Bioengineering最新文献

{"title":"Evaluation of the Validity and Reliability of NeuroSkin's Wearable Sensor Gait Analysis Device in Healthy Individuals.","authors":"Maël Descollonges, Baptiste Moreau, Nicolas Feppon, Oussama Abdoun, Perrine Séguin, Lana Popovic-Maneski, Julie Di Marco, Amine Metani","doi":"10.3390/bioengineering12090960","DOIUrl":"10.3390/bioengineering12090960","url":null,"abstract":"<p><p>Gait analysis plays a crucial role in assessing and monitoring the progress of individuals undergoing rehabilitation. This preliminary validation study aims to compare the performance of a new wearable system, NeuroSkin^®, equipped with embedded sensors (inertial measurement unit and pressure sensors), with the non-wearable gold standard, GAITRite^®, in assessing spatio-temporal parameters during gait. Data was collected from nine healthy participants wearing the NeuroSkin while walking on the GAITRite walkway. Temporal parameters were calculated using the pressure sensors of the NeuroSkin^® to detect heel strike (HS) and toe off (TO) on both sides. Distances were calculated using vertical hip acceleration with an inverted pendulum method. We found that the level of agreement between NeuroSkin^® and GAITRite^® measures was excellent for speed, cadence, as well as length and duration of stride and step (lower bound of intraclass correlation coefficients (ICCs) > 0.95), and moderate to excellent for stance and swing durations (ICC > 0.5). These levels of agreement are comparable to the known test-retest reliability of GAITRite^® measures. These results demonstrate the potential of NeuroSkin^® as an embedded gait assessment system for healthy subjects. As this study was conducted exclusively in healthy adults, the results are not directly generalizable to clinical populations. Thus, future studies are needed to investigate its use in patients.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172881","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}

{"title":"Eye Tracking-Enhanced Deep Learning for Medical Image Analysis: A Systematic Review on Data Efficiency, Interpretability, and Multimodal Integration.","authors":"Jiangxia Duan, Meiwei Zhang, Minghui Song, Xiaopan Xu, Hongbing Lu","doi":"10.3390/bioengineering12090954","DOIUrl":"10.3390/bioengineering12090954","url":null,"abstract":"<p><p>Deep learning (DL) has revolutionized medical image analysis (MIA), enabling early anomaly detection, precise lesion segmentation, and automated disease classification. However, its clinical integration faces two major challenges: reliance on limited, narrowly annotated datasets that inadequately capture real-world patient diversity, and the inherent \"black-box\" nature of DL decision-making, which complicates physician scrutiny and accountability. Eye tracking (ET) technology offers a transformative solution by capturing radiologists' gaze patterns to generate supervisory signals. These signals enhance DL models through two key mechanisms: providing weak supervision to improve feature recognition and diagnostic accuracy, particularly when labeled data are scarce, and enabling direct comparison between machine and human attention to bridge interpretability gaps and build clinician trust. This approach also extends effectively to multimodal learning models (MLMs) and vision-language models (VLMs), supporting the alignment of machine reasoning with clinical expertise by grounding visual observations in diagnostic context, refining attention mechanisms, and validating complex decision pathways. Conducted in accordance with the PRISMA statement and registered in PROSPERO (ID: CRD42024569630), this review synthesizes state-of-the-art strategies for ET-DL integration. We further propose a unified framework in which ET innovatively serves as a data efficiency optimizer, a model interpretability validator, and a multimodal alignment supervisor. This framework paves the way for clinician-centered AI systems that prioritize verifiable reasoning, seamless workflow integration, and intelligible performance, thereby addressing key implementation barriers and outlining a path for future clinical deployment.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172581","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}

{"title":"Porous Structures, Surface Modifications, and Smart Technologies for Total Ankle Arthroplasty: A Narrative Review.","authors":"Joshua M Tennyson, Michael O Sohn, Arun K Movva, Kishen Mitra, Conor N O'Neill, Albert T Anastasio, Samuel B Adams","doi":"10.3390/bioengineering12090955","DOIUrl":"10.3390/bioengineering12090955","url":null,"abstract":"<p><p>Surface engineering and architectural design represent key frontiers in total ankle arthroplasty (TAA) implant development. This narrative review examines biointegration strategies, focusing on porous structures, surface modification techniques, and emerging smart technologies. Optimal porous architectures with 300-600 µm pore sizes facilitate bone ingrowth and osseointegration, while functionally graded structures address regional biomechanical demands. Surface modification encompasses bioactive treatments (such as calcium phosphate coatings), topographical modifications (including micro/nanotexturing), antimicrobial approaches (utilizing metallic ions or antibiotic incorporation), and wear-resistant technologies (such as diamond-like carbon coatings). Multifunctional approaches combine strategies to simultaneously address infection prevention, enhance osseointegration, and improve wear resistance. Emerging technologies include biodegradable scaffolds, biomimetic surface nanotechnology, and intelligent sensor-based monitoring systems. While many innovations remain in the research stage, they demonstrate the potential to establish TAA as a comprehensive alternative to arthrodesis. Successful implant design requires integrated surface engineering tailored to the ankle joint's demanding biomechanical and biological environment.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172853","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}

