| 1. |
- Duan, Dongban, et al.
(author)
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Gadolinium Neutron Capture Reaction-Induced Nucleodynamic Therapy Potentiates Antitumor Immunity
- 2023
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In: CCS Chemistry. - : Chinese Chemical Society. - 2096-5745. ; 5:11, s. 2589-2602
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Journal article (peer-reviewed)abstract
- A nuclear reaction-induced dynamic therapy, denoted as nucleodynamic therapy (NDT), has been invented that triggers immunogenic cell death and successfully treats metastatic tumors due to its unexpected abscopal effect. Gadolinium neutron capture therapy (GdNCT) is binary radiotherapy based on a localized nuclear reaction that produces high-energy radiations (e.g., Auger electrons, γ-rays, etc.) in cancer cells when 157Gd is irradiated with thermal neutrons. Yet, its clinical application has been postponed due to the poor ability of Auger electrons and γ-rays to kill cells. Here, we engineered a 157Gd-porphyrin framework that synergizes GdNCT and dynamic therapy to efficiently produce both •OH and immunogenic 1O2 in cancer cells, thereby provoking a strong antitumor immune response. This study unveils the fact and mechanism that NDT heats tumor immunity. Another unexpected finding is that the Auger electron can be the most effective energy-transfer medium for radiation-induced activation of nanomedicines because its nanoscale trajectory perfectly matches the size of nanomaterials. In mouse tumor models, NDT causes nearly complete regression of both primary and distant tumor grafts. Thus, this 157Gd-porphyrin framework radioenhancer endows GdNCT with the exotic function of triggering dynamic therapy; its application may expand in clinics as a new radiotherapy modality that utilizes GdNCT to provoke whole-body antitumor immune response for treating metastases, which are responsible for 90% of all cancer deaths.
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| 2. |
- Wang, Kewei, et al.
(author)
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Reconfigurable Intelligent Surfaces Aided Energy Efficiency Maximization in Cell-Free Networks
- 2024
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In: IEEE Wireless Communications Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2162-2337 .- 2162-2345. ; 13:6, s. 1596-1600
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Journal article (peer-reviewed)abstract
- As we move towards next-generation wireless networks, the need for sustainability through energy efficiency (EE) concepts becomes more important than ever. Meanwhile, technology enablers, such as beamforming and reconfigurable intelligent surfaces (RISs), if appropriately used in a synergetic manner, can deliver profound excellence in terms of EE. Motivated by this, in this letter, we introduce an EE maximization policy that accounts for the rate demands of the end-users in RIS-assisted cell-free networks. The policy aims at performing joint optimization of the transmit beamforming vectors and the RIS phase-shift matrices in order to maximize the EE. In this direction, we first formulate the corresponding optimization problem, which is non-convex. To solve it, we rely on advanced optimization methods such as quadratic and Lagrangian dual transforms. Numerical results highlight the superiority of the presented policy in comparison to baseline approaches and reveal the most impactful network parameters.
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| 3. |
- Wang, Shuqi, et al.
(author)
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Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach
- 2024
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In: Electronics. - : Multidisciplinary Digital Publishing Institute (MDPI). - 2079-9292. ; 13:8
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Journal article (peer-reviewed)abstract
- Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm.
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