Preoperative blood-based nutritional biomarkers as significant prognostic factors after intravesical BCG therapy in patients with non-muscle-invasive bladder cancer.

The aim of this study was to investigate the prognostic role of blood-based nutritional biomarkers, including red blood cell (RBC count), hemoglobin (Hb), total protein (TP), albumin, the serum albumin to globulin ratio (AGR) and the prognostic nutritional index (PNI) in patients who underwent intravesical treatment for non-muscle invasive bladder cancer (NMIBC). A total of 501 NMIBC patients who received intravesical Bacillus Calmette-Guerin (BCG) treatment following transurethral resection of bladder tumor (TURBT) were included. The optimal cutoff values for these nutrition-based indicators were determined using receiver operating characteristic curve analysis. We observed a significantly higher recurrence-free survival (RFS) rate in patients with elevated levels of RBC count, Hb, TP, and albumin. Cox univariate and multivariate Cox regression analyses demonstrated that serum albumin (P = 0.002, HR = 0.51, 95%CI: 0.33-0.78), RBC count (P = 0.002, HR = 0.50, 95%CI: 0.32-0.77), TP (P = 0.028, HR = 0.62, 95%CI: 0.41-0.95), Hb (P = 0.004, HR = 0.53, 95%CI: 0.33-0.84), AGR (P = 0.003, HR = 0.46, 95%CI: 0.27-0.76) and PNI (P = 0.019, HR = 0.56, 95%CI: 0.35-0.91) were significant independent factors predicting RFS. These cost-effective and convenient blood-based nutritional biomarkers have the potential to serve as valuable prognostic indicators for predicting recurrence in NMIBC patients undergoing BCG-immunotherapy.

SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time.

The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual off-line algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SLAM)-based self-calibration method designed to achieve real-time, automatic and accurate calibration of the binocular stereo vision (BSV) rig's extrinsic parameters in a short period without auxiliary equipment and special calibration markers, assuming the intrinsic parameters of the left and right cameras are known in advance. The main contribution of this paper is to use the SLAM algorithm as our main tool for the calibration method. The method mainly consists of two parts: SLAM-based construction of 3D scene point map and extrinsic parameter calibration. In the first part, the SLAM mainly constructs a 3D feature point map of the natural environment, which is used as a calibration area map. To improve the efficiency of calibration, a lightweight, real-time visual SLAM is built. In the second part, extrinsic parameters are calibrated through the 3D scene point map created by the SLAM. Ultimately, field experiments are performed to evaluate the feasibility, repeatability, and efficiency of our self-calibration method. The experimental data shows that the average absolute error of the Euler angles and translation vectors obtained by our method relative to the reference values obtained by Zhang's calibration method does not exceed 0.5˚ and 2 mm, respectively. The distribution range of the most widely spread parameter in Euler angles is less than 0.2˚ while that in translation vectors does not exceed 2.15 mm. Under the general texture scene and the normal driving speed of the mobile robot, the calibration time can be generally maintained within 10 s. The above results prove that our proposed method is reliable and has practical value.