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Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange’s equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange’s equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

In order to improve the efficiency of heart valve simulation, we proposed a fast isogeometric simulation approach for time-dependent heart valve simulation algorithm with the idea of Geometric-Independent Field approximation (GIFT for short). For the solution of the blood flow field problem in a heart valve, the fluid background mesh is first simplified, then a Bézier tetrahedral mesh is generated based on the simplified mesh to maintain geometric precision, and finally, the fluid velocity field and pressure are solved. In addition, the GIFT idea is used to represent the geometry of computational domain geometry and approximate the physical field solution with different basis function spaces to obtain the numerical solution with the same precision as before simplification. In the structural mechanics simulation of valve leaflets, NURBS surfaces are used to represent the geometric model. To avoid degeneration on geometric boundary, a single leaflet geometric patch is subdivided into four patches. The immersion geometry strategy is adopted in solving the deformation problem of cardiac valve leaflets to achieve high simulation precision, and the dynamic augmented Lagrangian algorithm is used to couple fluid–structure control equations. For the time discretization, the generalized αmethod is used to control high-frequency dissipation. Numerical examples and comparisons with previous methods are also presented. The proposed algorithm can reduce the computing costs by about 54.3%, which proves the effectiveness of the proposed method.

Xylitol has reported to decrease gingival inflammation and nasopharyngeal pneumonia, which indicated that xylitol may have potential application in respiratory diseases. Although some studies have reported the inhalation toxicity of xylitol, however, the longest period tested was only for 14 days. The inhalation toxicity of xylitol is insufficient. This work investigated the potential subacute toxicity of xylitol according to the OECD TG 412. Rats were randomly divided into a control group and different dosage groups (2 g/m3, 3 g/m3, 5 g/m3), and exposed for 6 hours/day, 5 days/week for 28 days. At the end of the exposure or recovery period, clinical signs, mortality, body weight, food consumption, hematology, blood biochemistry, gross pathology, organ weight, and histopathology were examined. Compared with the control group, rats of both sexes in the exposure groups exhibited no significant changes in body weight, organ mass, and food uptake. After the xylitol exposure, aspartate aminotransferase activity in the xylitol group (3 g/m3) was significantly higher than that in the control group, while other blood indicators and pathological changes of liver and the analysis of the recovery group showed no changes, suggesting that xylitol exerted no observable toxic effect on the liver. Finally, other observations including the histopathology of target organs and hematology also showed no alterations. These results indicated that xylitol had no significant inhalation toxicity at doses up to 5 g/m3. These subacute inhalation toxicity results of xylitol showed that its no-observed-adverse-effect concentration (NOAEC) in rats was determined to 5 g/m3.

Macrophage phagocytosis is critical for the immune response, homeostasis regulation, and tissue repair. This intricate process involves complex changes in cell morphology, cytoskeletal reorganization, and various receptor-ligand interactions controlled by mechanical constraints. However, there is a lack of comprehensive theoretical and computational models that investigate the mechanical process of phagocytosis in the context of cytoskeletal rearrangement. To address this issue, we propose a novel coarse-grained mesoscopic model that integrates a fluid-like cell membrane and a cytoskeletal network to study the dynamic phagocytosis process. The growth of actin filaments results in the formation of long and thin pseudopods, and the initial cytoskeleton can be disassembled upon target entry and reconstructed after phagocytosis. Through dynamic changes in the cytoskeleton, our macrophage model achieves active phagocytosis by forming a phagocytic cup utilizing pseudopods in two distinct ways. We have developed a new algorithm for modifying membrane area to prevent membrane rupture and ensure sufficient surface area during phagocytosis. In addition, the bending modulus, shear stiffness, and cortical tension of the macrophage model are investigated through computation of the axial force for the tubular structure and micropipette aspiration. With this model, we simulate active phagocytosis at the cytoskeletal level and investigate the mechanical process during the dynamic interplay between macrophage and target particles.

Increased blood viscosity in type 2 diabetes mellitus (T2DM) is a risk factor for the development of insulin resistance and diabetes-related vascular complications; however, individuals with T2DM exhibit heterogeneous hemorheological properties, including cell deformation and aggregation. Using a multiscale red blood cell (RBC) model with key parameters derived from patient-specific data, we present a computational study of the rheological properties of blood from individual patients with T2DM. Specifically, one key model parameter, which determines the shear stiffness of the RBC membrane (μ) is informed by the high-shear-rate blood viscosity of patients with T2DM. At the same time, the other, which contributes to the strength of the RBC aggregation interaction (D0), is derived from the low-shear-rate blood viscosity of patients with T2DM. The T2DM RBC suspensions are simulated at different shear rates, and the predicted blood viscosity is compared with clinical laboratory-measured data. The results show that the blood viscosity obtained from clinical laboratories and computational simulations are in agreement at both low and high shear rates. These quantitative simulation results demonstrate that the patient-specific model has truly learned the rheological behavior of T2DM blood by unifying the mechanical and aggregation factors of the RBCs, which provides an effective way to extract quantitative predictions of the rheological properties of the blood of individual patients with T2DM.