Bioengineering最新文献

To evaluate cross-platform measurement consistency and diagnostic threshold requirements in shear wave elastography (SWE), this study presents a robotically controlled, phantom-based validation framework to quantify and interpret inter-vendor variability that limits clinical standardisation. A custom-fabricated polyvinyl chloride-graphite phantom containing eight spherical inclusions (15-25 mm diameter, 25-95 mm depth, 23.53-259.58 kPa stiffness), representing breast tissue mechanical properties, was evaluated using Samsung HS50 and Aixplorer ultrasound systems. Robotic automation standardised probe positioning and contact, eliminating operator-dependent variability and enabling direct, system-level comparison. Cross-platform reproducibility, accuracy against mechanically validated ground truth, and diagnostic threshold performance were assessed across 80 measurements. Both systems demonstrated excellent intra-machine reproducibility (coefficient of variation: Samsung 0.42%, Aixplorer 0.46%) with strong inter-machine correlation (r = 0.9951, p < 0.0001). However, systematic bias of 7.05 kPa and 95% limits of agreement spanning 38.7 kPa revealed substantial cross-platform measurement differences. All phantom inclusions (8/8) measured below their assigned ground truth stiffness on both systems, with systematic underestimation ranging from 0.33 kPa to 109.57 kPa, indicating parameter-dependent measurement distortion linked to inclusion size, depth, and stiffness. Dynamic range compression was observed (Samsung: 68.7%, Aixplorer: 59.1% of true phantom range), providing a mechanistic explanation for diagnostic threshold instability. This study contributes an interpretable validation methodology that links SWE measurement bias to physical lesion properties and imaging system characteristics, rather than relying on correlation alone. Despite strong reproducibility, the observed system-dependent bias demonstrates that SWE measurements are not directly transferable across ultrasound platforms, and system-specific collaboration is required to ensure reliable clinical interpretation.

This review develops a materials-to-clinic framework for patient-specific, functionally graded (FG) NiTi shape memory alloy (SMA) rods as a complementary paradigm for scoliosis correction that targets durable alignment with motion preservation. The article synthesizes the thermomechanical basis of NiTi (thermoelastic martensitic transformation, near constant superelastic plateau, and hysteretic damping) while leveraging additive manufacturing (AM) capabilities to spatially program transformation temperatures (e.g., A_f), effective stiffness, and geometric inertia along the rod. Consolidated process-structure-property linkages are provided for the PBF-LB, DED, and BJAM routes, together with contamination and composition-control strategies (mitigation of Ni volatilization; management of O/C uptake; gradient heat treatments) and segment-level quality assurance (DSC mapping, micro-CT, EBSD/indentation, and bench bending/torsion in physiologic media). Building on clinical curve classification, the methodology formalizes a grading mask and target moment vector that drive multi-objective optimization of the segmental A_f, relative density/architecture, and cross-section, followed by route-specific build plans and acceptance tolerances. A phenomenological constitutive description provides the forward map from local design variables to temperature-dependent moment-curvature loops for finite element verification and uncertainty control. Surgical handling and activation policies are codified (cold shaping in martensite and controlled intra-/postoperative warming within tissue-safe bounds), and a translational roadmap is outlined, encompassing prospective calibration of classification-to-design mappings, AM process maps with in situ monitoring, digital twin planning, and long-horizon fatigue/corrosion protocols. The proposed graded structures provide an adaptive transformation temperature gradient and tunable mechanical response, representing an important design direction toward 3D-printed, patient-specific SMA rods for durable, adjustable, and efficient scoliosis correction.

Accurate and real-time glottis localization is critical for ensuring intraoperative oxygenation and patient safety during nasotracheal intubation. However, representative foundation models exemplified by the Segment Anything Model exhibit notable limitations in medical applications, stemming from their rigid attention mechanisms, feature space misalignment, and insufficient generalization to complex glottal anatomies. To address these challenges, we propose Glottis-SAM, a lightweight and task-adaptive segmentation framework that integrates dynamic representation learning with multi-prior contextual modeling. Specifically, we introduce a hierarchical low-rank adaptation strategy that enables efficient fine-tuning of visual foundation models by preserving geometric priors while significantly reducing computational overhead. To further enhance semantic fusion and generalization, we design a feature aggregation module with dual-path dynamic feature pyramids, which enables complementary optimization from local textures to global semantic structures under varying anatomical conditions. Extensive experiments on three diverse datasets demonstrate that Glottis-SAM achieves state-of-the-art segmentation accuracy with 72.6% mDice, a compact 55.2 MB model size, and 44.3 FPS inference speed on clinical data. These results highlight the model's robustness, efficiency, and potential for deployment in visual guidance systems for nasotracheal intubation.

