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531 results on '"Bi Y."'

1. LHAASO-KM2A detector simulation using Geant4

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km^2). In this paper, the design of the KM2A simulation code G4KM2A based on Geant4 is introduced. The process of G4KM2A is optimized mainly in memory consumption to avoid memory overffow. Some simpliffcations are used to signiffcantly speed up the execution of G4KM2A. The running time is reduced by at least 30 times compared to full detector simulation. The particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented, which show good agreement.
Published: 2024
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2. Bidirectional causal associations between frailty measures and sleep disturbances: a two-sample Mendelian randomization study

Author: Che L, Zang H, Bi Y, Wen B, and Xu L
Subjects: frailty, sleep disturbance, mendelian randomization, the elderly, Psychiatry, RC435-571, Neurophysiology and neuropsychology, QP351-495
Abstract: Lu Che,&ast; Han Zang,&ast; Yaodan Bi, Bei Wen, Li Xu Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Li Xu, Email pumchxuli@163.comBackground: Observational studies have demonstrated a strong association between sleep disturbances and frailty. However, the causality remains inconclusive. We aimed to investigate the bidirectional causal relationships between frailty measures and sleep disturbances employing a two-sample Mendelian randomization (MR) analysis.Methods: Two-sample MR analyses were performed based on large-scale genome-wide association studies (GWAS) of the European population for frailty index (FI) (N = 175,226), Fried Frailty Score (FFS) (N = 386,565), insomnia (N = 283,595), sleep duration (N = 445,966) and sleep apnea (N = 523,366). We conducted the causal estimates using the inverse variance-weighted method (IVW), with sensitivity analyses using MR-Egger, weighted median, weighted mode, and MR pleiotropy residual sum and outlier (MR-PRESSO) analysis. Cochran’s Q test was performed to assess heterogeneity.Results: We found that genetically predicted FI was associated with shorter sleep duration and sleep apnea. The genetically predicted FFS was associated with insomnia, shorter sleep duration, and sleep apnea. In the reverse direction analysis, genetic liability to insomnia, short sleep duration, and long sleep duration were associated with an increase in FI. Genetic liability to short sleep duration and long sleep duration were associated with an increase in FFS.Conclusion: Our study provided genetic evidence supporting the bidirectional causality between frailty measures and sleep disturbances. The findings contribute to the prevention and management of frailty and sleep disturbances.Keywords: frailty, sleep disturbances, Mendelian randomization, the elderly
Published: 2025

3. Very high energy gamma-ray emission beyond 10 TeV from GRB 221009A

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, A., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The highest energy gamma-rays from gamma-ray bursts (GRBs) have important implications for their radiation mechanism. Here we report for the first time the detection of gamma-rays up to 13 TeV from the brightest GRB 221009A by the Large High Altitude Air-shower Observatory (LHAASO). The LHAASO-KM2A detector registered more than 140 gamma-rays with energies above 3 TeV during 230$-$900s after the trigger. The intrinsic energy spectrum of gamma-rays can be described by a power-law after correcting for extragalactic background light (EBL) absorption. Such a hard spectrum challenges the synchrotron self-Compton (SSC) scenario of relativistic electrons for the afterglow emission above several TeV. Observations of gamma-rays up to 13 TeV from a source with a measured redshift of z=0.151 hints more transparency in intergalactic space than previously expected. Alternatively, one may invoke new physics such as Lorentz Invariance Violation (LIV) or an axion origin of very high energy (VHE) signals., Comment: 49pages, 11figures
Published: 2023
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4. Differences in Haematological and Imaging Features of Lumbar Spine Fungal and Brucella Infections

Author: Fu XW, Bi Y, Wei JL, Qi M, Zhu L, Pu Y, Liu JL, Li X, and Cun XH
Subjects: mycosis, brucellosis, imaging, differential diagnosis, Infectious and parasitic diseases, RC109-216
Abstract: Xu-Wen Fu,1,&ast; Yan Bi,2,&ast; Jia-Lu Wei,3 Min Qi,3 Long Zhu,3 Ying Pu,3 Jing-Liang Liu,2 Xiang Li,3 Xin-Hua Cun4 1Department of Pharmacy, Kunming Third People’s Hospital/Yunnan Clinical Medical Center for Infectious Diseases, Kunming, 650041, People’s Republic of China; 2Department of Radiology, The People’s Hospital of Lincang, Lincang, 677000, People’s Republic of China; 3Department of Radiology, Kunming Third People’s Hospital/Yunnan Clinical Medical Center for Infectious Diseases, Kunming, Yunnan, 650200,People’s Republic of China; 4Department of Orthopaedics, Kunming Third People’s Hospital/Yunnan Clinical Medical Center for Infectious Diseases, Kunming, 650041, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiang Li, Department of Radiology, Kunming Third People’s Hospital/Yunnan Clinical Medical Center for Infectious Diseases, Kunming, Yunnan, 650041, People’s Republic of China, Tel +86 18108857532, Email li_xiang115@126.com Xin-Hua Cun, Department of Orthopaedics, Kunming Third People’s Hospital/Yunnan Clinical Medical Center for Infectious Diseases, Kunming, Yunnan, 650041, People’s Republic of China, Email xinhuacun_cun6@21cn.comObjective: To observe the clinical characteristics of fungal and Brucella infections of the lumbar spine and explore the key points for their differential diagnosis.Methods: The clinical data of 12 patients with fungal infection (the fungal group) and 31 patients with Brucella infection (the Brucella group) of the lumbar spine confirmed by microbiological culture and antigen test were retrospectively analysed. The differences in the clinical characteristics and imaging manifestations were observed between the two groups.Results: The peripheral blood neutrophil ratio, erythrocyte sedimentation rate, serum total protein and serum globulin levels in the fungal group were higher compared with the Brucella group, while the peripheral blood lymphocyte count, lymphocyte ratio and albumin-globulin ratio were lower in the fungal group compared with the Brucella group (P < 0.05). As for imaging examinations, the proportion of bone destruction centred on the intervertebral disc with surrounding osteosclerosis on computed tomography (CT) imaging showed a statistical difference between the Brucella group and the fungal group (P < 0.05). Fungal infection patients showed more osteosclerosis-free areas around the bone destruction on magnetic resonance imaging (MRI) than Brucella infection patients.Conclusion: There are certain similarities in clinical manifestations between fungal and Brucella infections of the lumbar spine, but the haematological indices and image features of CT and MRI can effectively differentiate between them, providing guidance for the clinical differential diagnosis.Keywords: mycosis, brucellosis, imaging, differential diagnosis
Published: 2024

5. Efficacy and Safety of Jiuxin Pill in the Treatment of Patients with Stable Angina Pectoris: A Protocol for a Randomized, Double-Blind, Placebo-Controlled, Multicenter Clinical Trial

Author: Liu T, Zhang J, Zhao Z, Bi Y, Zheng Y, Wang S, Dai X, Li J, Lin Q, Ni D, Wang C, Wu J, Xue Y, Zhu M, Wang X, and Mao J
Subjects: traditional chinese medicine, jiuxin pill, stable angina pectoris, protocol, randomized controlled trial., Medicine (General), R5-920
Abstract: Tongzuo Liu,1,2,&ast; Jingyi Zhang,1,2,&ast; Zhiqiang Zhao,1 Yingfei Bi,1 Ying Zheng,1 Shuai Wang,1 Xiaohua Dai,3 Jun Li,4 Qian Lin,5 Daimei Ni,6 Chenglong Wang,7 Jianguang Wu,8 Yitao Xue,9 Mingjun Zhu,10 Xianliang Wang,1,&ast; Jingyuan Mao1,&ast; 1Cardiovascular Department, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine/National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China; 2Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 3Cardiovascular Department, The First Affiliated Hospital of Anhui University of Chinese Medicine, Anhui, People’s Republic of China; 4Cardiovascular Department, Guang’anmen Hospital of China Academy of Chinese Medicine Science, Beijing, People’s Republic of China; 5Cardiovascular Department, Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, People’s Republic of China; 6Cardiovascular Department, Taihe Hospital of Traditional Chinese Medicine Affiliated to Anhui University of Chinese Medicine, Anhui, People’s Republic of China; 7Cardiovascular Department, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 8Cardiovascular Department, Affiliated Hospital of Jiangxi University of Chinese Medicine, Jiangxi, People’s Republic of China; 9Cardiovascular Department, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Shandong, People’s Republic of China; 10Cardiovascular Department, The First Affiliated Hospital of He’nan University of Traditional Chinese Medicine, Henan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jingyuan Mao; Xianliang Wang, Cardiovascular Department, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine/National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China, Tel +86-13820498886 ; +86-13920007825, Email jymao@126.com; xlwang1981@126.comBackground: Stable angina pectoris (SAP), as a common type of coronary heart disease (CHD), is characterized by transient retrosternal squeezing pain or suffocation induced by exercise, mood swings, or other stress. Most patients with stable angina pectoris do not benefit from interventional therapy and medication, so optimizing treatment plans has important clinical significance. Jiuxin pill is a Chinese patent medicine developed by Huatuo Chinese Medicine Co. Ltd. (Bozhou, China) to relieve the symptoms of stable angina pectoris (SAP). However, there is a lack of evidence support from high-quality clinical studies.Methods: In this randomized, double-blind, placebo-controlled, multicenter clinical trial, 170 patients with SAP were recruited from 11 centers in China. The patients were randomized to either the treatment group (Jiuxin pill, 2 pills, bid) or the control group (Jiuxin pill simulant, 2 pills, bid) without changing the original conventional western medicine. The trial was set up with a run-in period of 7 days, a treatment period of 28 ± 2 days, and a follow-up period of 28 ± 2 days. Total exercise time (TED) in the treadmill test and Seattle Angina Questionnaire (SAQ) scores were set as the main efficacy outcomes, and the 1-minute heart rate recovery (HRR1), metabolic equivalents (METs), maximum ST segment depression, Borg perceived exertion after exercise, the average number of angina attacks per week, usage of nitroglycerin, drug withdrawal and reduction rate, information scoring of four diagnostic methods in traditional Chinese medicine and incidence of major adverse cardiovascular events were set as the secondary efficacy outcomes. Adverse events were monitored throughout the trial.Discussion: In China, the use of Chinese patent medicine in the treatment of stable angina pectoris is more common. This trial evaluated the efficacy and safety of the Jiuxin pill in the treatment of patients with SAP, and the trial results provide high-quality research evidence for its clinical application.Trial Registration: This trial has been registered in the China Clinical Trial Registry on 11 June 2022 (Registration No.: ChiCTR2200060780, https://www.chictr.org.cn/showproj.html?proj=172352).Keywords: traditional Chinese medicine, Jiuxin pill, stable angina pectoris, protocol, randomized controlled trial
Published: 2024

