Bioengineering最新文献

Wound healing is a complex, multi-phase process requiring coordinated interactions among diverse cell types and molecular pathways to restore tissue integrity. Dysregulation can lead to chronic non-healing wounds or excessive scarring, posing major clinical and economic burdens. Single-omics interrogate individual molecular layers, such as the genome, transcriptome, proteome, metabolome, or epigenome, and have revealed key cellular players, but provide a limited view of dynamic wound repair. Single-cell technologies provide higher resolution to single-omic data by resolving cell-type and state-specific heterogeneity, enabling precise characterization of cellular populations. Multi-omics integrates multiple molecular layers at single-cell resolution, reconstructing regulatory networks, epigenetic landscapes, and cell-cell interactions underlying healing outcomes. Recent advances in single-cell and spatial multi-omics have revealed fibroblast subpopulations with distinct fibrotic or regenerative roles and immune-epithelial interactions critical for re-epithelialization. Integration with computational tools and artificial intelligence (AI) continues to reveal cellular interactions, predict healing outcomes, and guide development of personalized therapies. Despite technical and translational challenges, including data integration and cost, multi-omics are increasingly shaping the future of precision wound care. This review highlights how multi-omics is redefining understanding of wound biology and fibrosis and explores emerging applications such as smart biosensors and predictive models with potential to transform wound care.

Background: The integration of artificial intelligence (AI) into mobile health (mHealth) applications has been accelerated by the widespread adoption of smartphones and recent technological advances, particularly in the wake of the COVID-19 pandemic. This experience has expanded the role of AI-powered apps in real-time health monitoring, early detection, and personalized treatment pathways.

Aim: This review aims to summarize recent evidence on the use of AI in healthcare-related mobile applications, with a focus on clinical trends, practical implications, and future directions.

Methods: Studies were prioritized based on methodological rigor, with systematic reviews forming the core of the analysis. Additional literature was considered to capture emerging trends and applications where a relevant rigorous screening and scoring procedure was applied to ensure methodological quality and relevance. Only studies addressing healthcare applications, rather than computational or computer science frameworks, were included to reflect the journal's clinical scope.

Results and discussion: Fifty-six secondary studies were analyzed in detail. Thematic synthesis revealed a post-pandemic shift toward applications targeting mental health, chronic care management, and preventive services. Additional screening showed that, despite their increasing use in clinical contexts, few AI-based apps were formally classified as medical devices. This highlights a gap between technological innovation and regulatory oversight. Ethical concerns-including algorithm transparency, clinical responsibility, and data protection-were frequently reported across studies.

Conclusions: This review underscores the growing impact of AI in mobile health, while drawing attention to unresolved challenges related to regulation, safety, and clinical accountability. A more robust integration into health systems will require clearer governance frameworks, validation standards, and interdisciplinary dialogue between developers, clinicians, and regulators.

Alterations in the tumor extracellular environment and matrix stiffness promote tumor progression. Furthermore, correlational studies have identified enrichment of extracellular matrix (ECM) proteins (glycoproteins, collagens) in breast tumors. Despite these findings, there has yet to be an interdisciplinary analysis of both ECM composition and structural architecture in rare breast tumors, such as metaplastic breast cancer. Here, we explored changes in ECM protein expression and architecture in a triple-negative breast cancer (TNBC) metaplastic tumor through SEM, proteomics, and RNA sequencing. SEM revealed that the tumor pore size was larger compared to the control adipose tissue. Oscillating rheometry demonstrated increased ECM stiffness in the tumor compared to the control adipose breast adipose. Proteomic analysis of the metaplastic TNBC tumor showed significant enrichment for ECM proteins, notably glycoproteins compared to the control adipose. Interestingly, these samples showed no observed changes in expression for major fibrillar collagens COL1A1 and COL1A2, and a reduced expression of COL3A1. To determine the impact of less characterized ECMs in metaplastic TNBC, we overexpressed MFAP2 in primary metaplastic breast cancer cells and performed RNA sequencing. MFAP2 overexpression was associated with upregulation of epithelial-to-mesenchymal transition-related genes. Overall, our results establish an extracellular signature and onco-architecture for the metaplastic triple-negative tumor type.

Medical imaging is an indispensable tool in clinical diagnosis and therapeutic decision-making, encompassing a wide range of modalities such as radiography, ultrasound, CT, and MRI. With the rapid advancement of deep learning technologies, significant progress has been made in medical image analysis. However, existing deep learning methods are often limited by dataset size, which can lead to overfitting, while traditional approaches relying on hand-crafted features lack specificity and fail to fully capture complex pathological information. To address these challenges, we propose RadioGuide-DCN, an innovative radiomics-guided decorrelated classification network. Our method integrates radiomics features as prior information into deep neural networks and employs a feature decorrelation loss mechanism combined with an anti-attention feature fusion module to effectively reduce feature redundancy and enhance the model's capacity to capture both local details and global patterns. Specifically, we utilize a Kolmogorov-Arnold Network (KAN) classifier with learnable activation functions to further boost performance across various medical imaging datasets. Experimental results demonstrate that RadioGuide-DCN achieves an accuracy of 93.63% in BUSI image classification and consistently outperforms conventional radiomics and deep learning methods in multiple medical imaging classification tasks, significantly improving classification accuracy and AUC scores. Our study offers a novel paradigm for integrating deep learning with traditional imaging approaches and holds broad clinical application potential, particularly in tumor detection, image classification, and disease diagnosis.

