Two-component macrophage model for active phagocytosis with pseudopod formation.

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.

Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals.

Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.

Liver toxicity in rats after subchronic exposure to HTP aerosol and cigarette smoke.

Background: Heated tobacco product (HTP) considered to be a novel tobacco product which was reported safer than traditional cigarettes evidenced by lower potential harmful components released. Liver is an important detoxification organ of the body, the chemical components in aerosols are metabolized in the liver after absorbed, so it is necessary to explore the effect of HTP on the liver. Materials and Methods: The potential effect of HTP and cigarette smoke (CS) on SD rats was explored according to OECD 413 subchronic inhalation. The rats were randomly divided into Sham (air), different dosage of HTP groups (HTP_10, 23 and 50 µg nicotine/L aerosol) and Cig_23 (23 µg nicotine/L aerosol) group. After exposure, the clinical pathology, inflammation and oxidative stress were measured. Results: The clinical pathology results showed that both HTP_50 and Cig_23 led to abnormality of ALT for male rats. CS and HTP exposure reduced the expression of IL-1ß, IL-6 and TNF-α and mitochondrial medicated oxidative stress. In addition, the ATP production was reduced in Cig_23 group. Although inflammation and oxidative stress were displayed, no apoptosis were observed by TUNEL assay and these existed obvious pathological changes only in HTP_50 group, while in CS group with equivalent nicotine, hepatocytes swelling were observed in liver. Conclusion: CS exposure induced liver damage through mitochondrial mediated oxidative stress and inflammation, which was also observed in high concentration of HTP exposure group. For the same equivalent nicotine, HTP may show lower toxic effect on liver than CS.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics.

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).

Single-cell characterization of deformation and dynamics of mesenchymal stem cells in microfluidic systems: A computational study.

Understanding the homing dynamics of individual mesenchymal stem cells (MSCs) in physiologically relevant microenvironments is crucial for improving the efficacy of MSC-based therapies for therapeutic and targeting purposes. This study investigates the passive homing behavior of individual MSCs in micropores that mimic interendothelial clefts through predictive computational simulations informed by previous microfluidic experiments. Initially, we quantified the size-dependent behavior of MSCs in micropores and elucidated the underlying mechanisms. Subsequently, we analyzed the shape deformation and traversal dynamics of each MSC. In addition, we conducted a systematic investigation to understand how the mechanical properties of MSCs impact their traversal process. We considered geometric and mechanical parameters, such as reduced cell volume, cell-to-nucleus diameter ratio, and cytoskeletal prestress states. Furthermore, we quantified the changes in the MSC traversal process and identified the quantitative limits in their response to variations in micropore length. Taken together, the computational results indicate the complex dynamic behavior of individual MSCs in the confined microflow. This finding offers an objective way to evaluate the homing ability of MSCs in an interendothelial-slit-like microenvironment.

Testicular tissue response following a 90-day subchronic exposure to HTP aerosols and cigarette smoke in rats.

Background: Researches have shown that chronic inhalation of cigarette smoke (CS) disrupts male reproductive system, but it is unclear about the mechanisms behind reproductive damages by tobacco toxicants in male rats. This study was designed to explore the effects of heated tobacco products (HTP) aerosols and CS exposure on the testicular health of rats. Materials and Methods: Experiments were performed on male SD rats exposed to filtered air, HTP aerosols at 10 µg/L, 23 µg/L, and 50 µg/L nicotine-equivalent contents, and also CS at 23 µg/L nicotine-equivalent content for 90 days in five exposure groups (coded as sham, HTP_10, HTP_23, HTP_50 and Cig_23). The expression of serum testosterone, testicular tissue inflammatory cytokines (IL-1ß, IL-6, IL-10, TNF-α), reactive oxygen species (ROS), superoxide dismutase (SOD) and malondialdehyde (MDA), NLRP3 inflammasome-related mRNAs and proteins (NLRP3, ASC, and Caspase-1), the degree of pyroptosis and histopathology were investigated. Results: The results demonstrated that HTP_50 and Cig_23 caused varying degrees of oxidative damage to rat testis, resulting in a decrease of sperm quantity and serum testosterone contents, an increase in the deformity rate, expression levels of proinflammatory cytokines, and NLRP3 inflammasome-related mRNA, and an increase in the NLRP3, ASC, and Caspase-1-immunopositive cells, pyroptosis cell indices, and histopathological damage in the testes of rats. Responses from the HTP_10 and HTP_23 groups were less than those found in the above two exposure groups. Conclusion: These findings indicate that HTP_50 and Cig_23 induced oxidative stress in rat testes, induced inflammation and pyroptosis through the ROS/NLRP3/Caspase-1 pathway, and destroyed the integrity of thetesticular tissue structure.

The bionic neural network for external simulation of human locomotor system.

