Bioengineering最新文献

{"title":"Artificial Intelligence in Bioengineering: Innovations, Challenges, and Future Directions: A Narrative Review.","authors":"Daniele Giansanti","doi":"10.3390/bioengineering13101204","DOIUrl":"https://doi.org/10.3390/bioengineering13101204","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly transforming bioengineering, with applications ranging from diagnosis and biomedical imaging to wearable technologies, rehabilitation, biomaterials, biofabrication, and digital twins. This narrative review aims to examine the major innovations, challenges, and future directions of AI-enabled bioengineering, with particular attention to its clinical and translational development. The literature was searched in Scopus and PubMed, with emphasis on systematic reviews and meta-analyses and consideration of other relevant secondary literature across the main bioengineering domains. A total of 57 secondary publications were included in the principal evidence base. The review process was additionally informed by the <i>ANDJ Narrative Review Checklist</i> and an established qualification and scoring methodology applied to the selected secondary literature. The reviewed evidence shows a broad expansion of AI applications and increasing integration of intelligent technologies into bioengineering systems. However, important challenges remain, particularly regarding data quality and representativeness, validation and generalizability, reproducibility, safety, human oversight, ethical responsibility, equity, and regulatory requirements. Future developments are expected to increasingly involve multimodal, adaptive, connected, and human-centered systems integrating AI with sensing technologies, robotics, digital twins, and longitudinal data. Overall, AI-enabled bioengineering is progressing from promising technological applications toward increasingly integrated and translational systems. Its successful evolution will depend on combining technological innovation with robust clinical evidence, responsible development, and appropriate ethical and regulatory frameworks.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Gait-Conditioned Human-in-the-Loop Optimization Framework Based on Gait-Assistance-Response Relationship Model for Exoskeleton Assistance.","authors":"Jiaqi Han, Liping Wang, Jianquan Ding, Yuzhen Zhang, Xuelun Liu, Qichong Bian, Zhibo Jing, Juanjuan Zhang","doi":"10.3390/bioengineering13101210","DOIUrl":"https://doi.org/10.3390/bioengineering13101210","url":null,"abstract":"<p><p>The effectiveness of wearable exoskeletons critically depends on assistance patterns, yet their determination is challenging because physiological responses vary across individuals and gait conditions. Human-in-the-loop (HIL) optimization can personalize assistance, but its high experimental cost limits its application across diverse tasks. In this study, we developed a gait-conditioned HIL optimization framework based on a hierarchical gait-assistance-response model. The model characterizes assistance-response relationships using parameterized surfaces and predicts gait-dependent surface parameters through polynomial regression. The experiments focused on knee exoskeletons with extension assistance, where the physiological response was quantified as muscle activation based on normalized sEMG RMS from three major knee extensors on the assisted side. The model was built on data collected under predefined gait conditions and used to guide HIL optimization in validation experiments involving five healthy adults under unseen gait conditions. The validation results showed that, with the same CMA-ES optimizer but different configurations, two-generation model-guided HIL optimization showed a mean additional 6.4 percentage-point reduction in muscle activation compared with two-generation empirical HIL optimization, approaching four-generation empirical HIL optimization with only a mean 1.3 percentage-point difference. The preliminary results suggest the potential of the proposed strategy to reduce the number of optimization generations while maintaining comparable assistance performance by providing guidance for optimization configurations.