Annals of Biomedical Engineering最新文献

Skeletal metastases disrupt bone remodeling, compromising mechanical integrity and increasing fracture risk. Cancer therapies can further influence bone quality. This study aimed to evaluate the impact of systemic cancer therapies (docetaxel (DTX) and zoledronic acid (ZA)) on bone quality in a preclinical model of mixed femoral metastases. Mixed metastases were induced in 5-6-week-old athymic male rats via intracardiac injection of luciferase-transfected ACE-1 canine prostate cancer cells (day 0). Healthy and inoculated rats were randomly assigned to receive no treatment, DTX (5 mg/kg), or ZA (60 μg/kg) on day 10. Tumor development was confirmed with bioluminescence imaging (day 14 and 21). Animals were euthanized on day 21. Bilateral femora were excised and underwent μCT scanning. Trabecular bone in the left femora was segmented for microstructural analysis. The left distal femora were then cut to 1 cm length and stained with barium sulfate for high-resolution μCT imaging to assess microdamage. The cut femora were then loaded to failure under axial compression. The right femora were processed for histology to evaluate bone histoarchitecture and verify tumor presence. DTX, despite lower bone mineral density, bone volume fraction, and trabecular number, showed reduced microdamage accumulation and improved load to failure in inoculated animals. ZA improved microstructural parameters, reduced damage volume fraction, and enhanced load to failure compared to inoculated untreated animals. These findings highlight the differential impact of cancer therapies on the quality of healthy and metastatic bone. Understanding these effects is essential for optimizing treatment strategies and minimizing skeletal complications secondary to skeletal metastases.

Purpose: Seizure recurrence, often presenting as clusters, is a major clinical concern linked to increased morbidity. The immediate postictal period is a critical yet understudied window where recurrence frequently arises. This study evaluates whether EEG features from postictal intervals can distinguish postictal-to-ictal (P-I) from postictal-to-interictal (P-Inter) transitions, enabling early recurrence prediction.

Methods: EEG data from the CHB-MIT database were analyzed, comprising 73 postictal episodes from seven patients (44 P-I, 29 P-Inter). Each episode was segmented into 10-second windows, yielding 876 segments. Fifty wavelet-based features were extracted from low-sample entropy channels and classified using Decision Tree (DT) and Long Short-Term Memory (LSTM) models. Performance was evaluated using nested cross-validation and, to test inter-patient generalization, per-patient stratified nested cross-validation.

Results: In subject-independent nested CV, DT achieved accuracy 0.75 (95% CI ± 0.029), sensitivity 0.73 (±0.041), specificity 0.77 (±0.038), F1-score 0.70 (±0.032), AUC 0.75 (±0.028), and FPR 0.20 (±0.039). LSTM yielded accuracy 0.71 (±0.027), sensitivity 0.69 (±0.066), specificity 0.72 (±0.069), F1-score 0.65 (±0.027), AUC 0.73 (±0.035), and FPR 0.28 (±0.069). Under patient-stratified evaluation, accuracy decreased to 0.67 (±0.076) for DT and 0.65 (±0.093) for LSTM, reflecting inter-patient variability.

Conclusion: These proof-of-concept findings indicate that postictal EEG, particularly P-I transitions, may encode information relevant to seizure recurrence. While the observed performance remains moderate, these results provide preliminary evidence warranting further investigation rather than indicating immediate clinical applicability.

Purpose: Automated coronary artery segmentation from computed tomography coronary angiography (CTCA) can support quantitative analysis, yet performance varies with data characteristics and model configuration. This study systematically evaluates how dataset size, computed tomography (CT) window width and level (W/L) values, model architectures, and vessel geometry affect nnU-Net-based coronary artery segmentation.

Methods: Using 1000 annotated CTCA scans from the publicly available ImageCAS dataset, we conducted a series of experiments in which we varied the number of training cases, applied different W/L values, compared 2D and multiple 3D nnU-Net architectures (Full-Resolution, Low-Resolution, Cascade, and ensemble), and assessed performance across vessel curvature and tortuosity. All models were evaluated on an independent 200-case test set using Dice similarity coefficient (DSC) and intersection over union (IoU).

