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Hu J, Chen J, Ma T, Li Z, Hu J, Ma T, Li Z. Research advances in ZnO nanomaterials-based UV photode tectors: a review. Nanotechnology 2023; 34:232002. [PMID: 36848670 DOI: 10.1088/1361-6528/acbf59] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Ultraviolet photodetectors (UV PDs) have always been the research focus of semiconductor optoelectronic devices due to their wide application fields and diverse compositions. As one of the best-known n-type metal oxides in third-generation semiconductor electronic devices, ZnO nanostructures and their assembly with other materials have received extensive research. In this paper, the research progress of different types of ZnO UV PDs is reviewed, and the effects of different nanostructures on ZnO UV PDs are summarized in detail. In addition, physical effects such as piezoelectric photoelectric effect, pyroelectric effect, and three ways of heterojunction, noble metal local surface plasmon resonance enhancement and formation of ternary metal oxides on the performance of ZnO UV PDs were also investigated. The applications of these PDs in UV sensing, wearable devices, and optical communication are displayed. Finally, the possible opportunities and challenges for the future development of ZnO UV PDs are prospected.
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Affiliation(s)
- Jinning Hu
- School of Science, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
| | - Jun Chen
- Key Laboratory of Advanced Displaying Materials and Devices, Ministry of Industry and Information Technology, School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
| | - Teng Ma
- School of Science, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
| | - Zhenhua Li
- School of Science, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
| | - J Hu
- School of Science, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
| | - T Ma
- School of Science, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
| | - Z Li
- School of Science, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
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Zhang M, Li Z, Pei W, Li X, Yang H, Zhu X, Lü K. [M2 macrophage-derived exosomal lncRNA NR_028113.1 promotes macrophage polarization possibly by activating the JAK2/STAT3 signaling pathway]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:393-399. [PMID: 37087583 PMCID: PMC10122731 DOI: 10.12122/j.issn.1673-4254.2023.03.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 04/24/2023]
Abstract
OBJECTIVE To explore the effect of M2 macrophage-derived exosomal lncRNA NR_028113.1 on macrophage polarization and its possible mechanism. METHODS Bone marrow-derived macrophages (BMDMs) from BALB/c mice were isolated and cultured in vitro. After IL-4 treatment to induce M2 macrophage polarization, exosomes (M2-exo) were extracted from the supernatant of M2 macrophages and identified. The expression of lncRNA in M2-exo was detected by qRT-PCR. BMDMs were co-cultured with M2-exo (100 μg/mL) or PBS for 48 h, and the changes in cellular expression levels of Arg1, YM-1, FIZZ1, iNOS and TNF-α were detected using qRT-PCR and Western blotting. The percentage of CD206+ cells was analyzed using flow cytometry, and the phosphorylation levels of JAK2/STAT3 proteins were detected using Western blotting. A lncRNA smart silencer was designed to specifically inhibit the expression of lncRNA NR_028113.1 in the M2 macrophages, from which exosomes were extracted and co-cultured with BMDMs for 48 h. The mRNA expression levels of Arg1, YM-1, FIZZ1, iNOS and TNF-α, CD206+ cell percentage and the phosphorylation levels of JAK2/STAT3 proteins were detected using qRT-PCR, flow cytometry and Western blotting. RESULTS LncRNA NR_028113.1 was highly expressed in the exosomes of M2 macrophages (P < 0.05). Co-culture with M2-exo significantly increased mRNA expressions of M2 macrophage marker genes Arg1, YM-1 and FIZZ1 (P < 0.05), lowered the expressions of iNOS and TNF-α (P < 0.05), and increased CD206+ cell percentage and JAK2/STAT3 protein phosphorylation level in BMDMs (P < 0.05). After inhibiting the expression of lncRNA NR_028113.1 in M2 macrophages, the extracted M2-exo caused significant down-regulation of the mRNA expressions of Arg1, YM-1 and FIZZ1 and up-regulation of iNOS and TNF-α mRNA (P < 0.05), resulting also in signi-ficantly reduced CD206+ cell percentage and lowered phosphorylation levels of JAK2/STAT3 proteins in co-cultured BMDM (P < 0.05). CONCLUSIONS M2 macrophage-derived exosomal lncRNA NR_028113.1 can significantly promote M2 polarization of macrophages possibly by activating the JAK2/STAT3 signaling pathway.
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Affiliation(s)
- M Zhang
- Key Laboratory of Non- coding RNA Transformation Research of Anhui Higher Education Institution, Wannan Medical College, Wuhu 241001, China
- Central Laboratory, Yijishan Hospital, Wannan Medical College, Wuhu 241001, China
| | - Z Li
- Department of Rheumatology, Yijishan Hospital, Wannan Medical College, Wuhu 241001, China
| | - W Pei
- Key Laboratory of Non- coding RNA Transformation Research of Anhui Higher Education Institution, Wannan Medical College, Wuhu 241001, China
- Central Laboratory, Yijishan Hospital, Wannan Medical College, Wuhu 241001, China
| | - X Li
- Key Laboratory of Non- coding RNA Transformation Research of Anhui Higher Education Institution, Wannan Medical College, Wuhu 241001, China
- Central Laboratory, Yijishan Hospital, Wannan Medical College, Wuhu 241001, China
| | - H Yang
- Key Laboratory of Non- coding RNA Transformation Research of Anhui Higher Education Institution, Wannan Medical College, Wuhu 241001, China
- Central Laboratory, Yijishan Hospital, Wannan Medical College, Wuhu 241001, China
| | - X Zhu
- Key Laboratory of Non- coding RNA Transformation Research of Anhui Higher Education Institution, Wannan Medical College, Wuhu 241001, China
- Central Laboratory, Yijishan Hospital, Wannan Medical College, Wuhu 241001, China
| | - K Lü
- Key Laboratory of Non- coding RNA Transformation Research of Anhui Higher Education Institution, Wannan Medical College, Wuhu 241001, China
- Central Laboratory, Yijishan Hospital, Wannan Medical College, Wuhu 241001, China
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153
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Aboona BE, Adam J, Adamczyk L, Adams JR, Aggarwal I, Aggarwal MM, Ahammed Z, Anderson DM, Aschenauer EC, Atchison J, Bairathi V, Baker W, Ball Cap JG, Barish K, Bellwied R, Bhagat P, Bhasin A, Bhatta S, Bielcik J, Bielcikova J, Brandenburg JD, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chaloupka P, Chan BK, Chang Z, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Dale-Gau G, Das A, Daugherity M, Deppner IM, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Guryn W, Hamed A, Han Y, Harabasz S, Harasty MD, Harris JW, Harrison H, He W, He XH, He Y, Heppelmann S, Herrmann N, Holub L, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Jentsch A, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kelsey M, Khyzhniak YV, Kikoła DP, Kimelman B, Kincses D, Kisel I, Kiselev A, Knospe AG, Ko HS, Kosarzewski LK, Kramarik L, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Lacey R, Landgraf JM, Lauret J, Lebedev A, Lee JH, Leung YH, Lewis N, Li C, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Licenik R, Lin T, Lisa MA, Liu C, Liu F, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd E, Lu T, Lukow NS, Luo XF, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, McNamara G, Mi K, Mioduszewski S, Mohanty B, Mooney I, Mukherjee A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Niida T, Nishitani R, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Pani T, Paul A, Pawlik B, Pawlowska D, Perkins C, Pluta J, Pokhrel BR, Posik M, Protzman T, Prozorova V, Pruthi NK, Przybycien M, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Reed R, Ritter HG, Robertson CW, Robotkova M, Romero JL, Rosales Aguilar MA, Roy D, Roy Chowdhury P, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Sato S, Schmidke WB, Schmitz N, Seck FJ, Seger J, Seto R, Seyboth P, Shah N, Shanmuganathan PV, Shao M, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Smirnov N, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Stringfellow B, Su Y, Suaide AAP, Sumbera M, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Sweger ZW, Szymanski P, Tamis A, Tang AH, Tang Z, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Todoroki T, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Truhlar T, Trzeciak BA, Tsai OD, Tsang CY, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vassiliev I, Verkest V, Videbæk F, Voloshin SA, Wang F, Wang G, Wang JS, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wielanek D, Wieman H, Wilks G, Wissink SW, Witt R, Wu J, Wu J, Wu X, Wu Y, Xi B, Xiao ZG, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Measurement of Sequential ϒ Suppression in Au+Au Collisions at sqrt[s_{NN}]=200 GeV with the STAR Experiment. Phys Rev Lett 2023; 130:112301. [PMID: 37001106 DOI: 10.1103/physrevlett.130.112301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/30/2022] [Accepted: 01/26/2023] [Indexed: 06/19/2023]
Abstract
We report on measurements of sequential ϒ suppression in Au+Au collisions at sqrt[s_{NN}]=200 GeV with the STAR detector at the Relativistic Heavy Ion Collider (RHIC) through both the dielectron and dimuon decay channels. In the 0%-60% centrality class, the nuclear modification factors (R_{AA}), which quantify the level of yield suppression in heavy-ion collisions compared to p+p collisions, for ϒ(1S) and ϒ(2S) are 0.40±0.03(stat)±0.03(sys)±0.09(norm) and 0.26±0.08(stat)±0.02(sys)±0.06(norm), respectively, while the upper limit of the ϒ(3S) R_{AA} is 0.17 at a 95% confidence level. This provides experimental evidence that the ϒ(3S) is significantly more suppressed than the ϒ(1S) at RHIC. The level of suppression for ϒ(1S) is comparable to that observed at the much higher collision energy at the Large Hadron Collider. These results point to the creation of a medium at RHIC whose temperature is sufficiently high to strongly suppress excited ϒ states.
