Annals of Biomedical Engineering最新文献

Background and objective: Computed tomography-based fractional flow reserve computation (CT-FFR) is widely used in clinical practice, based on its efficacy demonstrated in many studies. However, major assumptions remain with the outflow boundary conditions (BCs) representing coronary microvasculature, especially in hyperaemia. We here propose a novel method to estimate patient-specific microvascular response to hyperaemia for CT-FFR calculations, based on patients' routinely available demographic data.

Methods: A statistical model to predict microvascular flow response (MFR) from routinely collected patient demographic parameters was derived using PET-based perfusion data of 101 patients with coronary artery disease. CT-FFR computations were then conducted with patient-specific anatomical models and outflow BCs derived from various MFR models including the proposed approach. The FFR values were calculated for an independent test cohort of 10 patients who had undergone CT coronary angiography, CT perfusion imaging and invasive FFR measurement. Computed FFR values were compared against invasive FFR and other CT-FFR algorithms.

Results: A multivariate regression model predicting patient-specific MFR was derived as a function of sex, diabetes and smoking status of the patient. FFR values computed using our model agreed well with the invasive FFR (0.76 ± 0.09 vs. 0.75 ± 0.10, P = 0.217). The FFRs predicted with our model were also comparable to those calculated using outflow BC tuned with patient-specific perfusion data (FFR: 0.74 ± 0.10, P = 0.233 vs. invasive FFR) and showed marked improvement over the conventional approach (FFR: 0.68 ± 0.11, P = 0.004 vs. invasive FFR). Diagnostic accuracy vs. invasive FFR were 100, 91 and 82% for CT-FFR with CTP-based MFR, demography-based MFR, and conventional approach, respectively.

Discussion: The proposed demography-based MFR model significantly improves FFR computation accuracy compared with a typical conventional model that assumes constant, healthy and population average MFR. Although its diagnostic accuracy is slightly lower than that of CT-FFR calibrated with patient-specific perfusion imaging data (91 vs. 100%), the demography-based model offers a substantial practical advantage by not requiring additional non-standard data acquisition, such as perfusion imaging. Consequently, it shows strong potential as a practical enhancement to conventional CT-FFR algorithms.

Purpose: Established in silico frameworks for assessing Torsade de Pointes (TdP) risk primarily rely on single-cell electrophysiological biomarkers, which have demonstrated strong predictive capabilities. However, ventricular transmural electrophysiological heterogeneity is known to influence repolarization dynamics and arrhythmogenic mechanisms. Explicitly incorporating endocardium, epicardium, and mid-myocardium representations may enhance physiological interpretability, but direct integration of multi-cell features can introduce severe multicollinearity and compromise model stability. To address this challenge, we propose an ordinal logistic regression (OLR) framework that integrates multi-cell qNet information through probability averaging, preserving physiological context while maintaining robust statistical behavior.

Methods: $I{C}_{50}$ values and Hill coefficients for 28 CiPA drugs were implemented in two ventricular cell models, the CiPAORdV1.0 and the ORd in silico models. qNet was computed independently for endocardium, epicardium, and mid-myocardium cells. Cell-specific OLR models produced class probabilities that were then averaged to generate the final prediction. Performance was compared against single-cell and direct multi-cell implementations across Manual and ChanTest datasets.

Results: For CiPA-ORd v1.0 using the ChanTest dataset, qNet achieved substantial performance, with AUCs for ROC1 and ROC2 of 1.000 and 0.958, respectively, and also meeting seven "excellent" classification criteria. In the ORd model, probability averaging consistently improved performance for both the Manual and ChanTest datasets relative to single-cell and direct multi-cell approaches.

Conclusion: Probability-averaged integration of multi-cell qNet predictions mitigates multicollinearity while preserving physiological relevance, yielding more stable and accurate in silico TdP risk classification and supporting broader applicability to preclinical safety assessment.

Purpose: Biomechanical parameters of the temporomandibular joint (TMJ), such as joint contact forces and intra-articular stresses, are suggested to contribute to the development of temporomandibular joint disorders, but are impractical to measure. In this study, we present a computational framework for evaluating these parameters by integrating a function assessment system and a patient-specific modeling approach.

Methods: The pipeline consists of acquiring patients' functional and morphological data and developing combined multibody dynamics and finite-element (MBD-FE) models for simulating their specific biting tasks. We demonstrate the approach in a pre-/post-orthognathic surgery scenario and present the measured and simulated outputs.

Results: In a three-patient cohort of one Class I control and two surgical patients (one Class II and one Class III patient), surgery was accompanied by functional changes such as increased bite force capacity and shifts in muscle-usage during unilateral first premolar clenching that brought the surgical cases closer to the control case. Also, morphological measurements showed postoperative adaptations in condylar size and joint space. Simulations demonstrated that contralateral joint forces exceeded ipsilateral forces during unilateral biting and predicted regions of concentrated disc stress that coincided with regions of reduced joint gap and poorer articular congruency, highlighting how morphology-function interactions shape local mechanics.

