A Blood Glucose Control Framework Based on Reinforcement Learning With Safety and Interpretability: In Silico Validation

Controlling blood glucose levels in diabetic patients is important for managing their health and quality of life. Several algorithms based on model predictive control and reinforcement learning (RL) have been proposed so far, most of which use prior knowledge of physiological systems, the mathematical structure of blood glucose dynamics, and many episodes including failures for training the policy network in RL. To be smoothly adopted in clinical settings, we propose a fast online learning method underlining safety and interpretability. A random forest regressor and a dual attention network were exploited for glucose prediction and extension of state variables. The soft actor-critic network to determine insulin dosing was guided by proportional-integral-derivative (PID) control in the early phase, and an adaptive safe actor with suspension and additional insulin dosing was incorporated. The performance of the models was validated using an FDA-approved type 1 diabetes simulator. The results showed comparable outcomes with PID control. Using this system, glucose dynamics could be captured despite minimal prior knowledge. The extended state variables were correlated with basic states such as glucose, insulin, and meal intake, their derivatives, and their integrals, which can be fundamental elements of mathematical modeling of physiological responses. Attention scores and attribution scores in the prediction and control models represented the focused features and the internal operation of the models with interpretability. We expect this study to provide some insights on how RL can be practically adopted in clinical environments and how interpretability can provide hints of machines' thoughts for clinical applications

Hardness Modulated Thermoplastic Poly(ether ester) Elastomers for the Automobile Weather-Strip Application

As a means of developing new material for automobile weather-stripping and seal parts replacing the conventional ethylene propylene diene monomer rubber/polypropylene vulcanizate, a series of poly(ether ester) elastomers are synthesized. The hardness is modulated by controlling chain extender composition after fixing the hard segment to soft segment ratio. Targeted hardness is achieved by partly substituting conventional chain extender 1,4-butandiol for soybean oil-originated fatty acid amide diol that bears a long chain branch. The crystallinity and phase separation behavior resultant elastomer are also tunable simply by modulating chain extender composition and hard to soft segment ratio

A development of a graph-based ensemble machine learning model for skin sensitization hazard and potency assessment

Many defined approaches (DAs) for skin sensitization assessment based on the adverse outcome pathway (AOP) have been developed to replace animal testing because the European Union has banned animal testing for cosmetic ingredients. Several DAs have demonstrated that machine learning models are beneficial. In this study, we have developed an ensemble prediction model utilizing the graph convolutional network (GCN) and machine learning approach to assess skin sensitization. The model integrates in silico parameters and data from alternatives to animal testing of well-defined AOP to improve DA predictivity. Multiple ensemble models were created using the probability produced by the GCN with six physicochemical properties, direct peptide reactivity assay, KeratinoSens (TM), and human cell line activation test (h-CLAT), using a multilayer perceptron approach. Models were evaluated by predicting the testing set's human hazard class and three potency classes (strong, weak, and non-sensitizer). When the GCN feature was used, 11 models out of 16 candidates showed the same or improved accuracy in the testing set. The ensemble model with the feature set of GCN, KeratinoSens (TM), and h-CLAT produced the best results with an accuracy of 88% for assessing human hazards. The best three-class potency model was created with the feature set of GCN and all three assays, resulting in 64% accuracy. These results from the ensemble approach indicate that the addition of the GCN feature could provide an improved predictivity of skin sensitization hazard and potency assessment

3D cell culture using a clinostat reproduces microgravity-induced skin changes

Exposure to microgravity affects human physiology in various ways, and astronauts frequently report skin-related problems. Skin rash and irritation are frequent complaints during space missions, and skin thinning has also been reported after returning to Earth. However, spaceflight missions for studying the physiological changes in microgravity are impractical. Thus, we used a previously developed 3D clinostat to simulate a microgravity environment and investigate whether physiological changes of the skin can be reproduced in a 3D in vitro setting. Our results showed that under time-averaged simulated microgravity (taSMG), the thickness of the endothelial cell arrangement increased by up to 59.75%, indicating skin irritation due to vasodilation, and that the diameter of keratinocytes and fibroblast co-cultured spheroids decreased by 6.66%, representing skin thinning. The alpha 1 chain of type I collagen was upregulated, while the connective tissue growth factor was downregulated under taSMG. Cytokeratin-10 expression was significantly increased in the taSMG environment. The clinostat-based 3D culture system can reproduce physiological changes in the skin similar to those under microgravity, providing insight for understanding the effects of microgravity on human health before space exploration