6. Red Blood Cell-Hitchhiking Delivery of Simvastatin to Relieve Acute Respiratory Distress Syndrome

Author: Sun M, Wei J, Su Y, He Y, Ge L, Shen Y, Xu B, Bi Y, and Zheng C
Subjects: acute lung injury, simvastatin, respiratory distress syndrome, ph response, Medicine (General), R5-920
Abstract: Mengjuan Sun,1,&ast; Jun Wei,1,&ast; Yanhui Su,1,&ast; Yangjingwan He,1 Liang Ge,1 Yan Shen,1 Bohui Xu,2 Yanlong Bi,3 Chunli Zheng1 1Department of Pharmaceutics, China Pharmaceutical University, Nanjing, People’s Republic of China; 2School of Pharmacy, Nantong University, Nantong, People’s Republic of China; 3Pediatric Intensive Care Unit, Children’s Hospital of Nanjing Medical University, Nanjing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Chunli Zheng; Yanlong Bi, Email zhengchunli@cpu.edu.cn; pulong123@163.comPurpose: The purpose of this study is to address the high mortality and poor prognosis associated with Acute Respiratory Distress Syndrome (ARDS), conditions characterized by acute and progressive respiratory failure. The primary goal was to prolong drug circulation time, increase drug accumulation in the lungs, and minimize drug-related side effects.Methods: Simvastatin (SIM) was used as the model drug in this study. Employing a red blood cell surface-loaded nanoparticle drug delivery technique, pH-responsive cationic nanoparticles loaded with SIM were non-covalently adsorbed onto the surface of red blood cells (RBC), creating a novel drug delivery system (RBC@SIM-PEI-PPNPs).Results: The RBC@SIM-PEI-PPNPs delivery system effectively extended the drug’s circulation time, providing an extended therapeutic window. Additionally, this method substantially improved the targeted accumulation of SIM in lung tissues, thereby enhancing the drug’s efficacy in treating ARDS and impeding its progression to ARDS. Crucially, the system showed a reduced risk of adverse drug reactions.Conclusion: RBC@SIM-PEI-PPNPs demonstrates promise in ARDS and ARDS treatment. This innovative approach successfully overcomes the limitations associated with SIM’s poor solubility and low bioavailability, resulting in improved therapeutic outcomes and fewer drug-related side effects. This research holds significant clinical implications and highlights its potential for broader application in drug delivery and lung disease treatment.Keywords: acute lung injury, simvastatin, respiratory distress syndrome, pH response
Published: 2024

7. Mesoporous Silicon with Strontium-Powered Poly(Lactic-Co-Glycolic acid)/Gelatin-Based Dressings Facilitate Skin Tissue Repair

Author: Li N, Zhang W, Wu S, Shafiq M, Xie P, Zhang L, Jiang S, and Bi Y
Subjects: electrospinning, nanofiber membrane, wound healing, mesoporous silicon, strontium, Medicine (General), R5-920
Abstract: Naijing Li,1,&ast; Weiying Zhang,2,&ast; Siyuan Wu,3,&ast; Muhammad Shafiq,4 Peihan Xie,3 Lixiang Zhang,5 Shichao Jiang,3 Yue Bi1 1Department of Orthopedic Oncology, Yantai Shan Hospital, Yantai, People’s Republic of China; 2Health Management Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People’s Republic of China; 3Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People’s Republic of China; 4Innovation Center of Nanomedicines, Kawasaki Institute of Industrial Promotion, Kawasaki, Japan; 5Department of Health Management, Characteristic Medical Center of Chinese People’s Armed Police Force, Tianjin, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yue Bi; Shichao Jiang, Email dr_yuebi@126.com; mailjsc@163.comPurpose: Functional inorganic nanomaterials (NMs) are widely exploited as bioactive materials and drug depots. The lack of a stable form of application of NMs at the site of skin injury, may impede the removal of the debridement, elevate pH, induce tissue toxicity, and limit their use in skin repair. This necessitates the advent of innovative wound dressings that overcome the above limitations. The overarching objective of this study was to exploit strontium-doped mesoporous silicon particles (PSiSr) to impart multifunctionality to poly(lactic-co-glycolic acid)/gelatin (PG)-based fibrous dressings (PG@PSiSr) for excisional wound management.Methods: Mesoporous silicon particles (PSi) and PSiSr were synthesized using a chemo-synthetic approach. Both PSi and PSiSr were incorporated into PG fibers using electrospinning. A series of structure, morphology, pore size distribution, and cumulative pH studies on the PG@PSi and PG@PSiSr membranes were performed. Cytocompatibility, hemocompatibility, transwell migration, scratch wound healing, and delineated angiogenic properties of these composite dressings were tested in vitro. The biocompatibility of composite dressings in vivo was assessed by a subcutaneous implantation model of rats, while their potential for wound healing was discerned by implantation in a full-thickness excisional defect model of rats.Results: The PG@PSiSr membranes can afford the sustained release of silicon ions (Si4+) and strontium ions (Sr2+) for up to 192 h as well as remarkably promote human umbilical vein endothelial cells (HUVECs) and NIH-3T3 fibroblasts migration. The PG@PSiSr membranes also showed better cytocompatibility, hemocompatibility, and significant formation of tubule-like networks of HUVECs in vitro. Moreover, PG@PSiSr membranes also facilitated the infiltration of host cells and promoted the deposition of collagen while reducing the accumulation of inflammatory cells in a subcutaneous implantation model in rats as assessed for up to day 14. Further evaluation of membranes transplanted in a full-thickness excisional wound model in rats showed rapid wound closure (PG@SiSr vs control, 96.1% vs 71.7%), re-epithelialization, and less inflammatory response alongside skin appendages formation (eg, blood vessels, glands, hair follicles, etc.).Conclusion: To sum up, we successfully fabricated PSiSr particles and prepared PG@PSiSr dressings using electrospinning. The PSiSr-mediated release of therapeutic ions, such as Si4+ and Sr2+, may improve the functionality of PLGA/Gel dressings for an effective wound repair, which may also have implications for the other soft tissue repair disciplines.Keywords: electrospinning, nanofiber membrane, wound healing, mesoporous silicon, strontium
Published: 2024

8. Stem Cell-Derived Extracellular Vesicles: Promising Therapeutic Opportunities for Diabetic Wound Healing

Author: Zhang B, Bi Y, Wang K, Guo X, Liu Z, Li J, and Wu M
Subjects: diabetes, wound healing, stem cells, regeneration, evs, ncrnas, Medicine (General), R5-920
Abstract: Boyu Zhang,1,&ast; Yajun Bi,2,&ast; Kang Wang,1,&ast; Xingjun Guo,3 Zeming Liu,1 Jia Li,1 Min Wu1 1Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China; 2Department of Pediatrics, Dalian Municipal Women and Children’s Medical Center (Group), Dalian Medical University, Dalian, Liaoning Province, 116011, People’s Republic of China; 3Department of Biliary-Pancreatic Surgery, Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jia Li; Min Wu, Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College 1095 Jiefang Avenue, Wuhan, Hubei, People’s Republic of China, Tel +86 18846818658 ; +86 13554254796, Email 4338311@qq.com; wumin@hust.edu.cnAbstract: Wound healing is a sophisticated and orderly process of cellular interactions in which the body restores tissue architecture and functionality following injury. Healing of chronic diabetic wounds is difficult due to impaired blood circulation, a reduced immune response, and disrupted cellular repair mechanisms, which are often associated with diabetes. Stem cell-derived extracellular vesicles (SC-EVs) hold the regenerative potential, encapsulating a diverse cargo of proteins, RNAs, and cytokines, presenting a safe, bioactivity, and less ethical issues than other treatments. SC-EVs orchestrate multiple regenerative processes by modulating cellular communication, increasing angiogenesis, and promoting the recruitment and differentiation of progenitor cells, thereby potentiating the reparative milieu for diabetic wound healing. Therefore, this review investigated the effects and mechanisms of EVs from various stem cells in diabetic wound healing, as well as their limitations and challenges. Continued exploration of SC-EVs has the potential to revolutionize diabetic wound care. Keywords: diabetes, wound healing, stem cells, EVs, ncRNAs, regeneration
Published: 2024

9. Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool

Author: Ding Q, Yu C, Xu X, Hou Y, Miao Y, Yang S, Chen S, Ma X, Zhang Z, and Bi Y
Subjects: mild cognitive impairment, type 2 diabetes mellitus, risk score, prescreening tool, Specialties of internal medicine, RC581-951
Abstract: Qun Ding,1– 4,&ast; Congcong Yu,2,3,&ast; Xiang Xu,1,3 Yinjiao Hou,1,3 Yingwen Miao,2,3 Sijue Yang,2,3 Shihua Chen,2,3 Xuelin Ma,2,3 Zhou Zhang,2,3 Yan Bi1– 3 1Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People’s Republic of China; 3Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, People’s Republic of China; 4Department of Endocrinology, the Second People’s Hospital of Lianyungang, Lianyungang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhou Zhang, Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Branch of National Clinical Research Centre for Metabolic Diseases, No. 321, Zhongshan Road, Nanjing, 210008, People’s Republic of China, Tel + 86 25-83106666, Email zhangzhou@smail.nju.edu.cn Yan Bi, Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Branch of National Clinical Research Centre for Metabolic Diseases, No. 321, Zhongshan Road, Nanjing, 210008, People’s Republic of China, Tel + 86 25-83105313, Email biyan@nju.edu.cnAim: Numerous evidence suggests that diabetes increases the risk of cognitive impairment. This study aimed to develop and validate a multivariable risk score model to identify mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM).Methods: This cross-sectional study included 1256 inpatients (age: 57.5 ± 11.2 years) with T2DM in a tertiary care hospital in China. MCI was diagnosed according to the criteria recommended by the National Institute on Aging-Alzheimer’s Association Workgroup, and a MoCA score of 19– 25 indicated MCI. Participants were randomly allocated into the derivation and validation sets at 7:3 ratio. Logistic regression models were used to identify predictors for MCI in the derivation set. A scoring system based on the predictors’ beta coefficient was developed. Predictive ability of the risk score was tested by discrimination and calibration methods.Results: Totally 880 (285 with MCI, 32.4%) and 376 (167 with MCI, 33.8%) patients were allocated in the derivation and validation set, respectively. Age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease were identified as predictors for MCI and constituted the risk score. The AUCs (95% CI) of the risk score were 0.751 (0.717, 0.784) in derivation set and 0.776 (0.727, 0.824) in validation set. The risk score showed good apparent calibration of observed and predicted MCI probabilities and was capable of stratifying individuals into 3 risk categories by two cut-off points (low risk: ≤ 3, medium risk: 4– 13, and high risk ≥ 14).Conclusion: The risk score based on age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease can effectively assess MCI risk in adults with T2DM at different age. It can serve as a practical prescreening tool for early detection of MCI in daily diabetes care.Keywords: mild cognitive impairment, type 2 diabetes mellitus, risk score, prescreening tool
Published: 2024

10. Adsorption–degradation of methylene blue by natural manganese ore: kinetics, characterization, and mechanism

Author: Cai, Y., Qiu, C., Yang, K., Tian, B., and Bi, Y.
Published: 2024
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11. Clinical Evidence of the Relationship Between Alanine Aminotransferase and Diabetic Kidney Disease

Author: Bi Y, Yang Y, Yuan X, Wang J, Liu Z, Tian S, and Sun C
Subjects: alanine aminotransferase, diabetic kidney disease, dose-response relationship, nhanes, Specialties of internal medicine, RC581-951
Abstract: Yaru Bi,1,&ast; Yang Yang,2,&ast; Xiaojie Yuan,3 Jiping Wang,3 Zhiyuan Liu,4 Suyan Tian,5 Chenglin Sun1,3 1Department of Endocrinology and Metabolism, First Hospital of Jilin University, Changchun, 130000, People’s Republic of China; 2Department of Health Examination Center, First Hospital of Jilin University, Changchun, 130000, People’s Republic of China; 3Department of Clinical Nutrition, First Hospital of Jilin University, Changchun, 130000, People’s Republic of China; 4Department of Clinical Medicine, Yanbian University, Yanji, 136200, People’s Republic of China; 5Division of Clinical Research, First Hospital of Jilin University, Changchun, 130000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Suyan Tian; Chenglin Sun, Email wmxt@jlu.edu.cn; chenglins@jlu.edu.cnAim: Multiple studies have investigated the association between alanine aminotransferase (ALT) and diabetes mellitus (DM); however, only a few studies have specifically examined the relationship between ALT and diabetic kidney disease (DKD). This study aimed to investigate the relationship between ALT and DKD using clinical data.Methods: A cross-sectional study was conducted on 668 individuals that included non-DM (N=281), DM without DKD (N=160), and DKD (N=227) patients. A generalized additive model (GAM) was used to examine the dose–response relationship between ALT and DKD risk. We also analyzed the data from the US National Health and Nutrition Examination Survey (NHANES) 2015– 2018 using the same statistical methods; 4481, 1110, and 671 individuals were included in the non-DM, DM without DKD, and DKD groups, respectively.Results: The changes in ALT activity among the non-DM, DM without DKD, and DKD groups showed a similar pattern in both our clinical data and the NHANES dataset. ALT activity increases with the onset of DM, whereas ALT activity decreases when DM progresses to DKD. The GAM revealed a nonlinear U-shaped relationship between ALT and DKD risk in the two datasets, and the lowest range of ALT was 40– 50 IU/L. Both lower (< 40 IU/L) and higher (> 50 IU/L) ALT activity were found to be positively associated with DKD risk.Conclusion: A U-shaped nonlinear association between ALT and DKD was found in our clinical data and NHANES data. DKD risk was increased by both lower or higher ALT activity. To confirm the causality of nonlinear relationship, larger prospective studies or Mendelian randomization analysis are required.Keywords: alanine aminotransferase, diabetic kidney disease, dose–response relationship, NHANES
Published: 2024

12. Therapeutic Potential of Downregulated Interleukin-6 Signaling for the Treatment of Chronic Pain: A Mendelian Randomization Study

Author: Bi Y, Zhu Y, and Tang S
Subjects: il-6 signaling, chronic pain, mendelian randomization, Medicine (General), R5-920
Abstract: Yaodan Bi,1,&ast; Yingchao Zhu,2,&ast; Shuai Tang1 1Department of Anesthesiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 2Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shuai Tang, Department of anesthesiology, Peking Union Medical College Hospital, Beijing, People’s Republic of China, Email tangshuai@pumch.cnIntroduction: While numerous studies have emphasized the pivotal involvement of the Interleukin 6 (IL-6) pathway in the development of chronic pain, the causal nature of this relationship remains uncertain.Methods: In this study, we opted to include genetic variants situated within the locus of the IL-6 receptor (IL-6R) that exhibited associations with C-reactive protein (CRP) levels. CRP serves as a downstream effector in the IL-6 pathway. Utilizing these variants as genetic proxies, we aimed to modulate IL-6 signaling. Employing a two-sample Mendelian randomization (MR) approach, we investigated the potential link between the genetic proxy and seven distinct subtypes of chronic pain, categorized based on their corresponding body locations. Moreover, we examined the relationship between chronic pain and an alternative instrument of IL-6 signaling that was weighted based on s-IL-6R levels. Furthermore, we conducted exploratory analyses to estimate the plausible causal association between CRP, gp130, and the subtypes of chronic pain.Results: Our analysis showed that genetic proxied downregulation of IL-6 signaling, weighted on CRP levels, was linked to a reduced risk of chronic back and knee pain. The sensitivity analyses across various MR methods confirmed the consistency of the findings and showed no evidence of horizontal pleiotropy or heterogeneity. Moreover, the results remained robust with different sets of instrument variables. A genetically increased level of s-IL-6R was also negatively associated with chronic back and knee pain. However, there was no causal relationship between CRP and gp130 with chronic pain.Conclusion: Based on our findings, there is evidence to suggest a potential causal relationship between IL-6 signaling and chronic back and knee pain. Consequently, the downregulation of IL-6 signaling holds promise as a potential therapeutic target for addressing chronic back and knee pain.Keywords: IL-6 signaling, chronic pain, Mendelian randomization
Published: 2023

13. Galvanic Corrosion Behavior of 7B04 Aluminum/TC16 Titanium Alloy Joints After Anodized Oxidation and Its Effect on Fatigue Life

Author: Zhang, T., Zhang, T., He, Y., Bi, Y., Hou, B., and Li, C. F.
Published: 2023
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14. The significant impact of the crystallization of cementite in high carbon steel wire on its torsional performance

Author: Bi, Y., Wang, Q., He, T., Liu, X.P., Liu, Y.D., Wang, Y.D., and Yao, Z.
Published: 2024
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15. The Emerging Roles of Exosomal miRNAs in Breast Cancer Progression and Potential Clinical Applications

Author: Li J, He D, Bi Y, and Liu S
Subjects: breast cancer, tumor microenvironment, exosome, mirnas, detection, therapy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Jie Li,1,&ast; Dejiao He,2,&ast; Yajun Bi,3,&ast; Shengxuan Liu4 1Department of Thyroid and Breast Surgery, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, 518067, People’s Republic of China; 2Department of Nephrology, Renmin Hospital of Wuhan University, Wuhan, 430060, People’s Republic of China; 3Department of Pediatrics, Dalian Municipal Women and Children’s Medical Center (Group), Dalian Medical University, Dalian, Liaoning Province, 116011, People’s Republic of China; 4Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shengxuan Liu, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, People’s Republic of China, Tel +86 133 8756 1016, Email tongjishengxuanl@163.comAbstract: Breast cancer remains the leading malignancy in terms of morbidity and mortality today. The tumor microenvironment of breast cancer includes multiple cell types, secreted proteins, and signaling components such as exosomes. Among these, exosomes have a lipid bilayer structure. Exosomes can reflect the biological traits of the parent cell and carry a variety of biologically active components, including proteins, lipids, small molecules, and non-coding RNAs, which include miRNA, lncRNA, and circRNA. MiRNAs are a group of non-coding RNAs of approximately 20– 23 nucleotides in length encoded by the genome, triggering silencing and functional repression of target genes. MiRNAs have been shown to play a significant role in the development of cancer owing to their role in the prognosis, pathogenesis, diagnosis, and treatment of cancer. MiRNAs in exosomes can serve as effective mediators of information transfer from parental cells to recipient cells and trigger changes in biological traits such as proliferation, invasion, migration, and drug resistance. These changes can profoundly alter the progression of breast cancer. Therefore, here, we systematically summarize the association of exosomal miRNAs on breast cancer progression, diagnosis, and treatment in the hope of providing novel strategies and directions for subsequent breast cancer treatment.Keywords: breast cancer, tumor microenvironment, exosome, miRNAs, detection, therapy
Published: 2023

