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The efficacy of tracheal stents (TSs) in treating malignant tracheal stenosis is often compromised by tumor overgrowth, leading to restenosis and other stent-related complications that conventional chemotherapy and commercial stents fail to adequately address. Drug-loaded tracheal stents have the potential to deliver chemotherapeutics directly to tumors while relieving stenosis, but their effectiveness has yet to be studied in vivo. The design of drug-loaded tracheal stents adapting to lesions to achieve optimal antitumor effects and minimal side effects remains an area worth exploring. In this study, a lesion-adaptive bionic tracheal stent (PTX-TS) designed for the dual purpose of treating tracheal tumors and associated stenosis was developed. This novel PTX-TS was evaluated using an orthotopic rabbit model of malignant tracheal stenosis, newly established in this study. The rabbit lesions were precisely scanned using computed tomography (CT) for 3D reconstruction, enabling the design of a PTX-TS that fit both the tumor and airway dimensions to ensure complete tumor coverage and effective dilation of the stenotic airway. The PTX-TS featured a bilayer structure including a surface layer of PTX/ethylene-vinyl acetate copolymer (EVA) blends for sustained PTX release and an inner layer of polycaprolactone (PCL)/EVA blends for appropriate mechanical performance. The stent was fabricated layer by layer using a custom-built 3D printer, and the drug-loaded surface layer was printed using a novel liquid printing technique developed in our lab, achieving a high drug loading of up to 80%. The dose of the PTX-TS was investigated and set as 7.5 mg/cm2, which leads to maximum tissue permeation. With its bionic cross-sectional C-shaped structure, the PTX-TS demonstrated excellent radial flexibility, allowing successful implantation at the lesion site using a specially designed delivery apparatus, where it self-expanded to relieve stenosis. Additionally, the PTX-TS effectively delivered PTX directly to the tracheal tumor, resulting in superior antitumor efficacy without significant toxicity or complications.

Herein, two sulfhydryl‐functionalized polymers, namely, EDT‐HCPS and TMPMP‐HCPS, were synthesized according to the Friedel‐Craft s alkylation and nucleophilic substitution, and they were used to adsorb Pd2+and Ag+from aqueous solution. The Friedel‐Crafts alkylation offered the polymers with hyper‐cross‐linking frameworks composed of plentiful rigid methylene. The nucleophilic substitution brought abundant functional SH groups (the sulfur contents were 12.6 and 9.4 wt%, respectively) to the polymers. The adsorption results showed that the capacities of Pd2+and Ag+on EDT‐HCPS were larger than TMPMP‐HCPS, and the higher quantities of functional SH groups and the porosity played a beneficial role. The equilibrium data can be characterized by the Langmuir model, and the maximum capacities of Pd2+and Ag+on EDT‐HCPS at 308 K were predicted to be 383.92 and 212.93 mg g−1, respectively. The produced polymers represented superior adsorption selectivity, reproducibility, and reusability, demonstrating their excellent performance for the adsorption of Pd2+and Ag+, and analysis of the adsorption mechanism demonstrated that the SH groups played a major role due to the strong chemical complexation of the functional SH with Pd2+/Ag+.

Reconfigurable intelligent surface (RIS) is a promising technique that smartly reshapes wireless propagation environment in the future wireless networks. In this paper, we apply RIS to an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network, in which the transmit signals from multiple UAVs to ground users are strengthened through RIS. Our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance. The formulated optimization problem is non-convex by jointly optimizing the position of UAVs, RIS reflection coefficients, transmit power, active beamforming vectors and decoding order, and thus is quite hard to solve optimally. To tackle this problem, we divide the resultant optimization problem into four independent subproblems, and solve them in an iterative manner. In particular, we first consider the sub-solution of UAVs placement which can be obtained via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we yield the closed-form expression for the RIS reflection coefficients. Subsequently, the transmit power is optimized using standard convex optimization methods. Finally, a dynamic-order decoding scheme is presented for optimizing the NOMA decoding order in order to guarantee fairness among users. Simulation results verify that our designed joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods, thus verifying the advantages of combining RIS into the multi-UAV assisted NOMA networks.