The study of double-network carboxymethyl chitosan/sodium alginate based cryogels for rapid hemostasis in noncompressible hemorrhage.

Developing an injectable hemostatic dressing with shape recovery and high blood absorption ratio for rapid hemostasis in noncompressible hemorrhage maintains a critical clinical challenge. Here, double-network cryogels based on carboxymethyl chitosan, sodium alginate, and methacrylated sodium alginate were prepared by covalent crosslinking and physical crosslinking, and named carboxymethyl chitosan/methacrylated sodium alginate (CM) cryogels. Covalent crosslinking was achieved by methacrylated sodium alginate in the freeze casting process, while physical crosslinking was realized by electrostatic interaction between the amino group of carboxymethyl chitosan and the carboxyl group of sodium alginate. CM cryogels exhibited large water swelling ratios (8167 ± 1062 %), fast blood absorption speed (2974 ± 669 % in 15 s), excellent compressive strength (over 160 kPa for CM100) and shape recovery performance. Compared with gauze and commercial gelatin sponge, better hemostatic capacities were demonstrated for CM cryogel with the minimum blood loss of 40.0 ± 8.9 mg and the lowest hemostasis time of 5.0 ± 2.0 s at hemostasis of rat liver. Made of natural polysaccharides with biocompatibility, hemocompatibility, and cytocompatibility, the CM cryogels exhibit shape recovery and high blood absorption rate, making them promising to be used as an injectable hemostatic dressing for rapid hemostasis in noncompressible hemorrhage.

Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue.

Most existing physiological testing systems broadly classify monitored physiological data into three categories: normal, abnormal, and highly abnormal, but do not consider differences in the importance of data within the same category, which may result in the loss of data of higher importance. In addition, the purpose of physiological monitoring is to detect health abnormalities in patients earlier and faster, thus enabling risk avoidance and real-time rescue. Therefore, we designed a system called the adaptive physiological monitoring and rescue system (APMRS) that innovatively incorporates emergency rescue functions into traditional physiological monitoring systems using the rescue of modified-MAC (RM-MAC) protocol. The relay selection (RS) algorithm of APMRS can select the appropriate relay to forward based on the importance of the physiological data, thus ensuring priority transmission of more important monitoring data. In addition, we apply deep learning target trajectory prediction technology to the indoor rescue module (IRM) of APMRS to provide high-performance scheduling of location tracking nodes in advance by trajectory prediction. It reduces network energy consumption and ensures perceptual tracking accuracy. When APMRS monitors abnormal physiological data that may endanger a patient's life, IRM can implement effective and fast location rescue to avoid risks.