16. Magnetic Resonance Imaging Manifestations in 13 Cases of Seminal Vesicle Tuberculosis

Author: Gan W, Bi Y, Fu X, Wei J, Qi M, He J, and Li X
Subjects: seminal vesicle tuberculosis, magnetic resonance imaging, genitourinary tuberculosis, Infectious and parasitic diseases, RC109-216
Abstract: Wei Gan,1,&ast; Yan Bi,2,&ast; Xuwen Fu,1 Jialu Wei,1 Min Qi,1 Jintang He,3 Xiang Li1 1Department of Radiology, Kunming Third People’s Hospital/Yunnan Clinical Medical Center for Infectious Diseases, Kunming, 650041, People’s Republic of China; 2Department of Radiology, The People’s Hospital of Lincang, Lincang, Yunnan, 677000, People’s Republic of China; 3Department of Surgery, Kunming Third People’s Hospital/Yunnan Clinical Medical Center for Infectious Diseases, Kunming, 650041, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiang Li, Department of Radiology, Kunming Third People’s Hospital /Yunnan Clinical Medical Center for Infectious Diseases, No. 319 of Wujing Street, Guandu District, Kunming, 650041, People’s Republic of China, Tel +86 871 6352 3507, Fax +86 871 6351 4717, Email li_xiang115@126.com Jintang He, Department of Surgery, Kunming Third People’s Hospital/Yunnan Clinical Medical Center for Infectious Diseases, No. 319 of Wujing Street, Guandu District, Kunming, 650041, People’s Republic of China, Tel +86 871 6842 3689, Fax +86 871 6351 4717, Email he_jintang06@21cn.comObjective: This study aimed to examine the radiographic manifestations of seminal vesicle tuberculosis (SVT) on magnetic resonance imaging to gain a deeper understanding of this disease.Methods: The clinical symptoms, general conditions, relevant laboratory tests and radiological data of 13 patients diagnosed with SVT were collected through bacteriological examination. A descriptive analysis was used to explore the composition ratio and rate values of the collected data.Results: All 13 cases (100.0%) showed isointense signals on T1WI and hypointense signals on T2WI in the affected seminal vesicles, with the disappearance of the multi-chambered high signal on T2WI in normal seminal vesicles. Eight cases (61.5%) showed diffusion restriction on DWI of the affected seminal vesicle and significant enhancement on the contrast scan, whereas five cases (38.5%) showed unrestricted diffusion and mild enhancement on the contrast scan. Patients with significant enhancements exhibited higher counts and neutrophil percentages than patients with mild enhancements, with statistically significant differences (Z = 2.196, P = 0.030; Z = 2.781, P = 0.003, respectively). The counts and percentage of lymphocytes, CD3+T cells and CD4+T cells were significantly lower in patients with significant enhancements than in those with mild enhancements, with statistically significant differences (Z = − 2.196, P = 0.030; Z = − 2.928, P = 0.002; Z = − 2.928, P = 0.002; Z = − 2.928, P = 0.002, respectively). Patients with significant enhancements were more likely to have active pulmonary tuberculosis than those with mild enhancements, with a statistically significant difference (P = 0.035).Conclusion: Magnetic resonance imaging reveals distinct radiographic features of SVT, and variations in imaging presentations can indicate a patient’s immune status.Keywords: seminal vesicle tuberculosis, magnetic resonance imaging, genitourinary tuberculosis
Published: 2023

17. Deep view of composite SNR CTA1 with LHAASO in γ-rays up to 300 TeV

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Bian, W., Bukevich, A. V., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, H. X., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Chu, M. C., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, J. H., Fang, K., Feng, C. F., Feng, H., Feng, L., Feng, S. H., Feng, X. T., Feng, Y., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Ge, T. T., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., Hannuksela, O. A., Hasan, M., He, H. H., He, H. N., He, J. Y., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Y. Y., Ji, X. L., Jia, H. Y., Jia, K., Jiang, H. B., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Karpikov, I., Khangulyan, D., Kuleshov, D., Kurinov, K., Li, B. B., Li, C. M., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, Jian, Li, Jie, Li, K., Li, S. D., Li, W. L., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, D. B., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Luo, Q., Luo, Y., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ng, K. C. Y., Ou, L. J., Pattarakijwanich, P., Pei, Z. Y., Qi, J. C., Qi, M. Y., Qiao, B. Q., Qin, J. J., Raza, A., Ruffolo, D., Saiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, D. X., Sun, Q. N., Sun, X. N., Sun, Z. B., Takata, J., Tam, P. H. T., Tang, Q. W., Tang, R., Tang, Z. B., Tian, W. W., Wan, L. H., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, Kai, Wang, Kai, Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, Q. W., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, Y. L., Xing, Y., Xiong, D. R., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, C. Y., Yang, F., Yang, F. F., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, W. X., Yao, Y. H., Yao, Z. G., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zhao, X. H., Zheng, F., Zhong, W. J., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhou, X. X., Zhu, B. Y., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., Zuo, X., and Li, B.
Published: 2025
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18. LHAASO detection of very-high-energy γ-ray emission surrounding PSR J0248+6021

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Bian, W., Bukevich, A. V., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, H. X., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Chu, M. C., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, J. H., Fang, K., Feng, C. F., Feng, H., Feng, L., Feng, S. H., Feng, X. T., Feng, Y., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Ge, T. T., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., Hannuksela, O. A., Hasan, M., He, H. H., He, H. N., He, J. Y., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Y. Y., Ji, X. L., Jia, H. Y., Jia, K., Jiang, H. B., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Karpikov, I., Khangulyan, D., Kuleshov, D., Kurinov, K., Li, B. B., Li, C. M., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, Jian, Li, Jie, Li, K., Li, S. D., Li, W. L., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, D. B., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Luo, Q., Luo, Y., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ng, K. C. Y., Ou, L. J., Pattarakijwanich, P., Pei, Z. Y., Qi, J. C., Qi, M. Y., Qiao, B. Q., Qin, J. J., Raza, A., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, D. X., Sun, Q. N., Sun, X. N., Sun, Z. B., Takata, J., Tam, P. H. T., Tang, Q. W., Tang, R., Tang, Z. B., Tian, W. W., Wan, L. H., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, Kai, Wang, Kai, Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, Q. W., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, Y. L., Xing, Y., Xiong, D. R., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, C. Y., Yang, F., Yang, F. F., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, W. X., Yao, Y. H., Yao, Z. G., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zhao, X. H., Zheng, F., Zhong, W. J., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhou, X. X., Zhu, B. Y., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Published: 2025
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19. Observation of the γ-ray emission from W43 with LHAASO

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Bian, W., Bukevich, A. V., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, H. X., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Chu, M. C., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, J. H., Fang, K., Feng, C. F., Feng, H., Feng, L., Feng, S. H., Feng, X. T., Feng, Y., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Ge, T. T., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., Hannuksela, O. A., Hasan, M., He, H. H., He, H. N., He, J. Y., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Y. Y., Ji, X. L., Jia, H. Y., Jia, K., Jiang, H. B., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Karpikov, I., Khangulyan, D., Kuleshov, D., Kurinov, K., Li, B. B., Li, C. M., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, Jian, Li, Jie, Li, K., Li, S. D., Li, W. L., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, D. B., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Luo, Q., Luo, Y., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ng, K. C. Y., Ou, L. J., Pattarakijwanich, P., Pei, Z. Y., Qi, J. C., Qi, M. Y., Qiao, B. Q., Qin, J. J., Raza, A., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, D. X., Sun, Q. N., Sun, X. N., Sun, Z. B., Takata, J., Tam, P. H. T., Tang, Q. W., Tang, R., Tang, Z. B., Tian, W. W., Wan, L. H., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, Kai, Wang, Kai, Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, Q. W., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, Y. L., Xing, Y., Xiong, D. R., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, C. Y., Yang, F., Yang, F. F., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, W. X., Yao, Y. H., Yao, Z. G., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zhao, X. H., Zheng, F., Zhong, W. J., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhou, X. X., Zhu, B. Y., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Published: 2025
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20. Changing Trends in the Disease Burden of Cataract and Forecasted Trends in China and Globally from 1990 to 2030

Author: Shu Y, Shao Y, Zhou Q, Lu L, Wang Z, Zhang L, and Bi Y
Subjects: china, cataract, disease burden, risk factors, Infectious and parasitic diseases, RC109-216
Abstract: Yiyang Shu,1 Yuting Shao,1 Qi Zhou,2 Lixia Lu,3 Zhiyue Wang,1 Li Zhang,1 Yanlong Bi1,4 1Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 2Exam Center of Tongji University School of Medicine, Shanghai, People’s Republic of China; 3Department of Biochemistry and Molecular Biology, Tongji Eye Institute, Tongji University School of Medicine, Shanghai, People’s Republic of China; 4Tongji Eye Institute, Tongji University School of Medicine, Shanghai, People’s Republic of ChinaCorrespondence: Yanlong Bi, Email biyanlong@tongji.edu.cnAim: To explore the trends in the prevalence and disease burden of cataract from 1990 to 2019, evaluate attributable risk factors, and predict trends over the next decade in China and globally.Methods: Data was obtained from Global Burden of Disease Study 2019. We calculated the age-standardized prevalence rate (ASR) and annual percentage change (EAPC) to show the trends of cataract in China and different regions. We calculated and reported the proportion of disability adjusted life years (DALYs) attributable to risk factors by sex in China and different regions. Then, the Bayesian age-period-cohort (BAPC) analysis model was also used to predict the prevalence trends from 2020 to 2030 in China and globally.Results: The ASR increased from 867.09 in 1990 to 991.56 in 2019 per 100,000 with an EAPC of 0.88 in China. The age-standardized DALY rate of females was higher than males. DALY rates were correlated to household air pollution from solid fuels, tobacco, high fasting plasma glucose and high body-mass index. The projective model indicates that the ASR for cataracts will rise to 1101.35× 106 for male and 1616.63× 106 for female by 2030.Conclusion: The trends from 1990 to 2030 suggested that the burden of cataract remains high in China. Maintaining good lifestyle habits such as switching to clean energy, reducing cigar intake, controlling blood glucose and weight can reduce the risk of cataracts. As aging increases, China should pay more attention to cataract-induced low vision and blindness and develop public policies to reduce the disease burden.Keywords: China, cataract, disease burden, risk factors
Published: 2023

