We investigated a system identification method for modeling the standing posture without external perturbation. Assuming that postural sway during quiet standing is caused by internal white noise, the method adopts an autocorrelation matrix consisting of body position and velocity parameters. We showed that the method can be applied to center-of-mass fluctuation measurement data. Since we used only a force plate to minimize the burden on the subjects, the velocity could not be measured directly. Therefore, we first demonstrated through simulation that ankle joint stiffness can be estimated by numerically deriving the velocity from the displacement. The velocity was calculated using the central difference approximation, and the ankle stiffness estimation error was obtained. The system identification method was then applied to the center-of-mass measurement data, and the ankle stiffness was estimated. Simulations showed that even when the velocity was calculated through numerical differentiation, the ankle joint stiffness could still be estimated with an accuracy of less than 1% error. The ankle stiffness estimated from the measured center of mass was slightly higher than the gravitational torque coefficient, but lower than that obtained in our previous electrical stimulation study (Uchiyama T: Adv Biomed Eng. 13, 223‒229, 2024). This suggests that the control method for ankle stiffness may depend on the presence or absence of external disturbances.

Urine-related information, such as voided volume and flow rate, is important for the diagnosis of dysuria. Uroflowmeters, which are used for obtaining urine information, measure mainly the weight gain of the collection cup or the rotation of the impeller. These devices pose hygiene-related problems because of the contact between urine and the device. In addition, the patients are forced to urinate in unfamiliar environments and positions, which can lead to unusual results. Therefore, there is a need for a non-contact uroflowmeter that can measure urine information in an environment and a posture to which the user is accustomed. A uroflowmeter that measures the rise of the water level in the toilet bowl is also available; however, it requires extensive installation. To address these problems, we have developed a scale-type uroflowmeter that measures weight loss during standing urination. The purpose of this study was to evaluate the measurement accuracy of our developed scale-type uroflowmeter. For this purpose, human urination was measured simultaneously using the developed uroflowmeter and three different medical uroflowmeters (Freeflow^®, P-Flowdiary^®, and PicoFlow2^®), to compare the voided volume, maximum flow rate, and average flow rate. The results of the developed uroflowmeter showed good agreement with the weight of urine measured by an electronic balance and the voided volume measured by the medical uroflowmeters. The error rates between the true voided volume measured by the electronic balance and the four uroflowmeters ranged from 0.9% to 22.5%. Comparing the maximum and average flow rates of the three medical uroflowmeters with the scale-type uroflowmeter, the error rates ranged from 9.4% to 25.4% and 17.4% to 25.2%, respectively.

Predicting spatiotemporal neural activity could lead to early detection of seizures and better understanding of cognitive processes in the brain. Parallel reservoir computing, where spatiotemporal data are split into regions and one reservoir predicts each region, has been proposed as a method to predict large spatiotemporal data with high efficiency and accuracy. However, no studies have extended parallel reservoir computing to predict large-scale brain activity. As the accuracy of the prediction will drop dramatically if the hyperparameters are not properly set, the relationship between the hyperparameters of parallel reservoir computing and parameters of neuronal simulations such as connection length, axonal conduction speed, and excitatory and inhibitory synaptic conductance should be explored. In this study, we systematically investigated the relationship between the appropriate hyperparameters of parallel reservoir computing and parameters from neuronal simulations. Specifically, we first simulated spiking neural networks and evaluated their spatial features while varying the parameters. Then, for each parameter setting, we investigated the hyperparameters of parallel reservoir computing that reproduced the temporal features of traveling waves. Our findings indicated that axonal conduction speed and connection length drastically affected the appropriate hyperparameters, while excitatory and inhibitory synaptic weights did not. In addition, spatial features alone did not necessarily determine the appropriate hyperparameters to predict neural activity. Our study reveals that more studies are needed to find a way to assess the dynamics of neuronal population, thereby paving the way for the prediction of large-scale brain activity.

