrero Bermudez, David Berard, Lawrence Holland, Austin J Ruiz, Jose M Gonzalez, Sofia I Hernandez Torres, Eric J Snider

{"title":"Comparison of Controller Logics for Automating Vasopressor Administration Using a Hardware-in-Loop Test Platform.","authors":"Michael D Lopez, Jonathan Marrero Bermudez, David Berard, Lawrence Holland, Austin J Ruiz, Jose M Gonzalez, Sofia I Hernandez Torres, Eric J Snider","doi":"10.3390/bioengineering13040454","DOIUrl":"10.3390/bioengineering13040454","url":null,"abstract":"Hemorrhagic shock remains one of the leading causes of preventable death for both civilian and military trauma. Fluid resuscitation is the primary treatment but requires constant monitoring, particularly for volume non-responsive patients susceptible to fluid overload, pulmonary edema, and other life-threatening conditions. To overcome fluid non-responsiveness, vasoactive drugs or vasopressors can be necessary adjuvants to fluid therapy but require tedious titrations that can be difficult to manage during mass-casualty situations. This study developed and evaluated automated closed-loop vasopressor controllers for hemorrhage scenarios. Ten physiological closed-loop controller (PCLC) configurations with different underlying functionalities were tuned to be either more aggressive or conservative to reach the target mean arterial pressure. A hardware-in-loop test platform with fluid-pressure responsiveness, derived from animal data, tested each controller across three different starting pressure scenarios. The platform successfully differentiated controller designs based on performance metrics. While some configurations overshot the target and others could not reach the target pressure, strong-performing PCLCs consistently reached and maintained the target quickly. Three candidate PCLCs outperformed the rest and will be evaluated across wider scenarios to develop a robust controller design. This work accelerates PCLC-driven vasopressor administration development, providing a necessary fluid resuscitation adjuvant for precise hemodynamic management in hemorrhagic trauma.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-Guided Deep Learning for Interpretable Biomedical Image Reconstruction and Pattern Recognition in Diagnostic Frameworks. 物理引导深度学习用于可解释的生物医学图像重建和诊断框架中的模式识别。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-13 DOI: 10.3390/bioengineering13040457

Akeel Qadir, Saad Arif, Prajoona Valsalan, Osama Khan

{"title":"Physics-Guided Deep Learning for Interpretable Biomedical Image Reconstruction and Pattern Recognition in Diagnostic Frameworks.","authors":"Akeel Qadir, Saad Arif, Prajoona Valsalan, Osama Khan","doi":"10.3390/bioengineering13040457","DOIUrl":"10.3390/bioengineering13040457","url":null,"abstract":"This study introduces a physics-guided deep learning architecture designed for the simulation, reconstruction, and pattern recognition of biomedical images. By explicitly integrating physical priors into the learning model, the framework addresses the black-box nature of traditional artificial intelligence (AI). It provides an explainable AI pathway that enhances diagnostic accuracy, robustness, and clinical interpretation. The proposed framework was evaluated through systematic simulation studies. It involved complex geometric configurations, multimodal physical fields, and noise-corrupted synthetic three-dimensional brain volumes. Quantitative analysis demonstrates consistent improvements in reconstruction fidelity, with the peak signal-to-noise ratio (PSNR) reaching 47 dB and the structural similarity index exceeding 0.90 across all scenarios. Notably, at moderate noise levels (0.05), the framework maintains a PSNR greater than 32 dB, ensuring structural integrity essential for computer-aided diagnosis. Volumetric brain experiments further reveal a 38-44% reduction in activation localization errors, highlighting the framework's utility in functional imaging and disease prognosis. By grounding deep learning in physical constraints, this study provides a transparent and robust solution for automated disease classification and advanced biomedical imaging tasks within clinical decision support systems.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Cortical Contributions to Turning Performance Through Muscle Synergies in Parkinson's Disease: A Mediation Study. 皮层通过肌肉协同作用对帕金森病患者翻身表现的影响：一项中介研究。

IF 3.7 3区医学

Bioengineering Pub Date : 2026-04-13 DOI: 10.3390/bioengineering13040453

Mirabel Ewura Esi Acquah, Zengguang Wang, Wei Chen, Dongyun Gu

{"title":"The Cortical Contributions to Turning Performance Through Muscle Synergies in Parkinson's Disease: A Mediation Study.","authors":"Mirabel Ewura Esi Acquah, Zengguang Wang, Wei Chen, Dongyun Gu","doi":"10.3390/bioengineering13040453","DOIUrl":"10.3390/bioengineering13040453","url":null,"abstract":"Turning impairment is a major contributor to falls in Parkinson's disease (PD), yet the mechanisms linking cortical dysfunction to altered motor behavior remain unclear. In particular, it is unknown whether disrupted cortical communication impairs turning by altering muscle coordination. This study investigates a novel mechanistic pathway: whether muscle synergy complexity mediates the relationship between cortical network connectivity and turning performance in PD. Specifically, electroencephalography (EEG) and electromyography (EMG) were recorded from 12 individuals with PD and 12 age-matched healthy controls during a 180° turning task. Directed cortical connectivity, muscle synergy complexity, and spatiotemporal turning performance were quantified. Mediation analysis was used to determine whether cortical influences on behavior operate indirectly through neuromuscular coordination. Compared to controls, individuals with PD performed slower turns with shorter stride lengths and reduced synergy complexity (p < 0.05), alongside altered frontal cortical connectivity (p < 0.05). Across participants, higher synergy complexity was associated with faster, longer strides (p < 0.04). Cortical connectivity strength strongly predicted synergy complexity (R2 = 0.66, p < 0.001) and exerted a significant indirect effect on turning performance (β = 0.312; 95% CI [0.072, 0.605]; p = 0.008). In PD, reliance on this indirect pathway increased with disease severity and poorer turning ability (r > 0.57, p < 0.03). This work establishes how muscle synergy complexity significantly mediates the relationship between cortical connectivity and turning performance in PD. Our findings provide evidence of a cortical-neuromuscular-behavioral pathway underlying turning deficits, highlighting coordination as a key target for neurorehabilitation.","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13113805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0