21. Reconstruction of Cherenkov image by multiple telescopes of LHAASO-WFCTA

Author: Aharonian, F., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Zhe, Cao, Zhen, Chang, J., Chang, J. F., Chen, E. S., Chen, Liang, Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, H. L., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., D’Ettorre Piazzoli, B., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Duan, K. K., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, W. H., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Levochkin, K., Li, B. B., Li, Cheng, Li, Cong, Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Long, W. J., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Masood, A., Min, Z., Mitthumsiri, W., Nan, Y. C., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. Y., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Z. B., Tian, W. W., Wang, B. D., Wang, C., Wang, H., Wang, H. G., Wang, J. C., Wang, J. S., Wang, L. P., Wang, L. Y., Wang, R., Wang, R. N., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., Yan, J. Z., Yang, C. W., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zeng, Z. K., Zha, M., Zhai, X. X., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, Lu, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Y. L., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, Y., Zhou, B., Zhou, H., Zhou, J. N., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Published: 2022
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22. CD133, but Not CD44, May Serve as a Novel Biomarker for Differential Diagnosis Between Basal Cell Carcinoma and Trichoblastomas

Author: Bi Y, Shi X, Chen D, and Zhao Y
Subjects: basal cell carcinoma (bcc), trichoblastomas (tb), differential diagnosis, cd133, cd44, immunohistochemistry, receiver operating characteristic (roc), Dermatology, RL1-803
Abstract: Yalan Bi,1 Xiaohua Shi,2 Dian Chen,1 Yi Zhao1 1Department of Dermatology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, People’s Republic of China; 2Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of ChinaCorrespondence: Yi Zhao, Department of Dermatology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, No. 168 Litang Road, Changping District, Beijing, People’s Republic of China, Tel/Fax +86 010 56119127, Email zhaoyimd@tsinghua.edu.cnPurpose: To investigate the clinical value of CD133 and CD44 as putative cancer stem cell markers in distinguishing between basal cell carcinoma (BCC) and trichoblastomas (TB).Patients and Methods: Tumor samples from 24 BCC and 23 TB patients were retrospectively retrieved for immunohistochemical staining of CD133 and CD44. The results were interpreted using a semiquantitative scoring system (H score). A receiver operating characteristic (ROC) curve was developed to identify an optimal cutoff value for differentiating between BCC and TB.Results: Expression of CD133 was significantly higher in BCC patients than in TB patients (median H score: 30 [IQR: 12.5– 56.3] vs 0 [IQR: 0– 2], P < 0.001). However, there was no significant difference in CD44 expression between the two groups (median H score: 105 [IQR: 63.8– 155.0] vs 60 [IQR: 30– 120], P = 0.095). The ROC analysis of CD133 immunostaining yielded an area under the curve (AUC) of 0.881 (95% CI: 0.756– 1.000) for differentiating between BCC and TB by using a H score of 7 as the cut-off value (98.5% sensitivity and 87.0% specificity). By contrast, immunostaining of CD44 showed a lower diagnostic value, with an AUC of 0.642 (95% CI: 0.476– 0.808) at the optimal cut-off value of 85 (62.5% sensitivity and 73.9% specificity). The positive and negative predictive values were 88.5% and 95.2% for CD133 and 71.4% and 65.4% for CD44, respectively. Additionally, CD133 expression was significantly associated with mitotic activity in BCC patients (r = 0.549, P = 0.005).Conclusion: Our study expanded upon previous studies of CD133 and CD44 expressions in skin tumors, suggesting that CD133, but not CD44, may serve as a novel biomarker for differential diagnosis of BCC, although future studies using a larger number of patients are needed to justify it further.Keywords: basal cell carcinoma, BCC, trichoblastomas, TB, differential diagnosis, CD133, CD44, immunohistochemistry, receiver operating characteristic, ROC
Published: 2022

23. Longitudinal investigations of anatomical and morphological development of the gastrointestinal tract in goats from colostrum to postweaning

Author: Abdelsattar, M.M., Zhuang, Y., Cui, K., Bi, Y., Haridy, M., and Zhang, N.
Published: 2022
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24. Tumor microenvironment and its clinicopathological and prognostic associations in surgically resected cutaneous angiosarcoma

Author: Bi, Y., Ge, L., Ren, X., Pang, J., Zhao, Y., and Liang, Z.
Published: 2022
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25. Tau Pathologies Mediate the Associations of Vascular Risk Burden with Cognitive Impairments in Non-demented Elders: The CABLE Study

Author: Yu, G.-X., Ou, Y.-N., Bi, Y.-L., Ma, Y.-H., Hu, H., Wang, Z.-T., Hou, X.-H., Xu, W., Tan, Lan, and Yu, Jin-Tai
Published: 2022
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26. Line-of-shower trigger method to lower energy threshold for GRB detection using LHAASO-WCDA

Author: Aharonian, F., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, H., Cai, J. T., Cao, Z., Cao, Z., Chang, J., Chang, J. F., Chang, X. C., Chen, B. M., Chen, J., Chen, L., Chen, L., Chen, L., Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Volpe, D. della, Piazzoli, B. D’Ettorre, Dong, X. J., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, Y. L., Gao, B., Gao, C. D., Gao, Q., Gao, W., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. C., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hou, X., Hu, H. B., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, Q. L., Huang, W. H., Huang, X. T., Huang, Z. C., Ji, F., Ji, X. L., Jia, H. Y., Jiang, K., Jiang, Z. J., Jin, C., Kuleshov, D., Levochkin, K., Li, B. B., Li, C., Li, C., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, K., Li, W. L., Li, X., Li, X., Li, X. R., Li, Y., Li, Y. Z., Li, Z., Li, Z., Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y. N., Liu, Z. X., Long, W. J., Lu, R., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Masood, A., Mitthumsiri, W., Montaruli, T., Nan, Y. C., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Ruffolo, D., Rulev, V., Sáiz, A., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. R., Song, H. C., Stenkin, Yu. V., Stepanov, V., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Z. B., Tian, W. W., Wang, B. D., Wang, C., Wang, H., Wang, H. G., Wang, J. C., Wang, J. S., Wang, L. P., Wang, L. Y., Wang, R. N., Wang, W., Wang, W., Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z., Wang, Z., Wang, Z. H., Wang, Z. X., Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, W. X., Wu, X. F., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, G., Xiao, H. B., Xin, G. G., Xin, Y. L., Xing, Y., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., Yang, C. W., Yang, F. F., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Zeng, H. D., Zeng, T. X., Zeng, W., Zeng, Z. K., Zha, M., Zhai, X. X., Zhang, B. B., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, J. W., Zhang, L., Zhang, L., Zhang, L. X., Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y., Zhang, Y., Zhang, Y. F., Zhang, Y. L., Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, Y., Zhou, B., Zhou, H., Zhou, J. N., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Published: 2021
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27. A dynamic range extension system for LHAASO WCDA-1

Author: Aharonian, F., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, H., Cai, J. T., Cao, Z., Cao, Z., Chang, J., Chang, J. F., Chang, X. C., Chen, B. M., Chen, J., Chen, L., Chen, L., Chen, L., Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Volpe, D. della, Piazzoli, B. D’Ettorre, Dong, X. J., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, Y. L., Gao, B., Gao, C. D., Gao, Q., Gao, W., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. C., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hou, X., Hu, H. B., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, Q. L., Huang, W. H., Huang, X. T., Huang, Y., Huang, Z. C., Ji, F., Ji, X. L., Jia, H. Y., Jiang, K., Jiang, Z. J., Jin, C., Kuleshov, D., Levochkin, K., Li, B. B., Li, C., Li, C., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, K., Li, W. L., Li, X., Li, X., Li, X. R., Li, Y., Li, Y. Z., Li, Z., Li, Z., Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y. N., Liu, Z. X., Long, W. J., Lu, R., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Masood, A., Mitthumsiri, W., Montaruli, T., Nan, Y. C., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Ruffolo, D., Rulev, V., Sáiz, A., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. R., Song, H. C., Stenkin, Yu. V., Stepanov, V., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Z. B., Tian, W. W., Wang, B. D., Wang, C., Wang, H., Wang, H. G., Wang, J. C., Wang, J. S., Wang, L. P., Wang, L. Y., Wang, R. N., Wang, W., Wang, W., Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z., Wang, Z., Wang, Z. H., Wang, Z. X., Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, W. X., Wu, X. F., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, G., Xiao, H. B., Xin, G. G., Xin, Y. L., Xing, Y., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., Yang, C. W., Yang, F. F., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Zeng, H. D., Zeng, T. X., Zeng, W., Zeng, Z. K., Zha, M., Zhai, X. X., Zhang, B. B., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, J. W., Zhang, L., Zhang, L., Zhang, L. X., Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y., Zhang, Y., Zhang, Y. F., Zhang, Y. L., Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, Y., Zhou, B., Zhou, H., Zhou, J. N., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Published: 2021
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28. On the characterization of primary iron-rich phase in a high-pressure die-cast hypoeutectic Al-Si alloy

Author: Jiao, X.Y., Liu, C.F., Guo, Z.P., Nishat, Harood, Tong, G.D., Ma, S.L., Bi, Y., Zhang, Y.F., Wiesner, Stuart, and Xiong, S.M.
Published: 2021
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29. Gender and age differences in the association between sleep characteristics and fasting glucose levels in Chinese adults

Author: Zheng, R., Niu, J., Wu, S., Wang, T., Wang, S., Xu, M., Chen, Y., Dai, M., Zhang, D., Yu, X., Tang, X., Hu, R., Ye, Z., Shi, L., Su, Q., Yan, L., Qin, G., Wan, Q., Chen, G., Gao, Z., Wang, G., Shen, F., Luo, Z., Qin, Y., Chen, L., Huo, Y., Li, Q., Zhang, Y., Liu, C., Wang, Y., Yang, T., Deng, H., Zhao, J., Mu, Y., Xu, Y., Li, M., Lu, J., Wang, W., Zhao, Z., Bi, Y., and Ning, G.
Published: 2021
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30. Increasing Age Affected Polymorphonuclear Neutrophils in Prognosis of Mycoplasma pneumoniae Pneumonia