Peripheral nerve regeneration is most rapid during the early post-injury period but gradually slows over time, often limiting functional recovery. Electrical stimulation (ES) delivered via percutaneous needle electrodes has been shown to modulate the local neural microenvironment and promote axonal regeneration; however, the optimal temporal window and duration of stimulation remain unclear. This study aimed to evaluate the time-dependent effects of needle-based ES on peripheral nerve regeneration in a rat model of sciatic nerve transection, using a well-established silicone nerve conduit as a stable and reproducible non-biodegradable repair model. Female Sprague-Dawley rats underwent sciatic nerve transection and repair. Postoperatively (PO), animals were randomly assigned to control (C) needle insertion or needle-based ES groups, receiving stimulation for either 3 weeks (C-3W-PO and ES-3W-PO, respectively) or 7 weeks (C-7W-PO and ES-7W-PO, respectively). Functional recovery was evaluated using cold plate latency and rotarod performance tests. Electrophysiological assessments included measurements of nerve conduction velocity (NCV), compound muscle action potential amplitude, and muscle action potential (MAP) area. Histomorphometric analysis of regenerated nerve tissue quantified total nerve cross-sectional area, endoneurial space, axon number, and axon density. Retrograde labeling with fluoro-gold (FG) was used to quantify reinnervated motor neurons. Immunohistochemical analyses of calcitonin gene-related peptide (CGRP) and macrophage-associated markers were conducted to assess sensory neuropeptide expression and immune cell infiltration within the regenerated nerve. ES significantly improved both sensory and motor recovery in a duration-dependent manner. Behavioral data showed increased cold pain thresholds and improved motor coordination in ES groups, with the most pronounced functional gains observed in the ES-7W-PO group. Electrophysiological measures revealed higher NCV, amplitude, and MAP area in ES-treated animals, with the most pronounced improvements at 7 weeks. Morphologically, ES enhanced nerve regeneration, as evidenced by increased total and endoneurial areas, axonal counts, and axon density. FG-labeled neuron counts were significantly elevated in ES groups, indicating enhanced motor reinnervation. At 3 weeks, ES induced higher CGRP expression and macrophage density, suggesting transient activation of sensory-associated and pro-regenerative immune responses during the early post-injury phase. These findings demonstrate that ES accelerates peripheral nerve repair in rats and that sustained stimulation across the early regenerative window yields superior structural and functional outcomes.

Introduction: Stereotactic radiosurgery is a highly precise radiotherapy technique widely used for the management of brain metastases. While VMAT enables highly conformal dose distributions, it is often associated with increased plan complexity and longer delivery times. Optimized dynamic conformal arc therapy (OptDCA) represents a less complex alternative that may achieve comparable dosimetric performance. In this retrospective study, dosimetric quality, deliverability, and plan complexity of VMAT and OptDCA were compared for single-isocenter SRS of multiple brain metastases.

Materials and methods: Thirty patients previously treated with VMAT were randomly selected and replanned using OptDCA with identical beam arrangements. Plan quality was evaluated using the Paddick conformity index, gradient index, target coverage, MUs, and brain V12Gy and V20Gy. Deliverability was assessed using gamma passing rates, and plan complexity was quantified using multiple complexity metrics.

Results: VMAT achieved a slightly higher CI (0.72 vs. 0.71) but required a higher number of MUs (5376 vs. 4820), while no significant differences were observed in GI or target coverage. OptDCA demonstrated significantly higher GPR (median 96.95% vs. 91.1%) and consistently lower plan complexity. Significant correlations were observed between GPR and several complexity metrics for both techniques.

Conclusion: Overall, OptDCA provides comparable plan quality to VMAT, while offering improved deliverability and reduced complexity, making it a viable alternative technique.

Deep Learning (DL) has undergone widespread adoption for medical image analysis and diagnosis. Numerous studies have explored mammographic image analysis for breast cancer screening. For this study, we assessed the hypothesis that stratifying mammography images based on the presence or absence of a corresponding region of interest (ROI) improves classification accuracy for both normal-abnormal and benign-malignant classifications. Our methodology involves independently training models and performing predictions on each subgroup with subsequent integration of the results. We used several DL models, including ResNet, EfficientNet, SwinTransformer, ConvNeXt, and MobileNet. For experimentation, we used the publicly available VinDr., CDD-CESM, and DMID datasets. Our comparison with prediction results obtained without ROI-based stratification demonstrated that the utility of considering ROI presence to enhance diagnostic accuracy in mammography increases along with the data volume. These findings support the usefulness of our stratification approach, particularly as a dataset's size grows.

