The effect of anti-VEGF drugs (bevacizumab and aflibercept) on the survival of patients with metastatic colorectal cancer (mCRC)

Significant progression has been achieved in the treatment of metastatic colorectal cancer (mCRC) in recent years. This has been partly attributed to successfully incorporating new drugs into combination chemotherapy. In addition to the traditional cytotoxic chemotherapeutic agents, molecularly targeted agents began to play an important role in the treatment of advanced solid tumors. To date, two classes of molecularly targeted agents have been approved for treatment of patients with mCRC: (1) antivascular endothelial growth factor (anti-VEGF) agents (such as bevacizumab and aflibercept) and (2) antiendothelial cell growth factor receptor (anti-EGFR) agents (such as cetuximab and panitumumab). Aflibercept is a new member of anti-VEGF agents which has demonstrated efficacy for treatment of mCRC. With the commencement of clinical trials and basic research into aflibercept, more data from the bedside and the bench have been obtained. This review will outline the application of anti-VEGF agents by reviewing clinic experiences of bevacizumab and aflibercept, and try to add perspectives on the use of anti-VEGF agents in mCRC

TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers

Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors. However, their performance on Video Object Detection (VOD) has not been well explored. In this paper, we present TransVOD, the first end-to-end video object detection system based on spatial-temporal Transformer architectures. The first goal of this paper is to streamline the pipeline of VOD, effectively removing the need for many hand-crafted components for feature aggregation, e.g., optical flow model, relation networks. Besides, benefited from the object query design in DETR, our method does not need complicated post-processing methods such as Seq-NMS. In particular, we present a temporal Transformer to aggregate both the spatial object queries and the feature memories of each frame. Our temporal transformer consists of two components: Temporal Query Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer Decoder (TDTD) to obtain current frame detection results. These designs boost the strong baseline deformable DETR by a significant margin (3%-4% mAP) on the ImageNet VID dataset. Then, we present two improved versions of TransVOD including TransVOD++ and TransVOD Lite. The former fuses object-level information into object query via dynamic convolution while the latter models the entire video clips as the output to speed up the inference time. We give detailed analysis of all three models in the experiment part. In particular, our proposed TransVOD++ sets a new state-of-the-art record in terms of accuracy on ImageNet VID with 90.0% mAP. Our proposed TransVOD Lite also achieves the best speed and accuracy trade-off with 83.7% mAP while running at around 30 FPS on a single V100 GPU device.Comment: Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), extended version of arXiv:2105.1092

Enhanced Boundary Learning for Glass-like Object Segmentation

Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models is available at \url{https://github.com/hehao13/EBLNet}.Comment: ICCV-2021 Code is availabe at https://github.com/hehao13/EBLNe

PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two critical issues: The one is an extremely foreground-background imbalanced distribution, and the other is multiple small objects along with the complex background. Such problems make the recent dense affinity context modeling perform poorly even compared with baselines due to over-introduced background context. To handle these problems, we propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features, which reduces the noise introduced by the background while keeping efficiency. In particular, we design a dual point matcher to select points from the salient area and object boundaries, respectively. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods. Especially, our methods achieve the best speed and accuracy trade-off on three aerial benchmarks. Further experiments on three general semantic segmentation datasets prove the generality of our method. Code will be provided in (https: //github.com/lxtGH/PFSegNets).Comment: accepted by CVPR202

An improved region growing algorithm in 3D laser point cloud identification of rock mass structural plane