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Affiliation(s)
- B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Adamczyk
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | | | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - R Bellwied
- University of Houston, Houston, Texas 77204
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - J Bielcik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J Bielcikova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | | | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - P Chaloupka
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - M Csanád
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S Fazio
- University of Calabria & INFN-Cosenza, Rende 87036, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | | | - T Galatyuk
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - W Guryn
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - S Harabasz
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | - H Harrison
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - W He
- Fudan University, Shanghai 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - S Heppelmann
- University of California, Davis, California 95616
| | - N Herrmann
- University of Heidelberg, Heidelberg 69120, Germany
| | - L Holub
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - C Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - A Jentsch
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - C Jin
- Rice University, Houston, Texas 77251
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - S Kagamaster
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | | | - D P Kikoła
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B Kimelman
- University of California, Davis, California 95616
| | - D Kincses
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - I Kisel
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L K Kosarzewski
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kramarik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Lauret
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - C Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - R Licenik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - M A Lisa
- The Ohio State University, Columbus, Ohio 43210
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - L Ma
- Fudan University, Shanghai 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - C Markert
- University of Texas, Austin, Texas 78712
| | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | | | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - A Mukherjee
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - A S Nunes
- Brookhaven National Laboratory, Upton, New York 11973
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - A Paul
- University of California, Riverside, California 92521
| | - B Pawlik
- Institute of Nuclear Physics PAN, Cracow 31-342, Poland
| | - D Pawlowska
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Perkins
- University of California, Berkeley, California 94720
| | - J Pluta
- Warsaw University of Technology, Warsaw 00-661, Poland
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- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - V Prozorova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - M Przybycien
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - R Reed
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - M Robotkova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J L Romero
- University of California, Davis, California 95616
| | | | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | | | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
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- Max-Planck-Institut für Physik, Munich 80805, Germany
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- Technische Universität Darmstadt, Darmstadt 64289, Germany
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- Creighton University, Omaha, Nebraska 68178
| | - R Seto
- University of California, Riverside, California 92521
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- Max-Planck-Institut für Physik, Munich 80805, Germany
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- Indian Institute Technology, Patna, Bihar 801106, India
| | | | - M Shao
- University of Science and Technology of China, Hefei, Anhui 230026
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- Fudan University, Shanghai 200433
| | - M Sharma
- University of Jammu, Jammu 180001, India
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- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Y Shen
- Fudan University, Shanghai 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana 47306
- Purdue University, West Lafayette, Indiana 47907
| | - N Smirnov
- Yale University, New Haven, Connecticut 06520
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- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - M Stefaniak
- The Ohio State University, Columbus, Ohio 43210
| | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A A P Suaide
- Universidade de São Paulo, São Paulo, Brazil 05314-970
| | - M Sumbera
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - Z W Sweger
- University of California, Davis, California 95616
| | - P Szymanski
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Truhlar
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B A Trzeciak
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- Rice University, Houston, Texas 77251
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Vanek
- Brookhaven National Laboratory, Upton, New York 11973
| | - I Vassiliev
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - D Wielanek
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - R Witt
- United States Naval Academy, Annapolis, Maryland 21402
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - X Wu
- University of California, Los Angeles, California 90095
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - H Zbroszczyk
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Lin L, Wang HJ, Mei SY, Li Z, Cheng YB. [A case of simultaneous haploinsufficiency of A20 and methylmalonic aciduria]. Zhonghua Er Ke Za Zhi 2023; 61:266-268. [PMID: 36849356 DOI: 10.3760/cma.j.cn112140-20220811-00722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Affiliation(s)
- L Lin
- Department of Emergency, Children's Hospital Affiliated to Zhengzhou University,Henan Province Engineering Research Center of Diagnosis and Treatment of Pediatric Infection and Critical Care,Zhengzhou 450000, China
| | - H J Wang
- Department of Emergency, Children's Hospital Affiliated to Zhengzhou University,Henan Province Engineering Research Center of Diagnosis and Treatment of Pediatric Infection and Critical Care,Zhengzhou 450000, China
| | - S Y Mei
- Department of Emergency, Children's Hospital Affiliated to Zhengzhou University,Henan Province Engineering Research Center of Diagnosis and Treatment of Pediatric Infection and Critical Care,Zhengzhou 450000, China
| | - Z Li
- Department of Pediatric Intensive Care Unit,Beijing Children's Hospital, National Center for Children's Hospital, Beijing 100045,China
| | - Y B Cheng
- Department of Pediatric Intensive Care Unit, Henan Children's Hospital, Zhengzhou 450000, China
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155
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Wang ZH, Liu G, Fan CN, Wang XD, Liu XH, Su J, Gao HM, Qian SY, Li Z, Cheng YB. [Diagnosis and treatment of pediatric septic shock in pediatric intensive care units from hospitals of different levels]. Zhonghua Er Ke Za Zhi 2023; 61:209-215. [PMID: 36849346 DOI: 10.3760/cma.j.cn112140-20221028-00916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Objective: To investigate the differences in clinical characteristics, diagnosis, and treatment of pediatric septic shock in pediatric intensive care unit (PICU) among hospitals of different levels. Methods: This retrospective study enrolled 368 children with septic shock treated in the PICU of Beijing Children's Hospital, Henan Children's Hospital, and Baoding Children's Hospital from January 2018 to December 2021. Their clinical data were collected, including the general information, location of onset (community or hospital-acquired), severity, pathogen positivity, consistence of guideline (the rate of standard attainment at 6 h after resuscitation and the rate of anti-infective drug administration within 1 h after diagnosis), treatment, and in-hospital mortality. The 3 hospitals were national, provincial, and municipal, respectively. Furthermore, the patients were divided into the tumor group and the non-tumor group, and into the in-hospital referral group and the outpatient or emergency admission group. Chi-square test and Mann-Whitney U test were used to analyze the data. Results: The 368 patients aged 32 (11, 98) months, of whom 223 were males and 145 females. There were 215, 107, and 46 patients with septic shock, with males of 141, 51, and 31 cases, from the national, provincial, and municipal hospitals, respectively. The difference in pediatric risk of mortality Ⅲ (PRISM Ⅲ) scores among the national,provincial and municipal group was statistically significant (26(19, 32) vs.19(12, 26) vs. 12(6, 19), Z=60.25,P<0.001). The difference in community acquired septic shock among the national,provincial and municipal group was statistically significant (31.6%(68/215) vs. 84.1%(90/107) vs. 91.3%(42/46), χ2=108.26,P<0.001). There were no significant differences in compliance with guidelines among the 3 groups (P>0.05). The main bacteria detected in the national group were Klebsiella pneumoniae (15.4% (12/78)) and Staphylococcus aureus (15.4% (12/78)); in the provincial group were Staphylococcus aureus (19.0% (12/63)) and Pseudomonas aeruginosa (12.7% (8/63)), and in the municipal group were Streptococcus pneumoniae (40.0% (10/25)) and Enteric bacilli (16.0% (4/25)). The difference in the proportion of virus and the proportion of 3 or more initial antimicrobials used among the national,provincial and municipal group was statistically significant (27.7% (43/155) vs. 14.9% (13/87) vs. 9.1% (3/33), 22.8%(49/215) vs. 11.2%(12/107) vs. 6.5%(3/46), χ2=8.82, 10.99, both P<0.05). There was no difference in the in-hospital mortality among the 3 groups (P>0.05). Regarding the subgroups of tumor and non-tumor, the national group had higher PRISM Ⅲ (31(24, 38) vs. 22 (21, 28) vs.16 (9, 22), 24 (18, 30) vs. 17(8, 24) vs. 10 (5, 16), Z=30.34, 10.45, both P<0.001), and it was the same for the subgroups of in-hospital referral and out-patient or emergency admission (29 (21, 39) vs. 23 (17, 30) vs. 15 (10, 29), 23 (17, 29) vs. 18 (10, 24) vs. 11 (5, 16), Z=20.33, 14.25, both P<0.001) as compared to the provincial and municipal group. There was no significant difference in the in-hospital mortality among the 2 pairs of subgroups (all P>0.05). Conclusion: There are differences in the severity, location of onset, pathogen composition, and initial antibiotics of pediatric septic shock in children's hospitals of different levels, but no differences in compliance with guidelines and in-hospital survival rate.
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Affiliation(s)
- Z H Wang
- Baoding Research Laboratory of Pediatric Severe Infectious Diseases, Department of Pediatric Intensive Care Medicine, Baoding Children's Hospital, Baoding 071051, China
| | - G Liu
- Department of Pediatric Intensive Care Unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - C N Fan
- Department of Pediatric Intensive Care Unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - X D Wang
- Department of Pediatric Intensive Care Unit, Henan Children's Hospital, Zhengzhou 450000, China
| | - X H Liu
- Baoding Research Laboratory of Pediatric Severe Infectious Diseases, Department of Pediatric Intensive Care Medicine, Baoding Children's Hospital, Baoding 071051, China
| | - J Su
- Department of Pediatric Intensive Care Unit, Henan Children's Hospital, Zhengzhou 450000, China
| | - H M Gao
- Department of Pediatric Intensive Care Unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - S Y Qian
- Department of Pediatric Intensive Care Unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Z Li
- Department of Pediatric Intensive Care Unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Y B Cheng
- Department of Pediatric Intensive Care Unit, Henan Children's Hospital, Zhengzhou 450000, China
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Yang J, Niu H, Pang S, Liu M, Chen F, Li Z, He L, Mo J, Yi H, Xiao J, Huang Y. MARK3 kinase: Regulation and physiologic roles. Cell Signal 2023; 103:110578. [PMID: 36581219 DOI: 10.1016/j.cellsig.2022.110578] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/27/2022]
Abstract
Microtubule affinity-regulating kinase 3 (MARK3), a member of the MARK family, regulates several essential pathways, including the cell cycle, ciliated cell differentiation, and osteoclast differentiation. It is important to understand the control of their activities as MARK3 contains an N-terminal serine/threonine kinase domain, ubiquitin-associated domain, and C-terminal kinase-associated domain, which perform multiple regulatory functions. These functions include post-translational modification (e.g., phosphorylation) and interaction with scaffolding and other proteins. Differences in the amino acid sequence and domain position result in different three-dimensional protein structures and affect the function of MARK3, which distinguish it from the other MARK family members. Recent data indicate a potential role of MARK3 in several pathological conditions, including congenital blepharophimosis syndrome, osteoporosis, and tumorigenesis. The present review focuses on the physiological and pathological role of MARK3, its regulation, and recent developments in the small molecule inhibitors of the MARK3 signalling cascade.
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Affiliation(s)
- Jingyu Yang
- Surgery of Mammary Gland and Thyroid Gland, the First People's Hospital of Yunnan Province, Panlong Campus, 157 Jinbi Road, Kunming 650032, Yunnan, People's Republic of China
| | - Heng Niu
- Surgery of Mammary Gland and Thyroid Gland, the First People's Hospital of Yunnan Province, Panlong Campus, 157 Jinbi Road, Kunming 650032, Yunnan, People's Republic of China
| | - ShiGui Pang
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China
| | - Mignlong Liu
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China
| | - Feng Chen
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China
| | - Zhaoxin Li
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China
| | - Lifei He
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China
| | - Jianmei Mo
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China
| | - Huijun Yi
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China
| | - Juanjuan Xiao
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China
| | - Yingze Huang
- Cancer Research Institute, The Affiliated Hospital of Guilin Medical University, Xiufeng Campus, 15 Lequn Road, Guilin 541001, Guangxi, People's Republic of China.