An intravascular shunt (arteriovenous anastomosis) created during hemodialysis is a common site of stenosis. Early detection of stenosis is crucial for treatment; hence, shunt sounds are gaining attention as an indicator for simple, noninvasive screening of stenosis. However, previous studies on screening methods using shunt sounds have not achieved sufficient detection accuracy for practical application. This study aimed to elucidate the mechanism of generation of shunt sounds by conducting computational fluid dynamics analysis of the fluctuations of pressure and magnitude of vorticity as potential sound sources. This insight may contribute to improving stenosis detection methods using shunt sounds. In this analysis, a side-to-end model was used as the typical anatomical geometry for vascular access. The power spectral densities of the pressure and vorticity magnitude fluctuations were estimated using an autoregressive model. Using shunt models with and without stenosis, we calculated the time fluctuations of the pressure and vorticity magnitude in the shunt blood vessel and investigated the differences in the power spectral densities of these fluctuations due to stenosis. The source of the shunt sounds was investigated by comparing the power spectral densities of the pressure and vorticity fluctuations to those of the shunt acoustic data, and a similar trend was observed. The power spectral densities of both the pressure and vorticity magnitude fluctuations were higher in the shunt model with stenosis than in the model without stenosis as the frequency increased. This analysis suggests that pressure and vorticity magnitude fluctuations in the shunt blood flow contribute to the generation of shunt sounds. Moreover, stenosis shifted these sounds to higher frequencies due to flow separation. Additionally, when the shunt acoustic spectrum at a specific point of the blood vessel exceeded those at adjacent points, especially within the 500-1200 Hz range, stenosis was likely to be present at that location. This finding demonstrates that stenosis can be detected with high accuracy by analyzing the spectral shifts in acoustic data at multiple points along a shunt. Improving software and hardware based on these insights may further enhance the accuracy of intradialytic shunt stenosis screening devices using shunt sounds.

Atrial fibrillation (AF) is the most common arrhythmia that increases the risk of cardiac diseases such as stroke; hence, its early detection is important. Although many studies have been conducted on automatic AF detection, the accuracy of identification needs to be improved for large amounts of data such as Holter electrocardiograms (ECGs). In rare clinical cases, AF shows regular R‒R intervals (RRIs). These cases are difficult to diagnose even by experienced cardiologists or to detect automatically. To detect AF accurately with minimum number of oversights, identification of AF including regular RRIs is necessary. This paper presents a new method for improving the accuracy of AF detection. The detection is realized in two stages with different AF identification models, after eliminating noise in the ECG waveforms using a finite impulse response bandpass filter. In the first stage, a hybrid convolutional neural network (CNN)‒long short-term memory model trained with segmented ECG waveforms and their RRIs is used to identify AF with irregular RRIs. In the second stage, non-AF events are input. AF cases with regular RRIs are identified using an independent CNN model trained with waveforms that include P- or fibrillatory waves. In this study, 24-hour Holter ECG data obtained from 60 subjects were used to train both models. For evaluation, Holter ECG data from ten subjects not used for training were used, which yielded a detection accuracy of 94.7% and a sensitivity of 98.5% for AF. In addition, evaluation using another untrained dataset from two subjects whose AF had regular RRIs showed an accuracy of 96.2% and a sensitivity of 98.8% for AF. These results demonstrate the feasibility of the proposed method for AF detection.

