Bioengineering最新文献

RNA-based interventions are particularly promising for next-generation therapeutic strategies and hold significant potential when integrated with diagnostic modalities. Among noncoding RNAs, microRNAs (miRNAs) regulate gene expression post-transcriptionally and represent compelling targets for cancer therapy. However, their clinical translation remains hindered by instability, off-target effects, and limited delivery efficiency. Here, we report the microfluidic synthesis of hybrid lipid-polymer nanoparticles (LiPoNs) that co-deliver an AntimiR-21 and the magnetic resonance imaging contrast agent gadolinium diethylenetriamine penta-acetic acid (Gd-DTPA). The LiPoNs were obtained using coupled Hydrodynamic Flow Focusing (cHFF), enabling precise control over lipid-polymer self-assembly and surpassing the compositional limitations reported with conventional micromixers. The resulting AntimiR-21-Gd-DTPA-LiPoNs exhibited an average hydrodynamic diameter of 124 nm, narrow polydispersity (PDI < 0.2), and encapsulation efficiency up to 60%. In MDA-MB-231 breast cancer cells, treatment with AntimiR-21-LiPoNs induced suppression of miR-21 and a corresponding decrease in migratory capacity, demonstrating effective functional delivery and gene expression modulation. These findings establish a versatile microfluidic platform for engineering multifunctional lipid-polymer nanostructures whose hybrid architecture combines the biocompatibility and membrane fusion capability of lipids with the structural robustness and controlled release properties of polymers, thereby advancing RNA-based theranostic design for precision oncology and related applications.

Background: Prostate cancer (PCa) is prevalent in men over 65 and requires effective clinical management. Standard PCa therapies often offer positive outcomes; however, its castration-resistant form (CRPC) is aggressive and associated with poor prognosis. The objective of this study is to characterize the microRNA profiles associated with the PCa to CRPC transition using a microfluidic PCa model.

Methods: LNCaP-derived hormone-sensitive PCa spheroids were cultured for 30 days under recirculating flow conditions mimicking hormone deprivation. Total RNA was isolated from the spheroids and perfusate at Day 5 and Day 30. Exosomal microRNAs were profiled by miRNA-seq. Differentially expressed miRNAs were used for target prediction across multiple databases, and gene set enrichment analysis (GSEA) was performed to identify pathways affected during prolonged hormone deprivation.

Results: Sustained hormone deprivation induced a shift in microRNA expression. Tumor-suppressive miRNAs were broadly reduced. To evaluate functional consequences, predicted targets were compiled for all regulated miRNAs. For the 33 intracellular miRNAs downregulated on Day 30, 430 genes were predicted as targets for at least 16 of these miRNAs, revealing strong convergence on shared regulatory pathways. Thirty-five genes overlapped with predicted targets of the single upregulated miRNA and were removed, yielding a refined set of 395 unique genes used for GSEA. Overall, the neuronal differentiation pathways observed reflect early features of a neuroendocrine-like phenotype.

Conclusions: This microfluidic PCa model captures early molecular events associated with progression toward CRPC. It provides a controlled system for studying disease evolution and supports the development of more precise therapeutic and diagnostic strategies.

Chronic wounds, including diabetic foot ulcers (DFUs), venous leg ulcers (VLUs), and pressure injuries, remain a major global health burden and contribute substantially to Medicare spending. Because traditional wound dressings fail to address the dynamic microenvironment of chronic wounds, bioactive materials that modulate inflammation and support tissue regeneration are needed. In this study, we evaluated the tissue response to our borate-based bioactive glass fiber matrix (BBGFM) designed to overcome limitations of existing fibrous wound dressings. Two Sus scrofa domesticus underwent creation of twelve 5 × 5 cm subcutaneous pockets each, which were treated with BBGFM at three thicknesses (25%, 50%, and 100%) or left untreated as controls. One animal was euthanized at three weeks and the other at six weeks for gross and histopathological evaluation of all wound sites. BBGFM-treated pockets demonstrated a dose-dependent increase in inflammation at three weeks that diminished by six weeks. Enhanced neovascularization and collagen matrix deposition were also seen at both time points. Collagen maturity increased across all groups by six weeks, and residual BBGFM correlated with initial implant thickness. These findings indicate that BBGFM promotes a controlled inflammatory response and supports neovascularization and matrix remodeling in a dose-dependent manner, suggesting its potential as an effective bioactive wound matrix.