Conclusion: By unifying individualized functional inputs and subject-specific geometries, the framework provides a practical basis for patient-tailored assessment of biomechanical parameters and decision support in TMJ care.

Purpose: Peripheral artery disease (PAD) predominantly affects the lower extremities, where complex biomechanical deformations during limb flexion contribute to disease progression and treatment failure. While human and cadaver studies have characterized these deformations, preclinical device testing requires large-animal models that replicate human arterial anatomy and biomechanics. Swine are commonly used, yet their biomechanical comparability to humans remains poorly defined.

Methods: We performed a detailed morphometric and biomechanical analysis of the external iliac (EIA), superficial femoral (SFA), and popliteal (PA) arteries in 20 Yucatan and 16 domestic swine using computed tomography angiography. Arteries were evaluated in straight and flexed limb postures to assess diameters, lengths, axial compression, tortuosity, bending angles, and inscribed sphere radii. Breed-specific effects of age and weight were also analyzed.

Results: Porcine arterial dimensions closely matched human lower extremity vessels. EIA diameters (4.9-7.2 mm) corresponded to human SFA, porcine SFA (4.1-5.9 mm) approximated human PA, and porcine PA (3.0-4.7 mm) resembled human tibial arteries. Segment lengths supported use of multiple devices. Flexion induced 12-33% axial compression, mimicking worst-case human scenarios. Tortuosity increased distally, and bending characteristics in porcine PAs aligned with human data. In Yucatan swine, vessel diameters were stable with age and weight, while domestic swine exhibited greater variability. Flexion-induced compression and tortuosity were not influenced by age or weight.

Conclusion: Swine are well-suited for modeling the geometry and biomechanics of human lower extremity arteries. Their anatomical compatibility and ability to replicate physiologic deformations make them valuable models for preclinical testing of PAD therapies and vascular devices.

The complexities of bone architecture, with its hierarchical organization and varying spatiotemporal scales, necessitate advanced modeling techniques to capture its mechanical behavior precisely. This review aims to highlight recent trends in capturing the multiscale nature of bone using two primary computational approaches: classical and data-driven frameworks. Each class is assessed regarding its versatility in achieving scale dimensions, modeling complex behavior, integrating biological data, and balancing computational efficiency and interpretability. In addition, hybrid techniques have been shown to offer future avenues for promising robust and generalizable modeling. Therefore, particular attention has been given to the synergy between these techniques. A hierarchical decision matrix is proposed to translate this review into actionable guidance, shedding light on the selection or combination of appropriate techniques based on specific application contexts, such as data availability, modeling objectives, and computational constraints. This review aims to serve as both a state-of-the-art synthesis and a practical reference for future advancements in multiscale bone biomechanics.

Purpose: Understanding how the neuromuscular system adapts to increasing force demands is essential for characterizing compensatory motor control. This study investigated force-dependent reconfiguration of muscle and cortical functional networks during isometric upper-limb tasks.

Methods: Twelve healthy participants performed isometric elbow flexion at 30%, 50%, and 70% of maximal voluntary contraction (MVC). Surface electromyography (sEMG) from eight upper-limb muscles and electroencephalography (EEG) from 21 scalp electrodes were recorded concurrently. Directed functional connectivity was estimated using generalized partial directed coherence (GPDC), and graph-theoretical metrics-average global efficiency (AGE), average clustering coefficient (ACC), and average path length (APL)-were computed separately for muscle and cortical networks.

Results: In the muscle network, a significant main effect of force level was observed. Compared with 30% MVC, AGE increased by 12.24% ( $P=0.043$ ) and APL decreased by 17.14% ( $P=0.031$ ) at 70% MVC, while ACC increased by 44.64% ( $P=0.018$ ). In the EEG beta band, AGE increased by 8.12% ( $P=0.048$ ) and APL decreased by 12.34% ( $P=0.036$ ) at 70% MVC relative to 30% MVC. Gamma band changes were limited or non-significant across conditions.

Conclusion: These results demonstrate systematic, force-dependent reconfiguration of both muscle and cortical functional networks during isometric force production. Rather than indicating improved performance or neural plasticity, the observed network changes suggest shifts in coordination strategies as force demands increase. The present framework provides quantitative network metrics that can be extended to clinical and longitudinal studies in future work.

Purpose: Assessment of muscle coordination during cycling can provide insight into motor control strategies and movement efficiency. This study evaluated muscle synergy patterns as indicators of neuromuscular coordination in the lower limbs across three power levels of cycling (LPL = Lowest Power Level, MPL = Middle Power Level, HPL = Highest Power Level).