{"title":"Defining a Simplified Process in Yeast for Production of Enveloped VLP Dengue Vaccine.","authors":"Salomé de Sá Magalhães, Stephen A Morris, Shinta Kusumawardani, Acep Riza Wijayadikusumah, Neni Nurainy, Eli Keshavarz-Moore","doi":"10.3390/bioengineering12090956","DOIUrl":"10.3390/bioengineering12090956","url":null,"abstract":"<p><p>Dengue is a rapidly spreading mosquito-borne viral infection, with increasing reports of outbreaks globally. According to the World Health Organization (WHO), by 30 April 2024, over 7.6 million dengue cases were reported, including 3.4 million confirmed cases, more than 16,000 severe cases, and over 3000 deaths. As dengue remains endemic in many regions, there is a critical need for the development of new vaccines and manufacturing processes that are efficient, cost-effective, and capable of meeting growing demand. In this study, we explore an alternative process development pathway for the future manufacturing of a dengue vaccine, utilizing <i>Komagataella phaffii</i> (<i>Pichia pastoris</i>) as the host organism, one of the most promising candidates for the expression of heterologous proteins in vaccine development. It combines the speed and ease of highly efficient prokaryotic platforms with some key capabilities of mammalian systems, making it ideal for scalable and cost-effective production. The key outcomes of our research include (i) demonstrating the versatility of the <i>Komagataella phaffii</i> platform in the production of dengue viral-like particles (VLPs); (ii) optimizing the culture process using Design of Experiments (DoE) approaches in small-scale bioreactors; (iii) developing a novel purification platform for enveloped VLPs (eVLPs), and (iv) establishing alternative biophysical characterization methods for the dengue vaccine prototype. These findings provide a promising foundation for efficient and scalable production of dengue vaccines, addressing both technical and operational challenges in vaccine manufacturing.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172774","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}

{"title":"Emulating Hyperspectral and Narrow-Band Imaging for Deep-Learning-Driven Gastrointestinal Disorder Detection in Wireless Capsule Endoscopy.","authors":"Chu-Kuang Chou, Kun-Hua Lee, Riya Karmakar, Arvind Mukundan, Pratham Chandraskhar Gade, Devansh Gupta, Chang-Chao Su, Tsung-Hsien Chen, Chou-Yuan Ko, Hsiang-Chen Wang","doi":"10.3390/bioengineering12090953","DOIUrl":"10.3390/bioengineering12090953","url":null,"abstract":"<p><p>Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172959","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}