A Study on the VR Goggle-based Vision System for Robotic Surgery

Robot-assisted surgery (RAS) using the da Vinci surgical system (dVSS) has been widely performed owing to its high-definition stereoscopic visualization and improved maneuverability, which has been developed from minimally invasive surgery. However, there was motivation to resolve the chronic fatigue suffered by surgeons because of stereo viewer, the vision system of the dVSS. Reflecting the clinical opinions, a virtual reality (VR) goggle was proposed to enhance the convenience by replacing the stereo viewer, and its applicability was investigated. Based on the da Vinci research kit, validation of the VR goggle was performed by analyzing the condition of its use and implementing the performance assessment. In addition, user evaluations, such as performance analysis, questionnaire surveys, and ergonomic analysis, were conducted to determine the difference in the performance and satisfaction between the stereo viewer and VR goggle. Following the IRB approval, a retrospective analysis of the results was performed. Based on the comparison between the vision systems, the VR goggle was evaluated positively by the surgeons and novices. Satisfaction with the ergonomic properties of the VR goggle averaged 3.9 on a five-point Likert scale, and there was no significant difference in the performance when using the VR goggle and stereo viewer in both groups. Adopting the VR goggle, the volume of the vision system could be decreased by 87.6%. Considering that the VR goggle was better than the stereo viewer in terms of satisfaction and ergonomic analysis with analogous performance, it has the main contribution that the VR goggle can be a promising candidate as a new vision baseline to research the enhancements of the RAS platform in further studies.N

Manufacturing and Control of a Robotic Device for Time-averaged Simulated Micro and Partial Gravity of a Cell Culture Environment

Gravity is omnipresent for all objects on Earth. However, in an environment of different gravitational stress (e.g., microgravity or partial gravity), cells and organs show different biological responses. So, researchers have attempted to achieve micro- or partial gravity on Earth through various approaches, such as parabolic flight or free fall. However, the duration of such ground experiments is highly limited, making it very difficult to conduct time-consuming tasks, such as cell culture. Thus, a three-dimensional (3D) clinostat is utilized as an alternative for experiments on the International Space Station. It provides time-averaged simulated micro- and partial gravity by using mechanical frames with two rotating actuators. This study proposes novel control algorithms for simulating micro- and partial gravity and validates them by applying it to the control of a manufactured 3D clinostat. First, the novel algorithm for time-averaged simulated microgravity (taSMG) provided a more uniformly distributed gravity field by reducing two poles the gravity-concentrated areas. The taSMG with reduced poles provides isotropic gravitational patterns, from which it is possible to minimize the unnecessary effect due to nonuniformity of the gravity vector direction. Second, the other suggested novel algorithm for time-averaged simulated partial gravity (taSPG) controls the pole sizes asymmetrically to generate the intended size of partial gravity. The suggested algorithms are based on mathematical models rather than totally randomized motions. Therefore, the convergence of gravity values, in the rotating frame over time, can be analytically predicted with improved accuracy compared with previously reported algorithms. The developed 3D clinostat hardware and algorithms will effectively provide well-validated taSMG and taSPG for cell growth experiments in future studies for space medicine

Current Status of Low-Density Lipoprotein Cholesterol Target Achievement in Patients with Type 2 Diabetes Mellitus in Korea Compared with Recent Guidelines

Background: We evaluated the achievement of low-density lipoprotein cholesterol (LDL-C) targets in patients with type 2 diabetes mellitus (T2DM) according to up-to-date Korean Diabetes Association (KDA), European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS), and American Diabetes Association (ADA) guidelines. Methods: This retrospective cohort study collected electronic medical record data from patients with T2DM (>_20 years) managed by endocrinologists from 15 hospitals in Korea (January to December 2019). Patients were categorized according to guidelines to assess LDL-C target achievement. KDA (2019): Very High-I (atherosclerotic cardiovascular disease [ASCVD]) <70 mg/dL; Very High-II (target organ damage [TOD], or cardiovascular risk factors [CVRFs]) <70 mg/dL; high (others) <100 mg/dL. ESC/EAS (2019): Very High-I (ASCVD): <55 mg/dL; Very High-II (TOD or >_3-CVRF) <55 mg/dL; high (diabetes >_10 years without TOD plus any CVRF) <70 mg/dL; moderate (diabetes <10 years without CVRF) <100 mg/dL. ADA (2019): Very High-I (ASCVD); Very High-II (age >_40+ TOD, or any CVRF), for high intensity statin or statin combined with ezetimibe. Results: Among 2,000 T2DM patients (mean age 62.6 years; male 55.9%; mean glycosylated hemoglobin 7.2%) ASCVD prevalence was 24.7%. Of 1,455 (72.8%) patients treated with statins, 73.9% received monotherapy. According to KDA guidelines, LDLC target achievement rates were 55.2% in Very High-I and 34.9% in Very High-II patients. With ESC/EAS guidelines, target attainment rates were 26.6% in Very High-I, 15.7% in Very High-II, and 25.9% in high risk patients. Based on ADA guidelines, most patients (78.9%) were very-high risk; however, only 15.5% received high-intensity statin or combination therapy. Conclusion: According to current dyslipidemia management guidelines, LDL-C goal achievement remains suboptimal in Korean patients with T2DM.N