Several training methods have been developed to obtain motion information during real-time walking and feed it back to trainees who adjust their gait to ensure that the measured gait parameters approach target value, which may not always be suitable for every trainee owing to physical differences between individuals. This paper proposes a method of setting this target value considering these physical differences and discusses the usefulness of the gait training method, wherein a multichannel deep convolutional neural network (MC-DCNN) gait classification model constructed by learning ideal or non-ideal gait features beforehand is used for trainee gait classification. Activation maximization is applied to the MC-DCNN model; data wherein the ideal walking features are activated are generated based on trainee gait data. However, the amounts of features to be activated to generate a possible and natural gait are restricted. The original trainee gait, beyond individual physical differences, and gait data generated based on the original gait data seem to yield the target value considering the physical differences among individuals. This study focused on gait related to stumbling. To verify its usefulness, a multivariate gait dataset consisting of kinematic and kinetic indices labeled as “gait rarely associated with stumbling” or “gait frequently associated with stumbling” was divided into a training set, validation set, and test set. The MC-DCNN model learned gait features for multivariate gait data classification in the training set. It classified the gait with 96.04±0.12% accuracy against the validation set. Finally, by applying the proposed method to the multivariate gait data contained in the test set, we generated multivariate gait data classified as “gait rarely associated with stumbling” based on the input data. In addition, the generated multivariate gait data include motion that increases the thumb-to-ground distance and describe possible and natural gait considering the physical differences among individuals.

Organ transplantation is the most effective therapy for end-stage organ failure. However, the demand for life-saving organ transplants far exceeds the supply of available organs owing to organ shortage. To address this problem, tissue engineering has offered potential strategies for in vitro construction of organs as medical and clinical applications. However, tissue-engineered organs are difficult to construct owing to the lack of functional vascular networks because avascular organs lead to tissue dysfunctions, such as hypoxia and clot formation. Therefore, establishing functional vascular networks is required for the construction and maintenance of organs in terms of morphology and function. Recent advances in tissue engineering have allowed the in vitro construction of a wide range of functional vascular networks, ranging from microvessels to organ-scale vascular networks, using self-organization and pre-designed approaches. In particular, various new models have been developed utilizing microfluidics, 3D bioprinting, and organ decellularization. These models have enabled the in vitro recapitulation of key features of physiological vascular networks, such as morphology (e.g., network formation, luminal structure, and perivascular cell coverage) and function (e.g., barrier and antithrombogenic functions). In this review, we summarize the progress and challenges in vascular tissue engineering based on two distinct categories: self-organization and pre-designed approaches. In addition, the advantages and limitations of these models are highlighted, and future perspectives are discussed. These models will provide useful insights for the construction of vascularized functional tissues and organs and can contribute to development in tissue engineering and regenerative medicine.

Partial volume effect is defined as the loss of accuracy for small objects caused by the low resolution of an imaging system. With low resolution computed tomography (CT), the trabecular bone and cavity are mixed and the brightness representing each of the spaces is averaged. Therefore, information regarding bony microstructure is absent. In this study, the partial volume effect was evaluated for multi-detector row CT (MDCT) and single-detector row CT (SDCT) in comparison with micro CT (μCT). Obvious and typical geometric patterns of healthy and osteoporotic bones were used to create virtual sectional images of various resolutions. Six parameters were evaluated: areal bone mineral density (aBMD), volumetric BMD (vBMD), bone volume (BV), bone mineral content (BMC), frequency distribution density of BMD (FDD) in the image, and the orientation angle of the bone. vBMD and BV values were dependent on the CT resolution, whereas aBMD and BMC values showed constant values independent of the resolution. Therefore, aBMD and BMC do not require high resolution CT and could be useful for clinically evaluating trabecular bone volume. Regarding FDD, the number of pixels with intermediate brightness increased as CT resolution decreased, and FDD converged on specific brightness representing aBMD. In addition, μCT estimated the bone orientation angle correctly, MDCT estimated the correct angle only for osteoporotic images, and SDCT was unable to estimate the angle. Many more cavities were present in the osteoporotic model than the Healthy model and the distribution of bone was sparse, which could have decreased the partial volume effect and enabled the major orientation angle of the bone to be distinguished. These findings suggest that MDCT could be useful for the clinical evaluation of osteoporotic bone structure.

