Shenghao Li

Chengdu University of Traditional Chinese Medicine

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Xinli Shi

Hebei University of Chinese Medicine

Zhongxuan Luo

Dalian University of Technology

Qunfei Zhao

Shanghai Jiao Tong University

Na Lei

Dalian University of Technology

Wen Fang

North China University of Technology

Junru Li

Shanghai Cancer Institute

Qiaoyang Xia

East China University of Science and Technology

Haoyao Chen

Harbin Institute of Technology

Guangyao Qi

University of Chinese Academy of Sciences

Liyuan Hao

Chengdu University of Traditional Chinese Medicine

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Applied Radar (United States)

261

Waterborne Environmental (United States)

260

Chinese Academy of Sciences

232

Facing Our Risk of Cancer Empowered

221

University of Chinese Academy of Sciences

118

Shanghai Jiao Tong University

Zhejiang University

Peking University

Huazhong University of Science and Technology

Sichuan University

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Regulatory Mechanism of PD-L1 Expression in Primary Liver Cancer

DOAJ (DOAJ: Directory of Open Access Journals) (2021)

Shenghao Li Xinli Shi

Primary (astronomy)

10.3971/j.issn.1000-8578.2021.21.0397

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Fast and Accurate Pose Estimation for Industrial Workpieces Robotic Picking

Qichuan Tang Xiaosong Gao Shenghao Li Qunfei Zhao

6DoF Pose estimation plays an important role in industrial robotic picking applications, and it is particularly challenging when dealing with complex-shaped workpieces, often with little texture. This paper proposes a complete approach to customize a fast and accurate workpiece picking system, based on dense reconstruction, object detection and point cloud registration schemes. For any target object, the required input is its CAD model. First, we use a depth camera and an eye-in-hand robot to capture the scene in RGB-D image form. Then, we align the CAD models to some reconstructed point clouds, and automatically generate datasets of annotated images with the help of projective rendering. The data is used to train a neural network object detector, in order to detect a region of interest in color images. Next, as the detected 2D region is projected into a 3D space, the depth information inside this space is extracted to conduct point cloud registration with the object's model for pose estimation, and its result guides the system to carry out an optical picking action. Moreover, our method is accelerated with the parallelized computation in GPU to raise the efficiency of the system.

RGB color model

10.1145/3475851.3475860

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Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia

Journal of Thoracic Disease (2021)

Qin Liu Baoguo Pang Haijun Li Bin Zhang Yumei Liu

To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness.A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC).The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913-1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757-1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906-1.000).A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

Triage

Lasso

10.21037/jtd-20-2580

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Citations (14)

Weak magnetic effect and free radical reaction mechanism on the dissolution of lightweight magnesia in CaO–Al2O3–SiO2 melts

Chemical Engineering Journal (2024)

Xinyu Chen Ao Huang Shenghao Li Huazhi Gu Lvping Fu

10.1016/j.cej.2024.154237

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Dihydroartemisinin breaks the immunosuppressive tumor niche during cisplatin treatment in Hepatocellular carcinoma

Acta Histochemica (2024)

Yanguang Yang Yuting Gao Yi Gong Junlan Lu Shenghao Li

Dihydroartemisinin

10.1016/j.acthis.2024.152171

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潤滑油構成成分が低速プレイグニッション(LSPI)に及ぼす影響についてのメカニズム解析

Hirabe Mamoru Yoshiki Wakabayashi Shenghao Li Jin Kusaka Yasuhiro Daisho

Source

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An optimal transport-guided diffusion framework with mitigating mode mixture

Neurocomputing (2024)

Shenghao Li Zhanpeng Wang Zhongxuan Luo Na Lei

Mode (computer interface)

10.1016/j.neucom.2024.128910

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Table of Contents

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) (2021)

Houcheng Li Long Cheng Zhengwei Li Guotao Li Xiaoxu Cao

Table (database)

10.1109/rcar52367.2021.9517432

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The current status and future of targeted-immune combination for hepatocellular carcinoma

Frontiers in Immunology (2024)

Liyuan Hao Shenghao Li Fanghang Ye Hengyi Wang Yuxin Zhong

Hepatocellular carcinoma (HCC) is one of the most common cancers and the third leading cause of death worldwide. surgery, transarterial chemoembolization (TACE), systemic therapy, local ablation therapy, radiotherapy, and targeted drug therapy with agents such as sorafenib. However, the tumor microenvironment of liver cancer has a strong immunosuppressive effect. Therefore, new treatments for liver cancer are still necessary. Immune checkpoint molecules, such as programmed death-1 (PD-1), programmed death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen-4 (CTLA-4), along with high levels of immunosuppressive cytokines, induce T cell inhibition and are key mechanisms of immune escape in HCC. Recently, immunotherapy based on immune checkpoint inhibitors (ICIs) as monotherapy or in combination with tyrosine kinase inhibitors, anti-angiogenesis drugs, chemotherapy agents, and topical therapies has offered great promise in the treatment of liver cancer. In this review, we discuss the latest advances in ICIs combined with targeted drugs (targeted-immune combination) and other targeted-immune combination regimens for the treatment of patients with advanced HCC (aHCC) or unresectable HCC (uHCC), and provide an outlook on future prospects. The literature reviewed spans the last five years and includes studies identified using keywords such as "hepatocellular carcinoma," "immune checkpoint inhibitors," "targeted therapy," "combination therapy," and "immunotherapy".

10.3389/fimmu.2024.1418965

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Experimental Research on the Performance of Recycled Waste Concrete Powder (RWCP) on Concrete

Materials (2024)

Shuai Wang Anxin Wang Xudong Fu Xue-Tao Zhang Zhe Li

Waste concrete is a large amount of solid waste produced in the process of urban construction and renewal in China. Its resource utilization is of great significance for saving mineral resources and improving urban environmental quality. The present study was designed to investigate the effects of mechanical grinding time on the particle size distribution and activity of recycled waste concrete powder (RWCP). Combined with unconfined compressive strength, slump, electric flux and chloride ion penetration resistance tests, the effects of RWCP on the mechanical properties, working performance and impermeability of concrete were analyzed, and the phase and microstructure of concrete containing RWCP were analyzed by XRD and SEM. The results showed that the RWCP is mainly composed of quartz, gismondine, C

10.3390/ma17215319

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Citations (1)