Amphibians possess high regeneration capacity of complicated structures such as articular joints. However, it is unknown when and how the joint function recovers during the regeneration process following a joint amputation. The present study examined the digit joint function in newt during regeneration process following complete amputation at its proximal interphalangeal (PIP) joint. The PIP joint of the hind middle digit of Iberian ribbed newt was amputated, and at several time points of the recovery period digit flexion test was performed to determine the relationship between mechanical loading applied to the flexor tendon and the flexion angle of PIP joints. There was a clear transition between 31 and 35 days post amputation (dpa) that the regenerated middle digit became flexible as the intact middle digit. The flexion of the PIP joint by mechanical loading at 28 dpa was significantly smaller than that of intact digits, and that of 31 dpa remained the same level. By contrast, the PIP joint flexion at 35 dpa was comparable to the intact digit. Histologically, the regenerated digit at 31 and 35 dpa showed no remarkable differences. Accordingly, potential mechanisms of regaining the flexion in the regenerated digit were that 1) the regenerated flexor tendon was reintegrated into the distal phalanx at 35 dpa, so that the pulling force from the flexor muscle generated sufficient torque to flex the regenerated PIP joint, 2) the regenerated PIP joint regained its interlocking structure and the flexibility was recovered to the intact level, or these mechanisms worked together.

Heart failure (HF) is a significant global public health issue, and accurate diagnosis and effective management are crucial for improving patient outcomes. This study aims to identify potential HF patient subgroups based on the pulse wave time series via unsupervised clustering algorithms and to quantify the importance ratio (IR) of pulse wave morphological features within these subgroups. We collected and normalized pulse wave time series and clinical characteristics from 380 HF patients, which were clustered by introducing the K-means++ algorithm and the clustering performance was assessed along with the clinical characteristic differences between clusters. We then extracted time-frequency features from the pulse waves, analyzed the differences in these features between clusters, and quantified their IRs using a Random Forest classifier. Our results show the optimal clustering performance when the number of clusters is 2, with Silhouette coefficient, Calinski-Harabasz index, and Davies-Bouldin index values of 0.74, 585, and 0.75, respectively. Noticeable differences were observed between clusters in terms of age, heart rate (HR), and ejection time (ET), resulting in two HF patient subgroups: Cluster 0 (47% elderly patients; 34% with tachycardia; 54% with low ET) and Cluster 1 (57% elderly patients; 23% with tachycardia; 46% with low ET). Additionally, through the Random Forest classifier, it was found that upstroke time, mean, downstroke time, and skewness were the significant important pulse wave morphological features, with IRs of 20.8%, 18.7%, 17.1%, and 10.1%, respectively. This study is the first to apply the K-means++ algorithm for unsupervised clustering of pulse wave time series in HF patients, successfully identifying two patient subgroups and revealing significant differences in age, HR, and ET between the clusters. The findings provide preliminary evidence for stratified management of HF patients using non-invasive and easily accessible pulse wave signals.

Among soft actuators, biohybrid actuators constructed by the integration of living tissue and flexible materials, show superior properties of intrinsic softness, environmental compatibility, self-repair function over traditional actuators with rigid body. Skeletal muscles, one of the most frequently used biohybrid actuators, are considered ideal options for driving sources. Research on biohybrid actuators has focused on fabricating actuator entities by tissue-culturing or three-dimensional printing and assembling them. These techniques are well-developed. However, they lack the model-based design approach found in existing actuators that achieve specific performances. A muscle contraction model applicable to skeletal muscle actuators has been developed to model the response of skeletal muscle contraction to external electrical stimuli and has been shown to be reproducible. Therefore, we used this model as a basis to establish the design approach of the biohybrid actuator. The performance requirements for the skeletal muscle actuator depend specifically on the target application and size. As the first step in the design process, we set the muscle mass as a design parameter and the contractile force as a design specification, and then used the model to establish a design method that achieves the required contractile force for a muscle mass. The contractile forces of toad gastrocnemius muscles with different masses were used in the experiments to determine the parameters of the model, specifically those related to muscle mass, thus completing the design model. Subsequently, the muscle mass obtained by targeting the actual contractile force was compared with the actual muscle mass in a verification test, and the results confirmed the feasibility of the designed model. This is the first study that attempts to establish a biohybrid actuator design method, which could lead to significant development in this field.

Awkward postures and repetitive forward bending movements in wall construction tasks indicate a risk of lower back pain (LBP). Trunk flexion during these tasks places a significant impact on lower back muscles, which are essential for maintaining spinal stability. However, the relationship between lower back muscles and trunk flexion in wall construction tasks remains unexplored. The aim of this study was to investigate the relationship between lower back muscle activity and trunk flexion during wall construction tasks. In a laboratory setting, twelve young male students (ages: 21.83±1.27 years) participated in simulated wall construction tasks. Inertial measurement unit (IMU) sensors were used to collect tasks movement data. A 3D musculoskeletal model was used to conduct inverse dynamics simulations to calculate muscle activity and trunk flexion angles. Maximum activity of the Iliopsoas (IL), Quadratus Lumborum (QL), Multifidus (MF), Erector Spinae (ES), and Gluteus Maximus (GM) muscles were compared with maximum trunk flexion during the performance of wall construction tasks at foot, knee, waist, and shoulder heights. Pearson correlation analysis was conducted to evaluate the relationships between maximum muscle activity and trunk flexion angles. In mortar spreading task, statistically significant relationships were found between the maximum muscle activity of the MF (r = .39), ES (r = .49), and QL (r = .47), with forward trunk flexion angles. In bricklaying task, a strong positive correlation was observed between trunk flexion angles and ES (r = .60), GM (r = .53), and QL (r = .75), muscles. Conversely, a moderate positive correlation was found between the trunk flexion and the IL (r = .23), and MF (r = .45) muscles. Wall construction at different heights demonstrated associations between lower back muscle activity and trunk flexions. Therefore, these findings may be helpful for developing interventions to reduce the risk of LBP among wall construction workers.

Understanding the flow characteristics of red blood cell (RBC) is crucial to comprehending the oxygen supply mechanism of microcirculation. Quantification of the RBC partitioning in bifurcating channels is necessary; however, most measurements are often performed manually, which has limitations in terms of labor intensive and reproducibility. Existing automatic detection methods are insufficient to identify RBCs obtained from in vitro experiments, due to heterogeneous backgrounds, minimal luminance variations, and unclear contours of RBCs. Furthermore, because RBCs are deformable, a method capable of tracking numerous moving and deforming RBCs is also required to investigate how capillary networks influence RBC deformation. We developed a convolutional neural networks (CNNs)-based method for detecting and simultaneously tracking multiple deformable RBCs in images obtained from in vitro experiment. The target images were obtained from a microfluidic channel with a ladder structure to understand the RBC heterogeneity mechanism in capillary networks. We also developed a method for automatically generating pseudo-RBC images based on actual RBC images to train CNNs. Moreover, we used the difference image between consecutive frames, along with RBC images, as inputs for CNNs. We validated the detection accuracy and evaluated the hematocrit and flow rate results obtained using the proposed method for each channel during in vitro experiments. The proposed method exhibited exceptional performance, with a precision of ≥0.925, F₁ score of ≥0.825, and low false positive rate across all frames of the RBC images used for testing. The proposed detection method enables the automated, accurate, and high-throughput quantification of RBC positions within in vitro experiments, facilitating more quantitative assessments of RBC flow characteristics within capillary networks. Moreover, it can be readily adapted for automated tracking of various cell types beyond RBCs, as well as in vivo experiments. This may lead to a more detailed understanding of microcirculatory dynamics and related physiological processes.