Annals of Biomedical Engineering最新文献

Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, which is a multistep, time-consuming process. To improve the model generation process, we herein present SeqSeg (sequential segmentation): a novel deep learning-based automatic tracing and segmentation algorithm for constructing image-based vascular models. SeqSeg leverages local U-Net-based inference to sequentially segment vascular structures from medical image volumes. We tested SeqSeg on CT and MR images of aortic and aortofemoral models and compared the predictions to those of benchmark 2D and 3D global nnU-Net models, which have previously shown excellent accuracy for medical image segmentation. We demonstrate that SeqSeg is able to segment more complete vasculature and is able to generalize to vascular structures not annotated in the training data.

Tissue engineering is a dynamic field focusing on the creation of advanced scaffolds for tissue and organ regeneration. These scaffolds are customized to their specific applications and are often designed to be complex, large structures to mimic tissues and organs. This study addresses the critical challenge of effectively characterizing these thick, optically opaque scaffolds that traditional imaging methods fail to fully image due to their optical limitations. We introduce a novel multi-modal imaging approach combining ultrasound, photoacoustic, and acoustic radiation force impulse imaging. This combination leverages its acoustic-based detection to overcome the limitations posed by optical imaging techniques. Ultrasound imaging is employed to monitor the scaffold structure, photoacoustic imaging is employed to monitor cell proliferation, and acoustic radiation force impulse imaging is employed to evaluate the homogeneity of scaffold stiffness. We applied this integrated imaging system to analyze melanoma cell growth within silk fibroin protein scaffolds with varying pore sizes and therefore stiffness over different cell incubation periods. Among various materials, silk fibroin was chosen for its unique combination of features including biocompatibility, tunable mechanical properties, and structural porosity which supports extensive cell proliferation. The results provide a detailed mesoscale view of the scaffolds’ internal structure, including cell penetration depth and biomechanical properties. Our findings demonstrate that the developed multimodal imaging technique offers comprehensive insights into the physical and biological dynamics of tissue-engineered scaffolds. As the field of tissue engineering continues to advance, the importance of non-ionizing and non-invasive imaging systems becomes increasingly evident, and by facilitating a deeper understanding and better characterization of scaffold architectures, such imaging systems are pivotal in driving the success of future tissue-engineering solutions.

Purpose

The patchy anatomical distribution of atherosclerosis has been attributed to variation in haemodynamic wall shear stress (WSS). The consensus is that low WSS and a high Oscillatory Shear Index (OSI) trigger the disease. We found that atherosclerosis at aortic branch sites correlates threefold better with transverse WSS (transWSS), a metric which quantifies multidirectional near-wall flow. Coronary artery disease has greater clinical significance than aortic disease but computation of WSS metrics is complicated by the substantial vessel motion occurring during each cardiac cycle. Here we present the first comparison of the distribution of atherosclerosis with WSS metrics computed for moving coronary arteries.

Methods

Maps of WSS metrics were computed using dynamic geometries reconstructed from angiograms of ten non-stenosed human right coronary arteries (RCAs). They were compared with maps of fatty streak prevalence derived from a previous study of 1852 RCAs.

Results

Time average WSS (TAWSS), OSI, transWSS and the cross-flow index (CFI), a non-dimensional form of the transWSS, gave non-significant or significant but low spatial correlations with lesion prevalence. The highest correlation coefficient (0.71) was for the relative residence time (RRT), a metric that decreases with TAWSS and increases with OSI. The coefficient was not changed if RRT was calculated using CFI, which captures multidirectional WSS only, rather than OSI, which encompasses both multidirectional and oscillatory WSS.

Conclusion

Contrary to our earlier findings in the aorta, low WSS in combination with highly multidirectional flow correlates best with lesion location in the RCA, explaining approximately half of its anatomical variation.

To elucidate the mechanisms of cellular mechanotransduction, it is necessary to employ biomaterials that effectively merge biofunctionality with appropriate mechanical characteristics. Agarose and collagen separately are common biopolymers used in cartilage mechanobiology and mechanotransduction studies but lack features that make them ideal for functional engineered cartilage. In this study, agarose is blended with collagen type I to create hydrogels with final concentrations of 4% w/v or 2% w/v agarose with 2 mg/mL collagen. We hypothesized that the addition of collagen into a high-concentration agarose hydrogel does not diminish mechanical properties. Acellular and cell-laden studies were completed to assess rheologic and compressive properties, contraction, and structural homogeneity in addition to cell proliferation and sulfated glycosaminoglycan production. Over 21 days in culture, cellular 4% agarose–2 mg/mL collagen I hydrogels seeded with primary murine chondrocytes displayed structural and bulk mechanical behaviors that did not significantly alter from 4% agarose-only hydrogels, cell proliferation, and continual glycosaminoglycan production, indicating promise toward the development of an effective hydrogel for chondrocyte mechanotransduction and mechanobiology studies.