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Chan J, Lee E, Li Z, Lee J, Lim A, Poon E. 104P Multi-omic profiling and real-time ex vivo modelling of imatinib-resistant dermatofibrosarcoma protuberans with fibrosarcomatous transformation. ESMO Open 2023. [DOI: 10.1016/j.esmoop.2023.101141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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158
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Wang R, Baxi V, Li Z, Locke D, Hedvat C, Sun Y, Walsh AM, Shao X, Basavanhally T, Greenawalt DM, Patah P, Novosiadly R. Pharmacodynamic activity of BMS-986156, a glucocorticoid-induced TNF receptor-related protein agonist, alone or in combination with nivolumab in patients with advanced solid tumors. ESMO Open 2023; 8:100784. [PMID: 36863094 PMCID: PMC10163007 DOI: 10.1016/j.esmoop.2023.100784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 11/02/2022] [Accepted: 01/04/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND The success of immune checkpoint inhibitors has revolutionized cancer treatment options and triggered development of new complementary immunotherapeutic strategies, including T-cell co-stimulatory molecules, such as glucocorticoid-induced tumor necrosis factor receptor-related protein (GITR). BMS-986156 is a fully agonistic human immunoglobulin G subclass 1 monoclonal antibody targeting GITR. We recently presented the clinical data for BMS-986156 with or without nivolumab, which demonstrated no compelling evidence of clinical activity in patients with advanced solid tumors. Here, we further report the pharmacodynamic (PD) biomarker data from this open-label, first-in-human, phase I/IIa study of BMS-986156 ± nivolumab in patients with advanced solid tumors (NCT02598960). MATERIALS AND METHODS We analyzed PD changes of circulating immune cell subsets and cytokines in peripheral blood or serum samples collected from a dataset of 292 patients with solid tumors before and during treatment with BMS-986156 ± nivolumab. PD changes in the tumor immune microenvironment were measured by immunohistochemistry and a targeted gene expression panel. RESULTS BMS-986156 + nivolumab induced a significant increase in peripheral T-cell and natural killer (NK) cell proliferation and activation, accompanied by production of proinflammatory cytokines. However, no significant changes in expression of CD8A, programmed death-ligand 1, tumor necrosis factor receptor superfamily members, or key genes linked with functional parameters of T and NK cells were observed in tumor tissue upon treatment with BMS-986156. CONCLUSIONS Despite the robust evidence of peripheral PD activity of BMS-986156, with or without nivolumab, limited evidence of T- or NK cell activation in the tumor microenvironment was observed. The data therefore explain, at least in part, the lack of clinical activity of BMS-986156 with or without nivolumab in unselected populations of cancer patients.
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Affiliation(s)
- R Wang
- Translational Medicine, Bristol Myers Squibb, Lawrenceville, USA
| | - V Baxi
- Informatics & Predictive Sciences, Bristol Myers Squibb, Lawrenceville, USA
| | - Z Li
- Lead Discovery and Optimization, Bristol Myers Squibb, Lawrenceville, USA
| | - D Locke
- Translational Medicine, Bristol Myers Squibb, Lawrenceville, USA
| | - C Hedvat
- Translational Medicine, Bristol Myers Squibb, Lawrenceville, USA
| | - Y Sun
- Translational Medicine, Bristol Myers Squibb, Lawrenceville, USA
| | - A M Walsh
- Informatics & Predictive Sciences, Bristol Myers Squibb, Lawrenceville, USA
| | - X Shao
- Translational Medicine, Bristol Myers Squibb, Lawrenceville, USA
| | - T Basavanhally
- Informatics & Predictive Sciences, Bristol Myers Squibb, Lawrenceville, USA
| | - D M Greenawalt
- Informatics & Predictive Sciences, Bristol Myers Squibb, Lawrenceville, USA
| | - P Patah
- Global Clinical Research, Bristol Myers Squibb, Lawrenceville, USA
| | - R Novosiadly
- Translational Medicine, Bristol Myers Squibb, Lawrenceville, USA.
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Aboona BE, Adam J, Adamczyk L, Adams JR, Aggarwal I, Aggarwal MM, Ahammed Z, Anderson DM, Aschenauer EC, Atchison J, Bairathi V, Baker W, Ball Cap JG, Barish K, Bellwied R, Bhagat P, Bhasin A, Bhatta S, Bielcik J, Bielcikova J, Brandenburg JD, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chaloupka P, Chan BK, Chang Z, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Dale-Gau G, Das A, Daugherity M, Deppner IM, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Guryn W, Hamed A, Han Y, Harabasz S, Harasty MD, Harris JW, Harrison H, He W, He XH, He Y, Heppelmann S, Herrmann N, Holub L, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Jentsch A, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kelsey M, Khyzhniak YV, Kikoła DP, Kimelman B, Kincses D, Kisel I, Kiselev A, Knospe AG, Ko HS, Kosarzewski LK, Kramarik L, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Lacey R, Landgraf JM, Lauret J, Lebedev A, Lee JH, Leung YH, Lewis N, Li C, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Licenik R, Lin T, Lisa MA, Liu C, Liu F, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd E, Lu T, Lukow NS, Luo XF, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, McNamara G, Mi K, Mioduszewski S, Mohanty B, Mooney I, Mukherjee A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Niida T, Nishitani R, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Pani T, Paul A, Pawlik B, Pawlowska D, Perkins C, Pluta J, Pokhrel BR, Posik M, Protzman T, Prozorova V, Pruthi NK, Przybycien M, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Reed R, Ritter HG, Robertson CW, Robotkova M, Romero JL, Rosales Aguilar MA, Roy D, Roy Chowdhury P, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Sato S, Schmidke WB, Schmitz N, Seck FJ, Seger J, Seto R, Seyboth P, Shah N, Shanmuganathan PV, Shao M, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Smirnov N, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Stringfellow B, Su Y, Suaide AAP, Sumbera M, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Sweger ZW, Szymanski P, Tamis A, Tang AH, Tang Z, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Todoroki T, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Truhlar T, Trzeciak BA, Tsai OD, Tsang CY, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vassiliev I, Verkest V, Videbæk F, Voloshin SA, Wang F, Wang G, Wang JS, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wielanek D, Wieman H, Wilks G, Wissink SW, Witt R, Wu J, Wu J, Wu X, Wu Y, Xi B, Xiao ZG, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Beam Energy Dependence of Fifth- and Sixth-Order Net-Proton Number Fluctuations in Au+Au Collisions at RHIC. Phys Rev Lett 2023; 130:082301. [PMID: 36898098 DOI: 10.1103/physrevlett.130.082301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/21/2022] [Accepted: 01/11/2023] [Indexed: 06/18/2023]
Abstract
We report the beam energy and collision centrality dependence of fifth and sixth order cumulants (C_{5}, C_{6}) and factorial cumulants (κ_{5}, κ_{6}) of net-proton and proton number distributions, from center-of-mass energy (sqrt[s_{NN}]) 3 GeV to 200 GeV Au+Au collisions at RHIC. Cumulant ratios of net-proton (taken as proxy for net-baryon) distributions generally follow the hierarchy expected from QCD thermodynamics, except for the case of collisions at 3 GeV. The measured values of C_{6}/C_{2} for 0%-40% centrality collisions show progressively negative trend with decreasing energy, while it is positive for the lowest energy studied. These observed negative signs are consistent with QCD calculations (for baryon chemical potential, μ_{B}≤110 MeV) which contains the crossover transition range. In addition, for energies above 7.7 GeV, the measured proton κ_{n}, within uncertainties, does not support the two-component (Poisson+binomial) shape of proton number distributions that would be expected from a first-order phase transition. Taken in combination, the hyperorder proton number fluctuations suggest that the structure of QCD matter at high baryon density, μ_{B}∼750 MeV at sqrt[s_{NN}]=3 GeV is starkly different from those at vanishing μ_{B}∼24 MeV at sqrt[s_{NN}]=200 GeV and higher collision energies.
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Affiliation(s)
- B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Adamczyk
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J R Adams
- Ohio State University, Columbus, Ohio 43210
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | | | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - R Bellwied
- University of Houston, Houston, Texas 77204
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - J Bielcik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J Bielcikova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | | | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - P Chaloupka
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - M Csanád
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S Fazio
- University of Calabria & INFN-Cosenza, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | | | - T Galatyuk
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - W Guryn
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - S Harabasz
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | - H Harrison
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - S Heppelmann
- University of California, Davis, California 95616
| | - N Herrmann
- University of Heidelberg, Heidelberg 69120, Germany
| | - L Holub
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - C Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | | | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - A Jentsch
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - C Jin
- Rice University, Houston, Texas 77251
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - S Kagamaster
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | | | - D P Kikoła
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B Kimelman
- University of California, Davis, California 95616
| | - D Kincses
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - I Kisel
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L K Kosarzewski
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kramarik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Lauret
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - C Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - R Licenik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - M A Lisa
- Ohio State University, Columbus, Ohio 43210
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - C Markert
- University of Texas, Austin, Texas 78712
| | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | | | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - A Mukherjee
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - A S Nunes
- Brookhaven National Laboratory, Upton, New York 11973
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - A Paul
- University of California, Riverside, California 92521
| | - B Pawlik
- Institute of Nuclear Physics PAN, Cracow 31-342, Poland
| | - D Pawlowska
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Perkins
- University of California, Berkeley, California 94720
| | - J Pluta
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - V Prozorova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - M Przybycien
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - R Reed
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - M Robotkova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J L Romero
- University of California, Davis, California 95616
| | | | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | | | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Schmitz
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - F-J Seck
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - R Seto
- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | | | - M Shao
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Shao
- Fudan University, Shanghai, 200433
| | - M Sharma
- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Purdue University, West Lafayette, Indiana 47907
- Ball State University, Muncie, Indiana, 47306
| | - N Smirnov
- Yale University, New Haven, Connecticut 06520
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- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
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- Purdue University, West Lafayette, Indiana 47907
| | | | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A A P Suaide
- Universidade de São Paulo, São Paulo, Brazil 05314-970
| | - M Sumbera
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - Z W Sweger
- University of California, Davis, California 95616
| | - P Szymanski
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Truhlar
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B A Trzeciak
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Valparaiso University, Valparaiso, Indiana 46383
- Argonne National Laboratory, Argonne, Illinois 60439
| | - I Upsal
- Rice University, Houston, Texas 77251
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Vanek
- Brookhaven National Laboratory, Upton, New York 11973
| | - I Vassiliev
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - D Wielanek
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - R Witt
- United States Naval Academy, Annapolis, Maryland 21402
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - X Wu
- University of California, Los Angeles, California 90095
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - H Zbroszczyk
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Cui Z, Zhang W, Li Z, Wang Z. Spatial–temporal transformer for end-to-end sign language recognition. COMPLEX INTELL SYST 2023. [DOI: 10.1007/s40747-023-00977-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
AbstractContinuous sign language recognition (CSLR) is an essential task for communication between hearing-impaired and people without limitations, which aims at aligning low-density video sequences with high-density text sequences. The current methods for CSLR were mainly based on convolutional neural networks. However, these methods perform poorly in balancing spatial and temporal features during visual feature extraction, making them difficult to improve the accuracy of recognition. To address this issue, we designed an end-to-end CSLR network: Spatial–Temporal Transformer Network (STTN). The model encodes and decodes the sign language video as a predicted sequence that is aligned with a given text sequence. First, since the image sequences are too long for the model to handle directly, we chunk the sign language video frames, i.e., ”image to patch”, which reduces the computational complexity. Second, global features of the sign language video are modeled at the beginning of the model, and the spatial action features of the current video frame and the semantic features of consecutive frames in the temporal dimension are extracted separately, giving rise to fully extracting visual features. Finally, the model uses a simple cross-entropy loss to align video and text. We extensively evaluated the proposed network on two publicly available datasets, CSL and RWTH-PHOENIX-Weather multi-signer 2014 (PHOENIX-2014), which demonstrated the superior performance of our work in CSLR task compared to the state-of-the-art methods.