Regular stress checks and health confirmations by supervisors are standard practices in high-risk environments such as construction sites. Therefore, effective health management of onsite workers is crucial for preventing health hazards and casualties at the workplace. Nevertheless, detection of falsified health declarations and unrecognized health issues remains a challenge. This study used facial thermography obtained by infrared thermal imaging to monitor the health conditions of workers. Thermal images of the face reflect the blood flow distribution in the skin, which is controlled by the autonomic nervous system and can serve as noninvasive health status indicators. However, ambient temperature and time-of-day variations affect skin temperature readings, making the application of facial thermal imaging challenging in uncontrolled environments outside laboratory setting. To address this issue, we proposed a method to correct the artifacts caused by environmental temperature and time-of-day variations in facial thermography. This method provides standard skin temperatures for any given environmental temperature and time of the day, allowing comparison with actual skin temperatures. We conducted a five-month longitudinal study in 279 construction workers, and collected 2,884 pairs of facial thermal images and health status data. The collected face thermal images were corrected for environmental temperature and time-of-day variations. The effectiveness of this method in assessing the health status of onsite workers was evaluated. The results showed that the proposed method effectively corrected artifacts from environmental temperature and time-of-day variations in facial thermography. Moreover, the corrected images revealed that a decline in health conditions was associated with a decrease in temperature around the nose and an increase in temperature in other areas. This is consistent with previous findings of the relationship between stress and facial skin temperature.

Phase-change nano-droplets (PCNDs) have been gaining attention for their potential applications in ultrasound-based therapeutic and diagnostic technologies. PCNDs transform from a droplet state to microbubbles in response to ultrasound or shockwaves. Although a negative pressure component contributes to this vaporization, previous studies have primarily used ultrasound with both positive and negative pressure components. Thus, the effect of the negative pressure component has not been sufficiently explored. In this study, we developed an experimental system that generates a negative pressure component using underwater shockwaves reflected at the water-air interface. Using PCNDs composed of perfluorohexane (PFH) with a boiling temperature of 59℃, we analyzed the bubble lifetime, response to pressure, and vaporization threshold when exposed to reflected waves with a rise time of 50 ns. The results showed a bubble lifetime of 11 μs, a linear response to negative peak pressure, and a threshold of vaporization between 1.5 MPa and 1.9 MPa. Moreover, the effects of vaporization of PCNDs on biological cells were evaluated by measuring cell viability before and after exposure to negative pressure.

Intercellular communication is mediated by fibrous structures known as tunneling nanotubes (TNTs). TNTs are present in diseased cells, including cancer cells, and facilitate the transport of intracellular substances crucial for cell survival. They contribute to cancer survival by enhancing metastatic and invasive abilities and are implicated in the drug resistance of cancer cells. However, previous studies have primarily focused on the biochemical signaling of TNTs, and their mechanical properties are largely unknown. We hypothesized that TNTs play a mechanical role in cell survival by regulating cell-cell distance, migration direction, and mechanical signaling between cells. In this study, we investigated the mechanical properties of TNTs in a human cervical cancer cell line, HeLa cells, through nanoindentation tests using atomic force microscopy (AFM). To minimize shape variation in the TNTs and enhance the efficiency of mechanical tests, the shape of the cells and the distance between neighboring cells were controlled using a microcontact printing method that regulates cell adhesion regions. By performing AFM indentation tests on the TNTs, we determined that their internal tension was 758.3 ± 372.5 pN, axial spring constant was 32.1 ± 14.2 pN/µm, and axial elastic modulus was 696.3 ± 578.0 kPa. Furthermore, we observed that the internal tension and axial elastic modulus correlated positively with TNT length, while the axial elastic modulus correlated negatively with TNT thickness; that is, the thinner and longer the TNTs, the stiffer and higher are their tension. This implies that longer TNTs can exert greater tension on neighboring cells, thereby facilitating the proliferation of cancer cells. These findings suggest that TNTs play a critical role in the mechanical communication of cancer cells and may influence various aspects of cell survival, significantly affecting the progression of cancer and other diseases.

