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Approximately 15–20% of COVID-19 patients will develop severe pneumonia, and about 10% of these will die if not properly managed. Earlier discrimination of potentially severe patients basing on routine clinical and laboratory changes and commencement of prophylactical management will not only save lives but also mitigate the otherwise overwhelming healthcare burden. In this retrospective investigation, the clinical and laboratory features were collected from 125 COVID-19 patients who were classified into mild (93 cases) or severe (32 cases) groups according to their clinical outcomes after 3–7 days post-admission. The subsequent analysis with single-factor and multivariate logistic regression methods indicated that 17 factors on admission differed significantly between mild and severe groups but that only comorbidity with underlying diseases, increased respiratory rate (>24/min), elevated C-reactive protein (CRP >10 mg/L), and lactate dehydrogenase (LDH >250 U/L) were independently associated with the later disease development. Finally, we evaluated their prognostic values with receiver operating characteristic curve (ROC) analysis and found that the above four factors could not confidently predict the occurrence of severe pneumonia individually, though a combination of fast respiratory rate and elevated LDH significantly increased the predictive confidence (AUC = 0.944, sensitivity = 0.941, and specificity = 0.902). A combination consisting of three or four factors could further increase the prognostic value. Additionally, measurable serum viral RNA post-admission independently predicted the severe illness occurrence. In conclusion, a combination of general clinical characteristics and laboratory tests could provide a highly confident prognostic value for identifying potentially severe COVID-19 pneumonia patients.

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS), a life-threatening disease, is typically induced by uncontrolled inflammatory responses and excessive production of reactive oxygen species (ROS). Astaxanthin (Ast) is known for its powerful natural antioxidant properties, showcasing excellent antioxidant, anti-inflammatory, and immunomodulatory effects. However, its poor water solubility and bioavailability significantly limit its efficacy. Taking inspiration from biomimetic biology, this study developed a nasal drug delivery system comprising macrophage membrane (Mϕ)-encapsulated Ast-loaded nanoparticles (Mϕ@Ast-NPs) for the treatment of ALI. Mϕ@Ast-NPs retain the original homing properties of Mϕ, enabling targeted delivery to inflamed lungs and enhancing the anti-inflammatory effects of Astaxanthin (Ast). In vitro and in vivo, Mϕ@Ast-NPs demonstrated excellent biocompatibility and safety, as evidenced by no hemolysis of red blood cells and no significant toxic effects on cells and major organs. To determine the inflammation-targeting of Mϕ@Ast-NPs, both healthy and ALI mice were intranasally administered with Mϕ@Ast-NPs, the results demonstrated that highly targeting to inflamed lungs and endothelia, while with minimal accumulation in healthy lungs and endothelia. Mϕ@Ast-NPs effectively inhibited ROS production, enhanced Nrf2 expression and nucleus translocation, and reduced the levels of pro-inflammatory factors such as IL-1β, IL-6, and tumor necrosis factor-α (TNF-α) in LPS-induced RAW264.7 cells and ALI mice. Our study provided a safe and effective nasal delivery platform for pulmonary diseases, and this biomimetic nano-formulation of Ast could be as functional foods in the future.

Kidney injury molecule-1 (Kim-1) and neutrophil gelatinase-associated lipocalin (NGAL) have been investigated as biomarkers for acute kidney injury (AKI). However, they are seldom investigated in patients with septic AKI treated with continuous renal replacement therapy (CRRT). The aim of the present study was to investigate the therapeutic effectiveness and possible mechanisms of CRRT in septic AKI by observing the changes in Kim-1 and NGAL levels. A group of 38 patients with septic AKI was randomly divided into the conventional drug treatment group (group A) and the CRRT group (group B). All patients were treated with standard antisepsis agents, and group B was additionally submitted to CRRT for 24 h. The levels of Kim-1 and NGAL in serum, urine and the ultrafiltrate of CRRT were measured prior to and at 12, 24, and 48 h after treatment. In group A, urinary Kim-1 (uKim-1) levels at 12, 24 and 48 h were lower than prior to treatment (P<0.05), whereas urinary NGAL (uNGAL) showed no difference among the various time points (P>0.05). In group B, uKim-1 was decreased at 24 and 48 h compared with before treatment (all P<0.05), whereas uNGAL was decreased at 48 h (P<0.05). Serum Kim-1 did not change with time in groups A and B (P>0.05), whereas serum NGAL was increased after treatment in group A (P<0.05) but did not change in group B (P>0.05). Kim-1 and NGAL were not detected in the ultrafiltrate of CRRT. uKim-1 and uNGAL decreased significantly after CRRT, and therefore may be used to reflect the change of renal function during CRRT and to evaluate the therapeutic effectiveness of the method.

Inducing the senescence of activated hepatic stellate cells (HSCs) has emerged as a promising therapeutic strategy for liver fibrosis, with potential connections to the Yes-associated protein (YAP)-controlled cGAS-STING pathway. However, the regulatory role of cytoskeletal dynamics on HSC senescence and its potential as a target for natural products have remained poorly understood. We employed preclinical and transcriptome analyses, experimental systems, Tmem173 mice and liver-specific STING knockdown mice to demonstrate the anti-fibrotic effects and mechanism of ligustilide (LIG). LIG selectively bound to monomeric globular actin (G-actin), thereby preventing its polymerization into polymeric filamentous actin (F-actin), which disturbed its interaction with intermediate filament component lamin A/C and initially destroyed the nuclear membrane. Moreover, the disruption of nuclear membrane caused YAP leakage from nuclear, which in turn suppressed lamin A/C and created a deleterious feedback loop that exacerbated nuclear membrane destabilization. Consequently, nuclear double stranded DNA (dsDNA) leakage caused by the above damage cascade ultimately triggered the activation of the cGAS-STING signaling pathway, promoting senescence-associated secretory phenotypes (SASPs) release and inducing HSC senescence. Moreover, the induction of HSC senescence and anti-fibrotic effects of LIG were completely abrogated in both whole-body STING knockout and liver-specific STING knockdown mice. By interacting with G-actin, LIG disrupted the cytoskeleton to compromise nuclear integrity with the involvement of YAP and further stimulated the cGAS-STING pathway, leading to the release of SASPs and HSC senescence, which ultimately mitigated liver fibrosis.