Author: Zhao Q, Zhang T, Zhu B, Bi Y, Jiang SW, Zhu Y, Zhao D, and Liu F
Subjects: refractory mycoplasma pneumoniae pneumonia, mycoplasma pneumoniae, polymorphonuclear neutrophils, reactive oxygen species, neutrophil extracellular traps, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Qianye Zhao,1,2,&ast; Teng Zhang,2,&ast; Beibei Zhu,1 Ying Bi,3 Shi-Wen Jiang,4 Yifan Zhu,1 Deyu Zhao,1 Feng Liu1 1Department of Respiratory Medicine, Children’s Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 2Department of Pediatrics, Lianyungang Maternal and Child Health Care Hospital, Lianyungang, Jiangsu, People’s Republic of China; 3Department of Respiratory Medicine, Xuzhou children’s Hospital, Xuzhou, Jiangsu, People’s Republic of China; 4Research Institute for Reproductive Health and Genetic Diseases, Center of Reproductive Medicine, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, Jiangsu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Deyu Zhao; Feng LiuDepartment of Respiratory Medicine, Children’s Hospital of Nanjing Medical University, No. 72, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210008, People’s Republic of ChinaTel +86 189 5176 9559; +86 189 5176 8373Fax +86 025 83117376Email zhaodeyu988@126.com; axsliu@163.comPurpose: It is well known that age is related to the incidence of Mycoplasma pneumoniae pneumonia (MPP), and how age and other factors contribute to MPP remains unclear. In this study, we investigate how age affects the prognosis of MPP.Patients and Methods: A total number of 1875 hospitalized children with pneumonia were enrolled in this study, including 52 children with refractory M. pneumoniae pneumonia (RMPP) and 298 children with non-RMPP. We used multiple logistic regression analysis to further identify the risk factors of RMPP, and found that age and polymorphonuclear neutrophils (PMNs) count were the key independent risk factors for the occurrence of RMPP. In order to improve specificity, 4.5 years old was taken as the cut-off value. Then, according to the cut-off value of age, 76 participants were recruited and divided into four groups: < 4.5y MPP group, ≥ 4.5y MPP group, < 4.5y health control (< 4.5yHC) and ≥ 4.5y HC group. We explored the diverse functions of primary PMNs from children of different ages with MPP at cellular level. Besides, we studied the relationship between lung injury and PMNs in mice model with MPP of different ages.Results: We found that the age and PMNs count of RMPP group were significantly higher than those of the non-RMPP group. Importantly, there is a linear correlation between the age of patients with RMPP and the percentage of PMNs. Further analysis showed that elderly patients infected with M. pneumoniae had more active PMNs function. Meanwhile, proteomics showed that children with M. pneumoniae infection in different age groups have differences in PMNs apoptosis, nicotinamide adenine dinucleotide phosphate, mitochondrial function and oxidative stress. Finally, we found that age is also involved in the pathogenesis of mouse model with MPP.Conclusion: We speculate that age may contribute to the development of RMPP.Keywords: refractory Mycoplasma pneumoniae pneumonia, Mycoplasma pneumoniae, polymorphonuclear neutrophils, reactive oxygen species, neutrophil extracellular traps
Published: 2021

31. Connecting Peripheral to Central Neuropathy: Examination of Nerve Conduction Combined with Olfactory Tests in Patients with Type 2 Diabetes

Author: Ni W, Zhang Z, Zhang B, Zhang W, Cheng H, Miao Y, Chen W, Liu J, Zhu D, and Bi Y
Subjects: type 2 diabetes mellitus, peripheral neuropathy, cognitive dysfunction, olfactory behavior, odor-induced brain activation, Specialties of internal medicine, RC581-951
Abstract: Wenyu Ni,1,&ast; Zhou Zhang,1,&ast; Bing Zhang,2 Wen Zhang,2 Haiyan Cheng,1 Yingwen Miao,1 Wei Chen,1 Jiani Liu,2 Dalong Zhu,1 Yan Bi1 1Department of Endocrinology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, People’s Republic of China; 2Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yan BiDepartment of Endocrinology, Drum Tower Hospital, Affiliated to Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing, 210008, People’s Republic of ChinaTel +86 25-83304616-61431Fax +86 25-83304616-61431Email biyan@nju.edu.cnAim: Few studies have investigated the associations between diabetic peripheral neuropathy (DPN) and cognitive decline. Olfactory impairment is related to neurodegenerative diseases and type 2 diabetes mellitus (T2DM); however, the cognitive alterations of patients with DPN and the role of olfactory function in DPN are not known. We explored alterations in cognition with DPN and the associations of neuropathy parameters with cognition and olfaction.Methods: Healthy controls (HCs) and patients with T2DM underwent nerve-conduction tests, detailed cognitive assessment, olfactory-behavior tests, and odor-induced functional magnetic resonance imaging (fMRI). T2DM patients were divided into two groups (non-DPN [NDPN] and DPN). Olfactory brain regions showing different activation between the two groups were selected for functional connectivity (FC) analyses. A structural equation model (SEM) was also generated to demonstrate the association among cognition, olfactory, and neuropathy parameters.Results: One hundred individuals (36 HCs, 36 NDPN, and 28 DPN) were matched for age, sex, and educational level. Compared with the NDPN group, the DPN group had significantly lower scores for memory and processing speed, as well as lower olfactory identification and memory scores, decreased activation of the left frontal lobe, and reduced seed-based functional connectivity in the right insula. The nerve conduction velocity in patients with T2DM was associated with cognitive functions. The association between nerve conduction and executive function was mediated by olfactory behavior.Conclusion: Patients with DPN had worse cognition than the NDPN patients in the domains of memory and processing speed. Cognitive dysfunction could be predicted by olfactory-behavior tests and electrophysiological examination.Keywords: type 2 diabetes mellitus, peripheral neuropathy, cognitive dysfunction, olfactory behavior, odor-induced brain activation
Published: 2021

32. Ultrahigh-energy photons up to 1.4 petaelectronvolts from 12 γ-ray Galactic sources

Author: Cao, Zhen, Aharonian, F. A., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, H., Cai, J. T., Cao, Zhe, Chang, J., Chang, J. F., Chang, X. C., Chen, B. M., Chen, J., Chen, L., Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., D′Ettorre Piazzoli, B., Dong, X. J., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, Y. L., Gao, B., Gao, C. D., Gao, Q., Gao, W., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. C., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hou, X., Hu, H. B., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, Q. L., Huang, W. H., Huang, X. T., Huang, Z. C., Ji, F., Ji, X. L., Jia, H. Y., Jiang, K., Jiang, Z. J., Jin, C., Kuleshov, D., Levochkin, K., Li, B. B., Li, Cong, Li, Cheng, Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, K., Li, W. L., Li, X., Li, Xin, Li, X. R., Li, Y., Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y. N., Liu, Z. X., Long, W. J., Lu, R., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Masood, A., Mitthumsiri, W., Montaruli, T., Nan, Y. C., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Ruffolo, D., Rulev, V., Sáiz, A., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. R., Song, H. C., Stenkin, Yu. V., Stepanov, V., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Z. B., Tian, W. W., Wang, B. D., Wang, C., Wang, H., Wang, H. G., Wang, J. C., Wang, J. S., Wang, L. P., Wang, L. Y., Wang, R. N., Wang, W., Wang, W., Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Zheng, Wang, Zhen, Wang, Z. H., Wang, Z. X., Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, W. X., Wu, X. F., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, G., Xiao, H. B., Xin, G. G., Xin, Y. L., Xing, Y., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., Yang, C. W., Yang, F. F., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Zeng, H. D., Zeng, T. X., Zeng, W., Zeng, Z. K., Zha, M., Zhai, X. X., Zhang, B. B., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, J. W., Zhang, L., Zhang, Li, Zhang, L. X., Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Yong, Zhang, Yi, Zhang, Y. F., Zhang, Y. L., Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, Y., Zhou, B., Zhou, H., Zhou, J. N., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Published: 2021
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33. The characterization of Fe-rich phases in a high-pressure die cast hypoeutectic aluminum-silicon alloy

Author: Jiao, X.Y., Liu, C.F., Guo, Z.P., Tong, G.D., Ma, S.L., Bi, Y., Zhang, Y.F., and Xiong, S.M.
Published: 2020
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34. Advances in Multiple Stimuli-Responsive Drug-Delivery Systems for Cancer Therapy

Author: Jia R, Teng L, Gao L, Su T, Fu L, Qiu Z, and Bi Y
Subjects: multi-stimuli responsive nanoparticles, drug delivery, tumor microenvironment, oncotherapy, Medicine (General), R5-920
Abstract: Ruixin Jia,1,* Lesheng Teng,2,* Lingyu Gao,1 Ting Su,1 Lu Fu,3 Zhidong Qiu,1 Ye Bi1,4 1College of Pharmacy, Changchun University of Chinese Medicine, Changchun, Jilin, People’s Republic of China; 2School of Life Science, Jilin University, Changchun, Jilin, People’s Republic of China; 3College of Life Science, Jilin Agricultural University, Changchun, Jilin, People’s Republic of China; 4Practice Training Center, Changchun University of Chinese Medicine, Changchun, Jilin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ye BiPractice Training Center, Changchun University of Chinese Medicine, 1035 Boshuo Road, Changchun, Jilin, People’s Republic of ChinaTel/Fax +86-431-86763979Email biye88@outlook.comZhidong QiuCollege of Pharmacy, Changchun University of Chinese Medicine, 1035 Boshuo Road, Changchun, Jilin, People’s Republic of ChinaTel/Fax +86-431-86763979Email qzdcczy@163.comAbstract: Nanomedicines afford unique advantages in therapeutic intervention against tumors. However, conventional nanomedicines have failed to achieve the desired effect against cancers because of the presence of complicated physiological fluids and the tumor microenvironment. Stimuli-responsive drug-delivery systems have emerged as potential tools for advanced treatment of cancers. Versatile nano-carriers co-triggered by multiple stimuli in different levels of organisms (eg, extracorporeal, tumor tissue, cell, subcellular organelles) have aroused widespread interest because they can overcome sequential physiological and pathological barriers to deliver diverse therapeutic “payloads” to the desired targets. Furthermore, multiple stimuli-responsive drug-delivery systems (MSR-DDSs) offer a good platform for co-delivery of agents and reversing multidrug resistance. This review affords a comprehensive overview on the “landscape” of MSR-DDSs against tumors, highlights the design strategies of MSR-DDSs in recent years, discusses the putative advantage of oncotherapy or the obstacles that so far have hindered the clinical translation of MSR-DDSs.Keywords: multi-stimuli responsive nanoparticles, drug delivery, tumor microenvironment, oncotherapy
Published: 2021