Near-infrared multispectral imaging (NIR-MSI) has attracted attention in the surgical field owing to its ability to penetrate tissues and analyze molecular vibrations with high spatial resolution, thus enabling the identification of deep or similarly colored tissues. However, conventional devices have limitations such as non-portability and long imaging times, rendering the surgical use of NIR-MSI challenging. This study developed a prototype NIR-MSI rigid endoscope system that can perform high-speed imaging and tissue classification. This system was constructed using a custom-made rigid endoscope that can relay NIR images, an InGaAs camera, and a lighting device containing nine high-power LEDs within the range of 940-1,535 nm. The developed device enables continuous processing of light irradiation, data acquisition, neural network analysis, and result display using customized software. The performance of the system was verified by testing tissue transparency and component classification. Tissue transparency test showed that text written on a letterboard could be recognized through a 5-mm thick layer of chicken breast meat. The system completed the imaging and neural network analysis of four color-matched transparent resins, which could not be distinguished readily under visible light, within 2 s, with an average accuracy of 96%. The proposed system may be applied for identifying deep or similarly colored tissues in the setting of non-invasive and real-time endoscopic surgery.

The development of cellular structures has garnered significant interest in regenerative medicine for the restoration of lost tissues, organs, and blood vessels. Our research group focuses on the construction of cellular architectures by exploiting the tendency of spheroids to coalesce. Understanding the dynamics of spheroid fusion is pivotal for advancing the construction of larger cellular assemblies and developing innovative methodologies for three-dimensional cellular structures. Therefore, the aim of this study was to perform a morphological analysis of spheroid fusion over time. Human mesenchymal stem cells derived from adipose tissue were used to generate spheroids. Fusion experiments were performed on the second and fifth day of culture using a spheroid formation system, with the number of spheroids ranging from two to four. Subsequently, we monitored the spheroid fusion process by time-lapse imaging at 10-min intervals over three days, utilizing a fluorescent microscope that preserved the culture environment. The images acquired facilitated the development of a novel evaluation method based on the changes in neck radius and circumference of the spheroids. A quantitative assessment of these time-dependent morphological changes revealed distinct fusion behaviors that varied according to the fusion time and the number of spheroids involved.

Purpose: In this study, we attempted to construct an improved anomaly detection system. In our previous study, we developed an anomaly detection system for the elderly. However, the detection performance may not be sufficient for relatively mild abnormalities such as fever or low peripheral oxygen saturation (SpO₂), which frequently occur in elderly people. Methods: The purposed method used 3 types of models: natural language processing model, data generation model, and anomaly detection model. Among the residents of a long-term care facility, 79 (16 males and 63 females, 88.54 ± 7.21 years of age) elderly people were selected as participants. Results: The success rate of predicting physical anomaly (mean ± standard deviation) using our previous method was 24.82 ± 32.55% and that using the method proposed in this study was 42.12 ± 39.20%, with a significant difference between the two methods. Conclusions: In this study, we succeeded to improve the performance of an anomaly detection system for the elderly. Difference in detection performance between subject was observed. Further research is needed to investigate the relationship between subject condition and detection performance.

Kinesthetic illusion induced by visual stimulation (KINVIS) is a phenomenon in which a virtual kinesthetic perception is induced by watching virtual limb movements mimicking actual limb movements. Recently, the KINVIS has been used for the rehabilitation of patients with stroke. Visual immersion is thought to be essential to achieve the KINVIS. Existing KINVIS rehabilitation equipment uses large devices, such as enclosed boxes or head-mounted displays, to improve immersion. To conveniently improve the effectiveness of the KINVIS, we combined it with the rubber hand illusion (RHI) and pulling illusion (PI), which can be induced by simple equipment. The RHI occurs when a fake hand is perceived as a real hand once tactile and visual stimuli are synchronized. The PI occurs when asymmetric vibrations induce the user to sense as though his/her body is being pulled. We developed a system that induces these three illusions by using an upper limb avatar on a laptop personal computer (PC) display and a vibrator. Participant placed the upper limb behind the laptop PC display overlapping with the image of the upper limb avatar. In this setup, participant experienced combinations of the illusions by watching the motion of the upper limb avatar and the vibrator object, and sensing the vibrations of the vibrator. Two evaluation indices; namely, sense of ownership (SoO) and sense of body motion (SoBM), were employed. SoO quantified the degree of recognition of a virtual body as one’s own, while SoBM quantified the degree of feeling that the body had moved. We conducted a questionnaire on the direction and strength of the participant’s SoBM and SoO. Results showed that the RHI increased SoO for the avatar, considered to enhance immersion, which in turn increased the effect of the KINVIS. However, there was no positive interaction between the PI and KINVIS; when the PI and KINVIS were in opposite directions, tactile and visual information did not integrate, and the KINVIS decreased the effect of the PI. The main contributions of this study are identifying effective and ineffective illusion combinations for enhancing the KINVIS and demonstrating that a simple device can achieve these illusions. These findings mark the first step toward a more accessible KINVIS.