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161
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Tao X, Ma F, Li Z, Kan X, Ye C, Sun E. [Genetic variations in four geographical isolates of Gohieria fusca based on cytochrome b and internal transcribed spacer genes]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:22-28. [PMID: 36974011 DOI: 10.16250/j.32.1374.2022193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
OBJECTIVE To investigate the genetic diversity and genetic differentiation of different geographical isolates of Gohieria fusca. METHODS G. fusca isolates were sampled from Wuhu (WH), Bengbu (BB) and Bozhou cities (BZ) of Anhui Province and Jiaxing City of Zhejiang Province (JX). Mitochondrial cytochrome b (Cytb) and ribosomal internal transcribed spacer (ITS) genes were amplified in WH, BB, BZ and JX isolates of G. fusca using PCR assay. The gene sequences were edited and aligned using the software Chromas 2 and DNASTAR 1.00, and the haplotype, haplotype diversity (Hd) and nucleotide polymorphism (Pi) of each isolate were calculated using the software DnaSP 5.10.00. The genetic differentiation among isolates (Fst) and gene flow value (Nm) were estimated using the software MEGA 10.2, and a phylogenetic tree was built. Tests of neutrality and analysis of molecular variance (AMOVA) were performed using the software Arlequin 3.1 and a haplotype network was built based on the Median-Joining network using the software Network 10.2. RESULTS PCR assay showed that the sizes of the Cytb and ITS genes were 372 bp and 1 301 to 1 320 bp, respectively. All four isolates of G. fusca presented high genetic diversity based on mitochondrial Cytb and ITS genes (Hd = 0.804, Pi = 0.006 91). AMOVA showed genetic differentiation among geographical isolates of G. fusca (Fst = 0.202 40, P < 0.05), and the genetic variation was mainly caused by intra-population variations (79.76%). Gene flow analysis showed a high level of gene flow among G. fusca isolates (Nm > 1). Tests of neutrality based on Cytb gene measured a Tajima's D value of -1.796 31 (P < 0.05) and a Fu's FS value of -3.293 98 (P < 0.05) in WH isolate of G. fusca, indicating population expansion in WH isolate of G. fusca. Haplotype network analysis and phylogenetic analysis revealed no remarkable geographical distribution pattern among different geographical isolates of G. fusca. All four isolates of G. fusca presented high genetic diversity (Hd = 0.985, Pi = 0.011 97). AMOVA showed moderate level of genetic differentiation between four isolates (Fst = 0.104 62, P < 0.05). The tests of neutrality based on ITS genes measured a Tajima's D value of -6.088 20 and a Fu's FS value of -1.935 99 (both P > 0.05) in the whole isolate of G. fusca, indicating no obviously population expansion. CONCLUSIONS The four geographical isolates of G. fusca have high genetic diversity and remarkable genetic differentiation. Since a high level of gene flow is detected among different geographical isolates of G. fusca, no obvious geographical distribution pattern of G. fusca is found.
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Affiliation(s)
- X Tao
- School of Laboratory Medicine, Wannan Medical College, Wuhu, Anhui 241002, China
| | - F Ma
- School of Laboratory Medicine, Wannan Medical College, Wuhu, Anhui 241002, China
| | - Z Li
- School of Laboratory Medicine, Wannan Medical College, Wuhu, Anhui 241002, China
| | - X Kan
- School of Laboratory Medicine, Wannan Medical College, Wuhu, Anhui 241002, China
| | - C Ye
- School of Laboratory Medicine, Wannan Medical College, Wuhu, Anhui 241002, China
| | - E Sun
- School of Laboratory Medicine, Wannan Medical College, Wuhu, Anhui 241002, China
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Xue Z, Ye G, Qiu T, Liu X, Wang X, Li Z. An objective, quantitative, dynamic assessment of facial movement symmetry changes after orthognathic surgery. Int J Oral Maxillofac Surg 2023; 52:272-281. [PMID: 35753942 DOI: 10.1016/j.ijom.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 01/11/2023]
Abstract
The aim of this study was to generate a quantitative dynamic assessment of facial movement symmetry changes after orthognathic surgery. Twenty-five patients diagnosed with skeletal class III malocclusion with facial asymmetry who underwent bimaxillary surgery were recruited. The patients were asked to perform a maximum smile that was recorded using a three-dimensional facial motion capture system preoperatively (T0), 6 months postoperatively (T1), and 12 months postoperatively (T2). Eleven facial landmarks were selected to analyse the cumulative distance and average speed during smiling. The absolute differences for the paired landmarks between the sides were analysed to reflect the symmetry changes. The results showed that the asymmetry index of the cheilions at T2 was significantly lower than that at T0 (P = 0.004), as was the index of the mid-lateral lower lips (P = 0.006). The mean difference in cheilions was 2.13 ± 1.41 mm at T0, 1.33 ± 1.09 mm at T1, and 1.00 ± 0.98 mm at T2. The facial total mobility at T1 was significantly lower than that at T0 (P < 0.001), while the total mobility at T2 was significantly higher than that at T1 (P = 0.012). The orthognathic surgical correction of facial asymmetry was able to improve the associated asymmetry of facial movements.
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Affiliation(s)
- Z Xue
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - G Ye
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - T Qiu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - X Liu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - X Wang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Z Li
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China.
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Li Z, Xue T, Jietian J, Xiong L, Wei L, Guo S, Han H. Infiltrating pattern and prognostic value of tertiary lymphoid structures, and predicting the efficacy of anti-PD-1 combination therapy in patients with penile cancer. Eur Urol 2023. [DOI: 10.1016/s0302-2838(23)00675-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Zhao MJ, Zhu PC, Li Z, Liu Z, Kang C. Stress analysis of self-tightness metal sealing against ultrahigh pressure medium. Inflamm Res 2023; 72:195-202. [PMID: 36385667 DOI: 10.1007/s00011-022-01583-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Stress is one of the most important factors in metal-to-metal sealing. In this paper, two methods (theoretical and empirical) were adopted to calculate the normal stress of the brass sealing surfaces against different ultrahigh pressure liquid. The theoretical formula was derived in terms of force balance, and the empirical formula was obtained by polynomial curve fitting, which the fitted data were from simulated results; besides, the results calculated using the empirical formula agree well with the results by theoretical formula. Meanwhile, the equivalent stresses of the brass seal, normal stress and contact stress on the brass seal surfaces were simulated by finite element method, and the simulated results indicated these stresses are increased with the increase of liquid pressure, and the maximum stresses always appear on the tip of the brass seal.
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Affiliation(s)
- M J Zhao
- The School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China.
| | - P C Zhu
- The School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
| | - Z Li
- The School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
| | - Z Liu
- The School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
| | - C Kang
- The School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
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Han J, Xu Y, Xu D, Niu Y, Li L, Li F, Li Z, Wang H. Mechanism of downward migration of quinolone antibiotics in antibiotics polluted natural soil replenishment water and its effect on soil microorganisms. Environ Res 2023; 218:115032. [PMID: 36502909 DOI: 10.1016/j.envres.2022.115032] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Reclaimed water is widely concerned as an effective recharge of groundwater and surface water, but trace organic pollutants produced by traditional wastewater treatment plants (WWTPs) would cause environmental pollution (water and soil) during infiltration. Therefore, the effects of reclaimed water containing ofloxacin (OFL) and ciprofloxacin (CIP) in antibiotics polluted natural soil (APNS) were investigated by simulating soil aquifer treatment systems (SATs). The experiment results showed that OFL and CIP in water were adsorbed and microbially degraded mainly at 30 cm, and the concentration of OFL and CIP in soil increased with depth, which were mainly due to the desorption from APNS. Concurrently, the change in replenishment water concentration also significantly affected OFL and CIP in pore water and soil. Although OFL and CIP inhibited the diversity of soil microbial community, they also promoted the growth of some microorganisms. As the dominant bacteria, Proteobacteria and Acidobacteriota can effectively participate in the degradation of OFL and CIP. The degradation effects of soil microorganisms on OFL and CIP were 45.48% and 42.39%, respectively, indicating that soil microorganisms selectively degraded pollutants. This experiment was carried out on APNS, which provided a reference for future studies on the migration of trace organic pollutants under natural conditions.