The rock mass structural plane constitutes the weakest part of the rock mass. Accurate and efficient identification of rock mass structural plane and extraction of characteristic information can provide an important basis for the rock mass stability evaluation. 3D laser scanning technology can greatly improve the efficiency and accuracy of structural surface survey; however, the current mainstream point cloud analysis algorithms exist the problems that the edge recognition of structural surfaces is blurred and the accuracy of point cloud segmentation cannot meet the accuracy of structural surface feature information extraction. Considering the spatial relationship between the position of the point cloud of the rock mass structural plane and its neighborhood, the region growth segmentation parameters were corrected by multiple eigenvalues. The KD-tree data structure was used to perform the nearest neighbor search. The voxel was sampled, and the structural plane was segmented to realize the extraction of the structure plane occurrence, spacing, and extension information, based on the normal vector difference of the point cloud and the characteristic final value. The effectiveness of this method in structural plane identification was also verified by indoor models. The results show that compared with the traditional Principal Component Analysis method and Random Sample Consensus method, this method has a higher recognition rate and accuracy in the same area among the 24 structural planes composed of indoor block models. It can not only ensure the complete recognition of data in the complex and changing plane area, but also better segment the edge points in the sharp position of the plane. Using this method, 24 structural planes can be divided into 6 groups, and the corresponding structural plane feature information can be obtained. Compared with the actual measurement results, the angle information error is approximately 1°, and the distance information error is within 1 cm. This method identified three groups of structural planes in the Mangshezhai slope rock mass successfully in the main stream of the Yangtze River. The method proposed in this study has a good verification effect on indoor model and field slope, which can provide robust and effective technical support for the identification and segmentation of rock mass structural plane

Prevalence of hyperuricemia and the population attributable fraction of modifiable risk factors: Evidence from a general population cohort in China

Data on updated hyperuricemia prevalence in Beijing-Tianjin-Hebei (BTH) region in China, which is one of the world-class urban agglomerations, is sparse. Overweight/obesity, alcohol consumption, smoking and sedentary behavior are modifiable risk factors (MRFs) for elevated serum uric acid (SUA), but their population attributable fractions (PAFs) for hyperuricemia is still unclear. Using baseline data from the BTH Physical Examination General Population Cohort, we calculated the crude- and adjusted-prevalence of hyperuricemia based on the 30,158 participants aged 18–80 years. Hyperuricemia was defined as SUA >420 μmol/L in men and >360 μmol/L in women, or currently use of uric acid lowering drugs. Overweight/obesity, alcohol consumption, smoking and sedentary behavior were considered as MRFs and their adjusted PAFs were estimated. The prevalence of hyperuricemia was 19.37%, 27.72% in men and 10.69% in women. The PAFs and 95% confidence intervals for overweight, obesity were 16.25% (14.26–18.25%) and 12.08% (11.40–12.77%) in men, 13.95% (12.31–15.59%) and 6.35% (5.97–6.74%) in women, respectively. Alcohol consumption can explain 4.64% (2.72–6.56%) hyperuricemia cases in men, but with no statistical significance in women. Cigarette smoking contributed to 3.15% (1.09–5.21%) cases in men, but a much lower fraction in women (0.85%, 0.49–1.22%). Compared with sedentary time <2 h per day, the PAFs of 2–4 h, 4–6 h, and more than 6 h per day were 3.14% (1.34–4.93%), 6.72% (4.44–8.99%) and 8.04% (4.95–11.13%) in men, respectively. Sedentary time was not found to be associated with hyperuricemia in women. These findings concluded that hyperuricemia is prevalent in this representative Chinese adult general population with substantial sex difference. Four MRFs (overweight/obesity, alcohol consumption, cigarette smoking and sedentary behavior) accounted for a notable proportion of hyperuricemia cases. The PAF estimations enable the exploration of the expected proportion of hyperuricemia cases that could be prevented if the MRFs were removed, which warrants the public health significance of life-style intervention

Whole exome sequencing identifies frequent somatic mutations in cell-cell adhesion genes in chinese patients with lung squamous cell carcinoma

Lung squamous cell carcinoma (SQCC) accounts for about 30% of all lung cancer cases. Understanding of mutational landscape for this subtype of lung cancer in Chinese patients is currently limited. We performed whole exome sequencing in samples from 100 patients with lung SQCCs to search for somatic mutations and the subsequent target capture sequencing in another 98 samples for validation. We identified 20 significantly mutated genes, including TP53, CDH10, NFE2L2 and PTEN. Pathways with frequently mutated genes included those of cell-cell adhesion/Wnt/Hippo in 76%, oxidative stress response in 21%, and phosphatidylinositol-3-OH kinase in 36% of the tested tumor samples. Mutations of Chromatin regulatory factor genes were identified at a lower frequency. In functional assays, we observed that knockdown of CDH10 promoted cell proliferation, soft-agar colony formation, cell migration and cell invasion, and overexpression of CDH10 inhibited cell proliferation. This mutational landscape of lung SQCC in Chinese patients improves our current understanding of lung carcinogenesis, early diagnosis and personalized therapy