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ContextCabozantinib combined with immune checkpoint inhibitors (ICIs) has brought a new therapeutic effect for the medical treatment of renal cell carcinoma (RCC).ObjectivesWe performed a meta-analysis of randomized controlled trials and single-arm trials to evaluate the efficacy and safety of cabozantinib plus ICIs in RCC.MethodsWe extracted data from PubMed, Cochrane, Medline and Embase databases, and rated literature quality through Cochrane risk of bias tool and MINORS. RevMan5.3 software was used to analyze the results of randomized controlled trials and single-arm trials.ResultsA total of 7 studies were included. Treatment with cabozantinib plus ICIs improved PFS [HR 0.75, (95%CI: 0.52, 1.08), p = 0.12] and the OS [HR 0.80, (95%CI: 0.60, 1.07), p = 0.13] in randomized controlled trials. Meanwhile, the result of the ORR in randomized controlled trials was [risk ratio (RR) 1.37, (95%CI: 1.21, 1.54), p < 0.00001] and in single-arm trials was [risk difference (RD) 0.49, (95%CI: 0.26, 0.71), p < 0.0001].ConclusionCabozantinib plus ICIs prolonged the PFS and OS, and improved ORR in patients with RCC. Our recommendation is to use cabozantinib plus ICIs to treat advanced RCC, and to continuous monitor and manage the drug-related adverse events.Systematic Review Registration:identifier CRD42023455878.

IntroductionDeep deterministic policy gradient (DDPG)-based path planning algorithms for intelligent robots struggle to discern the value of experience transitions during training due to their reliance on a random experience replay. This can lead to inappropriate sampling of experience transitions and overemphasis on edge experience transitions. As a result, the algorithm's convergence becomes slower, and the success rate of path planning diminishes.MethodsWe comprehensively examines the impacts of immediate reward, temporal-difference error (TD-error), and Actor network loss function on the training process. It calculates experience transition priorities based on these three factors. Subsequently, using information entropy as a weight, the three calculated priorities are merged to determine the final priority of the experience transition. In addition, we introduce a method for adaptively adjusting the priority of positive experience transitions to focus on positive experience transitions and maintain a balanced distribution. Finally, the sampling probability of each experience transition is derived from its respective priority.ResultsThe experimental results showed that the test time of our method is shorter than that of PER algorithm, and the number of collisions with obstacles is less. It indicated that the determined experience transition priority accurately gauges the significance of distinct experience transitions for path planning algorithm training.DiscussionThis method enhances the utilization rate of transition conversion and the convergence speed of the algorithm and also improves the success rate of path planning.

Abstract Cloud detection is an important step in remote sensing image processing and a prerequisite for subsequent analysis and interpretation of remote sensing images. Traditional cloud detection methods are difficult to accurately detect clouds and snow with very similar features such as color and texture. In this paper, the features of cloud and snow in remote sensing images are deeply extracted, and an accurate cloud and snow detection method is proposed based on the advantages of Unet3+ network in feature fusion. Firstly, color space conversion is performed on remote sensing images, RGB images and HIS images are used as input of Unet3+ network. Resnet 50 is used to replace the Unet3+ feature extraction network to extract remote sensing image features at a deeper level, and add the Convolutional Block Attention Module in Resnet50 to improve the network’s attention to cloud and snow. Finally, the weighted cross entropy loss is constructed to solve the problem of unbalanced sample number caused by high proportion of background area in the image. The results show that the proposed method has strong adaptability and moderate computation. The mPA value, mIoU value and mPrecision value can reach 92.76%, 81.74% and 86.49%, respectively. Compared with other algorithms, the proposed method can better eliminate all kinds of interference information in remote sensing images of different landforms and accurately detect cloud and snow in images.

Background: Delayed heart rate recovery (HRR) after exercise, an indicator of cardiac autonomic nervous function (CANF), has been found to be associated with metabolic syndrome (MetS) in general populations. However, this relationship has not been extensively studied in older adults. The present study aimed to investigate the association between MetS and HRRs at 1, 2, 3 and 6 minutes after exercise and cardiorespiratory fitness (CRF) in Chinese dwelling older adults. Methods: This retrospective, observational study consecutively enrolled participants aged 60 years or older who underwent physical examinations in Shenzhen District Yantian People's Hospital from September 2019 to July 2021. The participants were categorized into MetS and non-MetS groups according to the International Diabetes Federation criteria. Logistic regression analysis was applied to assess the association between MetS and CANF and CRF. Propensity score overlap weighting was used to adjust the covariates. Results: A total of 987 eligible participants were included (mean age±SD, 66± 4 years; male, 47.1%), of whom, 506 were diagnosed with MetS. MetS group showed significantly lower peak heart rate, HRR1-3, HRR6, peak metabolic equivalents, and peak oxygen consumption compared to the non-MetS group. Furthermore, peak systolic and diastolic blood pressures in the MetS group were significantly higher. Logistic regression analysis showed that MetS was significantly associated with HRR2, HRR3 (odds ratio [95% CI], 0.997 [0.995,0.999], both; P=0.009 and 0.005, respectively) and HRR6 (0.996 [0.994,0.998], P< 0.001). The association between MetS and CRF was significant (0.98 [0.97,0.98], P< 0.001). Conclusion: Elderly Chinese with MetS tend to exhibit reduced CANF and lower CRF. It is recommended that they boost physical activity and closely monitor heart rate and blood pressure during exercise to mitigate exercise-related risks. [ABSTRACT FROM AUTHOR]