35. Evaluation of a Novel Laboratory Candiduria Screening Protocol in the Intensive Care Unit

Author: He Z, Su C, Bi Y, Cheng Y, Lei D, and Wang F
Subjects: candiduria, intensive care unit, screening protocol, urinalysis, yeast like cell counting, Infectious and parasitic diseases, RC109-216
Abstract: Zhengxin He,1,* Chang Su,2,* Yuwang Bi,3 Yan Cheng,1 Daxin Lei,1 Fukun Wang1 1Laboratory Medicine, The 980th Hospital of PLA Joint Logistical Support Force (Bethune International Peace Hospital), Shijiazhuang, Hebei, 050082, People’s Republic of China; 2Cardiothoracic Surgery, The 980th Hospital of PLA Joint Logistical Support Force (Bethune International Peace Hospital), Shijiazhuang, Hebei, 050082, People’s Republic of China; 3Information Center, The 980th Hospital of PLA Joint Logistical Support Force (Bethune International Peace Hospital), Shijiazhuang, Hebei, 050082, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chang Su; Fukun Wang Tel +8631187978349; +8631187978537Email suchang987@163.com; wangfk8@sina.comBackground: Since urine cultures are only guaranteed for patients with obvious urinary symptoms in most cases, most of candiduria episodes are ignored in clinic.Objective: This study aimed to design a screening protocol to improve diagnostic efficiency of candiduria, and provide information of Candida species and drug susceptibility.Methods: All patients, who were admitted to the intensive care unit (ICU) of our hospital during December 1, 2018 and October 1, 2019, were enrolled in this study. Urinalysis was performed every three days for each subject from the first day of ICU admission. Urine specimens were sampled for fungal culture with either condition: (1) yeast-like cell counting (YLCC) ≥ 200; (2) positive YLCCs were observed in two consecutive tests, and at least one YLCC ≥ 100.Results: The screening protocol dramatically improved the candiduria diagnostic rate of ICU patients from 2.28% to 17.27%. However, compared to the historical control, the screening protocol has no time-saving advantage in candiduria diagnosing. Higher percentage of C. albicans in screening protocol-identified candiduria patients was observed, although there was no statistical difference. Our results indicated that female gender, pneumonia, diabetes and infarction/hemorrhage patients were more prone to develop candiduria. Non-candiduria patients showed a better tendency for survival and shorter ICU stay length. Multisite colonization was common in the surveyed candiduria patients, who were up to 70.83% showed Candida positive cultures in sputum.Conclusion: The screening protocol established in the study was a convenient and practical tool for early warning and feasible management of candiduria and IC.Keywords: candiduria, intensive care unit, screening protocol, urinalysis, yeast-like cell counting
Published: 2021

36. FTO Polymorphisms are Associated with Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) Susceptibility in the Older Chinese Han Population

Author: Gu Z, Bi Y, Yuan F, Wang R, Li D, Wang J, Hu X, He G, Zhang L, and Liu B
Subjects: mafld, fto, polymorphism, genetic association, susceptibility, Geriatrics, RC952-954.6
Abstract: Zhan Gu,1,* Yan Bi,2,* Fan Yuan,2,* Ruirui Wang,1 Dong Li,3 Jianying Wang,1 Xiaojuan Hu,1 Guang He,2 Lei Zhang,1 Bao-cheng Liu1 1Shanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, People’s Republic of China; 2Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, People’s Republic of China; 3Zhangjiang Community Health Service Center of Pudong New District, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bao-cheng Liu Email baochliu@shutcm.edu.cnBackground: As fat and obesity play a vital role in the pathophysiology of metabolic dysfunction-associated fatty liver disease (MAFLD), this study aims to investigate the association between the fat mass and obesity-associated gene (FTO) and MAFLD.Methods: Six SNPs (rs6499640, rs1421085, rs8050136, rs3751812, rs9939609 and rs9930506) within FTO were genotyped for 741 MAFLD patients (median age, 69.98; interquartile range, 66.55– 75.93) and 825 healthy people (median age, 69.94; interquartile range, 66.39– 75.64). Allele and genotype frequencies, pairwise linkage disequilibrium (LD) and haplotype analysis were calculated.Results: BMI, waist circumference, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, triglyceride, alanine transaminase, glutamyl transpeptidase and the prevalence of diabetes were found to be higher in the MAFLD individuals comparing to the control ones (P < 0.05). For rs1421085, the C allele frequency was remarkably higher in MAFLD after Bonferroni correction (OR [95% CI] =1.353 [1.095– 1.671]; Pcorr =0.030), and a significantly different genotype result was observed in log-additive model (OR [95% CI] =1.369 [1.108– 1.691]; Pcorr =0.024). For rs8050136, significantly increased A allele frequency was observed in MAFLD (OR [95% CI] =1.371 [1.109– 1.695]; Pcorr =0.024), and A-allele carriers showed increased MAFLD risk (OR [95% CI] =1.393 [1.103– 1.759]; Pcorr =0.030). For rs3751812, the T allele frequency was remarkably higher in MAFLD (OR [95% CI] =1.369 [1.108– 1.691]; Pcorr =0.024), and T-allele carriers demonstrated high MAFLD risk (OR [95% CI] =1.392 [1.103– 1.756]; Pcorr =0.030). For rs9939609, A allele frequency was also remarkably high in MAFLD (OR [95% CI] =1.369 [1.108– 1.691]; Pcorr =0.024), and A-allele carriers were more susceptible to MAFLD (OR [95% CI] =[1.103– 1.756]; Pcorr =0.030). A strong LD was found among rs1421085, rs8050136, rs3751812 and rs9939609 (r2 > 0.8), and individuals with C-A-T-A haplotype had an elevated MAFLD risk (P =0.005).Conclusion: The case-control study indicated that C variant of rs1421085, A variant of rs8050136, T variant of rs3751812 and A variant of rs9939609 are associated with elevated MAFLD risk in the older Chinese Han population.Keywords: MAFLD, FTO, polymorphism, genetic association, susceptibility
Published: 2020

37. Human Adipose Tissue-Derived Mesenchymal Stem Cells in Parkinson’s Disease: Inhibition of T Helper 17 Cell Differentiation and Regulation of Immune Balance Towards a Regulatory T Cell Phenotype

Author: Bi Y, Lin X, Liang H, Yang D, Zhang X, Ke J, Xiao J, Chen Z, Chen W, Wang S, and Liu CF
Subjects: parkinson’s disease, adipose-derived mesenchymal stem cells, cd4+t cell, t helper 17 cell, t regulatory cell, peripheral blood mononuclear cells, leukemia inhibitory factor, Geriatrics, RC952-954.6
Abstract: Yong Bi,1– 3 Xiaobin Lin,4 Huazheng Liang,2 Dehao Yang,4 Xiaowei Zhang,4 Jianming Ke,4 Jingjing Xiao,2 Zhilin Chen,2 Weian Chen,4 Xu Zhang,4 Shaoshi Wang,2 Chun-Feng Liu1,3 1Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China; 2Department of Neurology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, People’s Republic of China; 3Institute of Neuroscience, Soochow University, Suzhou, People’s Republic of China; 4Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of ChinaCorrespondence: Chun-Feng Liu Email liuchunfeng@suda.edu.cnBackground: Parkinson’s disease (PD) is a neurodegenerative disorder displaying a typical neuroinflammation pathology that may result from an imbalance between regulatory T cells (Treg) and T helper 17 (Th17) cells. Human adipose tissue-derived mesenchymal stem cells (Ad-MSCs) exert immunomodulatory effects by inhibiting effector T cell responses and have been used to treat diverse immune disorders. We aimed to investigate the modulating effect of human Ad-MSCs on peripheral blood mononuclear cells (PBMCs) of patients with PD, focusing on differentiation into Th17 and Treg cells.Methods: We isolated human peripheral blood CD4+T cells and co-cultured them with Ad-MSCs at a ratio of 4:1 under either Th17 or Treg cell polarizing conditions for 4 days to detect the proportions of IL-17-producing CD4+T (Th17) and CD4+CD25+Foxp3+regulatory T (Treg) cells by flow cytometry. We also determined the mRNA expression levels of the retinoid-related orphan nuclear receptor (RORγt) transcription factor and those of interleukin-6 receptor (IL-6R), interleukin-23 receptor (IL-23R), leukemia inhibitory factor (LIF), and LIF receptor (LIFR) by quantitative reverse transcription PCR. We detected levels of cytokines in the supernatant (including LIF, IL-6, IL-23, IL-10, and TGF-β) using ELISA.Results: Our results showed that Ad-MSCs specifically inhibited the differentiation of PBMCs of patients with PD into IL-17-producing CD4+T cells by decreasing expressions of IL-6R, IL-23R, and RORγt (the key transcription factor for Th17 cells). Moreover, Ad-MSCs induced a functional CD4+CD25+Foxp3+T regulatory cell phenotype as evidenced by the secretion of IL-10. The levels of IL-6, IL-23, and TGF-β remained constant after co-culture under either the Th17 or the Treg cell polarizing condition. In addition, levels of LIF protein and its receptor mRNA were significantly increased under both polarizing conditions.Conclusion: The present in vitro study found that Ad-MSCs from healthy participants were able to correct the imbalance between Th17 and Treg found in PBMCs of PD patients, which were correlated with an increase in LIF secretion and a decrease in expression of IL-6R, IL-23R, and RORγt. These findings should be confirmed by in vivo experiments.Keywords: Parkinson’s disease, adipose-derived mesenchymal stem cells, CD4+T cell, T helper 17 cell, T regulatory cell, peripheral blood mononuclear cells, leukemia inhibitory factor
Published: 2020