The cerebellum has a unique morphology characterized by fine folds called folia. During cerebellar morphogenesis, folia formation (foliation) proceeds with granule cell (GC) proliferation in an external granular layer, and subsequent cell migration to an internal granular layer (IGL). GC migration is guided along Bergmann glial (BG) fibers, whose orientation depends on the deformation of cerebellar tissue during folia formation. The aim of this study is to investigate the contribution of the fiber-guided GC migration on folia formation from a mechanical viewpoint. Based on a continuum mechanics model of cerebellar tissue deformation and GC dynamics, we simulated foliation process caused by GC proliferation and migration. By changing migration speeds, we showed that the fiber-guided GC migration caused the non-uniform accumulation of GCs and folia lengthening. Furthermore, the simulation of impaired GC migration under pathological conditions, where GCs did not migrate along BG fibers, revealed that fiber-guided GC migration was necessary for folia lengthening. These simulation results successfully recapitulated the features of physiological and pathological foliation processes and validated the mechanisms that guidance of GC migration by BG fibers causes folia lengthening accompanied by non-uniform IGL. Our computational approach will help us understand biological and physical morphogenesis mechanisms, facilitated by interactions between cellular activities and tissue behaviors.

Several training methods have been developed to acquire motion information during real-time walking; these methods also feed the information back to the trainee. Trainees adjust their gait to ensure that the measured value approaches the target value, which may not always be suitable for each trainee. Therefore, we aim to develop a gait feedback training system that considers individual differences, classifies the gait of the trainee, and identifies adjustments for body parts and timing. A convolutional neural network (CNN) has a feature extraction function and is robust in terms of each feature position; therefore, it can be used to classify a gait as ideal or non-ideal. Additionally, when the gradient-weighted class activation mapping (Grad-CAM) is applied to the gait classification model, the output measures the influence degree contributed by the trainee’s each body part to the classification results. Thus, the trainee can visually determine the body parts that need to be adjusted through the use of the output. In this study, we focused on gaits related to stumbling. We measured the kinematics and kinetics data for participants and generated multivariate gait data, which were labeled as “gait rarely associated with stumbling” class or “gait frequently associated with stumbling” class using clustering with dynamic time warping. Next, the multichannel deep CNN (MC-DCNN) was used to learn the gait using the multivariate gait data and the corresponding classes. Finally, the data for verification were input into the MC-DCNN model, and we visualized the influence degrees of each place of the multivariate gait data for classification using Grad-CAM. The MC-DCNN model classified gaits with a high accuracy of 97.64±0.40%, and it learned the features that determine the thumb-to-ground distance. The output of the Grad-CAM indicated body parts, timing, and the relative strength of features that have an important effect on the thumb-to-ground distance.

Elderly people often use a cane to walk; it is an important part of their daily life. The cane must be made of a light weight material of high stiffness. In addition, stress relaxation on impact is required to make the cane easy to grasp. All these factors are affected by the shape design. Therefore, an effective shape design considering the stress relaxation on impact load and the weight of the cane is important. Traditionally, straight-type canes are widely used in the market. In this study, a bioinspired shape design methodology is proposed to produce canes. The basis vector method is used, and a multi-objective design optimization for minimizing the total volume and the maximum stress is performed. A sequential approximate optimization is then adopted to determine the Pareto optimal solutions. The superiority of the proposed method over the straight-type cane is confirmed through numerical results. The optimal cane shapes have more than 90% lower impact stress than the straight-type canes. Finally, a prototype of the optimized cane is produced using the braiding technology. Carbon fiber reinforced plastic is the selected cane material owing to its light weight and high stiffness.