Polymeric micelles are extensively used for the delivery of hydrophobic drugs, which, however, suffer from unsatisfactory drug loading, colloidal uniformity, formulation stability, and drug release. Herein, we demonstrate a convenient strategy to prepare micelles with ultrahigh drug loading via the incorporation of polymer-drug coordination interactions. An amphiphilic copolymer containing pendant phenylboronic acid as electron acceptor unit was synthesized, which afforded donor-acceptor coordination with doxorubicin to obtain micelles with ultrahigh drug loading (∼50%), nearly quantitative loading efficiency (>95%), uniform size, and colloidal stability. Besides, the encapsulated drug can be effectively and selectively released in response to the high reactive oxygen species levels in cancer cells, which potentiated the anticancer efficacy and reduced systemic toxicity. Apart from doxorubicin, the current platform could be extended to other drugs with electron-donating groups (e.g., epirubicin and irinotecan), rendering a simple and robust strategy for enabling high drug loading in polymeric micelles and cancer-specific drug release.

Natural triterpenes represent a group of pharmacologically active and structurally diverse organic compounds. The focus on these phytochemicals has been enormous in the past few years, worldwide. Asiatic acid (AA), a naturally occurring pentacyclic triterpenoid, is found mainly in the traditional medicinal herb Centella asiatica. Triterpenoid saponins, which are the primary constituents of C. asiatica, are commonly believed to be responsible for their extensive therapeutic actions. Published research work has described the molecular mechanisms underlying the various biological activities of AA and its derivatives, which vary for each chronic disease. However, a compilation of the various pharmacological properties of AA has not yet been done. Herein, we describe in detail the pharmacological properties of AA and its derivatives that inhibit multiple pathways of intracellular signaling molecules and transcription factors that are involved in the various stages of chronic diseases. Furthermore, the pharmacological activities of AA were compared with two natural compounds: curcumin and resveratrol. This review summarizes the research on AA and its derivatives and helps to provide future directions in the area of drug development.

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The introduction of donor-receptor coordination between micelles and drug payloads provides a precise co-delivery strategy for two different chemo-drugs with high drug loading and ROS responsiveness.

The success of intracellular protein therapy demands efficient delivery and selective protein activity in diseased cells. Therefore, a cascaded nanozymogen consisting of a hypoxia‐activatable pro‐protein, a hypoxia‐inducing protein, and a hypoxia‐strengthened intracellular protein delivery nanovehicle was developed. RPAB, an enzymatically inactive pro‐protein of RNase, reversibly caged with hypoxia‐cleavable azobenzene, was delivered with glucose oxidase (GOx) using hypoxia‐responsive nanocomplexes (NCs) consisting of azobenzene‐cross‐linked oligoethylenimine (AOEI) and hyaluronic acid (HA). Upon NC‐mediated delivery into cancer cells, GOx catalyzed glucose decomposition and aggravated tumoral hypoxia, which drove the recovery of RPAB back to the hydrolytically active RNase and expedited the degradation of AOEI to release more protein cargoes. Thus, the catalytic reaction of the nanozymogen was self‐accelerated and self‐cycled, ultimately leading to a cooperative anti‐cancer effect between GOx‐mediated starvation therapy and RNase‐mediated pro‐apoptotic therapy.

With the booming development of flexible wearable sensing devices, flexible stretchable strain sensors with crack structure and high sensitivity have been widely concerned. However, the narrow sensing range has been hindering the development of crack-based strain sensors. In addition, the existence of the crack structure may reduce the interface compatibility between the elastic matrix and the sensing material. Herein, to overcome these problems, integrated core-sheath fibers were prepared by coaxial wet spinning with partially added carbon nanotube sensing materials in thermoplastic polyurethane elastic materials. Due to the superior interface compatibility and the change in the conductive path during stretching, the fiber strain sensor exhibits excellent durability (5000 tensile cycles), high sensitivity (>10 4 ), large stretchability (500%), a low detection limit (0.01%), and a fast response time of ∼60 ms. Based on these outstanding strain sensing performances, the fiber sensor is demonstrated to detect subtle strain changes (e.g., pulse wave and swallowing) and large strain changes (e.g., finger joint and wrist movement) in real time. Moreover, the fabric sensor woven with the core-sheath fibers has an excellent performance in wrist bending angle detection, and the smart gloves based on the fabric sensors also show exceptional recognition ability as a wireless sign language translation device. This integrated strategy may provide prospective opportunities to develop highly sensitive strain sensors with durable deformation and a wide detection range.

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.