Results: Segmentation performance increased with dataset size and plateaued beyond 100 training cases. W/L values of 800/200 Hounsfield units (HU) and 1300/350 HU slightly improved performance over original images. 3D models outperformed the 2D model, with the 3D ensemble achieving the highest accuracy (DSC 0.8337; 95% confidence interval [CI]: 0.8269-0.8405, IoU 0.7178; 95% CI: 0.7080-0.7275). Performance remained consistent across vessels with different curvature and tortuosity, indicating model robustness to geometric complexity.

Conclusion: The nnU-Net framework achieves accurate and generalizable coronary artery segmentation across diverse dataset sizes, W/L values, model architectures, and vessel geometries. Optimizing input settings and architectural strategies further enhances segmentation accuracy.

Purpose: Axial headfirst impacts can cause catastrophic cervical spine injuries when the head is rapidly decelerated and the cervical spine is compressed by the torso's inertia. The goal of this study was to quantify the cervical vertebral translations, rotations, eccentricity and curvature, and the head rotation in human subjects exposed to non-impact inverted freefalls that represented the pre-impact dynamics of a headfirst impact.

Methods: Eleven human subjects were exposed to 4 headfirst freefalls (2 relaxed, 2 with pre-bracing) while secured in a race car seat fixed to a carriage that was inverted and released to freefall over 312.5 ms (0.479 m) before being decelerated to rest. Sagittal fluoroscopy of the cervical spine was acquired and analyzed at freefall onset and end to extract vertebral and head posture variables. Linear mixed models were used to assess the effect of time (freefall onset/end) and condition (relaxed/braced) on the posture variables.

Results: Subjects consistently moved their cervical spine anteriorly and inferiorly, rotated their vertebrae and head in flexion, and increased their spinal eccentricity. Small changes in spinal curvature and intervertebral angles suggested that the subjects responded using an "en bloc" rotation of the cervical spine and head about a point inferior to C6. Compared to the relaxed condition, pre-freefall bracing produced a different initial posture but a similar end posture.

Conclusion: Despite considerable inter-subject variability, a consistent neck and head reorientation was observed, albeit with variable underlying segmental cervical spine posture, providing valuable input for cadaveric tests and computational models simulating headfirst impacts to improve injury prediction.

Purpose: Physical liver models are essential for surgical training and biomechanical research, offering standardized, reproducible, and ethically compliant experimental platforms. However, accurately replicating the nonlinear mechanical behavior of real liver tissue remains a challenge for existing simulant materials.

Methods: This study proposed a design strategy for particle-reinforced hyperelastic composites based on an improved Mori-Tanaka method. An incremental constitutive model under finite deformation was established to theoretically predict the nonlinear stress-strain response of the composite material. Using Ecoflex silicone as the matrix and Dragon Skin microspheres as the reinforcing phase, composites with volume fractions ranging from 10 to 30% were fabricated and subjected to quasi-static uniaxial compression tests to validate the model. By leveraging stress-strain data from both bovine and human livers, inverse optimization was employed to determine the optimal material formulations that match the mechanical behavior of liver tissues.

Results: The mean relative errors between the model predictions and experimental results were 16.11, 16.98, and 10.86% for volume fractions of 10, 20, and 30%, respectively, validating the accuracy of the theoretical model. The composites with an EF10-30% matrix and a reinforcement volume fraction of 16.14%, as well as those with an EF10-50% matrix and volume fraction of 19.37%, closely matched the mechanical properties of bovine and human liver tissue, respectively.

Conclusion: This study establishes the quantitative framework for designing particle-reinforced liver simulant material, integrating micromechanical modeling. The methodology achieves precise matching of nonlinear tissue mechanics and provides systematic guidelines for developing high-fidelity simulants for surgical training and biomechanical applications.

Drug resistance, which arises from a variety of biological mechanisms such as drug efflux by ATP-binding cassette transporters, epithelial-to-mesenchymal transition (EMT), the presence of cancer stem cells (CSCs), immune system evasion, and changes in the tumor microenvironment (TME), poses serious treatment challenges for breast cancer. This paper offers a thorough analysis of various resistance mechanisms, going into great detail into the molecular, cellular, and genetic contributors. We next look at how nanotechnology-based approaches, such multifunctional nanocarriers, smart nanoparticle systems, and targeted drug delivery, can be developed to get around these resistance mechanisms, building on this biological basis. The use of particular nanotechnology platforms (such as liposomes, micelles, and dendrimers) to target CSCs, circumvent efflux pumps, alter immunological responses, and improve treatment precision is highlighted. Important issues in clinical translation and regulatory considerations are also covered in the study. This two-part investigation seeks to improve therapeutic outcomes for drug-resistant breast cancer by bridging the gap between molecular insight and nanotechnological innovation.