38. Association between type 1 and type 2 diabetes and risk of non-Hodgkin's lymphoma: A meta-analysis of cohort studies

Author: Wang, Y., Liu, X., Yan, P., Bi, Y., Liu, Y., and Zhang, Z.-J.
Published: 2020
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39. Interactive effects of dietary vitamin K3 and Bacillus subtilis PB6 on the growth performance and tibia quality of broiler chickens with sex separate rearing

Author: Guo, S., Xv, J., Li, Y., Bi, Y., Hou, Y., and Ding, B.
Published: 2020
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40. Influence of the RF magnetic field on beam dynamics in SC200 cyclotron

Author: Karamysheva, G., Popov, D., Karamyshev, O., Malinin, V., Karamysheva, T., Shirkov, G., Shirkov, S., Bi, Y., Chen, G., Chuansong, W., Ding, K., and Song, Y.
Published: 2019
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41. Type 1 innate lymphoid cells are associated with type 2 diabetes

Author: Liu, F., Wang, H., Feng, W., Ye, X., Sun, X., Jiang, C., Chu, X., Zhang, P., Wang, Y., Zhu, D., and Bi, Y.
Published: 2019
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42. Trends and age-period-cohort effect on incidence and mortality of prostate cancer from 1990 to 2017 in China

Author: Liu, X., Yu, C., Bi, Y., and Zhang, Z.J.
Published: 2019
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43. Characterization of high-pressure die-cast hypereutectic Al-Si alloys based on microstructural distribution and fracture morphology

Author: Jiao, X.Y., Wang, J., Liu, C.F., Guo, Z.P., Tong, G.D., Ma, S.L., Bi, Y., Zhang, Y.F., and Xiong, S.M.
Published: 2019
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44. Model-informed drug development approach supporting approval of the 4-week (Q4W) dosing schedule for nivolumab (Opdivo) across multiple indications: a regulatory perspective

Author: Bi, Y., Liu, J., Furmanski, B., Zhao, H., Yu, J., Osgood, C., Ward, A., Keegan, P., Booth, B.P., Rahman, A., and Wang, Y.
Published: 2019
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45. Histological verification of seminiferous tubule structure injury a cause of asthenozoospermia in chicken

Author: Guo, Q., Xu, L., Bi, Y., Zhang, K., Xu, S., Yuan, Q., Xue, Y., Lu, W., Ren, L., Qiu, L., Zhang, Y., Xu, Q., Chang, G., and Chen, G.
Published: 2019
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46. The function of phytogenic mounds in the accumulation and conservation of soil seed banks in semiarid areas with water erosion.

Author: Nie, W. J., Du, H. D., Xie, S. S., and Bi, Y. L.
Subjects: SOIL seed banks, SOIL conservation, EROSION, PLANT canopies, COMPOSITION of seeds, PARTICLE size distribution, SOIL erosion, WATERSHEDS
Abstract: Background: Phytogenic mounds are a type of microtopography formed under perennial plants canopies in water erosion areas. However, the function of phytogenic mounds in seed assemblages and their ecological consequences remain poorly understood in semiarid areas with water erosion. Thus, understanding the characteristics of seed banks on mounds is crucial for ecosystem conservation and management in water-eroded areas. Methods: We compared the quantity and composition of soil seed banks on the upslope and downslope parts of mounds and intercanopy surfaces along four slope gradients. We also explored the relationships among the soil seed bank, aboveground vegetation, and environmental factors. Furthermore, the species similarity between the soil seed bank and aboveground vegetation was analyzed to clarify the important ecological consequences of phytogenic mounds for plant community construction in serious soil erosion area. Results: For slopes with a = 46.6%, the intercanopy surfaces had greater soil seed bank species composition, density, and diversity than did the phytogenic mounds, and these characteristics showed no significant differences between the upslope and downslope parts of the mounds. As the slope increased, the soil seed bank density and species composition increased on the upslope part of the mound, and reached a maximum for slopes with a > 70%, while the downslope part of the mound negatively effected on seed aggregation. The sediment accumulation rate, soil moisture, particle size distribution, pH, organic matter carbon, and hardness were significantly correlated with the soil seed bank density and diversity in the study area. For slopes with 0 < a = 26.8%, the species similarity coefficient between the soil seed bank and aboveground vegetation was the highest for the intercanopy surface. This species similarity on the upslope part of the mound showed an increasing trend with increasing slope gradient, while the downslope part of the mound had the opposite trend. For slopes with a > 70%, the upslope part of the mound did not only have more species in the soil seed bank but also had more species in aboveground vegetation than did the downslope part of the mound and intercanopy surface. Conclusion: For slopes with a = 46.6%, phytogenic mounds had barely impact soil seed bank accumulation and conservation in semiarid and eroded areas. For slopes with a > 46.6%, the mounds (particularly on the upslope part of the mound) showed seed assemblage functions, which are coupled with improving edaphic conditions and decreasing microhabitat stress; thus, phytogenic mounds, or areas of microtopography, can be used to promote restoration success in semiarid eroded areas. [ABSTRACT FROM AUTHOR]
Published: 2024
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47. Changes in flavour- and taste-presenting substances in Hypsizygus marmoreus mushroom packaged in polypropylene nanofilm.

Author: Wang, B., He, K., Yun, J., Zhao, Y., Qu, Y., Bi, Y., and Yao, L.
Subjects: ORGANIC acids, TREHALOSE, HIGH performance liquid chromatography, LIQUID chromatography-mass spectrometry, GAS chromatography/Mass spectrometry (GC-MS), POLYPROPYLENE, GLUTAMIC acid
Abstract: Hypsizygus marmoreus mushroom is known for its pleasant flavour and delicious taste. However, its flavour quality deteriorates rapidly during postharvest storage. In the present work, we explored the dynamic changes in flavour-presenting substances and taste components in H. marmoreus packaged in polyethylene nanofilm (0.05 mm) during low temperature storage (12 days). We used headspace solid-phase microextraction combined with gas chromatography-mass spectrometry to analyse the volatile flavour components; high performance liquid chromatography and liquid chromatography-mass spectrometry to analyse the non-volatile flavour (taste-presenting) substances (e.g., soluble sugars, taste nucleotides, free amino acids, and organic acids); and the relative odour activity value (ROAV) and taste active value (TAV) methods to determine the flavour substances in fresh and stored H. marmoreus. Results revealed that n-octanal, 1-octene-3-one, and 1- octene-3-ol were the characteristic volatile flavour substances, and that trans-2-octenal was the key substance affecting the formation of volatile flavours in fresh H. marmoreus. Trans,trans-2,4-nonadienal, and 3-octanone were the key volatile substances that resulted in flavour deterioration. Among the non-volatile flavour substances, trehalose, 5'-GMP, glutamic acid, alanine, and arginine were the key taste-presenting substances. In addition, oxalic acid and lysine were the key taste substances after H. marmoreus deterioration. These results provided a theoretical basis for rapid quality detection, flavour identification, and shelf-life prediction of H. marmoreus. [ABSTRACT FROM AUTHOR]
Published: 2024
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48. Low serum bilirubin levels contribute to the presence and progression of distal symmetrical polyneuropathy in Chinese patients with type 2 diabetes

Author: Jin, J., Wang, W., Gu, T., Chen, C., Sun, J., Chen, W., Bi, Y., and Zhu, D.
Published: 2019
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49. Different trends in colorectal cancer mortality between age groups in China: an age-period-cohort and joinpoint analysis

Author: Liu, X., Bi, Y., Wang, H., Meng, R., Zhou, W., Zhang, G., Yu, C., and Zhang, Z.-J.
Published: 2019
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50. Construction and on-site performance of the LHAASO WFCTA camera

Author: Aharonian, F., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, H., Cai, J. T., Cao, Z., Cao, Z., Chang, J., Chang, J. F., Chang, X. C., Chen, B. M., Chen, J., Chen, L., Chen, L., Chen, L., Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Volpe, D. della, Piazzoli, B. D’Ettorre, Dong, X. J., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, Y. L., Gao, B., Gao, C. D., Gao, Q., Gao, W., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. C., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hou, X., Hu, H. B., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, Q. L., Huang, W. H., Huang, X. T., Huang, Z. C., Ji, F., Ji, X. L., Jia, H. Y., Jiang, K., Jiang, Z. J., Jin, C., Kuleshov, D., Levochkin, K., Li, B. B., Li, C., Li, C., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, K., Li, W. L., Li, X., Li, X., Li, X. R., Li, Y., Li, Y. Z., Li, Z., Li, Z., Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y. N., Liu, Z. X., Long, W. J., Lu, R., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Masood, A., Mitthumsiri, W., Montaruli, T., Nan, Y. C., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Ruffolo, D., Rulev, V., Sáiz, A., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. R., Song, H. C., Stenkin, Yu. V., Stepanov, V., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Z. B., Tian, W. W., Wang, B. D., Wang, C., Wang, H., Wang, H. G., Wang, J. C., Wang, J. S., Wang, L. P., Wang, L. Y., Wang, R. N., Wang, W., Wang, W., Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z., Wang, Z., Wang, Z. H., Wang, Z. X., Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, W. X., Wu, X. F., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, G., Xiao, H. B., Xin, G. G., Xin, Y. L., Xing, Y., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., Yang, C. W., Yang, F. F., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Zeng, H. D., Zeng, T. X., Zeng, W., Zeng, Z. K., Zha, M., Zhai, X. X., Zhang, B. B., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, J. W., Zhang, L., Zhang, L., Zhang, L. X., Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S S., Zhang, X., Zhang, X. P., Zhang, Y., Zhang, Y., Zhang, Y. F., Zhang, Y. L., Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, Y., Zhou, B., Zhou, H., Zhou, J. N., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Published: 2021
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