University of California, San Diego

Measuring the forces of individual muscles in a muscle group around a joint is non-trivial, and researchers have suggested using surrogates for individual muscle forces instead. Traditionally, experimentalists have shown that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure (IMP) generated by the muscle belly. However, IMP proves difficult to measure in vivo, due to variations from sensor placement and invasiveness of the procedure. Numerical biomechanical simulations offer a tool to analyze muscle contractions, enabling new insights into the correlations among non-invasive experimentally measurable quantities, such as strains and the force output. In this work, we investigate the correlations between the muscle force output, the principal, shear and volumetric strains experienced by the muscle, as well as the pressure developed within the muscle belly as the tissue undergoes isometric contractions with varying activation profiles and magnitudes. It is observed that pressure does not correlate well with force output under higher sub-maximal and maximal activation levels, especially at locations away from the center of the muscle belly due to pressure relaxation effects. This study reveals strong correlations between force output and the strains at all locations of the belly, irrespective of the type of activation considered. This observation offers evidence for further in vivo studies using experimentally measurable principal and volumetric strains in the muscle belly as proxies for the force generation by the individual muscle and consequently enables the estimation on the contribution of various muscle groups to the total force.

• Developed a simplified lumped-parameter model for small heave plate WECs. • Validated model predictions against extensive experimental data. • The simplified model captured key WEC dynamics efficiently. • Linked laboratory behavior to ocean-relevant operational conditions. Wave energy converters (WECs) are increasingly being adapted for smaller-scale applications, such as powering unmanned oceangoing vehicles. For free-floating ocean platforms, there is a need for a simplified modeling framework that can be efficiently applied and used as a foundation for future optimization and design insight. We present the development and validation of a lumped-parameter model representative of a small heave plate WEC designed for short-period waves (1-5 s) typical of moderate wind conditions. The modeled system consists of a negatively buoyant drag plate and a surface buoy connected by a spring-damper power take-off (PTO) that captures the relative motion between the two bodies. To validate the model, a physical prototype of the heave plate WEC was constructed. A linear actuator simulated wave excitation, replicating the idealized vertical forcing experienced by a buoy in a wave field. Experiments varied wave conditions, system mass, and PTO parameters to assess their influence on the model and measured response. Results show that lower non-dimensional spring constants ( K *) improve broadband performance, while higher non-dimensional masses ( M *) yield only marginal increases in mean power. These findings confirm the model’s accuracy and provide guidance for optimizing WEC design for free-floating platforms.

We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, retrieving data from PubMed, Elsevier, Web of Science, and Cochrane Central Register of Controlled Trials . Primary outcomes included changes from baseline in acute headache medication days and monthly migraine days (MMD). Secondary outcomes comprised the incidence of adverse events (AEs), common AEs, and the proportion of patients achieving a ≥ 50% reduction in MMDs. Five randomized controlled trials including 875 patients (mean age of 42.7 years) were analyzed. Erenumab treatment was associated with significant reductions in acute headache medication days (mean difference = -1.72; 95% confidence interval [CI]: -2.81 to -0.62; p = 0.002) and MMDs (mean difference = -1.88; 95% CI: -2.68 to -1.07; p < 0.001). Whereas erenumab increased the risk of common AEs such as constipation (risk ratio [RR] = 1.43; 95% CI: 1.17 to 1.76; I2 = 14%), the overall incidence of AEs was not significantly different compared to placebo (RR = 1.02; 95% CI: 0.92 to 1.14; I2 = 57%). A higher proportion of patients achieved a ≥50% reduction in MMDs after 3 months in the erenumab group (RR = 1.49; 95% CI: 1.26 to 1.77; I2 = 22%). Erenumab appears effective in reducing migraine frequency and symptomatic medication use among patients with chronic migraine MOH, with an acceptable tolerability profile.

High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due to the resolution requirement of the target reconstruction, conventional deep learning-based methods often suffer from computational resource constraints or limited granularity. Conversely, implicit modeling is favored due to its computational efficiency and continuous representation at any resolution. We propose a neural implicit function-based method to learn a 3D surface to achieve anatomy-aware, precise pulmonary segment reconstruction, represented as a shape by deforming a learnable template. Additionally, we introduce two clinically relevant evaluation metrics to comprehensively assess the quality of the reconstruction. Furthermore, to address the lack of publicly available shape datasets for benchmarking reconstruction algorithms, we developed a shape dataset named Lung3D, which includes the 3D models of 800 labeled pulmonary segments and their corresponding airways, arteries, veins, and intersegmental veins. We demonstrate that the proposed approach outperforms existing methods, providing a new perspective for pulmonary segment reconstruction. Code and data will be available at https://github.com/HINTLab/ImPulSe.

Abstract Atherosclerotic lesions comprise different populations of macrophages, including lipid-laden macrophage foam cells and non-foamy, inflammatory macrophages, which play distinct roles in disease progression. Non-foamy macrophages express higher levels of inflammarafts – enlarged, cholesterol-rich lipid rafts hosting assemblies of inflammatory receptors – compared to foam cells in atherosclerotic lesions of Ldlr −/− mice. Apolipoprotein A-I binding protein (AIBP) has been shown to control lipid raft dynamics. This study investigated the effect of systemic AIBP deficiency on inflammaraft expression in foam cells and non-foamy macrophages in atherosclerotic lesions of hypercholesterolemic mice. A larger number of foam cells, with increased neutral lipid accumulation, populated atherosclerotic lesions in Apoa1bp −/− Ldlr −/− mice compared to Ldlr −/− mice. Importantly, AIBP-deficient foam cells expressed higher levels of TLR4 dimers and lipid rafts (markers of inflammarafts) than control mice, accompanied by larger atherosclerotic lesions and larger necrotic cores compared to Ldlr −/− mice. In a model of foam cells, Apoa1bp −/− bone marrow-derived macrophages incubated with oxidized LDL had increased expression of inflammation and atherosclerosis related genes. These results indicate that AIBP deficiency results in a phenotype shift in foam cells, characterized by increased lipid accumulation and increased expression of inflammarafts, and it correlates with the development of advanced atherosclerotic plaques.