Controlling cell orientation is of paramount importance in bioengineering processes. While several surface modification techniques have emerged to guide cell orientation, they often involve complex, repetitive procedures for each experiment. Thus, a streamlined approach for cell orientation is necessary. In this study, we present a potentially reusable metallic culture surface that induces an anisotropic cell orientation due to its unique geometric morphology. Using a femtosecond laser, periodic nanostructures were produced on the metallic culture surface, resulting in a distinctive macro stripe design composed of both laser-treated and mirrored areas. Myoblast cells cultured on this surface showed a pronounced orientation. The macro stripe design provided stronger control of cell orientation compared to the simple laser-treated surface with periodic nanostructures. Initial random cell adherence was followed by migration toward the mirror surface, culminating in the desired orientation. This shift can be attributed to the mirrored areas providing superior cell adhesion and reduced wettability compared to the laser-treated areas. This innovative culture surface has significant potential for advancing bioengineering endeavors, especially in the field of tissue engineering.

Background: Ankle sprains are very common in badminton, and chronic ankle instability (CAI) often develops in players after these injuries. CAI badminton players (CAIBP) are more susceptible to injuries during high-intensity tasks, such as jumping landings, due to decreased ankle stability. This study aims to explore the variations in lower limb biomechanics between CAIBP and normal badminton players (NBP) during single-leg medial landing tasks. Methods: Sixteen CAIBP and sixteen NBP university badminton players volunteered to participate in this experiment. The study used OpenSim open-source software to simulate and calculate lower limb joint angles, moments, and joint stiffness for the CAI group and healthy controls during a single-leg medial landing task, and utilized Delsys EMG to assess muscle pre-activation and activation levels. Independent samples t-tests and one-dimensional statistical parametric mapping were used to analyze the experimental results. Results: In terms of kinematics, before and after initial contact (IC) during landing, CAIBP showed significantly greater hip adduction and flexion angles than NBP (p < 0.001). Pre-IC, CAIBP exhibited less knee flexion (p = 0.004). Both pre- and post-IC, CAIBP exhibited significantly greater dorsiflexion angles (p = 0.045) and inversion angles (p < 0.001). In terms of muscle activation and dynamics, pre-IC, CAIBP had significantly less pre-activation of the peroneus longus than NBP (p = 0.007), and significantly more gastrocnemius lateral (p = 0.021) and gastrocnemius medial (p < 0.001) pre-activation. Post-IC, CAIBP had significantly greater muscle activation of the tibialis anterior (p < 0.001). Post-IC, peak knee extension moments (p = 0.012) and peak ankle plantarflexion moments (p = 0.001) were significantly greater in CAIBP than in NBP. In addition, CAIBP reported significantly higher knee stiffness (p = 0.001) and ankle stiffness (p < 0.001). Conclusions: During the medial landing task, CAIBP exhibited increased hip adduction and flexion, altered sagittal plane motion of the ankle, and increased activation of certain lower extremity muscles compared to NBP. Although these altered landing mechanisms contribute to enhanced stability during landing to some extent, they may also increase the potential risk of knee injury.