Combination regimens of immunotherapy plus chemotherapy have been approved as the first-line and standard of care for extensive-stage small cell lung cancer (ES-SCLC). Novel regimens are continuously being explored, with the ETER701 study being the representative randomized controlled trial (RCT). ETER701 study has assessed the efficacy and safety of chemotherapy with or without anlotinib (multi-target angiogenesis inhibitor) + benmelstobart (programmed cell death ligand 1 inhibitor) (Anl/Ben/CT). There is no evidence-based medicine available proving that Anl/Ben/CT is the optimal regimen due to the lack of direct or indirect comparisons among varying immunotherapy-based regimens. In this study, we aimed to identify the optimal regimen to assist in clinical decision-making. The eligible RCTs were identified by searching PubMed, Embase, Cochrane Library databases, and major international conferences. Then, the network meta-analysis was analyzed to compare the efficacy and safety among 15 first-line regimens in ES-SCLC. The Cochrane Risk of Bias Tool was used to assess the risk of bias in included studies. A total of 12 immunotherapy-related RCTs covering 15 interventions and 6,178 patients with ES-SCLC were included. Overall, most RCTs exhibited a low risk of bias across multiple domains. The results indicated that most immunotherapy-based regimens could significantly prolong progression-free survival (PFS) compared with chemotherapy alone, especially Anl/Ben/CT [hazard ratio (HR) 0.32, 95% confidence interval (CI): 0.25-0.40]. Similar results were observed regarding overall survival (OS), that is, most immunotherapy-related regimens dramatically reduced the risk of death in ES-SCLC, with Anl/Ben/CT being the most prominent (HR 0.61, 95% CI: 0.47-0.80). The Bayesian ranking probabilities showed that Anl/Ben/CT ranked first and serplulimab plus chemotherapy ranked second in both PFS and OS among 15 regimens. Regarding safety, Anl/Ben/CT ranked 3rd, and serplulimab plus chemotherapy ranked 7th. Adding anlotinib and benmelstobart to chemotherapy significantly improved PFS and OS compared with chemotherapy alone or chemotherapy plus immunotherapy, with an acceptable safety profile in patients with ES-SCLC. In conclusion, Anl/Ben/CT could be a new, preferable first-line treatment option but further clinical studies are needed to validate its efficacy and safety.
The FeO content of sinter is closely related to the drum strength and reduction performance of sinter and reflects the heat control level of the sintering process. It is one of the key indicators to measure the quality of sinter and the level of production operation. However, due to the large lag of sintering process and quality detection, the detected FeO content can only reflect the production state several hours ago, so the early prediction of FeO content is very important. In this paper, the theoretical basis of FeO content prediction is expounded from the aspects of FeO formation mechanism, influencing factors and prediction difficulties of sinter. The advantages, disadvantages and applicable scenarios of traditional FeO detection methods are compared and analysed. Then, the evolution, application and latest progress of the prediction technology of FeO content in sinter are summarised from the perspectives of process parameters and tail section, and the technical bottleneck and future direction of FeO content prediction are pointed out.
In this paper, an event-based control method is proposed to study the trajectory tracking of a two-wheel differential nonholonomic mobile robot. Firstly, the system model of the mobile robot is proposed and the control law of the two-wheel differential nonholonomic mobile robot is designed. The stability of the equilibrium point is proved by using the linearization method and Lyapunov function. Then, the stability of the system with trigger condition and Zeno phenomenon is proved. Finally, the simulation clearly shows that the proposed event triggering method can reduce the sampling frequency.
A new quantitative analysis algorithm based on data extraction and representative data selection in the LIBS field to establish the prediction model with a small sample size is proposed.
This paper proposes a novel finger-individuating exoskeleton system with a non-contact leader-follower control strategy that effectively combines motion functionality and individual adaptability. Our solution comprises the following two interactive components: the leader side and the follower side. The leader side processes joint angle information from the healthy hand during motion via a Leap Motion Controller as the system input, providing more flexible and active operations owing to the non-contact manner. Then, as the follower side, the exoskeleton is driven to assist the user's hand for rehabilitation training according to the input. The exoskeleton mechanism is designed as a universal module that can adapt to various digit sizes and weighs only 40 g. Additionally, the current motion of the exoskeleton is fed back to the system in real time, forming a closed loop to ensure control accuracy. Finally, four experiments validate the design effectiveness and motion performance of the proposed exoskeleton system. The experimental results indicate that our prototype can provide an average force of about 16.5 N for the whole hand during flexing, and the success rate reaches 82.03% in grasping tasks. Importantly, the proposed prototype holds promise for improving rehabilitation outcomes, offering diverse options for different stroke stages or application scenarios.
ABSTRACT In this article, we present a novel approach for estimating the conditional average treatment effect in models with binary responses. Our proposed method involves model averaging, and we establish a weight choice criterion based on jackknife model averaging. We analyze the theoretical properties of this approach, including its asymptotic optimality in achieving the lowest possible squared error and the convergence rate of the weights assigned to correctly specified models. Additionally, we introduce a new matching method that combines partition and nearest neighbor pairing, leveraging the strengths of both techniques. To evaluate the performance of our method, we conduct comparisons with existing approaches via a Monte Carlo study and a real data analysis. Overall, our results demonstrate the effectiveness and practicality of our proposed approach for estimating the conditional average treatment effect in binary response models.
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