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Label-Free Biochemical Characterization and Subtype Classification of Human Breast Pathologies Using Fingerprint Raman Spectroscopy and Machine Learning.","authors":"Nguyen Van Hung, Mai Huu Thuan, Ngo Duc Quan","doi":"10.3390/bioengineering13101203","DOIUrl":"https://doi.org/10.3390/bioengineering13101203","url":null,"abstract":"<p><p>Breast cancer remains a major cause of cancer-related mortality, highlighting the need for improved molecular characterization of breast pathologies. Raman spectroscopy (RS) provides a label-free, non-destructive approach for probing tissue biochemical composition. This study investigated Raman spectral alterations across five human breast histopathological categories and explored their potential for machine learning-assisted classification. Formalin-fixed, paraffin-embedded (FFPE) tissue specimens were obtained from five patients, with one patient representing each category: normal breast tissue, intraductal papilloma, high-grade ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC). A total of 100 Raman spectra (20 per patient) were acquired using 785 nm excitation over the fingerprint region (500-2000 cm^-1). Spectral preprocessing involved Savitzky-Golay smoothing, asymmetrically reweighted penalized least-squares (arPLS) baseline correction, and vector normalization. The investigated spectra exhibited reduced relative lipid-associated contributions near 1063, 1300, and 1445 cm^-1, accompanied by increased relative protein- and nucleic-acid-associated features near 1003, 1420, and 1600-1650 cm^-1. The phenylalanine-to-lipid intensity ratio (I₁₀₀₃/I₁₄₄₅) increased from ≤0.2 in normal tissue to ≥1.5 in invasive lesions, suggesting a potential spectroscopic descriptor of biochemical remodeling. Exploratory subtype-associated differences were also observed, including a prominent aromatic C=C-associated feature near 1618 cm^-1 in IDC and an enhanced carbohydrate-associated contribution near 890 cm^-1 in ILC. A support vector machine (SVM) classifier was evaluated using patient-independent leave-one-patient-out (LOPO) cross-validation. Across the five held-out patients, the model correctly classified 89 of 100 spectra, achieving an overall spectrum-level accuracy of 89.0%, sensitivity of 90.0%, specificity of 87.5%, and F1-score of 90.8%. Although these findings demonstrate encouraging discrimination within the investigated cohort, the limited number of biologically independent patients and the representation of each histopathological category by a single patient restrict the generalizability of the results. This study provides preliminary proof-of-concept evidence supporting the integration of Raman-derived biochemical features with machine learning for breast pathology characterization, warranting further validation in larger, multi-patient and multicenter cohorts.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping the Global Landscape and Thematic Evolution of fNIRS-Gait Research: A Systematic Bibliometric and Network Visualization Analysis.","authors":"Yunis Akkaş, Senem Güner, Naime Uluğ","doi":"10.3390/bioengineering13101198","DOIUrl":"https://doi.org/10.3390/bioengineering13101198","url":null,"abstract":"<p><strong>Background: </strong>Functional near-infrared spectroscopy (fNIRS) has emerged as an essential optical neuroimaging modality in human locomotion, yet a comprehensive computational mapping of its structural and thematic evolution remains absent. This study provides a systematic, data-driven scientometric evaluation of the global fNIRS-gait research landscape across a quarter-century (2002-April 2026).</p><p><strong>Methods: </strong>A refined corpus of 348 peer-reviewed documents from the Web of Science Core Collection was computationally analyzed using VOSviewer and Bibliometrix.</p><p><strong>Results: </strong>The literature exhibits a 12.53% compound annual growth rate, shifting from stationary motor imagery toward ecologically valid overground walking. The United States leads global outputs, supported by European collaborative corridors and Chinese national infrastructures. Healthy aging (27.30%), Parkinson's disease (10.92%), and stroke (9.20%) represent the primary clinical subdomains, centering on prefrontal cortex hyper-activation during dual-task walking (20.11%). Frontiers have migrated toward multimodal Mobile Brain/Body Imaging (MoBI) integrating virtual reality, exoskeletons, and machine learning. However, technical integration is hindered by severe signal processing heterogeneity and unstandardized short-separation channel regressions.</p><p><strong>Conclusions: </strong>Establishing standardized preprocessing pipelines and short-separation regressions may prove pivotal for transitioning mobile neuroimaging into objective, closed-loop clinical neurorehabilitation.