{"title":"Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization.","authors":"Roberta Fusco, Vincenza Granata, Teresa Petrosino, Paolo Vallone, Maria Assunta Daniela Iasevoli, Mauro Mattace Raso, Sergio Venanzio Setola, Davide Pupo, Gerardo Ferrara, Annarita Fanizzi, Raffaella Massafra, Miria Lafranceschina, Daniele La Forgia, Laura Greco, Francesca Romana Ferranti, Valeria De Soccio, Antonello Vidiri, Francesca Botta, Valeria Dominelli, Enrico Cassano, Charlotte Marguerite Lucille Trombadori, Paolo Belli, Giovanna Trecate, Chiara Tenconi, Maria Carmen De Santis, Luca Boldrini, Antonella Petrillo","doi":"10.3390/bioengineering12090952","DOIUrl":"10.3390/bioengineering12090952","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the prediction of the tumor molecular profile.</p><p><strong>Methods: </strong>A total of 153 patients with malignant and benign lesions were analyzed and underwent MRI examinations. Considering the histological findings as the ground truth, three different types of findings were used in the analysis: (1) benign versus malignant lesions; (2) G1 + G2 vs. G3 classification; (3) the presence of human epidermal growth factor receptor 2 (HER2+ vs. HER2-). Radiomic features (n = 851) were extracted from manually segmented regions of interest using the PyRadiomics platform, following IBSI-compliant protocols. Highly correlated features were excluded, and the remaining features were standardized using z-score normalization. A feature selection process based on Elastic Net regularization (α = 0.5) was used to reduce dimensionality. Synthetic balancing of the training data was applied using the ROSE method to address class imbalance. Model performance was evaluated using repeated 10-fold cross-validation and AUC-based metrics.</p><p><strong>Results: </strong>Among the 153 patients enrolled in the studies, 113 were malignant lesions. Among the 113 malignant lesions, 32 had high grading (G3) and 66 had the HER2+ receptor. Radiomic features derived from both CEM and DCE-MRI showed strong discriminative performance for malignancy detection, with several features achieving AUCs above 0.80. Gradient Boosting Machine (GBM) achieved the highest accuracy (0.911) and AUC (0.907) in differentiating benign from malignant lesions. For tumor grading, the neural network model attained the best accuracy (0.848), while LASSO yielded the highest sensitivity (0.667) for detecting high-grade tumors. In predicting HER2+ status, the neural network also performed best (AUC = 0.669), with a sensitivity of 0.842.</p><p><strong>Conclusions: </strong>Radiomics-based machine learning models applied to multiparametric CEM and DCE-MRI images offer promising, non-invasive tools for breast cancer characterization. The models effectively distinguished benign from malignant lesions and showed potential in predicting histological grade and HER2 status. These results demonstrate that radiomic features extracted from CEM and DCE-MRI, when analyzed through machine and deep learning models, can support accurate breast cancer characterization. Such models may assist clinicians in early diagnosis, histological grading, and biomarker assessment, potentially enhancing personalized treatment planning and non-invasive decision-making in routine practice.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172732","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}

{"title":"Time Series Classification of Autism Spectrum Disorder Using the Light-Adapted Electroretinogram.","authors":"Sergey Chistiakov, Anton Dolganov, Paul A Constable, Aleksei Zhdanov, Mikhail Kulyabin, Dorothy A Thompson, Irene O Lee, Faisal Albasu, Vasilii Borisov, Mikhail Ronkin","doi":"10.3390/bioengineering12090951","DOIUrl":"10.3390/bioengineering12090951","url":null,"abstract":"<p><p>The clinical electroretinogram (ERG) is a non-invasive diagnostic test used to assess the functional state of the retina by recording changes in the bioelectric potential following brief flashes of light. The recorded ERG waveform offers ways for diagnosing both retinal dystrophies and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and Parkinson's disease. In this study, different time-series-based machine learning methods were used to classify ERG signals from ASD and typically developing individuals with the aim of interpreting the decisions made by the models to understand the classification process made by the models. Among the time-series classification (TSC) algorithms, the Random Convolutional Kernel Transform (ROCKET) algorithm showed the most accurate results with the fewest number of predictive errors. For the interpretation analysis of the model predictions, the SHapley Additive exPlanations (SHAP) algorithm was applied to each of the models' predictions, with the ROCKET and KNeighborsTimeSeriesClassifier (TS-KNN) algorithms showing more suitability for ASD classification as they provided better-defined explanations by discarding the uninformative non-physiological part of the ERG waveform baseline signal and focused on the time regions incorporating the clinically significant a- and b-waves of the ERG. With the potential broadening scope of practice for visual electrophysiology within neurological disorders, TSC may support the identification of important regions in the ERG time series to support the classification of neurological disorders and potential retinal diseases.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172953","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}

{"title":"Structure-Preserving Histopathological Stain Normalization via Attention-Guided Residual Learning.","authors":"Nuwan Madusanka, Prathiksha Padmanabha, Kasunika Guruge, Byeong-Il Lee","doi":"10.3390/bioengineering12090950","DOIUrl":"10.3390/bioengineering12090950","url":null,"abstract":"<p><p>Staining variability in histopathological images compromises automated diagnostic systems by affecting the reliability of computational pathology algorithms. Existing normalization methods prioritize color consistency but often sacrifice critical morphological details essential for accurate diagnosis. This work proposes a novel deep learning framework, integrating enhanced residual learning with multi-scale attention mechanisms for structure-preserving stain normalization. The approach decomposes the transformation process into base reconstruction and residual refinement components, incorporating attention-guided skip connections and progressive curriculum learning. The method was evaluated on the MITOS-ATYPIA-14 dataset containing 1420 paired H&E-stained breast cancer images from two scanners. The framework achieved exceptional performance with a structural similarity index (SSIM) of 0.9663 ± 0.0076, representing 4.6% improvement over the best baseline (StainGAN). Peak signal-to-noise ratio (PSNR) reached 24.50 ± 1.57 dB, surpassing all comparison methods. An edge preservation loss of 0.0465 ± 0.0088 demonstrated a 35.6% error reduction compared to the next best method. Color transfer fidelity reached 0.8680 ± 0.0542 while maintaining superior perceptual quality (FID: 32.12, IS: 2.72 ± 0.18). The attention-guided residual learning framework successfully maintains structural integrity during stain normalization, with superior performance across diverse tissue types, making it suitable for clinical deployment in multi-institutional digital pathology workflows.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172628","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}

