Synergistic integration of the Internet of Things (IoT), cloud computing, and big data technologies in healthcare have led to the notion of "smart health." Smart health is an emerging concept that refers to the provision of healthcare services for prevention, diagnosis, treatment, and follow-up management at any time or any place by connecting information technologies and healthcare. As a significant breakthrough in smart healthcare development, IoT-enabled smart devices allow medical centers to carry out preventive care, diagnosis, and treatment more competently. This review focuses on recently developed patient health monitoring platforms based on IoT-enabled smart devices that can collect real-time patient data and transfer information for assessment by healthcare providers, including doctors, hospitals, and clinics, or for self-management. We aimed to summarize the available information about recently approved devices and state-of-the-art developments through a comprehensive, systematic literature review. In this review, we also discuss possible future directions for the integration of cloud computing and blockchain, which may offer unprecedented breakthroughs in on-demand medical services. The combination of IoT with real-time, remote patient monitoring empowers patients to assert more control over their care, thereby allowing them to actively monitor their particular health conditions.

As the regenerative mechanisms of biological organisms, self-healing provides useful functions for soft electronics or associated systems. However, there have been few examples of soft electronics where all components have self-healing properties while also ensuring compatibility between components to achieve multifunctional and resilient bio-integrated electronics. Here, we introduce a stretchable, biodegradable, self-healing conductor constructed by combination of two layers: (i) synthetic self-healing elastomer and (ii) self-healing conductive composite with additives. Abundant dynamic disulfide and hydrogen bonds of the elastomer and conductive composite enable rapid and complete recovery of electrical conductivity (~1000 siemens per centimeter) and stretchability (~500%) in response to repetitive damages, and chemical interactions of interpenetrated polymer chains of these components facilitate robust adhesion strength, even under extreme mechanical stress. System-level demonstration of soft, self-healing electronics with diagnostic/therapeutic functions for the urinary bladder validates the possibility for versatile, practical uses in biomedical research areas.

We introduce a soft, expandable electronic and optoelectronic system designed to monitor and cure urinary bladder diseases.

In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (CIs) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction.

Several studies have investigated the use of invasive and non-invasive stimulation methods to enhance nerve regeneration, and varying degrees of effectiveness have been reported. However, due to the use of different parameters in these studies, a fair comparison between the effectiveness of invasive and non-invasive stimulation methods is not possible. The present study compared the effectiveness of invasive and non-invasive stimulation using similar parameters. Eighteen Sprague Dawley rats were classified into three groups: the iES group stimulated with fully implantable device, the tES group stimulated with transcutaneous electrical nerve stimulation (TENS), and the injury group (no stimulation). The iES and tES groups received stimulation for 6 weeks starting immediately after the injury. Motor function was evaluated using the sciatic functional index (SFI) every week. The SFI values increased over time in all groups; faster and superior functional recovery was observed in the iES group than in the tES group. Histological evaluation of the nerve sections and gastrocnemius muscle sections were performed every other week. The axon diameter and muscle fiber area in the iES group were larger, and the g-ratio in the iES group was closer to 0.6 than those in the tES group. To assess the cause of the difference in efficiency, a 3D rat anatomical model was used to simulate the induced electric fields in each group. A significantly higher concentration and intensity around the sciatic nerve was observed in the iES group than in the tES group. Vector field distribution showed that the field was orthogonal to the sciatic nerve spread in the tES group, whereas it was parallel in the iES group; this suggested that the tES group was less effective in nerve stimulation. The results indicated that even though rats in the TENS group showed better recovery than those in the injury group, it cannot replace direct stimulation yet because rats stimulated with the invasive method showed faster recovery and superior outcomes. This was likely attributable to the greater concentration and parallel distribution of electric field with respect to target nerve.