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Group Postural Rehabilitation in Patients with Parkinson's Disease and Camptocormia: Preliminary Data from an Observational Pilot Study.","authors":"Rossella Calciano, Paola Emilia Ferrara, Alberto Cutaia, Letizia Tarallo, Virginia Capolongo, Rosa Cioffi, Diego Ricciardi, Ludovica Tassi, Maria Rita Lo Monaco, Daniele Coraci, Gianpaolo Ronconi","doi":"10.3390/bioengineering13101197","DOIUrl":"https://doi.org/10.3390/bioengineering13101197","url":null,"abstract":"<p><p><b>Background and Objectives:</b> Parkinson's disease (PD) is frequently associated with postural instability, including camptocormia, an involuntary anterior flexion of the thoracolumbar spine. This retrospective observational pilot study evaluated the effects of a postural group rehabilitation protocol on the stabilometric parameters, depressive symptoms, and quality of life in PD patients with camptocormia, and whether gains were maintained during a subsequent two-month period after completion of supervised group rehabilitation, during which patients were provided with a general home-based exercise program. <b>Materials and Methods:</b> Forty patients with idiopathic PD (Hoehn and Yahr II-III) were assessed at baseline (T0), after two months of twice-weekly rehabilitation (T1). At T2, only stabilometric follow-up was performed after two months without group exercise for 38 patients. Outcomes included SPPB, Tinetti, Berg Balance Scale, Hamilton Depression Rating Scale, PDQ-39, occiput-to-wall distance and stabilometric analysis. <b>Results:</b> At T1, Hamilton scores decreased from 11.44 ± 4.59 to 3.33 ± 3.04 and PDQ-39 from 82.22 ± 22.36 to 69.72 ± 16.54 (both <i>p</i> < 0.001). Occiput-to-wall distance improved significantly in three of six positions. The EO/EC Perimeter Ratio decreased significantly from T0 to T1 (<i>p</i> = 0.016). Between T1 and T2, EO Perimeter continued improving (<i>p</i> = 0.010), whereas the Perimeter Ratio worsened (<i>p</i> = 0.046), and EC Ellipse area, though non-significant, increased rather than decreased. <b>Conclusions:</b> A two-month protocol was followed by apparent improvements in depression, quality of life, postural alignment, and selected stabilometric parameters. After discontinuation, some parameters appeared to improve while others seemed to worsen, suggesting these effects may not be sustained with uniformity. These preliminary findings should be confirmed with an expanded sample size in larger studies.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-Dimensional MRI-Based Statistical Shape Analysis of Knee Bone Morphology in Individuals with Generalized Joint Hypermobility.","authors":"Ye Luo, Luqi Guo, Zhitao Zhu, Feng Li, Xinwei Huang, Shaobai Wang, Yuanbiao Luo","doi":"10.3390/bioengineering13101202","DOIUrl":"https://doi.org/10.3390/bioengineering13101202","url":null,"abstract":"<p><strong>Background: </strong>Generalized joint hypermobility (GJH) is associated with ligamentous and capsular laxity and an increased risk of knee injury. Whether GJH is associated with three-dimensional knee bone morphology remains unclear. This study compared femoral, tibial, and patellar morphology between young adults with GJH and healthy controls using magnetic resonance imaging (MRI)-based statistical shape modeling (SSM).</p><p><strong>Methods: </strong>This cross-sectional study included 80 participants aged 18-25 years, comprising 40 individuals with GJH (Beighton score ≥ 6) and 40 healthy controls (Beighton score ≤ 2). One right knee per participant was analyzed. High-resolution 3.0-T MRI was used to segment and model the femur, tibia, and patella separately in ShapeWorks. The primary SSMs were scale-normalized using Procrustes alignment to remove global isotropic scale differences. Principal components (PCs) accounting for at least 95% of cumulative shape variance were retained. Overall group differences were assessed using permutational multivariate analysis of variance (PERMANOVA), with multivariate dispersion assessed using PERMDISP. Individual PCs were compared with Benjamini-Hochberg false discovery rate (FDR) correction.