Abstract. Objective:. Laboratory diagnosis of neurosyphilis (NS) remains a great challenge. This study was the aimed to identify miRNA candidates as biomarkers to distinguish between NS, non-neurosyphilis, and healthy controls (HCs). Methods:. We analyzed miRNA expression profiles in peripheral blood mononuclear cells (PBMCs) from six patients with NS, eight patients with secondary syphilis (SS), and five HCs using microarray technology. The differentially expressed miRNAs were validated in 33 NS samples, 31 SS samples, and 30 HC samples using TaqMan miRNA real-time qPCR (qRT-PCR). Results:. Thirty-nine miRNAs were differentially expressed in SS and NS patients compared with HCs. Thirteen miRNAs were randomly selected to validate their expression levels in the same samples used in microarray assay by qRT-PCR. All miRNAs were upregulated in SS and NS samples compared with HC. qRT-PCR analysis of the expression of the 13 miRNAs in a second cohort (76 samples) showed that the average expression levels of nine miRNAs were higher in SS than in NS (SS: 0.185, NS: 0.136, P=3.8E-10), while the expressions of the other four miRNAs were lower in SS than in NS (SS: 0.000757, NS: 0.000873, P=0.022). ROC curve analysis of the 13 miRNAs showed the area under the curve value to be 1.00 for distinguishing SS patients from HCs, 1.00 for distinguishing NS patients from HCs, 1.00 for distinguishing SS and NS patients from HCs, and 0.968 for distinguishing NS from SS patients. Conclusion:. The present study is the first one that identified differentially expressed miRNAs in PBMCs from patients with NS. Our results suggest that the 13 candidate miRNAs in PBMCs may be novel noninvasive biomarkers for NS diagnosis.

ZnO nanoparticles in a spherical-like structure were synthesized via filtration and calcination methods, and different amounts of ZnO nanoparticles were added to MgH2 via ball milling. The SEM images revealed that the size of the composites was about 2 μm. The composites of different states were composed of large particles with small particles covering them. After the absorption and desorption cycle, the phase of composites changed. The MgH2-2.5 wt% ZnO composite reveals excellent performance among the three samples. The results show that the MgH2-2.5 wt% ZnO sample can swiftly absorb 3.77 wt% H2 in 20 min at 523 K and even at 473 K for 1 h can absorb 1.91 wt% H2. Meanwhile, the sample of MgH2-2.5 wt% ZnO can release 5.05 wt% H2 at 573 K within 30 min. Furthermore, the activation energies (Ea) of hydrogen absorption and desorption of the MgH2-2.5 wt% ZnO composite are 72.00 and 107.58 KJ/mol H2, respectively. This work reveals that the phase changes and the catalytic action of MgH2 in the cycle after the addition of ZnO, and the facile synthesis of the ZnO can provide direction for the better synthesis of catalyst materials.

Loop closure detection is a crucial part of VSLAM. However, the traditional loop closure detection algorithms are difficult to adapt to complex and changeable scenes. In this paper, we fuse Gist features, semantic features and appearance features of the image to detect the loop closures quickly and accurately. Firstly, we take advantage of the fast extraction speed of the Gist feature by using it to screen the loop closure candidate frames. Then, the current frame and the candidate frame are semantically segmented to obtain the mask blocks of various types of objects, and the semantic nodes are constructed to calculate the semantic similarity between them. Next, the appearance similarity between the images is calculated according to the shape of the mask blocks. Finally, based on Gist similarity, semantic similarity and appearance similarity, the image similarity calculation model can be built as the basis for loop closure detection. Experiments are carried out on both public and self-filmed datasets. The results show that our proposed algorithm can detect the loop closure in the scene quickly and accurately when the illumination, viewpoint and object change.

Integral imaging is a type of true three‐dimensional (3D) display technology that uses a lens array to reconstruct vivid 3D images with full parallax and true color. In order to present a high‐quality 3D image, it's vital to correct the axial position error caused by the misalignment and deformation of the lens array which makes the reconstructed lights deviate from the correct directions, resulting in severe voxel drifting and image blurring. We proposed a sub‐pixel marking method to measure the axial position error of the lenses with great accuracy by addressing the sub‐pixels under each lens and forming a homologous sub‐pixel pair. The proposed measurement method relies on the geometric center alignment of image points, which makes the measurement accuracy high. Additionally, a depth‐based sub‐pixel correction method was proposed to eliminate the voxel drifting. The proposed correction method takes the voxel depth into consideration. The experimental results well confirmed that the proposed measuring and correction methods can greatly suppress the voxel drifting caused by the axial position error of the lenses, and greatly improve the 3D image quality. [ABSTRACT FROM AUTHOR]