Islet encapsulation has the potential to enable transplantation without requirement for life-long immunosuppression. The period between implantation and revascularisation is most harmful for encapsulated islets as they receive nutrients and oxygen exclusively via diffusion. This critical time gap must be bridged with a temporary oxygen supply to prevent inflammation and apoptosis. Hence, we compared the efficiency of individual components of an oxygen-delivering matrix (hyaluronic acid (HA); HA + perfluorodecalin nanoemulsion; HA + perfluorodecalin nanoemulsion + oxygen) to provide a substitute for the extracellular matrix and to facilitate human islet survival. The islets were loaded into silicone-based macroencapsulation devices with multi-scale porous membranes designed to optimise revascularisation. Four to five days of normoxic culture revealed that non-oxygen-charged nanoemulsion prevented islet disintegration but did not reduce necrosis or apoptosis. Oxygen supply decreased the generation of reactive oxygen species and chemokines, thereby increasing islet yield. Stimulated insulin secretion of encapsulated islets was marginal and severely delayed. Islets incubated in oxygen-precharged nanoemulsion were characterised by the highest stimulation index. These data suggest that islet survival in macroencapsulation devices can be optimised with a multi-functional matrix providing mechanical support and temporary oxygen supply to reduce the production of pro-inflammatory mediators. Suitable oxygen delivery systems with an extended life span must identified before in vivo experiments can be undertaken.

Structural valve deterioration (SVD) remains a key limitation in bioprosthetic heart valve (BHV) usage influenced by patient age. A deeper understanding of SVD pathogenesis, particularly of the immune-mediated processes altering current BHV materials, is therefore critical. To this end, commercially available BHV tissues of bovine, porcine, and equine origin were investigated following subcutaneous implantation into α1,3-galactosyltransferase-knockout (Gal KO) mice. We compared the immune responses between adult and aged animals via histological assessments of explants and measurement of serum anti-galactose α1,3-galactose (Gal) and anti-non-Gal antibodies at 2 months post-implantation. In contrast to adult mice, old Gal KO mice did not show increased levels of serum anti-Gal or -non-Gal antibodies after receiving specific BHV tissue (i.e., Freedom-Solo). Instead, a significant decrease in serum anti-Gal IgM was found in old recipients of Freedom-Solo. Furthermore, the overall cellular immune response was attenuated in explants from old mice compared with adults (i.e., ATS 3f and Crown). Nevertheless, the Freedom-Solo (bovine) and the Hancock-II (porcine) tissues still elicited strong cellular immune infiltration in the old cohorts. Therefore, the Gal KO mouse model offers a valuable platform to investigate age-related differences regarding cellular and humoral immune responses to various BHV tissues, contributing to our understanding of SVD.

Tissue engineering is entering a new era, one defined not by passive scaffolds but by smart, adaptive biomaterials that can sense, think, and respond to their surroundings. These next-generation materials go beyond simply providing structure; they interact with cells and tissues in real time. Recent advances in mechanically responsive hydrogels and dynamic crosslinking have demonstrated how materials can adjust their stiffness, repair themselves, and transmit mechanical cues that directly influence cell behavior and tissue growth. Meanwhile, in vivo studies are demonstrating how engineered materials can harness the body's own mechanical forces to activate natural repair programs without relying on growth factors or additional ligands, paving the way for minimally invasive, force-based therapies. The emergence of electroactive and conductive biomaterials has further expanded these capabilities, enabling two-way electrical communication with excitable tissues such as the heart and nerves, supporting more coordinated and mature tissue growth. Meanwhile, programmable bioinks and advanced bioprinting technologies now allow for precise spatial patterning of multiple materials and living cells. These printed constructs can adapt and regenerate after implantation, combining architectural stability with flexibility to respond to biological changes. This review brings together these cross-cutting advances, dynamic chemical design, mechanobiology-guided engineering, bioelectronic integration, and precision bio-fabrication to provide a comprehensive view of the path forward in this field. We discuss key challenges, including scalability, safety compliance, and real-time sensing validation, alongside emerging opportunities such as in situ stimulation, personalized electromechanical sites, and closed loop "living" implants. Taken together, these adaptive biomaterials represent a transformative step toward information-rich, self-aware scaffolds capable of guiding regeneration in patient-specific pathways, blurring the boundary between living tissue and engineered material.