</p><p><strong>Results: </strong>Significant overall scale-normalized shape differences were observed between the GJH and healthy control groups for the femur (pseudo-F = 8.14, R² = 0.0945, <i>p</i> = 0.0009), tibia (pseudo-F = 9.13, R² = 0.1048, <i>p</i> = 0.0006), and patella (pseudo-F = 2.00, R² = 0.0250, <i>p</i> = 0.0255), with no significant between-group differences in multivariate dispersion. After FDR correction, femoral PC1 (Hedges' g = 0.765, q = 0.0171) and tibial PC1 (g = 0.841, q = 0.00621) differed significantly between groups, whereas no individual patellar PC remained significant. Descriptive surface maps showed visually larger group-mean surface displacements in periarticular osseous contours than across the major articular contact surfaces.</p><p><strong>Conclusions: </strong>Significant three-dimensional differences in femoral, tibial, and patellar morphology remained detectable between individuals with GJH and healthy controls after removal of global isotropic scale. Femoral and tibial differences were primarily reflected in PC1, whereas the patella showed an overall multivariate difference without an individual FDR-significant PC. After global isotropic scale was removed, between-group shape differences persisted, indicating that they were not solely attributable to overall bone size.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research and Application of Tongue Image Segmentation Method Based on Lightweight Deep Learning.","authors":"Yulin Gou, Ziyang Zhang, Haibei Song, Alan Wang","doi":"10.3390/bioengineering13101201","DOIUrl":"https://doi.org/10.3390/bioengineering13101201","url":null,"abstract":"<p><p>Intelligent tongue diagnosis demands high-precision segmentation on resource-constrained mobile devices. All deep learning models outperform conventional approaches, improving mean intersection over union (mIoU) by approximately 9% and accelerating inference by more than two orders of magnitude. Among the evaluated models, UNet++ achieves the highest test mIoU (0.9543) and Dice (0.9764), with paired bootstrap analysis confirming statistically significant advantages over BiSeNet, PP-LiteSeg, and HRNet. BiSeNet achieves the fastest inference speed (310.03 FPS) and the highest mPA (0.9766), making it the most efficient model for real-time deployment. HRNet and PP-LiteSeg provide a favorable accuracy-efficiency balance, suitable for mobile-based tongue diagnosis. Deployment of PP-LiteSeg on three Android devices achieved inference latency of 233-262 ms and throughput of 3.2-3.4 FPS, demonstrating the feasibility of lightweight models on commodity mobile hardware. These findings establish an evidence-based framework for model selection in mobile tongue diagnosis, supporting AI deployment on portable devices for clinical assistance and remote monitoring.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infant Movement Classification Using Pose-Based Motion Features and a Learned Hybrid k-Nearest Neighbor Model.","authors":"Ali Ari, Ecem Yıldız Çangur, Kamile Uzun Akkaya, Pelin Atalan Efkere, Berna Gurler Ari, Bülent Elbasan, Abdulkadir Sengur, Yan Tian","doi":"10.3390/bioengineering13101200","DOIUrl":"https://doi.org/10.3390/bioengineering13101200","url":null,"abstract":"<p><p><b>Background/Objectives:</b> Objective assessment of infant movement remains challenging because clinical assessments mostly depend on expert visual inspection and subjective scoring. This study introduces a pose-based movement analysis framework that converts raw RGB infant videos into resilient spatiotemporal representations and executes multi-class infant movement classification utilizing a lightweight learned hybrid k-nearest neighbor (kNN) model. <b>Methods:</b> The proposed pipeline extracts 2D body keypoints from each video frame, applies shoulder-centered spatial normalization to reduce scale and translation variability, and divides recordings into overlapping temporal windows. Within each segment, a small set of interpretable kinematic descriptors captures joint speed statistics, bilateral symmetry, inter-limb coordination, postural distances, movement smoothness, directional variability, entropy, and ratio-based relational features. This results in a fixed 60-dimensional feature vector for each segment. The pose-derived features were evaluated using a learned hybrid kNN classifier with adaptive similarity weighting and conventional classifiers under the same subject-wise evaluation protocol. The framework was evaluated using both segment-wise stratified 5-fold cross-validation and subject-wise grouped 5-fold cross-validation. <b>Results:</b> Segment-wise CV yielded an accuracy of 96.44%, while subject-wise grouped CV achieved 94.80%, indicating strong performance under both evaluation settings. <b>Conclusions:</b> The study contributes a pose-based framework and the BERA-GAZI-RGB dataset for infant movement classification. Learned Hybrid kNN performed similarly to conventional kNN, without demonstrating a consistent advantage of hybrid weighting. Further validation is needed to assess its potential for early movement screening.