This study investigates the functional capabilities and accessibility limitations of current knee prostheses while developing and evaluating a three-stage prosthetic system (V1-V3). The primary objective is to design a cost-effective knee prosthesis featuring anatomically compatible motion, high kinematic accuracy, and a modular architecture. The methodology integrates a technical review of commercial prostheses, CAD modeling in SolidWorks, kinematic evaluation through Motion Simulation, and experimental testing of the V2 prototype. The results demonstrate the structural limitations of the initial V1 design, the complete assembly and improved functional performance of the V2 prototype, and the advanced mechanical behavior achieved in the final V3 concept. The V3 model provides an extended range of motion, reduced mass and lowered center of gravity, smoother dynamic response, and compatibility with a fully modular foot-ankle-knee configuration. Overall, the findings indicate that the V3 design represents a promising engineering solution that brings the system closer to clinical applicability and establishes a foundation for the development of a fully modular lower-limb prosthetic platform.

In the original publication [...].

Fetal heart rate (FHR) monitoring is ubiquitous in antenatal care, yet human visual interpretation poorly predicts adverse pregnancy outcomes. Meanwhile, preterm gestations carry a high burden of stillbirth and severe fetal compromise, where earlier identification of high-risk pregnancies may justify iatrogenic preterm delivery to prevent avoidable fetal death. We analyzed 4867 antepartum FHR recordings from pre-term pregnancies meeting at least one of ten adverse outcome criteria alongside 4014 term uncomplicated controls. Seven clinically validated FHR features were extracted from each trace, and six machine-learning classifiers were trained on 80% of the data (7105 samples) using k-fold cross-validation; the remaining 20% (1776 samples) formed an internal validation cohort. The random forest demonstrated the best performance, achieving an area under the receiver-operating characteristic curve (AUC) of 0.88 (95% confidence interval [CI] 0.87-0.88) during training and 0.88 (95% CI 0.86-0.90) on validation, with good calibration (Brier score 0.14). Median AUC across individual adverse outcomes was 0.85 (interquartile range [IQR] 0.81-0.89) and exceeded 0.80 at all gestational ages assessed; sensitivity and specificity at the Youden threshold were 76.2% and 87.5%, respectively. Decision-curve analysis demonstrated net benefit across a range of clinically relevant probability thresholds. These findings indicate that data-driven interpretation of antepartum FHR can stratify risk in pre-term pregnancies with high accuracy and may support earlier, evidence-based clinical decision-making, particularly in resource-limited settings where specialist expertise is limited.

Physical activity (PA) is an important factor for maintaining health and well-being, especially in older adults. This study aims to apply machine learning methods to predict PA patterns and identify key factors influencing these behaviors among community-dwelling older adults. Linear and Logistic Regression, Elastic Net, and Light Gradient Boosting Machine (LightGBM) models were used to analyze cross-sectional data. While longitudinal data collected over 14 days were analyzed using LightGBM, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). The most important predictors identified in the cross-sectional analysis were the Exercise Self-efficacy Scale (ESES) for PA levels and the Geriatric Depression Scale (GDS) for the International Physical Activity Questionnaire (IPAQ) as a continuous measurement. In the longitudinal analysis, using a seven-day sequence of step count data provided the best performance for forecasting physical activity for the entire next day. Overall, the findings indicate that combining wearable sensor data with machine learning and deep learning methods can provide valuable insights into physical activity behaviors among older adults. In the cross-sectional analysis, psychological and motivational factors such as self-efficacy were identified as important factors for activity levels, while in the longitudinal analysis, using a week of past step count data provided the most reliable predictions of future-day physical activity.

