Cyberdyne (Japan)

Background: Exercise therapy is essential for diabetes management, but adherence remains challenging for many patients. The hybrid assistive limb (HAL) lumbar type is a wearable robot that has shown effectiveness in various conditions, yet its application in diabetes patients has not been investigated.Objective: To evaluate the feasibility and safety of exercise therapy using the HAL lumbar type for patients with diabetes and to exploratorily investigate its effects on physical function and metabolic parameters.Methods: This pilot, prospective, single-arm study included nine patients with diabetes (mean age: 69.00±13.23 years). Participants underwent an exercise program using the HAL lumbar type, consisting of eight sessions of 60 minutes each. The program included stretching, squatting, and walking exercises, performed 1-2 times per week, with the goal of completion within 4 to 8 weeks. The primary outcome was the Sit-To-Stand test (STS test). Secondary outcomes included changes in body weight, body mass index (BMI), and metabolic parameters.Results: All participants completed the intervention without adverse events. Significant improvements were observed in the STS test (15.86±3.87 to 10.43±2.27 seconds, p<0.005), body weight (72.68±15.73 to 70.71±15.15 kg, p<0.05), and lipid metabolism parameters including total cholesterol (200.11±32.85 to 182.67±21.45 mg/dL, p<0.05) and LDL cholesterol (114.00±28.51 to 101.56±24.52 mg/dL, p<0.005).Conclusion: Exercise therapy using the HAL lumbar type was safely implemented in diabetes patients and showed potential benefits in improving physical function and lipid metabolism. These findings suggest that HAL-assisted exercise therapy might be a promising new approach for diabetes management.

Unlike the outpatient cardiac rehabilitation (CR) program for chronic heart failure (HF), the acute-phase inpatient CR program is not well established. We aimed to examine whether CR using lumbar-type hybrid assistive limb (HAL; lumbar HAL-CR) therapy improves physical functions in hospitalized aged HF patients. Decompensated and hospitalized HF patients were recruited from 2018 to 2019. As soon as possible after the decompensated HF phase, during the same hospitalization, participants underwent 40-min daily sessions of sit-to-stand and squat exercises with the lumbar-type HAL (lumbar HAL-CR program). The outcome measure was the score on the Short Physical Performance Battery (SPPB) before and after lumbar HAL-CR therapy. Twenty-eight HF patients (median age 84.5 years; 18 males) were enrolled. The median number of lumbar HAL-CR therapy sessions was 5.0. The SPPB score (7.0 [5.3-8.8] to 9.0 [7.0-11.0] points; P<0.001) and quadriceps isometric strength (0.25 [0.22-0.38] to 0.30 [0.26-0.37] kgf/kg; P=0.040) were significantly improved. Patients with lower nutrition status had more improvement in SPPB score. For the inpatient CR program, lumbar-type HAL therapy improved physical functions in elderly HF patients. Lumbar-type HAL therapy may improve physical functions in aged HF patients within the current short-term period of hospitalization for acute-phase HF.

Fibrosis is a key factor in diseases such as interstitial pneumonia and cirrhosis. Conventional approaches in cell therapy, which involves transplanting living cells into patients, are able to address these conditions. However, cell cultivation involves numerous intricate procedures. Given the demands for a substantial quantity of cells in the therapy, manual cultivation has its limitations. Furthermore, removing cells from a CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> incubator during culturing can cause damage to the cells, leading to reduced cell viability. To improve the efficacy of cell therapy, it is crucial to cultivate cells with minimal damage. The purpose of this study is to develop a robotic cell culture system that enables the automatic cultivation of fibroblast cells in large quantities for demands of cell therapy, in an environment similar to that of a CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> incubator and allows to monitor the culture environment. The system enables observation of cells under a microscope without altering the culture conditions and automates the medium exchange process. To confirm the feasibility of the system, we conducted the basic experiment set comparing the developed system to manual cultivation of fibroblast cells. The results showed the developed system improved the fibroblast viability and proliferation rate compared with manual cultivation. In conclusion, we confirmed the feasibility of the developed robotic cell culture system in its capability for cultivating fibroblasts with an efficiency equivalent to or greater than conventional manual cultivation.

We aimed to estimate the rate of kidney function decline over 10 years in the general population and develop a machine learning model to predict it. We used the JMDC database from 2012 to 2021, which includes company employees and their family members in Japan, where annual health checks are mandated for people aged 40-74 years. We estimated the slope (average change) of estimated glomerular filtration rate (eGFR) over a period of 10 years. Then, using the annual health-check results and prescription claims for the first five years from 2012 to 2016 as predictor variables, we developed an XGBoost model, evaluated its prediction performance with the root mean squared error (RMSE), R2, and area under the receiver operating characteristic curve (AUROC) for rapid decliners (defined as the slope <-3 ml/min/1.73 m2/year) using 5-fold cross validation, and compared these indicators with those of (i) the simple application of the eGFR slope from 2012 to 2016 and (ii) the adjusted linear regression model. We included 126,424 individuals (mean age, 45.2 years; male, 82.4%; mean eGFR, 79.0 ml/min/1.73 m2 in 2016). The mean slope was -0.89 (standard deviation, 0.96) ml/min/1.73 m2/year. The predictive performance of the XGBoost model (RMSE, 0.78; R2, 0.35; and AUROC, 0.89) was better than that of either the simple application of the eGFR slope from 2012 to 2016 (RMSE, 1.94; R2, -3.03; and AUROC, 0.79) or the adjusted linear regression model (RMSE, 0.81; R2, 0.30; and AUROC, 0.87). We estimated the rate of kidney function decline over 10 years in the general population as well as demonstrated that application of machine learning to annual health-check and claims data provides better predictive performance compared to traditional methods.