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced SynPID by Tracking Varying Targets Applied to General Anaesthesia.","authors":"Carlos E Castañeda, Onofre Orozco-López, Alfredo Abad-Gurumeta, Agustín Rodríguez-Herrero","doi":"10.3390/bioengineering13101199","DOIUrl":"https://doi.org/10.3390/bioengineering13101199","url":null,"abstract":"<p><p>Closed-loop anaesthesia systems use automated controllers to regulate drug delivery and improve anaesthetic management. Although PID-based strategies have shown promising performance, challenges related to patient variability, robustness, setpoint definition, and clinical implementation remain open. This work presents an enhanced synergistic PID strategy, denoted as ESynPID, for multivariable drug delivery in general anaesthesia. The proposed approach extends the previous SynPID formulation by incorporating a variable setpoint generation mechanism through a PID Inversor, a symmetric weighting strategy for the setpoint feedforward terms, and an updated symmetric control-to-range module. The virtual patient is represented through pharmacokinetic-pharmacodynamic models for propofol, remifentanil, and rocuronium, numerically propagated in a sampled-data framework. The resulting architecture combines tracking-oriented target generation, weighted errors PID control, range correction, open-loop/closed-loop phase switching, and a pump emulation block including safety, quantification, and zero-order hold stages. The proposed controller is evaluated in a cohort of virtual patients under surgical stimulation scenarios and compared with the previous SynPID approach. The results show that ESynPID modifies the perfusion profiles by anticipating drug delivery around stimulus changes and by producing more active tracking behaviour during the maintenance phase. This improves the regulation of the hypnotic monitor, while revealing a trade-off in the nociception response. Overall, the study shows that the proposed tracking and symmetry mechanisms increase the adaptability of the control law, although their effect depends on the monitored variable and on the interaction between the weighting strategy, the inversion mechanism, and the pharmacokinetic dynamics of the patient.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Therapeutic Validation of a Microfluidic Metastasis Model: Cimetidine but Not Cilengitide Inhibits Physiological Tumor Cell Extravasation.","authors":"Claudia Kühlbach, Lea Olschewski, Frank Baganz, Volker C Hass, Margareta M Mueller","doi":"10.3390/bioengineering13101192","DOIUrl":"https://doi.org/10.3390/bioengineering13101192","url":null,"abstract":"<p><p>Interactions between tumor cells and the endothelial lining of blood vessels under flow conditions are essential for successful metastasis. These interactions are mainly facilitated by adhesive proteins, like integrins or selectins and by proteases. While expression of specific integrins (α_vβ₃ and α_vβ₅) and selectins correlate with tumor progression in cancer and seem to mediate tumor cell adhesion, analysis of a therapeutic intervention in these processes is frequently limited to systems lacking physiological cell-cell interactions. In this study on the adhesion of different tumor cell types in monoculture and in coculture on endothelial cell layers was evaluated under different conditions, i.e., different static setups and under flow conditions in a microfluidic device and under treatment with the integrin inhibitor Cilengitide and the selectin inhibitor Cimetidine. This careful comparative experimental validation revealed that while Cilengitide inhibits H838-, HCT116- and U87-tumor cell adhesion in tumor cell monocultures in all assay set-ups its effect is abolished by the presence of endothelial cells. In contrast, Cimetidine, a Histamine 2 receptor antagonist and E-selectin inhibitor significantly reduced H838 lung carcinoma cell adhesion and trans-endothelial migration suggesting a significant role of E-selectin in tumor progressions and metastasis.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}