Targeting GSTP1-dependent ferroptosis in lung cancer radiotherapy: Existing evidence and future directions

Radiotherapy is applied in about 70% patients with tumors, yet radioresistance of tumor cells remains a challenge that limits the efficacy of radiotherapy. Ferroptosis, an iron-dependent lipid peroxidation regulated cell death, is involved in the development of a variety of tumors. Interestingly, there is evidence that ferroptosis inducers in tumor treatment can significantly improve radiotherapy sensitivity. In addition, related studies show that Glutathione S-transferase P1 (GSTP1) is closely related to the development of ferroptosis. The potential mechanism of targeting GSTP1 to inhibit tumor cells from evading ferroptosis leading to radioresistance has been proposed in this review, which implies that GSTP1 may play a key role in radiosensitization of lung cancer via ferroptosis pathway

Force Analysis on Coiled Tubing Plug Drilling Operation in Ultra-Deep Well with Long Horizontal Section

For purpose of accurate landing depth prediction and process safety assessment of coiled tubing （CT） plug drilling operation， considering the factors such as wellbore trajectory， buoyant weight of CT， friction of pipe sleeve， CT buckling and boundary conditions of bottomhole assembly （BHA）， based on the soft rope model， a mechanical model of CT operations was constructed by incorporating variable friction factor and CT with variable wall thickness， and taking into account the influence of well fluid drag force. Moreover， the estimates of different models were compared using the actual data from an ultra-deep well S9P1H. The results show that the well fluid drag force leads to enhanced load on CT， while the variable friction factor is conducive to the decreased load on CT， and the variable wall thickness helps reduce the stress on CT and relieve the fatigue of CT， prolonging its service life. The use of CT of 60.3 mm in diameter enables drilling to the bottomhole even at the weight on bit （WOB） of 20 kN and 25 kN， suggesting that the mechanical model incorporating variable friction factor and variable stiffness maximizes the plug drilling capability within the safe range. The research results provide guidance for addressing insufficient extension of CT in plug drilling operation in ultra-deep wells

An Effective Training Method for Counterfactual Multi-Agent Policy Network Based on Differential Evolution Algorithm

Due to the advantages of a centralized critic to estimate the Q-function value and decentralized actors to optimize the agents’ policies, counterfactual multi-agent (COMA) stands out in most multi-agent reinforcement learning (MARL) algorithms. The sharing of policy parameters can improve sampling efficiency and learning effectiveness, but it may lead to a lack of policy diversity. Hence, to balance parameter sharing and diversity among agents in COMA has been a persistent research topic. In this paper, an effective training method for a COMA policy network based on a differential evolution (DE) algorithm is proposed, named DE-COMA. DE-COMA introduces individuals in a population as computational units to construct the policy network with operations such as mutation, crossover, and selection. The average return of DE-COMA is set as the fitness function, and the best individual of policy network will be chosen for the next generation. By maintaining better parameter sharing to enhance parameter diversity, multi-agent strategies will become more exploratory. To validate the effectiveness of DE-COMA, experiments were conducted in the StarCraft II environment with 2s_vs_1sc, 2s3z, 3m, and 8m battle scenarios. Experimental results demonstrate that DE-COMA significantly outperforms the traditional COMA and most other multi-agent reinforcement learning algorithms in terms of win rate and convergence speed