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Affiliation(s)
- Jinlong Han
- Tangshan Key Laboratory of Bioelectrochemical Water Pollution Control Technology, North China University of Science and Technology, Tangshan, 063210, PR China; Beijing Institute of Water Science and Technology, Beijing, 100048, PR China; School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin, 300401, PR China
| | - Yufei Xu
- Tangshan Key Laboratory of Bioelectrochemical Water Pollution Control Technology, North China University of Science and Technology, Tangshan, 063210, PR China
| | - Duo Xu
- Tangshan Key Laboratory of Bioelectrochemical Water Pollution Control Technology, North China University of Science and Technology, Tangshan, 063210, PR China
| | - Yunxia Niu
- Tangshan Key Laboratory of Bioelectrochemical Water Pollution Control Technology, North China University of Science and Technology, Tangshan, 063210, PR China; Hebei Mining Area Ecological Restoration Industry Technology Research Institute Tangshan, 063000, PR China
| | - Lei Li
- Beijing Institute of Water Science and Technology, Beijing, 100048, PR China
| | - Fuping Li
- Hebei Mining Area Ecological Restoration Industry Technology Research Institute Tangshan, 063000, PR China
| | - Zhaoxin Li
- Beijing Institute of Water Science and Technology, Beijing, 100048, PR China; School of Water Conservancy and Hydroelectric Power, Hebei University of Engineering, Handan, 056038, PR China.
| | - Hao Wang
- Tangshan Key Laboratory of Bioelectrochemical Water Pollution Control Technology, North China University of Science and Technology, Tangshan, 063210, PR China; Hebei Mining Area Ecological Restoration Industry Technology Research Institute Tangshan, 063000, PR China.
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Hou S, Wang X, Yu Y, Ji H, Dong X, Li J, Li H, He H, Li Z, Yang Z, Chen W, Yao G, Zhang Y, Zhang J, Bi M, Niu S, Zhao G, Zhu R, Liu G, Jia Y, Gao Y. Invasive fungal infection is associated with antibiotic exposure in preterm infants: a multi-centre prospective case-control study. J Hosp Infect 2023; 134:43-49. [PMID: 36646139 DOI: 10.1016/j.jhin.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/31/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023]
Abstract
BACKGROUND Previous antibiotic exposure is an important risk factor for invasive fungal infection (IFI). Antibiotic overexposure is common in lower-income countries; however, multi-centre studies concerning IFI in relation to antibiotic exposure are scarce. AIM This prospective, multi-centre matched case-control study explored the correlation of IFI and antibiotic exposure in very preterm infants or very-low-birthweight infants admitted to 23 tertiary hospitals in China between 2018 and 2021. METHODS Using a 1:2 matched design for gestational age, birth weight and early-onset sepsis (yes/no), the risk factors between infants diagnosed with IFI and infection-free controls were compared. The antibiotic use rate (AUR) was calculated using calendar days of antibiotic therapy in the 4 weeks preceding IFI onset divided by onset day of IFI. FINDINGS In total, 6368 infants were included in the study, of which 90 (1.4%) were diagnosed with IFI. Median AUR, length of antibiotic therapy (LOT) and days of antibiotic therapy (DOT) within the 4 weeks preceding IFI onset were 0.90, 18 days and 30 days, respectively. Multi-variate analysis showed that a 10% increase in AUR, each additional day of DOT and LOT, and each additional day of third-generation cephalosporins and carbapenems were notably associated with IFI. CONCLUSION Prolonged antibiotic therapy is common before the onset of IFI, and is an important risk factor, especially the use of third-generation cephalosporins and carbapenems. Antibiotic stewardship should be urgently developed and promoted for preterm infants in order to reduce IFI in lower-income countries such as China.
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Affiliation(s)
- S Hou
- Department of Paediatrics, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - X Wang
- Department of Paediatrics, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Y Yu
- Department of Neonatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China; Department of Neonatology, Shandong Provincial Hospital, Shandong University, Jinan, China.
| | - H Ji
- Department of Neonatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China; Department of Neonatology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - X Dong
- Department of Neonatology, Shandong Provincial Maternal and Child Health Hospital, Jinan, Shandong, China
| | - J Li
- Department of Neonatology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - H Li
- Department of Neonatology, Hebei PetroChina Central Hospital, Langfang, China
| | - H He
- Department of Neonatology, Baogang Third Hospital of Hongci Group, Baotou, Inner Mongolia, China
| | - Z Li
- Department of Neonatology, W.F. Maternal and Child Health Hospital, Weifang, China
| | - Z Yang
- Department of Neonatology, Taian Maternal and Child Health Care Hospital, Taian, Shandong, China
| | - W Chen
- Department of Neonatology, People's Hospital of Rizhao, Rizhao, China
| | - G Yao
- Department of Neonatology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, Shandong, China
| | - Y Zhang
- Department of Neonatology, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - J Zhang
- Department of Neonatology, Qilu Hospital of Shandong University, Jinan, China
| | - M Bi
- Department of Neonatology, Jinan Central Hospital, Jinan, China
| | - S Niu
- Department of Neonatology, Zibo Maternal and Child Health Hospital, Zibo, China
| | - G Zhao
- Department of Neonatology, Binzhou Medical University Hospital, Binzhou, China
| | - R Zhu
- Department of Neonatology, Zibo Municipal Hospital, Zibo, China
| | - G Liu
- Department of Neonatology, Yidu Central Hospital of Weifang, Weifang, China
| | - Y Jia
- Department of Neonatology, Shanxi Province Shangluo Central Hospital, Shanluo, China
| | - Y Gao
- Department of Neonatology, Qilu Hospital of Shandong University Dezhou Hospital, Shanluo, China
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Dong X, Li Z, Zhao S, Liu J, Luo S, Zhang Y, Xu Q, Chen G, Zhang Y. Molecular cloning and expression analysis of Myxovirus resistance gene in Yangzhou goose ( Anser cygnoides domesticus). Br Poult Sci 2023:1-9. [PMID: 36637331 DOI: 10.1080/00071668.2022.2163617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
1. Myxovirus resistance (Mx) is a protein produced by the interferon-induced natural immune response with broad spectrum antiviral function. However, the role and expression characteristics of the Mx gene in immune defence against viral infection in goose have not yet been reported.2. This study found a 2576 bp genomic sequence and a 2112 bp mRNA sequence for Mx, encoding 703 amino acids. Multiple sequence alignments of the amino acid sequences showed that the Yangzhou goose Mx (goMx) had 86.99% similarity to the mallard duck (Anas platyrhynchos).3. Tissue-specific expression profiling revealed that the expression of goMx was highest in the lung and spleen. Both poly (I:C) and GPV were found to elevate the expression of goMx. The upregulated expression of goMx was associated with interferon pathway-related genes IRF7, JAK1, STAT1, and STAT2. Furthermore, overexpression of goMx significantly activated the transcription of poly (I:C) induced TNF-α, IL-1β, IL-6, and IL-18.4. The findings of this study suggest that the goMx modulation of the antiviral response is mediated by the interferon pathway.
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Affiliation(s)
- X Dong
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Z Li
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - S Zhao
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - J Liu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - S Luo
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Y Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.,Joint International Research Laboratory of Agriculture & Agri-Product Safety of Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China
| | - Q Xu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.,Joint International Research Laboratory of Agriculture & Agri-Product Safety of Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China
| | - G Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.,Joint International Research Laboratory of Agriculture & Agri-Product Safety of Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China
| | - Y Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.,Joint International Research Laboratory of Agriculture & Agri-Product Safety of Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China
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168
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Peng Y, Li Z, Song DJ. [Anatomical classification of adductor magnus perforator flap and its application in head and neck reconstruction]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:37-41. [PMID: 36603864 DOI: 10.3760/cma.j.cn115330-20220530-00314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Objective: To investigate the anatomical classification of adductor magnus perforator flap and its application in head and neck reconstruction. Methods: From January 2017 to January 2020, Hunan Cancer Hospital treated 27 cases of oral tumor patients (15 cases of tongue cancer, 9 cases of gingival cancer and 3 cases of buccal cancer), including 24 males and 3 females, aged 31-56 years old. The course of disease was 1-12 months. Secondary soft tissue defects with the sizes of 5.0 cm × 3.5 cm to 11.0 cm × 8.0 cm were left after radical resection of the tumors, and were repaired with free adductor magnus perforator flaps. The flaps based on the origing locations of perforator vessels were divided into three categories: ① intramuscular perforator: vessel originated between the gracilis muscle and the adductor magnus or passed through a few adductor magnus muscles; ② adductor magnus middle layer perforator: vessel run between the deep and superficial layers of adductor magnus; ③ adductor magnus deep layer perforator: vessel run between the deep layer of adductor magnus and the semimembranous muscle. Descriptive analysis was used in this research. Results: Perforator vessels of adductor magnus were found in all cases, with a total of 62 perforator branches of adductor magnus. The anatomical classification of the perforator vessels was as follows: 12 branches for class ①, 31 branches for class ② and 19 branches for class ③. The vascular pedicles of the free adductor major perforator flaps included type ① for 3 cases, type ② for 16 cases and type ③ for 8 cases. All 27 flaps survived and the donor sites were closed directly. In 18 cases, the perforator arteries and the venae comitan were respectively anastomosed with the superior thyroid arteries and veins. In 9 cases, the pedicle arteries and the venae comitan were respectively anastomosed with the facial arteries and veins. Follow up for 12-40 months showed that the appearances of the flaps and the swallowing and language functions of patients were satisfactory, apart from linear scars were left in the donor sites with no significant affect on the functions of thigh. Local recurrence occurred in 3 cases and radical surgeries were performed again followed by repairs with pedicled pectoralis major myocutaneous flaps. Cervical lymph node metastasis occurred in 2 cases and cervical lymph node dissection was performed again. Conclusions: The adductor magnus perforator flap has soft texture, constant perforator vessel anatomy, flexible donor location and harvesting forms, and less damage to the donor site. It is an ideal choice for postoperative reconstruction in head and neck tumors.