Tripping is the largest cause of falls and low swing foot ground clearance during the mid-swing phase, particularly at the critical gait event known as Minimum Foot Clearance (MFC) is the major risk factor for tripping-related falls. Intervention strategies to increase MFC height can be effective if applied in real-time based on feed-forward prediction. The current study investigated the capability of machine learning models to classify the MFC into various categories using toe-off kinematics data. Specifically, three MFC sub-categories (less than 1.5cm, between 1.5-2.0cm and higher than 2.0cm) were predicted applying machine learning approaches. A total of 18,490 swing phase gait cycles’ data were extracted from six healthy young adults, each walking for 5-minutes at a constant speed of 4km/h on a motorised treadmill. Both K-Nearest Neighbour (KNN) and Random-Forest were utilised for prediction based on the data from toe-off for five consecutive frames (0.025s duration). Foot kinematics data were obtained from inertial measurement unit attached to the mid-foot, recording tri-axial linear accelerations and angular velocities of the local coordinate. KNN and Random-Forest achieved 84% and 86% accuracy, respectively, in classifying MFC into the three sub-categories with run time of 0.39 seconds and 13.98 seconds respectively. The KNN-based model was found to be more effective if incorporated into an active exoskeleton as the intelligent system to control MFC based on the preceding gait event, toe-off due to its quicker computation time. The machine learning based prediction model shows promise for the prediction of critical MFC data indicating higher tripping risk.

Visualizing blood vessel networks in 3D is helpful in many superficial vascular imaging applications. We previously reported the feasibility of LED-based photoacoustic imaging in visualizing human vasculature in 3D by linear translation of combined photoacoustic/ultrasound imaging probe. In this work, we improved this 3D imaging functionality by 1) automatic removal of skin PA signal in different 2D slices by ultrasound-based lineation , 2) encoding depth information with different colors and 3)improving the grayscale and hot color maps. All these new features were implemented in the system software and we validated these using phantom and human in vivo imaging experiments. Results demonstrate that the newly implemented features significantly improved the 3D imaging capability in our LED-based photoacoustic and ultrasound imaging system. We believe that the improved 3D imaging functionality in the system will be potentially useful for multiple preclinical and clinical applications in which the visualization of vasculature in 3D is a prerequisite.

Since hypoxia is an early marker for cancer and several microvascular disorders, imaging oxygen saturation with high resolution has profound importance in multiple preclinical and clinical imaging studies. In this work, we demonstrate the potential of dual-wavelength (690/850 nm) LED-based photoacoustic imaging in high resolution real-time oxygen saturation imaging in vivo. We performed two live rat imaging experiments in which the oxygen saturation of the blood vessels in the left hindlimb was imaged for 20 seconds while the animal was breathing pure nitrogen gas to induce hypoxia. For a comparison, we continuously monitored the oxygen level using a pulse oximeter connected to the forelimb. In another experiment, we performed imaging of oxygen saturation changes in the tail vein of the rat while hypoxia was induced over the period of 40 seconds. In this case also, pulse oximeter readings were recorded for a comparison. In all the experiments, photoacoustic-based oxygen saturation values measured over time followed the same trend as the reference values provided by the pulse oximeter. Also, our two wavelength LED-based photoacoustic imaging approach was found to be sensitive even for 3% change in oxygen saturation. Results give a direct confirmation about the potential of LED-based photoacoustic imaging in detection of oxygen saturation with high spatio-temporal resolution, making it an ideal tool for hypoxia detection in longitudinal preclinical and clinical imaging studies.

Accurate burn depth assessment is crucial to determine treatment plans for burn patients. We have previously proposed a method for performing burn depth assessments based on photoacoustic (PA) imaging, and we have demonstrated the validity of this method, which allows the successful detection of PA signals originating from the blood under the bloodless burned tissue, using rat burn models. Based on these findings, we started a clinical study in which we faced two technical issues: (1) When the burn depth was shallow, PA signals due to skin contamination and/or melanin in the epidermis (surface signals) could not be distinguished from PA signals originating from the blood in the dermis; (2) the size of the system was too large. To solve these issues, we propose a burn depth diagnosis based on dual-wavelength light emitting diodes (LEDs)-excited PA imaging. The use of LEDs rendered the system compact compared to the previous one that used a conventional solid-state laser. We replicated human burned skin by applying a titrated synthetic melanin solution onto the wound surface in albino rat burn models and measured their burn depths by PA excitation at 690 and 850 nm, where melanin and haemoglobin show greatly different absorption coefficients. As a result, the surface signals were eliminated by subtracting the PA signals at 690 nm from those at 850 nm. The resultant estimated burn depths were strongly correlated with the histological assessment results. The validity of the proposed method was also examined using a burn model of rats with real melanin.

Ankle Hybrid Assistive Limb (HAL) is the wearable cyborg, specifically aiming to improve impaired ankle joint motions. The current pilot-study investigated whether a single 5-minute HAL session would change ankle joint motions especially during the swing phase of the gait cycle. Minimum foot clearance (MFC) is the mid-swing phase event with the lowest vertical swing toe displacement from the walking surface and insufficient MFC height poses the high tripping risk. In the current pilot-study, one male participant's gait patterns (38 yrs., 174cm., 68kg) were tested before and after the HAL session. Positive changes to MFC height and ankle joint control were observed. Most interesting was improved consistency in gait patterns, indicated by reduced standard deviation over multiple gait cycles' data possibly due to HAL's interactive biofeedback approach. The current pilot-study showed the great potential of HAL to reduce the risk of tripping falls.