Fabrication of a Molecularly-Imprinted-Polymer-Based Graphene Oxide Nanocomposite for Electrochemical Sensing of New Psychoactive Substances

As new psychoactive substances (commonly known as “the third generation drugs”) have characteristics such as short-term emergence, rapid updating, and great social harmfulness, there is a large gap in the development of their detection methods. Herein, graphite oxide (GO) was first prepared and immobilized with a reversible addition-fragmentation chain transfer (RAFT) agent, then a new psychoactive substance (4-MEC) was chosen as a template, and then the surface RAFT polymerization of methacrylamide (MAAM) was carried out by using azobisisobutyronitrile (AIBN) as an initiator and divinylbenzene (DVB) as a cross-linker. After the removal of the embedded template, graphene oxide modified by molecularly imprinted polymers (GO-MIPs) was finally obtained. Owing to the specific imprinted cavities for 4-MEC, the satisfactory selectivity and stability of the GO-MIP nanocomposite have been demonstrated. The GO-MIP nanocomposite was then used to fabricate the electrochemical sensor, which displayed a high selectivity in detecting 4-MEC over a linear concentration range between 5 and 60 μg mL−1 with a detection limit of 0.438 μg mL−1. As a result, the GO-MIPs sensor developed an accurate, efficient, convenient, and sensitive method for public security departments to detect illicit drugs and new psychoactive substances

Adversarial Counterfactual Environment Model Learning

A good model for action-effect prediction, named environment model, is important to achieve sample-efficient decision-making policy learning in many domains like robot control, recommender systems, and patients' treatment selection. We can take unlimited trials with such a model to identify the appropriate actions so that the costs of queries in the real world can be saved. It requires the model to handle unseen data correctly, also called counterfactual data. However, standard data fitting techniques do not automatically achieve such generalization ability and commonly result in unreliable models. In this work, we introduce counterfactual-query risk minimization (CQRM) in model learning for generalizing to a counterfactual dataset queried by a specific target policy. Since the target policies can be various and unknown in policy learning, we propose an adversarial CQRM objective in which the model learns on counterfactual data queried by adversarial policies, and finally derive a tractable solution GALILEO. We also discover that adversarial CQRM is closely related to the adversarial model learning, explaining the effectiveness of the latter. We apply GALILEO in synthetic tasks and a real-world application. The results show that GALILEO makes accurate predictions on counterfactual data and thus significantly improves policies in real-world testing

Programmable Photoswitchable Microcapsules Enable Precise and Tailored Drug Delivery from Microfluidics

The development of precision personalized medicine poses a significant need for the next generation of advanced diagnostic and therapeutic technologies, and one of the key challenges is the development of highly time-, space-, and dose-controllable drug delivery systems that respond to the complex physiopathology of patient populations. In response to this challenge, an increasing number of stimuli-responsive smart materials are integrated into biomaterial systems for precise targeted drug delivery. Among them, responsive microcapsules prepared by droplet microfluidics have received much attention. In this study, we present a UV-visible light cycling mediated photoswitchable microcapsule (PMC) with dynamic permeability-switching capability for precise and tailored drug release. The PMCs were fabricated using a programmable pulsed aerodynamic printing (PPAP) technique, encapsulating an aqueous core containing magnetic nanoparticles and the drug doxorubicin (DOX) within a poly(lactic-co-glycolic acid) (PLGA) composite shell modified by PEG-b-PSPA. Selective irradiation of PMCs with ultraviolet (UV) or visible light (Vis) allows for high-precision time-, space-, and dose-controlled release of the therapeutic agent. An experimentally validated theoretical model was developed to describe the drug release pattern, holding promise for future customized programmable drug release applications. The therapeutic efficacy and value of patternable cancer cell treatment activated by UV radiation is demonstrated by our experimental results. After in vitro transcatheter arterial chemoembolization (TACE), PMCs can be removed by external magnetic fields to mitigate potential side effects. Our findings demonstrate that PMCs have the potential to integrate embolization, on-demand drug delivery, magnetic actuation, and imaging properties, highlighting their immense potential for tailored drug delivery and embolic therapy