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Affiliation(s)
- Y Peng
- Department of Otolaryngology, Changsha Fourth Hospital, Changsha 410008, China
| | - Z Li
- Department of Oncology Plastic Surgery, Hunan Cancer Hospital, Changsha 410008, China
| | - D J Song
- Department of Oncology Plastic Surgery, Hunan Cancer Hospital, Changsha 410008, China
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169
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Zhu KZ, He C, Li Z, Wang PJ, Wen SX, Wen KX, Wang JY, Liu J, Xiao H, Guo CL, Chen AN, Zhang JH, Lu X, Zeng M, Liu Z. Development and multicenter validation of a novel radiomics-based model for identifying eosinophilic chronic rhinosinusitis with nasal polyps. Rhinology 2023; 61:132-143. [PMID: 36602548 DOI: 10.4193/rhin22.361] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Reliable noninvasive methods are needed to identify endotypes of chronic rhinosinusitis with nasal polyps (CRSwNP) to facilitate personalized therapy. Previous computed tomography (CT) scoring system has limited and inconsistent performance in identifying eosinophilic CRSwNP. We aimed to develop and validate a radiomics-based model to identify eosinophilic CRSwNP. METHODS Surgical patients with CRSwNP were recruited from Tongji Hospital and randomly divided into training (n = 232) and internal validation cohort (n = 61). Patients from two additional hospitals served as external validation cohort-1 (n = 84) and cohort-2 (n = 54), respectively. Data were collected from October 2013 to May 2021. Eosinophilic CRSwNP was determined by histological criterion. The least absolute shrinkage and selection operator and the logistic regression (LR) algorithm were used to develop a radiomics model. Univariate and multivariate LR were employed to build models based on CT scores, clinical characteristics, and the combination of radiological and clinical characteristics. Model performance was evaluated by assessing discrimination, calibration, and clinical utility. RESULTS The radiomics model based on 10 radiomic features achieved an area under the curve (AUC) of 0.815 in the training cohort, significantly better than the CT score model based on ethmoid-to-maxillary sinus score ratio with an AUC of 0.655. The combination of radiomic features and blood eosinophil count had a further improved performance, achieving an AUC of 0.903. The performance of these models was confirmed in all validation cohorts with satisfying predictive calibration and clinical application value. CONCLUSIONS A CT radiomics-based model is promising to identify eosinophilic CRSwNP. This radiomics-based method may provide novel insights in solving other clinical concerns, such as guiding personalized treatment and predicting prognosis in patients with CRSwNP.
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Affiliation(s)
- K-Z Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Insititue of Allergy and Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Hubei Clinical Research Center for Nasal Inflammatory Diseases, Wuhan, P.R. China
| | - C He
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Insititue of Allergy and Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Hubei Clinical Research Center for Nasal Inflammatory Diseases, Wuhan, P.R. China
| | - Z Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - P-J Wang
- Department of Otolaryngology-Head and Neck Surgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, P.R. China
| | - S-X Wen
- Department of Otolaryngology-Head and Neck Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, P.R. China
| | - K-X Wen
- Department of Otolaryngology-Head and Neck Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, P.R. China
| | - J-Y Wang
- Department of Otolaryngology-Head and Neck Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, P.R. China
| | - J Liu
- Department of Otolaryngology-Head and Neck Surgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, P.R. China
| | - H Xiao
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Insititue of Allergy and Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Hubei Clinical Research Center for Nasal Inflammatory Diseases, Wuhan, P.R. China
| | - C-L Guo
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Insititue of Allergy and Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Hubei Clinical Research Center for Nasal Inflammatory Diseases, Wuhan, P.R. China
| | - A-N Chen
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Insititue of Allergy and Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Hubei Clinical Research Center for Nasal Inflammatory Diseases, Wuhan, P.R. China
| | - J-H Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - X Lu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Insititue of Allergy and Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Hubei Clinical Research Center for Nasal Inflammatory Diseases, Wuhan, P.R. China
| | - M Zeng
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Insititue of Allergy and Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Hubei Clinical Research Center for Nasal Inflammatory Diseases, Wuhan, P.R. China
| | - Z Liu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Insititue of Allergy and Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.,Hubei Clinical Research Center for Nasal Inflammatory Diseases, Wuhan, P.R. China
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170
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Lan W, Li Z, Li YH, Song SZ. Acupuncture combined with exercise training at different time points on nerve repair of cerebral ischemia-reperfusion injury in rats and its effects on the expressions of Nestin, bFGF and EGF. Eur Rev Med Pharmacol Sci 2023; 27:38-45. [PMID: 36651839 DOI: 10.26355/eurrev_202301_30851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE The aim of the study was to observe the neuroreparative effect of electroacupuncture in rats with cerebral ischemia-reperfusion injury, and to explore the difference in the therapeutic effect of acupuncture on different acupoint groups after cerebral ischemia-reperfusion. MATERIALS AND METHODS Experimental rats were randomly divided into: sham operation group, model group, electroacupuncture group, rehabilitation group, and Diankang group (electroacupuncture + rehabilitation training). There were 24 rats in each group, and the focal cerebral ischemia-reperfusion model was established by Zea-Longa suture method. After modeling, it took 4 hours to electroacupuncture at Baihui and Dazhui points, which was used to observe the changes of nerve function in rats with signs of keel nerve function defect. Protein expression was detected by immunohistochemistry. RESULTS Compared with the model group, the EA 3d, 7d, 10d groups and the rehabilitation group had no significant difference in promoting the expression of Nestin (p>0.05). There was a significant difference (p<0.01). After cerebral ischemia-reperfusion injury, the expression of bFGF and EGF on the ischemic side was stronger. The peak of bFGF expression appeared earlier, and the peak of EGF expression appeared later. The expression of bFGF and EGF in cerebral ischemic cortex at different time points of ischemia in electroacupuncture group, rehabilitation group and Diankang group was increased, and the response was enhanced. The effect of Diankang group on the upregulation of bFGF and EGF was more significant (p<0.01, p<0.05). CONCLUSIONS Under the influence of different effects, Diankang is superior to simple treatment in improving ischemic neurological dysfunction. This may be related to the fact that Diankang can promote the proliferation of neural stem cells and the expression of neurotrophic factors on the ischemic side of the rat brain.
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Affiliation(s)
- W Lan
- School of Acupuncture and Massage, Anhui University of Traditional Chinese Medicine, Anhui, China.
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171
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Feng L, Zheng Y, Liu Y, Zhao Y, Lei M, Li Z, Fu S. Hair Zinc and Chromium Levels Were Associated with a Reduced Likelihood of Age Related Cognitive Decline in Centenarians and Oldest-Old Adults. J Nutr Health Aging 2023; 27:1012-1017. [PMID: 37997723 DOI: 10.1007/s12603-023-2008-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/27/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Cognitive function has inevitable decline with advancing age in nature, and age-related cognitive decline (ARCD) is of increasing concern to aging population. Scarce study has involved the associations between hair trace elements and ARCD in older adults, especially in centenarians and oldest-old adults. This study was to investigate the associations between hair trace elements and ARCD in centenarians and oldest-old adults. METHODS Based on the household registration information of centenarians and oldest-old adults provided by the Civil Affairs Department of Hainan Province, China, the investigators conducted a one-to-one household survey among centenarians (≥100 years old) and oldest-old adults (80-99 years old). All 50 centenarians had a median age of 103 years and females accounted for 68.0%. All 73 oldest-old adults aged 80-99 years had a median age of 90 years and females accounted for 82.2%. Basic information were obtained with questionnaire interview, physical examination, biological test and hair collection by pre-trained local doctors and nurses. An inductively coupled plasma mass spectrometer was used to measure hair trace elements. All data in this study comes from China. Age, sex, body mass index, systolic blood pressure, diastolic blood pressure, smoking, drinking, hemoglobin, albumin, fasting blood pressure, zinc, chromium, copper, selenium, iron, manganese, strontium, lead, magnesium, potassium, and barium were simultaneously included in multivariate Logistic regression analysis. One adjusted model was done with all hair trace elements together. RESULTS Zinc and chromium levels were significantly lower in participants with ARCD than those without ARCD (P < 0.05 for all). Multivariate Logistic regression analysis indicated that zinc [odds ratio (OR): 0.988, 95%confidence interval (95%CI): 0.977-0.999] and chromium (OR: 0.051, 95%CI: 0.004-0.705) were associated with a reduced likelihood of ARCD (P < 0.05 for all). CONCLUSIONS Hair zinc and chromium levels were associated with a reduced likelihood of ARCD in centenarians and oldest-old adults. Further studies are necessary to verify if zinc and chromium supplementation has the potential to improve cognitive function and prevent ARCD development.
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Affiliation(s)
- L Feng
- Shihui Fu, Department of Cardiology, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China. E-mail: ; Zhirui Li, Department of Orthopedics, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China. E-mail: ; Mingxing Lei, Chinese People's Liberation Army Medical School, Beijing, China. E-mail: ; Yali Zhao, Central Laboratory, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China. E-mail:
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172
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Zhang H, Li Z, Zheng S, Zheng P, Liang X, Li Y, Bu X, Zou X. Range-aided drift-free cooperative localization and consistent reconstruction of multi-ground robots. IEEE Robot Autom Lett 2023. [DOI: 10.1109/lra.2023.3244721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Affiliation(s)
- H. Zhang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Z. Li
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - S. Zheng
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - P. Zheng
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - X. Liang
- State Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Y. Li
- State Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - X. Bu
- State Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - X. Zou
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
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173
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Li Z, Liu A, Xu J, Zhang C. Resveratrol Attenuates Heat-Stress-Impaired Immune and Inflammatory Responses of Broilers by Modulating Toll-Like Receptor-4 Signaling Pathway. Braz J Poult Sci 2023. [DOI: 10.1590/1806-9061-2022-1668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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174
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Xing B, Yu J, Liu Y, He S, Chen X, Li Z, He L, Yang N, Ping F, Xu L, Li W, Zhang H, Li Y. High Dietary Zinc Intake Is Associated with Shorter Leukocyte Telomere Length, Mediated by Tumor Necrosis Factor-α: A Study of China Adults. J Nutr Health Aging 2023; 27:904-910. [PMID: 37960914 DOI: 10.1007/s12603-023-1992-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/30/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVES Diet can influence peripheral leukocyte telomere length (LTL), and various micronutrients have been reported to correlate with it. Zinc is known for its antioxidant properties and immunomodulatory effects. However, there are few epidemiological investigations on the relationship between dietary zinc intake and LTL. This study analyzed the association between dietary zinc and LTL and the potential role of inflammation and oxidative stress among them. DESIGN Cross-sectional and community-based study. SETTING AND PARTICIPANTS 599 participants from rural communities in the Changping suburb of Beijing, China, were recruited. MEASUREMENTS Serum lipid profile, glycosylated hemoglobin (HbA1c), oxidative stress marker, and inflammatory cytokines levels were measured. Detailed dietary data were obtained using a 24 h food recall. LTL was assessed using a real-time PCR assay. Spearman analysis, restricted cubic splines (RCS), and general linear regression models were used to determine the association between dietary zinc intake and LTL. Simple regulatory models were also applied to analyze the role of inflammation and oxidative stress among them. RESULTS A total of 482 subjects were ultimately included in this analysis. Spearman analysis showed that dietary zinc intake and zinc intake under energy density were negatively correlated with LTL (r=-0.142 and -0.126, all P <0.05) and positively correlated with tumor necrosis factor-α (TNF-α) (r=0.138 and 0.202, all P <0.05) while only dietary zinc without energy adjustment had a positive correlation with superoxide dismutase (SOD). RCS (P for non-linearity=0.933) and multiple linear regression (B=-0.084, P=0.009) indicated a negative linear association between dietary zinc and LTL. The adjustment of TNF-α rather than SOD could abolish the relationship. The mediation model suggested that the unfavorable effect of dietary zinc on LTL was mediated by TNF-α. CONCLUSIONS High dietary zinc may correlate with telomere attrition, and TNF-α can act as a mediator in this relationship. In the future, more extensive cohort studies are needed to further explore the relationship between dietary zinc and cellular aging and the specific mechanisms.
