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Aims Catheter ablation (CA) of idiopathic ventricular arrhythmias (VAs) from the epicardial left ventricular summit is challenging. The endocardial approach targets two sites: the endocardial closest site (ECS) to the epicardial earliest activation site (epi-EAS) and the endocardial earliest activation site (endo-EAS). We aimed to differentiate between cases where CA at the ECS was effective and where CA at the endo-EAS yielded success. Methods and results Fifty-eight patients (47 men; age 60 ± 13 years) were analysed with VAs in which the EAS was observed in the coronary venous system (CVS). Overall, VAs were successfully eliminated in 42 (72%) patients: 8 in the CVS, 8 where the ECS matched with the endo-EAS, 11 at the ECS, and 15 at the endo-EAS. A successful ECS ablation was associated with a shorter epi-EAS–ECS distance (10.2 ± 4.7 vs. 18.8 ± 5.3 mm; P < 0.001) and shorter epi-EAS–left main coronary trunk (LMT) ostial distance (20.3 ± 7.6 vs. 30.3 ± 8.4 mm; P = 0.005), with optimal cut-off values of ≤12.6 and ≤24.0 mm, respectively. A successful endo-EAS ablation was associated with an earlier electrogram at the endo-EAS [23 (8, 36) vs. 15 (0, 19) ms preceding the QRS; P < 0.001] and shorter epi-EAS–endo-EAS interval [6 (1, 8) vs. 22 (12, 25) ms; P < 0.001], with optimal cut-off values of ≥18 and ≤9 ms, respectively. Conclusion Shorter anatomical distances between the epi-EAS and ECS, and between the epi-EAS and LMT ostium, predict a successful ECS ablation. The prematurity of the endo-EAS electrogram and a shorter interval between the epi-EAS and endo-EAS predicted a successful endo-EAS ablation. [ABSTRACT FROM AUTHOR]

Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot's state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot's perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods. [ABSTRACT FROM AUTHOR]

Author(s): Kobe C. Yuen [sup.1] , Li-Fen Liu [sup.1] , Vinita Gupta [sup.1] , Shravan Madireddi [sup.1] , Shilpa Keerthivasan [sup.1] , Congfen Li [sup.1] , Deepali Rishipathak [sup.1] , [...], Although elevated plasma interleukin-8 (pIL-8) has been associated with poor outcome to immune checkpoint blockade.sup.1, this has not been comprehensively evaluated in large randomized studies. Here we analyzed circulating pIL-8 and IL8 gene expression in peripheral blood mononuclear cells and tumors of patients treated with atezolizumab (anti-PD-L1 monoclonal antibody) from multiple randomized trials representing 1,445 patients with metastatic urothelial carcinoma (mUC) and metastatic renal cell carcinoma. High levels of IL-8 in plasma, peripheral blood mononuclear cells and tumors were associated with decreased efficacy of atezolizumab in patients with mUC and metastatic renal cell carcinoma, even in tumors that were classically CD8.sup.+ T cell inflamed. Low baseline pIL-8 in patients with mUC was associated with increased response to atezolizumab and chemotherapy. Patients with mUC who experienced on-treatment decreases in pIL-8 exhibited improved overall survival when treated with atezolizumab but not with chemotherapy. Single-cell RNA sequencing of the immune compartment showed that IL8 is primarily expressed in circulating and intratumoral myeloid cells and that high IL8 expression is associated with downregulation of the antigen-presentation machinery. Therapies that can reverse the impacts of IL-8-mediated myeloid inflammation will be essential for improving outcomes of patients treated with immune checkpoint inhibitors. In a retrospective analysis of data from three clinical trials, increased baseline peripheral and tumor IL-8 levels were associated with worse clinical outcomes in patients with metastatic urothelial carcinoma and metastatic renal cell carcinoma treated with anti-PD-L1 therapy.

Depression is a severe and chronic psychiatric disease, affecting 350 million subjects worldwide. Although multiple antidepressants have been used in the treatment of depressive symptoms, their beneficial effects are limited. The soluble epoxide hydrolase (sEH) plays a key role in the inflammation that is involved in depression. Thus, we examined here the role of sEH in depression. In both inflammation and social defeat stress models of depression, a potent sEH inhibitor, TPPU, displayed rapid antidepressant effects. Expression of sEH protein in the brain from chronically stressed (susceptible) mice was higher than of control mice. Furthermore, expression of sEH protein in postmortem brain samples of patients with psychiatric diseases, including depression, bipolar disorder, and schizophrenia, was higher than controls. This finding suggests that increased sEH levels might be involved in the pathogenesis of certain psychiatric diseases. In support of this hypothesis, pretreatment with TPPU prevented the onset of depression-like behaviors after inflammation or repeated social defeat stress. Moreover, sEH KO mice did not show depression-like behavior after repeated social defeat stress, suggesting stress resilience. The sEH KO mice showed increased brain-derived neurotrophic factor (BDNF) and phosphorylation of its receptor TrkB in the prefrontal cortex, hippocampus, but not nucleus accumbens, suggesting that increased BDNF-TrkB signaling in the prefrontal cortex and hippocampus confer stress resilience. All of these findings suggest that sEH plays a key role in the pathophysiology of depression, and that epoxy fatty acids, their mimics, as well as sEH inhibitors could be potential therapeutic or prophylactic drugs for depression.