Calcium vanadate nanorods with Ca10V6O25 phase have been synthesized by a hydrothermal process without any surfactants. Hydrothermal temperature, reaction time and calcium (Ca) raw materials play important roles in the formation and size of the calcium vanadate nanorods. The nucleation and crystal growth combined with crystal splitting process have been proposed to explain the formation and growth of calcium vanadate nanorods. The calcium vanadate nanorods are used as glassy carbon electrode-modified materials to analyze the electrochemical behaviors of tartaric acid. The calcium vanadate nanorod-modified glassy carbon electrode exhibits good performance for the electrochemical detection of tartaric acid with a detection limit of 2.4 μM and linear range of 0.005–2 mM. The analytical performance and straightforward fabrication method make the calcium vanadate nanorods promising for the development of electrochemical sensors for tartaric acid.

Despite the increasing prevalence of robots in daily life, their navigation capabilities are still limited to environments with prior knowledge, such as a global map. To fully unlock the potential of robots, it is crucial to enable them to navigate in large-scale unknown and changing unstructured scenarios. This requires the robot to construct an accurate static map in real-time as it explores, while filtering out moving objects to ensure mapping accuracy and, if possible, achieving high-quality pedestrian tracking and collision avoidance. While existing methods can achieve individual goals of spatial mapping or dynamic object detection and tracking, there has been limited research on effectively integrating these two tasks, which are actually coupled and reciprocal. In this work, we propose a solution called S\\(^2\\)MAT (Simultaneous and Self-Reinforced Mapping and Tracking) that integrates a front-end dynamic object detection and tracking module with a back-end static mapping module. S\\(^2\\)MAT leverages the close and reciprocal interplay between these two modules to efficiently and effectively solve the open problem of simultaneous tracking and mapping in highly dynamic scenarios. We conducted extensive experiments using widely-used datasets and simulations, providing both qualitative and quantitative results to demonstrate S\\(^2\\)MAT's state-of-the-art performance in dynamic object detection, tracking, and high-quality static structure mapping. Additionally, we performed long-range robotic navigation in real-world urban scenarios spanning over 7 km, which included challenging obstacles like pedestrians and other traffic agents. The successful navigation provides a comprehensive test of S\\(^2\\)MAT's robustness, scalability, efficiency, quality, and its ability to benefit autonomous robots in wild scenarios without pre-built maps.

This paper aims at exploring how to synthesize close-to-real blurs that existing video deblurring models trained on them can generalize well to real-world blurry videos. In recent years, deep learning-based approaches have achieved promising success on video deblurring task. However, the models trained on existing synthetic datasets still suffer from generalization problems over real-world blurry scenarios with undesired artifacts. The factors accounting for the failure remain unknown. Therefore, we revisit the classical blur synthesis pipeline and figure out the possible reasons, including shooting parameters, blur formation space, and image signal processor~(ISP). To analyze the effects of these potential factors, we first collect an ultra-high frame-rate (940 FPS) RAW video dataset as the data basis to synthesize various kinds of blurs. Then we propose a novel realistic blur synthesis pipeline termed as RAW-Blur by leveraging blur formation cues. Through numerous experiments, we demonstrate that synthesizing blurs in the RAW space and adopting the same ISP as the real-world testing data can effectively eliminate the negative effects of synthetic data. Furthermore, the shooting parameters of the synthesized blurry video, e.g., exposure time and frame-rate play significant roles in improving the performance of deblurring models. Impressively, the models trained on the blurry data synthesized by the proposed RAW-Blur pipeline can obtain more than 5dB PSNR gain against those trained on the existing synthetic blur datasets. We believe the novel realistic synthesis pipeline and the corresponding RAW video dataset can help the community to easily construct customized blur datasets to improve real-world video deblurring performance largely, instead of laboriously collecting real data pairs.

This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark. The documentation first starts with the background and motivation of the dataset. Follow it, we briefly describe the method of collecting the dataset and the processing method from raw dataset to the final release dataset, specifically, the version 1.0. We also provide the detailed usage of the dataset and the evaluation metric for submitted the result for the 2019 Hyperspectral City Challenge.

TP751; 随着深度学习方法在计算机视觉领域的崛起,如何将其应用于具有全天时、全天候等优点的SAR图像也成为一大研究重点.相较于传统图像,SAR图像由于其难判读、应用人群较少等原因难以获得大量标注数据.本文提出一种基于场景合成和锚点约束的SAR图标检测方法.通过区域生长算法和阈值法对SAR车辆目标及其阴影进行分割,然后随机嵌入SAR复杂场景中的合理区域来合成目标检测数据集.针对SAR车辆目标的几何特性、图像分辨率参数,对Faster-RCNN中的锚点大小进行约束,减少不符合SAR车辆目标检测框尺寸的候选框,大量约简冗余计算,提升训练、测试效率及精度.