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Affiliation(s)
- B Xing
- Wei Li, Huabing Zhang, Yuxiu Li, Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, Wei Li, ; Huabing Zhang, ; Yuxiu Li,
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175
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Wang R, Li Z, Zhai Z, Li Y. Congenital hypoplasia of depressor-labii-inferioris-muscle: An uncommon cause of asymmetric facies. Asian J Surg 2022; 46:2205-2206. [PMID: 36564296 DOI: 10.1016/j.asjsur.2022.11.100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/24/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Rong Wang
- Plastic Surgery Institute of Weifang Medical University, Weifang, 261053, China
| | - Zhaoxin Li
- Weifang Hospital of Traditional Chinese Medicine, Weifang, 261053, China
| | - Zhaohui Zhai
- Plastic Surgery Institute of Weifang Medical University, Weifang, 261053, China.
| | - Yuli Li
- Plastic Surgery Institute of Weifang Medical University, Weifang, 261053, China; School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao, 266071, China.
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176
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Li Z, Zhou C, Chen Y, Ma W, Cheng Y, Chen J, Bai Y, Luo W, Li N, Du E, Li S. Egfr signaling promotes juvenile hormone biosynthesis in the German cockroach. BMC Biol 2022; 20:278. [PMID: 36514097 PMCID: PMC9749228 DOI: 10.1186/s12915-022-01484-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In insects, an interplay between the activities of distinct hormones, such as juvenile hormone (JH) and 20-hydroxyecdysone (20E), regulates the progression through numerous life history hallmarks. As a crucial endocrine factor, JH is mainly synthesized in the corpora allata (CA) to regulate multiple physiological and developmental processes, including molting, metamorphosis, and reproduction. During the last century, significant progress has been achieved in elucidating the JH signal transduction pathway, while less progress has been made in dissecting the regulatory mechanism of JH biosynthesis. Previous work has shown that receptor tyrosine kinase (RTK) signaling regulates hormone biosynthesis in both insects and mammals. Here, we performed a systematic RNA interference (RNAi) screening to identify RTKs involved in regulating JH biosynthesis in the CA of adult Blattella germanica females. RESULTS We found that the epidermal growth factor receptor (Egfr) is required for promoting JH biosynthesis in the CA of adult females. The Egf ligands Vein and Spitz activate Egfr, followed by Ras/Raf/ERK signaling, and finally activation of the downstream transcription factor Pointed (Pnt). Importantly, Pnt induces the transcriptional expression of two key enzyme-encoding genes in the JH biosynthesis pathway: juvenile hormone acid methyltransferase (JHAMT) and methyl farnesoate epoxidase (CYP15A1). Dual-luciferase reporter assay shows that Pnt is able to activate a promoter region of Jhamt. In addition, electrophoretic mobility shift assay confirms that Pnt directly binds to the - 941~ - 886 nt region of the Jhamt promoter. CONCLUSIONS This study reveals the detailed molecular mechanism of Egfr signaling in promoting JH biosynthesis in the German cockroach, shedding light on the intricate regulation of JH biosynthesis during insect development.
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Affiliation(s)
- Zhaoxin Li
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China ,grid.20561.300000 0000 9546 5767Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China ,grid.263785.d0000 0004 0368 7397Guangmeiyuan R&D Center, Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, South China Normal University, Meizhou, China
| | - Caisheng Zhou
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China
| | - Yumei Chen
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China
| | - Wentao Ma
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China
| | - Yunlong Cheng
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China
| | - Jinxin Chen
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China
| | - Yu Bai
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China
| | - Wei Luo
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China
| | - Na Li
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China ,grid.263785.d0000 0004 0368 7397Guangmeiyuan R&D Center, Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, South China Normal University, Meizhou, China
| | - Erxia Du
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China ,grid.20561.300000 0000 9546 5767Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Sheng Li
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China ,grid.20561.300000 0000 9546 5767Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China ,grid.263785.d0000 0004 0368 7397Guangmeiyuan R&D Center, Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, South China Normal University, Meizhou, China
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177
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Cui Z, Chen Z, Li Z, Wang Z. A Pyramid Semi-Autoregressive Transformer with Rich Semantics for Sign Language Production. Sensors (Basel) 2022; 22:9606. [PMID: 36559975 PMCID: PMC9785616 DOI: 10.3390/s22249606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/30/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
As a typical sequence to sequence task, sign language production (SLP) aims to automatically translate spoken language sentences into the corresponding sign language sequences. The existing SLP methods can be classified into two categories: autoregressive and non-autoregressive SLP. The autoregressive methods suffer from high latency and error accumulation caused by the long-term dependence between current output and the previous poses. And non-autoregressive methods suffer from repetition and omission during the parallel decoding process. To remedy these issues in SLP, we propose a novel method named Pyramid Semi-Autoregressive Transformer with Rich Semantics (PSAT-RS) in this paper. In PSAT-RS, we first introduce a pyramid Semi-Autoregressive mechanism with dividing target sequence into groups in a coarse-to-fine manner, which globally keeps the autoregressive property while locally generating target frames. Meanwhile, the relaxed masked attention mechanism is adopted to make the decoder not only capture the pose sequences in the previous groups, but also pay attention to the current group. Finally, considering the importance of spatial-temporal information, we also design a Rich Semantics embedding (RS) module to encode the sequential information both on time dimension and spatial displacement into the same high-dimensional space. This significantly improves the coordination of joints motion, making the generated sign language videos more natural. Results of our experiments conducted on RWTH-PHOENIX-Weather-2014T and CSL datasets show that the proposed PSAT-RS is competitive to the state-of-the-art autoregressive and non-autoregressive SLP models, achieving a better trade-off between speed and accuracy.
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Affiliation(s)
- Zhenchao Cui
- Hebei Machine Vision Engineering Research Center, School of Cyber Security and Computer, Hebei University, Baoding 071002, China
| | - Ziang Chen
- Hebei Machine Vision Engineering Research Center, School of Cyber Security and Computer, Hebei University, Baoding 071002, China
| | - Zhaoxin Li
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhaoqi Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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178
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Yao H, Li Z, Zhai Z, Li Y. Primary benign mature teratoma of facial fronto-temporal region in adolescent: A case report. Asian J Surg 2022; 46:2203-2204. [PMID: 36528533 DOI: 10.1016/j.asjsur.2022.11.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Affiliation(s)
- Haifeng Yao
- Plastic Surgery Institute of Weifang Medical University, Weifang, 261053, China
| | - Zhaoxin Li
- Affiliated Traditional Chinese Medicine Hospital of Weifang Medical University, Weifang, 261053, China
| | - Zhaohui Zhai
- Plastic Surgery Institute of Weifang Medical University, Weifang, 261053, China.
| | - Yuli Li
- Plastic Surgery Institute of Weifang Medical University, Weifang, 261053, China; School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao, 266071, China.
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179
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Cousins A, Olivares O, Markert E, Manoharan A, Bubnova X, Bresolin S, Degn M, Li Z, Silvestri D, McGregor G, Tumanov S, Sumpton D, Kamphorst JJ, Michie AM, Herzyk P, Valsecchi MG, Yeoh AE, Schmiegelow K, Te Kronnie G, Gottlieb E, Halsey C. Central nervous system involvement in childhood acute lymphoblastic leukemia is linked to upregulation of cholesterol biosynthetic pathways. Leukemia 2022; 36:2903-2907. [PMID: 36289348 PMCID: PMC9712090 DOI: 10.1038/s41375-022-01722-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/28/2022] [Accepted: 10/04/2022] [Indexed: 11/09/2022]
Affiliation(s)
- A Cousins
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - O Olivares
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - E Markert
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Glasgow, UK
| | - A Manoharan
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - X Bubnova
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - S Bresolin
- Department of Women's and Children's Health, University of Padova, Padova, Italy
| | - M Degn
- Department of Pediatrics and Adolescent Medicine, The Juliane Marie Centre, The University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Z Li
- VIVA-NUS Centre for Translational Research in Acute Leukaemia, Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
| | - D Silvestri
- Center of Biostatistics for Clinical Epidemiology, Department of Health Science, University of Milano-Bicocca, Milano, Italy
| | - G McGregor
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Glasgow, UK
| | - S Tumanov
- Cancer Research UK Beatson Institute, Glasgow, UK
| | - D Sumpton
- Cancer Research UK Beatson Institute, Glasgow, UK
| | - J J Kamphorst
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Glasgow, UK
| | - A M Michie
- Paul O'Gorman Leukaemia Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - P Herzyk
- Glasgow Polyomics, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Institute of Molecular, Cell and Systems Biology, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - M G Valsecchi
- Center of Biostatistics for Clinical Epidemiology, Department of Health Science, University of Milano-Bicocca, Milano, Italy
| | - A E Yeoh
- VIVA-NUS Centre for Translational Research in Acute Leukaemia, Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
- VIVA-University Children's Cancer Centre, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, National University Health System, Singapore, 119228, Singapore
| | - K Schmiegelow
- Department of Pediatrics and Adolescent Medicine, The Juliane Marie Centre, The University Hospital Rigshospitalet, Copenhagen, Denmark
- Institute of Clinical Medicine, Faculty of Medicine, University of Copenhagen and Juliane Marie Centre, the University Hospital Rigshospitalet, Copenhagen, Denmark
| | - G Te Kronnie
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - E Gottlieb
- The Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
| | - C Halsey
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
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180
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Li Z, Ott M, Greene W. PP 1.5 – 00040 Mapping of genetic interaction networks identifies a nucleosomal modification complex for silencing HIV. J Virus Erad 2022. [DOI: 10.1016/j.jve.2022.100110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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181
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Li Z, Wang H, Xiao S. A mechanism-based fate model of pesticide solutions on the plant surface under aerial application. SAR QSAR Environ Res 2022; 33:933-952. [PMID: 36448373 DOI: 10.1080/1062936x.2022.2148738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 11/11/2022] [Indexed: 06/16/2023]
Abstract
Pesticide residues on plant surfaces are a primary source of pesticide bioaccumulation in crops. In this context, we propose a mechanism-based model for understanding the pesticide fate on the plant surface following aerial application, taking into account fate modelling of the pesticide spray solution on the plant surface. Using chlorothalonil as an example, the simulation results revealed that the spray solution dissipated rapidly after aerial application, resulting in the formation of a saturated pesticide solution, which facilitated the diffusion process of the pesticide residue from the plant surface into the peel tissue. The proposed model generated higher simulated residue concentrations in the peel or pulp than the current model, owing to the proposed model's assumption of rapid dissipation of the spray solution. This indicated that the proposed model specified the influence of the spray solution on the plant's exposure to residues via the surface deposition pathway, whereas the current modelling approach presented a generic estimate of the residue dissipation on the plant surface that linked to the residue's fate in the soil.