This study aims to propose a non-pharmacological approach to pain relief by analyzing changes in electrocardiogram (ECG) parameters following transcutaneous microcurrent stimulation generated according to the pulse train characteristics of intensity and frequency. Therefore, we analyze and interpret stimulation methods that induce parasympathetic nervous system (PNS) activity, which is the clinical basis for pain relief. There were 14 male participants, with a height of 176.08 ± 7.05 cm, a weight of 77.07 ± 10.32 Kg, and an age of 26.35 ± 1.71 years, and 10 female participants, with a height of 160.6 ± 5.88 cm, a weight of 52.9 ± 9.03 Kg, and an age of 24 ± 1.61 years. The microcurrent stimulation patch was attached to the left wrist. In order to observe the PNS induction effect of the measured electrocardiograms, time and frequency domains were analyzed and additional nonlinear analysis was performed. Data measurements had a rest period of more than 1 h depending on the intensity, and more than 1 day depending on the frequency to ensure sufficient stabilization time. Although physiological changes were shown differently in various pulse trains, among them, after 7 Vpp microcurrent stimulation at 1 Hz, the values of the square root of the mean squared differences of successive R-R intervals and instantaneous RR interval variability, which indicate PNS activity in the subjects, significantly increased from 41.31 ± 34.13, 29.23 ± 24.14 ms to 65.09 ± 32.46, 44.56 ± 37.92 ms (p < 0.05). Activation of PNS, which can relieve pain, was confirmed only in the 7 Vpp with 1 Hz stimulation. This suggests that microcurrent stimulation can relieve pain in a non-pharmacological way by inducing activation of PNS.

The human gut microbiota plays a key role in neurochemical communication, especially through the gut-brain axis. There is growing evidence that the gut microbiota influences dopamine metabolism through both production and consumption mechanisms. Two key bacterial enzymes are central to this process: tyrosine decarboxylase (TDC), which primarily catalyzes the decarboxylation of tyrosine to tyramine but can also act on L-DOPA to produce dopamine in certain bacterial strains, and aromatic L-amino acid decarboxylase (AADC), which can convert precursors such as L-DOPA, tryptophan, or 5-hydroxytryptophan into bioactive amines including dopamine, tryptamine, and serotonin. Identifying the bacterial families corresponding to TDC and AADC enzymes opens new avenues for clinical intervention, particularly in neuropsychiatric and neurodegenerative disorders, such as Parkinson's disease. Moreover, elucidating strain-specific microbial contribution and host-microbe interactions may enable personalized therapeutic strategies, such as selective microbial enzyme inhibitors or tailored probiotics, to optimize dopamine metabolism. Emerging technologies, including biosensors and organ-on-chip platforms, offer new tools to monitor and manipulate microbial dopamine activity. This article explores the bacterial taxa capable of producing or consuming dopamine, focusing on the enzymatic mechanisms involved and the methodologies available for studying these processes in vivo.

Infantile Epileptic Spasms Syndrome (IESS) is a devastating epileptic encephalopathy of infancy that carries a high risk of lifelong neurodevelopmental disability. Timely diagnosis is critical, as every week of delay in effective treatment is associated with worse cognitive outcomes. Although synchronized electroencephalogram (EEG) and surface electromyography (EMG) recordings capture both the electrophysiological and motor signatures of spasms, accurate automated detection remains challenging due to the non-stationary nature of the signals and the absence of physiologically plausible inter-modal fusion in current deep learning approaches. We introduce IESS-FusionNet, an end-to-end dual-stream framework specifically designed for accurate, real-time IESS detection from simultaneous EEG and EMG. Each modality is processed by a dedicated Unimodal Encoder that hierarchically integrates Continuous Wavelet Transform, Spatio-Temporal Convolution, and Bidirectional Mamba to efficiently extract frequency-specific, spatially structured, local and long-range temporal features within a compact module. A novel Cross Time-Mixing module, built upon the linear recurrent attention of the Receptance Weighted Key Value (RWKV) architecture, subsequently performs efficient, time-decaying, bidirectional cross-modal integration that explicitly respects the causal and physiological properties of cortico-muscular coupling during spasms. Evaluated on an in-house clinical dataset of synchronized EEG-EMG recordings from infants with confirmed IESS, IESS-FusionNet achieves 89.5% accuracy, 90.7% specificity, and 88.3% sensitivity, significantly outperforming recent unimodal and multimodal baselines. Comprehensive ablation studies validate the contribution of each component, while the proposed cross-modal fusion requires approximately 60% fewer parameters than equivalent quadratic cross-attention mechanisms, making it suitable for real-time clinical deployment. IESS-FusionNet delivers an accurate, computationally efficient solution with physiologically inspired cross-modal fusion for the automated detection of infantile epileptic spasms, offering promise for future clinical applications in reducing diagnostic delay.