Periodontitis is a chronic inflammatory disease characterized by dysbiotic biofilms and host-mediated destruction of periodontal tissues. This study evaluated the efficacy of a novel needle-shaped floating electrode-dielectric barrier discharge (FE-DBD) plasma probe in treating experimental periodontitis. Using a split-mouth design in a rat model of ligature-induced periodontitis, subgingival microbiome changes were analyzed via 16S rRNA sequencing, while gene expression of inflammatory mediators and osteoclastogenic factors was quantified by qRT-PCR. Histopathological evaluation and osteoclast activity were assessed through H&E and TRAP staining, respectively. FE-DBD treatment significantly shifted the subgingival microbiome by reducing pathobionts such as Bacteroidota and Fusobacteriota and increasing health-associated taxa including Proteobacteria and Actinobacteriota. The therapy also exerted immunomodulatory effects by suppressing pro-inflammatory genes (TNF-α, ICAM-1, CCL2) and elevating anti-inflammatory IL-10 expression. Moreover, FE-DBD favorably modulated bone remodeling by downregulating RANK and RANKL, upregulating OPG, and raising the OPG/RANKL ratio 3.72-fold, accompanied by reduced inflammatory infiltration and osteoclast numbers. These findings demonstrate that FE-DBD plasma effectively ameliorates periodontitis by simultaneously targeting pathogenic biofilms, host inflammation, and osteoclastogenesis, highlighting its potential as a multifaceted adjunctive therapy for periodontal disease.

Collagen sponges are widely used for oral tissue regeneration, due to their extracellular matrix-mimetic architecture and excellent biocompatibility. However, in practical biomedical applications, collagen sponges may exhibit hydration-induced structural instability, and there can be associated inflammatory responses under physiological conditions, potentially compromising their regenerative performance. In this study, we investigated how two cross-linking strategies-transglutaminase (TG) and 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide/N-hydroxysuccinimide (EDC/NHS)-modulate the structural stability and inflammatory profiles of collagen sponges. TG-cross-linked sponges exhibited microstructural collapse, associated with macrophage activation and engagement of the Itgαvβ3/5-Src-RhoC-NF-κB signaling axis. In contrast, EDC/NHS-cross-linked sponges preserved a stable porous architecture, effectively suppressing this signaling cascade and establishing a low-inflammatory microenvironment. These findings elucidate a key mechanism by which cross-linking regulates the microstructural integrity of collagen scaffolds and provides in vitro-derived preliminary design principles for developing next-generation collagen biomaterials with low-inflammatory properties.

Facial expressions are crucial in conveying emotions and for engaging in social interactions. The facial musculature activations and their pattern of movements under emotions are similar in all humans; hence, facial expressions are considered a behavioral phenotype. Facial features related to the expression of various emotions change under different health impairments, including cognitive decline and pain experience. Hence, evaluating these facial expression deviations in comparison to healthy baseline conditions can help in the early detection of health impairments. Recent advances in machine learning and computer vision have introduced a multitude of tools for extracting human facial features, and researchers have explored the application of these tools in early screening and detection of different health conditions. Advances in these studies can especially help in telemedicine applications and in remote patient monitoring, potentially reducing the current excessive demand on the healthcare system. In addition, once developed, these technologies can assist healthcare professionals in emergency room triage, early diagnosis, and treatment. The aim of the present review is to discuss the available tools that can objectively measure facial features and to record the studies that use these tools in various health assessments. Our findings indicate that analyzing facial expressions for the detection of multiple health impairments is indeed feasible. However, for these technologies to achieve reliable real-world deployment, they must incorporate disease-specific facial features and address existing limitations, including concerns related to patient privacy.

Low back pain (LBP) remains one of the most prevalent and disabling musculoskeletal conditions worldwide, shaped by interacting mechanical, neurophysiological, inflammatory, vascular, and behavioral factors. Conventional care often relies on generalized exercise programs and episodic, predominantly subjective assessment, which can underrepresent inter-individual heterogeneity and longitudinal change. Recent bioengineering advances enable continuous, multimodal monitoring of objective correlates of function-neuromuscular activation and coordination (sEMG/polyEMG), movement patterns and activity exposure (IMU), and complementary physiological context (e.g., autonomic and perfusion-related signals). Rather than measuring pain directly, these signals can contextualize symptoms, support treatment stratification within non-surgical care, and enable trajectory monitoring with early non-response flags to guide timely rehabilitation adjustment under clinician oversight. When integrated with transparent, implementation-oriented analytics, biosensing can also support incremental closed-loop rehabilitation through patient-facing feedback and adaptive progression rules. This review synthesizes current and emerging biosensing approaches for LBP and highlights key translational requirements-outcome-linked validation, standardization, and workflow integration-to bridge engineering innovation with clinically usable, data-informed rehabilitation.