Purpose: 1) Characterize the magnitude and consistency of dog-leash tension during routine walks in an at-home setting and 2) determine the effects of walking with a dog on-leash on gait intensity and variability.

Methods: A wireless system was designed to simultaneously measure dog-leash tension and gait kinematics. Twenty human subjects took their dog for one routine walk in their home environment and repeated the same walk without their dog for comparison. Peak force and peak loading rate were computed during periods of non-gait and during each stride and characterized using standard statistical metrics. Gait intensity and variability metrics were compared between trials (walking with a dog versus walking alone) using paired samples t-tests.

Results: The largest pulling event recorded was during a period of non-gait, with a force of 412.5 N and loading rate of 2868.7 N/s. Half of our participants experienced leash tension of 125 N or greater when walking their dog. Participants had increased stride time variability (p = 0.035) and decreased power spectral density amplitude in the vertical (p = 0.016) and anteroposterior directions (p = 0.003) when walking with a dog compared to walking alone. Gait intensity metrics were not different between walking conditions.

Conclusion: Some dog owners routinely experience dog-pulling forces that pose a risk of injury to musculoskeletal tissue directly and indirectly through accidental falls. There is a large amount of between- and within-subject variability in leash pulling metrics. Walking a dog on-leash challenges dynamic balance, as reflected through increased gait variability.

Developing a therapeutic transcatheter device for cardiovascular diseases requires precise preclinical large animal models and robust evaluation methods. Three key aspects are essential for evaluating the safety and efficacy of a device: (1) types of swine heart failure models, (2) appropriate echocardiographic methods, and (3) the specific implantation approaches in swine. Common heart failure models include rapid pacing heart failure models, volume overload models, and ischemic models. Among the ischemic models, several approaches are performed to replicate the pathological conditions, such as coronary artery ligation, ameroid constrictor implantation, balloon occlusion, and coronary embolization. The optimal choice of echocardiographic method varies based on the procedure and includes transthoracic, transesophageal, intracardiac, and epicardial techniques. Echocardiographic imaging serves as an invaluable tool in both preclinical and clinical settings, providing structural and functional assessments, procedural guidance during device placement, and comprehensive evaluations of cardiac function and structural changes before and after implantation. Implantation approaches, such as trans-atrial, trans-apical, trans-septal, and trans-right pulmonary methods, each offer unique advantages and challenges. A thorough understanding of these approaches, including the orientation of the catheter tip relative to critical anatomical structures like the mitral annulus or coronary vasculature, is essential for ensuring procedural success. This review explores these aspects to contribute to the development and refinement of catheter-based cardiovascular interventions, ensuring reliability in preclinical data and successful clinical translation.

Purpose: To investigate the contribution of knee adduction moment (KAM) lever arm components and examine their relationships with gait variables in Early-mild and Severe osteoarthritis (OA) groups.

Methods: Female patients with medial knee OA were classified into two groups: Early-mild (Kellgren-Lawrence (KL) grade 1-2, n = 46) and Severe (KL 3-4, n = 40). At the first peak of KAM, we measured the lever arm; the lateral positions of the body center of mass, knee joint center, and foot center of pressure (foot COP); and the frontal plane angles of the trunk, pelvis, hip, and knee. Additionally, foot progression angle and step width were measured. Hierarchical multiple regression determined the contribution of lever arm components, and partial correlation assessed their relationships with gait variables.

Results: In both groups, smaller knee varus angle (Early-mild: β = 0.563; Severe: β = 0.346) and more lateral foot COP position (Early-mild: β = - 0.316; Severe: β = - 0.686) were associated with a shorter lever arm. Knee varus angle and foot COP explained 46.4% and 4.8% of lever arm variance in Early-mild group, and 11.3% and 35.6% in Severe group. Smaller contralateral pelvic drop and greater hip adduction related to smaller knee varus angle. Especially in Early-mild OA, greater toe-in was associated with smaller knee varus. Additionally, greater toe-in and wider step width were related to foot COP closer to the knee.

Conclusions: The characteristics of the contributions of the lever arm components and related gait variables were revealed in Early-mild and Severe OA. These findings may inform gait strategies to reduce KAM.