The concomitant use of the renin-angiotensin system inhibitors and survival outcomes of patients with pancreatic adenocarcinoma: an analysis from a tertiary center.

Background: The limited efficacy of chemotherapy in improving survival in pancreatic ductal adenocarcinoma (PDAC) necessitates the exploration of novel strategies to overcome treatment resistance. Objectives: This study aimed to investigate the impact of combining renin-angiotensin system (RAS) blockers with chemotherapy on survival outcomes in patients with PDAC. Design: Patients with PDAC were enrolled in the retrospective study. Methods: We analyzed patients with PDAC (n = 384) at our institution between 2014 and 2021. Survival outcomes, including event-free survival (EFS) and overall survival (OS), were analyzed according to the concomitant use of RAS blockers. Results: Among the 384 patients in the study, 70 (18.2%) concomitantly received angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs). Patients in the ACEI/ARB group, characterized by older age and more comorbidities, displayed a significantly superior 12-month EFS rate (22.86% versus 13.69%, p = 0.008) compared to the non-ACEI/ARB group, while OS remained similar between the groups. In the multivariate analysis, the use of ACEI/ARB was associated with better 12-month EFS (hazards ratio = 0.71, 95% confidence interval: 0.52-0.96; p = 0.024). Poor performance, advanced disease status, and higher CA19-9 levels were associated with poor survival outcomes. Conclusion: Concomitant use of ACEIs/ARBs in patients with pancreatic cancer resulted in significantly better 12-month EFS. Age, performance status, disease status, and higher CA19-9 levels were independent predictors of survival. The combination strategy might provide better treatment outcomes in patients with PDAC.

A model-based strategy for quadruped running with differentiated fore- and hind-leg morphologies.

This article introduces a model-based strategy for a quadruped robot with differentiated fore- and hind-leg ground reaction force patterns to generate animal-like running behavior. The proposed model comprises a rigid body and two eccentric spring-loaded inverted pendulum (eSLIP) legs with dampers. The eSLIP model extends the traditional SLIP model by adding a bar to offset the spring direction. The proposed two-leg eSLIP (TL-eSLIP) model's fore- and hind legs were designed to have the same offset magnitude but in opposite offset directions, producing different braking and thrusting force patterns. The TL-eSLIP model's reference leg trajectories were designed based on the fixed-point motion of the eSLIP model. Additionally, the legs were clock torque-controlled to modulate leg motion and stabilize the model to follow its natural dynamics. The model's equations for motion were derived, and the model's dynamic behavior was simulated and analyzed. The simulation results indicate that the model with leg offsets and in either trotting or pronking has differentiated leg force patterns, and it is more stable and has larger basins of attraction than the model without leg offsets. A quadruped robot was built for experimental validation. The experimental results demonstrate that the robot with differentiated legs ran with differentiated ground reaction force patterns and ran more stably than another robot with the same leg morphology.

DoA Estimation for FMCW Radar by 3D-CNN.

A method of direction-of-arrival (DoA) estimation for FMCW (Frequency Modulated Continuous Wave) radar is presented. In addition to MUSIC, which is the popular high-resolution DoA estimation algorithm, deep learning has recently emerged as a very promising alternative. It is proposed in this paper to use a 3D convolutional neural network (CNN) for DoA estimation. The 3D-CNN extracts from the radar data cube spectrum features of the region of interest (RoI) centered on the potential positions of the targets, thereby capturing the spectrum phase shift information, which corresponds to DoA, along the antenna axis. Finally, the results of simulations and experiments are provided to demonstrate the superior performance, as well as the limitations, of the proposed 3D-CNN.