{"title":"The Effects of Rehabilitation Programs Incorporating Breathing Interventions on Chronic Neck Pain Among Patients with Forward Head Posture: A Systematic Review and Meta-Analysis.","authors":"Seri Park, Kihyun Kim, Minbong Kang","doi":"10.3390/bioengineering12090947","DOIUrl":"10.3390/bioengineering12090947","url":null,"abstract":"<p><p>The effectiveness of breathing interventions on postural alignment, pain reduction, and functional improvement in patients with forward head posture (FHP) and chronic neck pain remains uncertain. Previously conducted randomized controlled trials (RCTs) that involved breathing interventions were identified through searches of the PubMed, Cochrane Library, Web of Science, and Scopus databases. Studies were included if they applied diaphragmatic breathing, breathing muscle training, or feedback breathing exercises for at least 2 weeks to chronic neck pain (duration ≥ 3 months) and/or forward head posture. The craniovertebral angle (CVA), the visual analog scale (VAS), and the neck disability index (NDI) were the primary outcome measures. The results showed that breathing interventions had a moderate effect size in terms of improving the CVA. Limited effects were observed for pain reduction, and improvements in neck disability approached statistical significance. However, despite these positive findings, the overall evidence was rated as 'very low certainty' in the GRADE assessment, primarily due to high heterogeneity among studies, limited sample sizes, and the potential for unit-of-analysis errors in diagnosis-based subgroup analyses. Consequently, their overall effectiveness in chronic neck pain was limited. Future research is needed to explore a multidisciplinary approach to neck pain using standardized protocols and larger samples.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172779","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}

{"title":"Applications of Adipose Tissue Micrografts (ATM) and Dermis Micrografts (DMG) in Wound Healing: A Scoping Review of Clinical Studies.","authors":"Konstantinos Zapsalis, Orestis Ioannidis, Elissavet Anestiadou, Maria Pantelidou, Konstantinos Siozos, Christos Xylas, Georgios Gemousakakis, Angeliki Cheva, Chryssa Bekiari, Antonia Loukousia, Savvas Symeonidis, Stefanos Bitsianis, Manousos-Georgios Pramateftakis, Efstathios Kotidis, Ioannis Mantzoros, Stamatios Angelopoulos","doi":"10.3390/bioengineering12090948","DOIUrl":"10.3390/bioengineering12090948","url":null,"abstract":"<p><p>Adipose tissue micrografts (ATM) and dermis micrografts (DMG) have emerged as promising autologous therapies in regenerative wound care, leveraging mechanically disaggregated cell-matrix constructs to modulate the wound microenvironment and promote tissue repair. This scoping review systematically analyzed clinical studies investigating ATMs and DMGs in acute and chronic wounds. Eight studies, comprising randomized controlled trials, observational studies, and case series, were identified, involving diverse wound types such as burns, ulcers, surgical dehiscence, and posttraumatic defects. All interventions utilized mechanical disaggregation (Rigenera^® system) to produce micrografts, which were applied via perilesional injection, scaffold-assisted delivery, or topical administration. Outcomes consistently demonstrated accelerated re-epithelialization, enhanced angiogenesis, improved scar remodeling, and low complication rates. In select studies, micrografts were combined with platelet-rich fibrin or stromal vascular fraction, suggesting potential synergistic effects. While one randomized trial showed superior healing outcomes with DMGs over collagen scaffolds, others yielded mixed results, likely reflecting heterogeneity in methodology and outcome measures. Overall, the available clinical evidence supports the safety, feasibility, and biological activity of micrograft-based therapies. However, larger, standardized, and mechanistically driven studies are required to validate their efficacy and define optimal protocols across wound etiologies.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172837","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}