Methods: Twenty recreational cyclists performed a graded cycling test on a stationary bicycle ergometer. Electromyography (EMG) was recorded bilaterally from seven lower-limb muscles and muscle synergies were extracted using non-negative matrix factorization. The Synergy Index (SI) and Synergy Coordination Index (SCI) were calculated to assess muscle coordination patterns.

Results: Four muscle synergies were identified consistently across power levels, with changes in synergy composition and activation timing correlated with increasing muscular demands. At the dominant hip, SI remained consistent across power levels (0.50 ± 0.11 at LPL, 0.56 ± 0.15 at MPL, 0.54 ± 0.15 at HPL). At the dominant knee, SI decreased with increasing power (0.47 ± 0.07 at LPL to 0.34 ± 0.05 at HPL; p < 0.01, η_p² = 0.51). At the dominant ankle, SI increased with increasing power (0.19 ± 0.09 at LPL to 0.27 ± 0.10 at HPL; p < 0.01, η_p² = 0.41). The SCI increased with increasing power level (0.08 ± 0.04 at LPL, 0.13 ± 0.08 at MPL, 0.24 ± 0.11 at HPL; p < 0.01, Kendall's W = 0.59).

Conclusion: These findings provide insight into how the central nervous system modulates its response to increasing mechanical demands. Combining synergy indices offers a promising approach to assess motor control, inform rehabilitation, and optimize performance in cycling tasks.

Purpose: Atherosclerotic vascular disease remains a leading cause of morbidity and mortality worldwide. Current treatments such as angioplasty, stenting, and atherectomy are invasive and limited by restenosis, thrombosis, and incomplete long-term efficacy. Pulsed field ablation (PFA), a nonthermal electroporation-based modality, has demonstrated safety in other cardiovascular applications, but it has not been applied for the treatment of endoluminal vascular diseases. We investigated whether pulsed electric fields could be delivered within the coronary artery and if PFA could selectively ablate the cellular components of atherosclerotic plaques.

Methods: An endoluminal bipolar PFA probe was fabricated using a balloon catheter with flexible electrodes and evaluated in potato and ex vivo porcine hearts. The electrical conductivities of human atherosclerotic plaques were derived from previous impedance measurements for patient-specific multi-tissue and single-cell electroporation modeling. PFA was then evaluated for selective decellularization within an electrical conductivity-matched 3D fibrotic atherosclerosis tissue mimic using high concentrations of human macrophages and aggregated oxidized low-density lipoproteins, encapsulated within a collagen matrix.

Results: Endoluminal probe positioning and high-voltage pulsed electric field delivery feasibility was established within the porcine left coronary arteries, with susequent potato lesions experiments demonstrating maximum ablations (6.99 cm²) and current (13 A) with equal treatment parameters. The multi-tissue model then indicated that endoluminal PFA can effectively cover > 95% of severe and thick plaques with irreversible electroporation, with single-cell modeling supporting the electroporation of foam cells within the plaque. The 3D atherosclerosis mimic validated the ability of PFA to completely ablate the foam cells with fibrotic tissue at > 1000 V/cm.

Conclusions: This study provides practical demonstration of PFA for the treatment of atherosclerotic vascular disease. By combining experimental validation with computational modeling, we establish proof-of-concept that endoluminal PFA can selectively ablate diseased cells while preserving extracellular architecture, laying the groundwork for future translational development of this therapy.

Current research in predicting traumatic brain injury risk focuses on the relationship between head impacts and the likelihood of white matter damage, often overlooking the role of neurovascular coupling in the injury response. To fill this gap, we combined biomechanical and neurodynamic principles to simulate alterations to large-scale resting-state brain activity following head impacts. We simulated cortical neural activity with a network of Kuramoto phase oscillators, using structural connectivity to define coupling and a vascular-informed local resource term to capture neurovascular coupling. By combining the neurodynamic model with a brain mechanics model, we investigated two mechanistic pathways of network dysfunction: (1) white matter damage, reflected in degrading network edges, and (2) local tissue oxygenation decline, reflected in adjusting the resource term at each network node. We simulated 53 real-world head impacts using a vasculature template to evaluate the changes in simulated functional connectivity (FC) and neural dynamics relative to injury outcomes (concussion vs. no concussion). To assess vascular variability, simulations were repeated across 41 individual vasculature maps. Our results show simulated FC changes (measured by Pearson's correlation) consistently correlated well with injury outcomes, regardless of injury mechanism (AUC = 0.89 and 0.90), However, the two injury models yielded distinct FC patterns as indicated by graph metrics. Vascular variability substantially influenced how injury affected FC, with some brains exhibiting resilience to synchrony disruption depending on their vascular structure. These findings offer testable insight into the neurovascular mechanism of brain dysfunction after TBI and have important implications for individualized protection and treatment.