Recent advances in microfluidic cell cultures enable the construction of in vitro human skin models that can be used for drug toxicity testing, disease study. However, current in vitro skin model have limitations to emulate real human skin due to the simplicity of model. In this paper, we describe the development of ‘skin-on-a-chip’ to mimic the structures and functional responses of the human skin. The proposed model consists of 3 layers, on which epidermal, dermal and endothelial components originated from human, were cultured. The microfluidic device was designed for co-culture of human skin cells and each layer was separated by using porous membranes to allow interlayer communication. Skin inflammation and edema were induced by applying tumor necrosis factor alpha on dermal layer to demonstrate the functionality of the system. The expression levels of proinflammatory cytokines were analyzed to illustrate the feasibility. In addition, we evaluated the efficacy of therapeutic drug testing model using our skin chip. The function of skin barrier was evaluated by staining tight junctions and measuring a permeability of endothelium. Our results suggest that the skin-on-a-chip model can potentially be used for constructing in vitro skin disease models or for testing the toxicity of cosmetics or drugs.

The clinical use of bioactive molecules in bone regeneration has been known to have side effects, which result from uncontrolled and supraphysiological doses. In this study, we demonstrated the synergistic effect of two bioactive molecules, bone morphogenic protein-2 (BMP-2) and alendronate (ALN), by releasing them in a sequential manner. Collagen-hydroxyapatite composite scaffolds functionalized using BMP-2 are loaded with biodegradable microspheres where ALN is encapsulated. The results indicate an initial release of BMP-2 for a few days, followed by the sequential release of ALN after two weeks. The composite scaffolds significantly increase osteogenic activity owing to the synergistic effect of BMP-2 and ALN. Enhanced bone regeneration was identified at eight weeks post-implantation in the rat 8-mm critical-sized defect. Our findings suggest that the sequential delivery of BMP-2 and ALN from the scaffolds results in a synergistic effect on bone regeneration, which is unprecedented. Therefore, such a system exhibits potential for the application of cell-free tissue engineering.

No abstract

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.INDEX TERMS blood glucose control, reinforcement learning, safe and interpretable control, in silico validation, simulation for clinical application

Background Patient verification by unique identification is an important procedure in health care settings. Risks to patient safety occur throughout health care settings by failure to correctly identify patients, resulting in the incorrect patient, incorrect site procedure, incorrect medication, and other errors. To avoid medical malpractice, radio-frequency identification (RFID), fingerprint scanners, iris scanners, and other technologies have been implemented in care settings. The drawbacks of these technologies include the possibility to lose the RFID bracelet, infection transmission, and impracticality when the patient is unconscious. Objective The purpose of this study was to develop a mobile health app for patient identification to overcome the limitations of current patient identification alternatives. The development of this app is expected to provide an easy-to-use alternative method for patient identification. Methods We have developed a facial recognition mobile app for improved patient verification. As an evaluation purpose, a total of 62 pediatric patients, including both outpatient and inpatient, were registered for the facial recognition test and tracked throughout the facilities for patient verification purpose. Results The app was developed to contain 5 main parts: registration, medical records, examinations, prescriptions, and appointments. Among 62 patients, 30 were outpatients visiting plastic surgery department and 32 were inpatients reserved for surgery. Whether patients were under anesthesia or unconscious, facial recognition verified all patients with 99% accuracy even after a surgery. Conclusions It is possible to correctly identify both outpatients and inpatients and also reduce the unnecessary cost of patient verification by using the mobile facial recognition app with great accuracy. Our mobile app can provide valuable aid to patient verification, including when the patient is unconscious, as an alternative identification method.

This article presents the efficacy of heat-induced MPC-grafting against excessive fibrous capsule formation and related inflammation in tissues surrounding silicone breast implants inserted in a pig model.

The surface of human silicone breast implants is covalently grafted at a high density with a 2-methacryloyloxyethyl phosphorylcholine (MPC)-based polymer. Addition of crosslinkers is essential for enhancing the density and mechanical durability of the MPC graft. The MPC graft strongly inhibits not only adsorption but also the conformational deformation of fibrinogen, resulting in the exposure of a buried amino acid sequence, γ377−395, which is recognized by inflammatory cells. Furthermore, the numbers of adhered macrophages and the amounts of released cytokines (MIP-1α, MIP-1β, IL-8, TNFα, IL-1α, IL-1β, and IL-10) are dramatically decreased when the MPC network is introduced at a high density on the silicone surface (cross-linked PMPC-silicone). We insert the MPC-grafted human silicone breast implants into Yorkshire pigs to analyze the in vivo effect of the MPC graft on the capsular formation around the implants. After 6 month implantation, marked reductions of inflammatory cell recruitment, inflammatory-related proteins (TGF-β and myeloperoxidase), a myoblast marker (α-smooth muscle actin), vascularity-related factors (blood vessels and VEGF), and, most importantly, capsular thickness are observed on the crosslinked PMPC-silicone. We propose a mechanism of the MPC grafting effect on fibrous capsular formation around silicone implants on the basis of the in vitro and in vivo results.

Cholesterol and squalene are fatty materials of latent fingermarks that can be utilized for dating methodologies and visualization techniques. Previous studies have suggested these compounds undergo degradation in fingermarks as a function of time (days) and light at ambient temperature. However, studies assessing how their composition changes at low and high temperatures over short periods of time (hours) have not been published previously. Here, we performed quantitative analysis of cholesterol and squalene in natural fingermark residue using PVDF membrane, after exposure to a range of temperatures (−20 to 100°C) for 4 and 8 h. We found that levels of both fatty materials remained constant at −20 to 60°C, but both showed significant reduction at 100°C, over short exposure times. These results indicate that cholesterol and squalene are detectable at −20 to 60°C, whereas at 100°C or higher, both are lost due to rapid thermal degradation.