Muscle forces and joint kinematics estimated with musculoskeletal (MSK) modeling techniques offer useful metrics describing movement quality. Model-based computational MSK models can interpret the dynamic interaction between the neural drive to muscles, muscle dynamics, body and joint kinematics, and kinetics. Still, such a set of solutions suffers from high computational time and muscle recruitment problems, especially in complex modeling. In recent years, data-driven methods have emerged as a promising alternative due to the benefits of flexibility and adaptability. However, a large amount of labeled training data is not easy to be acquired. This paper proposes a physics-informed deep learning method based on MSK modeling to predict joint motion and muscle forces. The MSK model is embedded into the neural network as an ordinary differential equation (ODE) loss function with physiological parameters of muscle activation dynamics and muscle contraction dynamics to be identified. These parameters are automatically estimated during the training process which guides the prediction of muscle forces combined with the MSK forward dynamics model. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The results demonstrate that the proposed deep learning method can effectively identify subject-specific MSK physiological parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion and muscle forces predictions.

In silico modeling of patient-specific blood rheology in type 2 diabetes mellitus.

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.

Quantifying Shear-induced Margination and Adhesion of Platelets in Microvascular Blood Flow.

Platelet margination and adhesion are two critical and closely related steps in thrombus formation. Using dissipative particle dynamics (DPD) method that seamlessly models blood cells, blood plasma, and vessel walls with functionalized surfaces, we quantify the shear-induced margination and adhesion of platelets in microvascular blood flow. The results show that the occurrence of shear-induced RBC-platelet collisions has a remarkable influence on the degree of platelet margination. We characterize the lateral motion of individual platelets by a mean square displacement analysis of platelet trajectories, and find that the wall-induced lift force and the shear-induced displacement in wall-bounded flow cause the variation in near-wall platelet distribution. We then investigate the platelet adhesive dynamics under different flow conditions, by conducting DPD simulations of blood flow in a microtube with fibrinogen-coated wall surfaces. We find that the platelet adhesion is enhanced with the increase of fibrinogen concentration level but decreased with the increase of shear rate. These results are consistent with available experimental results. In addition, we demonstrate that the adherent platelets have a negative impact on the margination dynamics of the circulating platelets, which is mainly due to the climbing effect induced by the adherent ones. Taken together, these findings provide useful insights into the platelet margination and adhesion dynamics, which may facilitate the understanding of the predominant processes governing the initial stage of thrombus formation.

Probing the Interaction Between Supercarrier RBC Membrane and Nanoparticles for Optimal Drug Delivery.

Red blood cell (RBC) membrane-hitchhiking nanoparticles (NPs) have been an increasingly popular supercarrier for targeted drug delivery. However, the kinetic details of the shear-induced NP detachment process from RBC in blood flow remain unclear. Here, we perform detailed computational simulations of the traversal dynamics of an RBC-NP composite supercarrier with tunable properties. We show that the detachment of NPs from RBC occurs in a shear-dependent manner which is consistent with previous experiment results. We quantify the NP detachment rate in the microcapillary flow, and our simulation results suggest that there may be an optimal adhesion strength span of 25-40 µJ/m2 for rigid spherical NPs to improve the supercarrier performance and targeting efficiency. In addition, we find that the stiffness and the shape of NPs alter the detachment efficiency by changing the RBC-NP contact areas. Together, these findings provide unique insights into the shear-dependent NP release from the RBC surface, facilitating the clinical utility of RBC-NP composite supercarriers in targeted and localized drug delivery with high precision and efficiency.

The Relationship between Traditional Music in Xinjiang and the Geographical Environment of the Gobi Desert.

The natural environment and geographical environment provide the possibility for human survival and development and are also the premise of the formation of music culture. When studying the style characteristics and cultural types of music, more comprehensive and correct conclusions can be drawn only when considering various regional factors such as geography and topographic environment. If we want to explore the music of Xinjiang, we must understand the regional culture. The special geographical location of the Silk Road gives Xinjiang a foreign style compatible with Chinese and Western cultures. At the same time, because of the geographical environment of the Gobi Desert, the music style of Xinjiang is unique. This paper explores the relationship between the traditional music of Xinjiang and the Gobi desert, through the investigation and study of Xinjiang and various musical styles, which have certain reference significance for the study of the Chinese traditional culture.

Evaluation of inhalation toxicology after a 90-day xylitol aerosol exposure in Sprague-Dawley rats.

Xylitol is a hygroscopic compound known to protect nasal cavity against bacteria. It has also been developed into nasal spray and evaluated as a potential candidate drug for respiratory diseases. Consequently, it is necessary to study its inhalation toxicity. Based on our previous study on its subacute inhalation toxicity, this study aimed to investigate the safety of xylitol inhalation for long-term use. According to the OECD Test Guideline 413, Sprague-Dawley rats were randomly divided into six groups and exposed with different concentrations of xylitol aerosol or air. After exposure for 90-day, the recovery groups were continued to observe for a recovery period of 28-day. No significant changes in body weight were observed between sham and xylitol groups. Several significant differences in hematological, clinical chemistry, bronchoalveolar lavage fluid were observed, which either had no dose-effect relationship for both male and female rats or were restored during the recovery period. Finally, except for high dose group of xylitol, two rats showed a small amount of inflammatory exudate in alveolar and bronchial cavities, which was restored in the recovery period. The rest of rats showed no obvious difference. For the recovery groups, no significant difference was observed between these two groups. In conclusion, the no observable adverse effect level (NOAEL) of xylitol in our subchronic inhalation toxicological experiments was 2.9 mg/L, which indicated that xylitol for rats' long-time inhalation is tolerant and safe.

Toxicity evaluation in rats following 28 days of inhalation exposure to xylitol aerosol.

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.