Accurate localization of thoracic lymph nodes during endobronchial ultrasound (EBUS) is crucial for lung cancer staging, treatment planning, and prognostication. Artificial intelligence (AI) has the potential to support this process. Deep learning (DL) models often lack transparency but can benefit from explainable AI (XAI) tools like Gradient-weighted Class Activation Mapping (Grad-CAM). However, no prior study has quantitatively assessed whether model attention in EBUS imaging corresponds to relevant anatomy. This study developed a convolutional neural network (CNN) to classify thoracic lymph node stations and evaluated the anatomical relevance of Grad-CAM activations using a structured annotation framework. Applied on 35,527 labeled EBUS images, the CNN achieved 63.1% accuracy, with the highest F1-score in stations 4L, 4R, and 10R. Three expert bronchoscopists independently annotated Grad-CAM maps from 3131 test images. Activations predominantly aligned with lymph nodes and/or blood vessels, yielding an accuracy of 65.9% and an F1-score of 58.4%, with moderate interobserver agreement. These findings indicate that DL can aid lymph node station classification and that XAI offers meaningful insight into model behavior. The proposed framework may enhance anatomical orientation and operator training during EBUS, although further optimization and multicenter validation are required.

Degraded mechanical properties in the aortic wall can lead to the formation of aortic aneurysms, potentially resulting in life-threatening ruptures. Current diagnostic criteria using maximum aortic diameter often fail to predict this critical moment, underscoring the need for more accurate patient-based prediction methods. A hospital-compatible experimental apparatus was designed for quasi-static ex vivo inflation testing of intact Ascending Thoracic Aortic Aneurysm (ATAA) specimens with 360° full-field three-dimensional digital image correlation (3D-DIC). Given hospital handling constraints, liquid pressurization was not feasible; instead, pressure was applied via a balloon-driven pneumatic system, and synchronized stereo imaging was used to measure surface displacement fields between 80 and 120 mmHg. The system was validated using a CT-derived ATAA silicone phantom. Full-field displacement measurements showed close agreement with finite element simulations, supporting the mechanical reliability of the apparatus and the repeatability of the measurement workflow. In addition, a frozen-thawed healthy porcine thoracic aorta was tested to demonstrate biological feasibility, particularly regarding the speckle application and DIC tracking, without aiming to extract tissue constitutive parameters. Overall, the setup provides a practical framework for acquiring full-field inflation-induced deformation data from intact aortic specimens in a hospital setting, enabling future studies on resected human ATAA tissue and model calibration that may contribute to more accurate methods for rupture prediction.

Tissue decellularization aims to obtain bioscaffolds for regenerative applications by removing all cellular components while preserving the extracellular matrix (ECM) architecture. Although decellularization removes the majority of linear nuclear DNA (nDNA), residual amounts remain detectable. However, the fate of circular mitochondrial DNA (mtDNA) after decellularization has not yet been reported. Cell death or injury can cause the release of mtDNA, which is resistant to breakdown by exonucleases. Extracellular mtDNA acts as a damage-associated molecular pattern (DAMP) that can trigger immune responses. The aim of this study is to assess the presence of residual mtDNA in the liver, bile duct, and vascular scaffolds after decellularization and whether this causes inflammatory responses in macrophages. Decellularized tissues showed a marked reduction in total DNA content well below the threshold of 50 ng/mg tissue. However, in liver and vascular scaffolds, a relative increase in the mtDNA:nDNA ratio was detected in the remnant DNA fraction. Residual mtDNA in bioscaffolds acted as DAMPs causing macrophage activation, as shown by increased cell proliferation and cytokine production. Strategies to further reduce remnant mtDNA were tested. We found that treatment with the endonuclease enzyme HpaII was effective in degrading residual mtDNA. Importantly, mtDNA removal resulted in a significantly reduced macrophage activation. In conclusion, our study shows that mtDNA is relatively resistant to the decellularization procedure and can act as a DAMP in bioscaffolds. This underscores the importance of removing mtDNA from decellularized bioscaffolds to improve the immunocompatibility for biomedical applications.