Ureteral inflammatory edema grading clinical application

PurposeTo evaluate the relationship between endoscopic ureteral inflammatory edema (UIE) and ureteral lumen, formulate a preliminary grading method for the severity of UIE, and analyze the impact of different grades of UIE on endoscopic ureteral calculi surgery and prognosis.Materials and methodsWe retrospectively analyzed 185 patients who underwent ureteroscopic lithotripsy (URSL) for upper urinary tract stones between January 2021 and November 2021. The UIE grade and lumen conditions were assessed by endoscopic observation. The effect of UIE grade on URSL and on patient prognosis were analyzed by multiple linear regression and binary logistic regression.ResultsA total of 185 patients were included in the study. UIE grade showed a significant correlation with age, hydronephrosis grading (HG), ureteroscope placement time (UPT), surgery time (ST), hemoglobin disparity value (HDV), and postoperative ureteral stenosis (PUS) (P < 0.05). Logistics regression analysis showed a gradual increase in intraoperative UPT and ST with increase in UIE grade. The severity of UIE showed a negative correlation with improvement of postoperative hydronephrosis (IPH) and the appearance of PUS. HDV was significantly increased in patients with UIE grade 3.ConclusionsUIE grading can be used as an adjunctive clinical guide for endoscopic treatment of upper urinary tract stones. The postoperative management measures proposed in this study can help inform treatment strategy for ureteral stones

OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. It comprises three primary sub-tasks, including the 3D lane detection inherited from OpenLane, accompanied by corresponding metrics to evaluate the model's performance. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.Comment: Accepted by NeurIPS 2023 Track on Datasets and Benchmarks | OpenLane-V2 Dataset: https://github.com/OpenDriveLab/OpenLane-V

Topology Reasoning for Driving Scenes

Understanding the road genome is essential to realize autonomous driving. This highly intelligent problem contains two aspects - the connection relationship of lanes, and the assignment relationship between lanes and traffic elements, where a comprehensive topology reasoning method is vacant. On one hand, previous map learning techniques struggle in deriving lane connectivity with segmentation or laneline paradigms; or prior lane topology-oriented approaches focus on centerline detection and neglect the interaction modeling. On the other hand, the traffic element to lane assignment problem is limited in the image domain, leaving how to construct the correspondence from two views an unexplored challenge. To address these issues, we present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks. To capture the driving scene topology, we introduce three key designs: (1) an embedding module to incorporate semantic knowledge from 2D elements into a unified feature space; (2) a curated scene graph neural network to model relationships and enable feature interaction inside the network; (3) instead of transmitting messages arbitrarily, a scene knowledge graph is devised to differentiate prior knowledge from various types of the road genome. We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2, where our approach outperforms all previous works by a great margin on all perceptual and topological metrics. The code would be released soon

Immobilization of Microcystin by the Hydrogel–Biochar Composite to Enhance Biodegradation during Drinking Water Treatment

Microcystin-LR (MC-LR), the most common algal toxin in freshwater, poses an escalating threat to safe drinking water. This study aims to develop an engineered biofiltration system for water treatment, employing a composite of poly(diallyldimethylammonium chloride)-biochar (PDDA-BC) as a filtration medium. The objective is to capture MC-LR selectively and quickly from water, enabling subsequent biodegradation of toxin by bacteria embedded on the composite. The results showed that PDDA-BC exhibited a high selectivity in adsorbing MC-LR, even in the presence of competing natural organic matter and anions. The adsorption kinetics of MC-LR was faster, and capacity was greater compared to traditional adsorbents, achieving a capture rate of 98% for MC-LR (200 μg/L) within minutes to tens of minutes. Notably, the efficient adsorption of MC-LR was also observed in natural lake waters, underscoring the substantial potential of PDDA-BC for immobilizing MC-LR during biofiltration. Density functional theory calculations revealed that the synergetic effects of electrostatic interaction and π-π stacking predominantly contribute to the adsorption selectivity of MC-LR. Furthermore, experimental results validated that the combination of PDDA-BC with MC-degrading bacteria offered a promising and effective approach to achieve a sustainable removal of MC-LR through an "adsorption-biodegradation" process

CD36 and Its Role in Regulating the Tumor Microenvironment

CD36 is a transmembrane glycoprotein that binds to a wide range of ligands, including fatty acids (FAs), cholesterol, thrombospondin-1 (TSP-1) and thrombospondin-2 (TSP-2), and plays an important role in lipid metabolism, immune response, and angiogenesis. Recent studies have highlighted the role of CD36 in mediating lipid uptake by tumor-associated immune cells and in promoting tumor cell progression. In cancer-associated fibroblasts (CAFs), CD36 regulates lipid uptake and matrix protein production to promote tumor proliferation. In addition, CD36 can promote tumor cell adhesion to the extracellular matrix (ECM) and induce epithelial mesenchymal transition (EMT). In terms of tumor angiogenesis, CD36 binding to TSP-1 and TSP-2 can both inhibit tumor angiogenesis and promote tumor migration and invasion. CD36 can promote tumor angiogenesis through vascular mimicry (VM). Overall, we found that CD36 exhibits diverse functions in tumors. Here, we summarize the recent research findings highlighting the novel roles of CD36 in the context of tumors