Blast traumatic brain injury (bTBI) is a prominent military health concern. The pervasiveness and long-term impacts of this injury highlight the need for investigation of the physiological outcomes of bTBI. Preclinical models allow for the evaluation of behavioral and neuropathological sequelae associated with bTBI. Studies have implemented rodent models to investigate bTBI due to the relative small size and low cost; however, a large animal model with similar neuroanatomical structure to humans is essential for clinical translation. Small blast simulators are used to induce bTBI in rodents, but a large animal model demands a larger device. This study describes a large advanced blast simulator (ABS4) that is a gas-detonation-driven system consisting of 5 sections totaling 40 ft in length with a cross-section of 4 × 4 ft at the test section. It is highly suitable for large animals and human surrogate investigations. This work characterized the ABS4 in preparation of large-scale bTBI testing. An array of tests were conducted with target overpressures in the test section ranging from 10 to 50 psi, and the pressure-time profiles clearly illustrate the essential characteristics of a free-field blast wave, specifically a sharp peak pressure and a defined negative phase. Multiple blast tests conducted at the same target pressure produced very similar pressure profiles, exhibiting the reproducibility of the ABS4 system. With its extensive range of pressures and substantial size, the ABS4 will permit military-relevant translational blast testing.

The migration of healthcare professionals from the Philippines, known as the ‘brain drain,’ poses a significant challenge to the nation’s health system. The shortfall in healthcare workers, exacerbated by this exodus, threatens disease control and overall public health. However, the rise of public medical schools offers a strategic response to this crisis. With new programs approved by the Commission on Higher Education, state universities are expanding access to medical education, particularly in underserved regions. This initiative is crucial for addressing the immediate shortage of healthcare professionals and building a more resilient and self-sustaining healthcare workforce in the Philippines.

The need for safe and effective methods to manage deep vein thrombosis (DVT), given the risks associated with anticoagulants and thrombolytic agents, motivated research into innovative approaches to resolve blood clots. In response to this challenge, sonothrombolysis is being explored as a technique that combines microbubbles, ultrasound, and thrombolytic agents to facilitate the aggressive dissolution of thrombi. Prior studies have indicated that relatively large microbubbles accelerate the dissolution process, either in an in vitro or an arterial model. However, sonothrombolysis using large microbubbles must be evaluated in venous thromboembolism diseases, where blood flow velocity is not comparable. In this study, the efficacy of sonothrombolysis was validated in a murine model of pre-existing DVT. During therapy, microfluidically produced microbubbles of 18 μm diameter and recombinant tissue plasminogen activator (rt-PA) were administered through a tail vein catheter for 30 min, while ultrasound was applied to the abdominal region of the mice. Three-dimensional ultrasound scans were performed before and after therapy for quantification. The residual volume of the thrombi was 20% in animals post sonothrombolysis versus 52% without therapy ( $p=0.012<0.05$ ), indicating a significant reduction in DVT volume. Histological analysis of tissue sections confirmed a reduction in DVT volume post-therapy. Therefore, large microbubbles generated from a microfluidic device show promise in ultrasound-assisted therapy to address concerns related to venous thromboembolism.

Purpose: Individuals with walking impairment, such as those with cerebral palsy, often face challenges in leading physically active lives due to the high energy cost of movement. Assistive devices like powered exoskeletons aim to alleviate this burden and improve mobility. Traditionally, optimizing the effectiveness of such devices has relied on time-consuming laboratory-based measurements of energy expenditure, which may not be feasible for some patient populations. To address this, our study aimed to enhance the state-of-the-art predictive model for estimating steady-state metabolic rate from 2-min walking trials to include individuals with and without walking disabilities and for a variety of terrains and wearable device conditions.

Methods: Using over 200 walking trials collected from eight prior exoskeleton-related studies, we trained a simple linear machine learning model to predict metabolic power at steady state based on condition-specific factors, such as whether the trial was conducted on a treadmill (level or incline) or outdoors, as well as demographic information, such as the participant's weight or presence of walking impairment, and 2 minutes of metabolic data.

Results: We demonstrated the ability to predict steady-state metabolic rate to within an accuracy of 4.71 ± 2.7% on average across all walking conditions and patient populations, including with assistive devices and on different terrains.

Conclusion: This work seeks to unlock the use of in-the-loop optimization of wearable assistive devices in individuals with limited walking capacity. A freely available MATLAB application allows other researchers to easily apply our model.