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Affiliation(s)
- Z Li
- School of Public Health, Sun Yat-sen University, Shenzhen, China
| | - H Wang
- School of Public Health, Sun Yat-sen University, Shenzhen, China
| | - S Xiao
- School of Public Health, Sun Yat-sen University, Shenzhen, China
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182
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Peng Y, Li Z, Hu J, Wu T. Palladium-Catalyzed Denitrative Mizoroki–Heck Reactions of Aryl or Alkyl Olefins with Nitrobenzenes. Russ J Org Chem 2022. [DOI: 10.1134/s1070428022120168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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183
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Li Z, Xia L, Li X, Guan Y, He H, Jin L. Body mass index and the risk of abdominal hernia: a Mendelian randomization study. Hernia 2022; 27:423-429. [PMID: 36441335 DOI: 10.1007/s10029-022-02703-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 10/21/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Abdominal hernia (AH) is one of the most common clinical diseases. A large number of observational studies have found that obesity is an important risk factor for AH. However, the causal relationship between obesity and AH cannot be determined because of the clinical studies on AH induced by obesity are relatively few and only have some small- or medium-scale observational studies. Observational studies have so many confounding factors and reverse causality due to their shortcomings. From an evidence-based medicine perspective, they are not sufficiently convincing. Therefore, there is still a lack of high-quality, evidence-based medical evidence supporting a causal relationship between obesity and AH. A causal relationship between obesity and AH is also almost impossible to confirm by randomized controlled trials (RCTs). Our study based on Mendelian randomization (MR) may provide a higher level of evidence-based medical support for the relationship between obesity and AH. Body mass index (BMI) is the most common measure used for defining obesity. Finally, we employed two-sample Mendelian randomization (TSMR) to explore the causal relationship between BMI and AH. METHODS AH-related single nucleotide polymorphisms (SNPs) data were obtained from the FinnGen Biobank (FB), and BMI-related single nucleotide polymorphisms (SNPs) data were obtained from the UK Biobank (UKB). Genetic loci are used as instrumental variables (IVs), methods such as inverse variance weighted (IVW) were used for two-sample Mendelian randomization analysis, and the odds ratio (OR) value was used to evaluate the causal relationship between BMI and AH. RESULTS The results of the horizontal pleiotropy test were calculated by Egger-intercept method: p = 0.34 > 0.05. The Cochran Q test of MR-Egger method and IVW method showed heterogeneity P = 0.03 < 0.05, so the IVW random effect model was used as the gold standard. We found a genetically determined 1-standard deviation (SD) increment of BMI causally increased a 66.0% risk of AH (N = 371 SNPs, OR = 1.66, 95% CI 1.46-1.89, p = 1.55E-14) based on the IVW random effect model which was almost consistent with the results of other seven methods. CONCLUSIONS Our MR found genetic evidence for BMI and AH. The risk of developing AH increases with the number of BMI. This finding provides further evidence that maintaining a healthy BMI can prevent the development of AH. In addition, clinicians may need to focus on the potential risk of AH on some high-BMI patients.
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Affiliation(s)
- Z Li
- School of Clinical Medicine, Dali University, Dali, 671000, China
| | - L Xia
- School of Clinical Medicine, Dali University, Dali, 671000, China
| | - X Li
- College of Life Science, Shaanxi Normal University, Xi'an, 710000, China
| | - Y Guan
- The First Affiliated Hospital of Dali University, Dali, 671000, China
| | - H He
- The First Affiliated Hospital of Dali University, Dali, 671000, China
| | - L Jin
- The First Affiliated Hospital of Dali University, Dali, 671000, China.
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184
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Li Z, Zhang QL, Shen YH, Shu XH, Cheng LL. [Evaluation of left ventricular function with left atrio-ventricular longitudinal strain in patients with lymphoma underwent anthracycline therapy]. Zhonghua Xin Xue Guan Bing Za Zhi 2022; 50:1064-1068. [PMID: 36418273 DOI: 10.3760/cma.j.cn112148-20220727-00583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To analyze the value of 3-dimensional speckle tracking echocardiograghy (3D-STE) derived strain parameters on the detection of subclinical myocardial deformation alterations in patients with lymphoma treated with anthracycline agents. Methods: This study was a retrospective study. A total of 37 patients with newly diagnosed diffuse large B cell non-Hodgkin lymphoma between December 2012 and December 2014 in Cancer Center, Fudan university were included. 3D-STE strain measurements were performed at baseline (T0),after the completion of two therapy circles (T1) and at the end of anthracycline regimen chemotherapy (Te). Echocardiography images were analyzed on the TTA workstation, and the indexes included left atrial minimum volume (LAVmin), left atrial emptying index (LAEF), left atrial active emptying index (LAAEF), as well as the left ventricular global longitudinal strain (LVGLS), left ventricular global circumferential strain (LVGCS), left atrial global longitudinal strain (LAGLS). The overall left atrioventricular longitudinal strain (LAVGLS) was calculated, which was the sum of the absolute values of LVGLS and LAGLS. The changes of left ventricular strain indexes measured by 3D-STE at different time points of patients were evaluated. Results: Thirty-seven patients with DLBCL, aged (48.3±12.1)years, including 23 males (63.9%), were enrolled. Compared with baseline, LVGLS (T1: (-18.63±4.73)% vs. (-22.13±4.40)%, P=0.001; Te:(-18.26±4.64)% vs. (-22.13±4.40)%, P<0.001), LAGLS (T1: (20.41±5.56)% vs. (23.98±5.59)%, P=0.003; Te: (17.60±3.96)% vs. (23.98±5.59)%, P<0.001) and LAVGLS (T1: (39.05±7.60)% vs. (46.11±7.77)%, P<0.001; Te: (40.34±8.55)% vs. (46.11±7.77)%, P<0.001) were all deteriorated at the T1 and Te. While LVGCS ((-21.98±5.82)% vs. (-26.15±7.51)%, P=0.010), LAVmin ((23.93±7.29)ml vs. (20.33±7.03)ml, P=0.029), LAEF ((28.94±11.16)% vs. (35.79±11.12)%, P=0.002) and LAAEF ((11.93±10.00)% vs. (18.10±9.96)%, P=0.013) were decreased only until Te. Conclusions: 3D-STE strain measurements could detect early myocaridial function alteration in patients receiving anthracycline regimen chemotherapy, thus may provide a novel approach to monitor anthracycline caused myocardial toxicity.
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Affiliation(s)
- Z Li
- Department of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai 200032, China Shanghai Institute of Cardiovascular disease, Shanghai 200032, China Shanghai Institute of Medical Imaging, Shanghai 200032, China National Clinical Research Center for Interventional Medicine, Shanghai 200032, China
| | - Q L Zhang
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032 China
| | - Y H Shen
- Department of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai 200032, China Shanghai Institute of Cardiovascular disease, Shanghai 200032, China
| | - X H Shu
- Department of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai 200032, China Shanghai Institute of Cardiovascular disease, Shanghai 200032, China Shanghai Institute of Medical Imaging, Shanghai 200032, China Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - L L Cheng
- Department of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai 200032, China Shanghai Institute of Cardiovascular disease, Shanghai 200032, China Shanghai Institute of Medical Imaging, Shanghai 200032, China National Clinical Research Center for Interventional Medicine, Shanghai 200032, China
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185
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Li Z, Zhang J, Peterchev AV, Goetz SM. Modular pulse synthesizer for transcranial magnetic stimulation with fully adjustable pulse shape and sequence. J Neural Eng 2022; 19:10.1088/1741-2552/ac9d65. [PMID: 36301685 PMCID: PMC10206176 DOI: 10.1088/1741-2552/ac9d65] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 10/25/2022] [Indexed: 01/11/2023]
Abstract
The temporal shape of a pulse in transcranial magnetic stimulation (TMS) influences which neuron populations are activated preferentially as well as the strength and even direction of neuromodulation effects. Furthermore, various pulse shapes differ in their efficiency, coil heating, sensory perception, and clicking sound. However, the available TMS pulse shape repertoire is still very limited to a few biphasic, monophasic, and polyphasic pulses with sinusoidal or near-rectangular shapes. Monophasic pulses, though found to be more selective and stronger in neuromodulation, are generated inefficiently and therefore only available in simple low-frequency repetitive protocols. Despite a strong interest to exploit the temporal effects of TMS pulse shapes and pulse sequences, waveform control is relatively inflexible and only possible parametrically within certain limits. Previously proposed approaches for flexible pulse shape control, such as through power electronic inverters, have significant limitations: The semiconductor switches can fail under the immense electrical stress associated with free pulse shaping, and most conventional power inverter topologies are incapable of generating smooth electric fields or existing pulse shapes. Leveraging intensive preliminary work on modular power electronics, we present a modular pulse synthesizer (MPS) technology that can, for the first time, flexibly generate high-power TMS pulses (one-side peak ∼4000 V, ∼8000 A) with user-defined electric field shape as well as rapid sequences of pulses with high output quality. The circuit topology breaks the problem of simultaneous high power and switching speed into smaller, manageable portions, distributed across several identical modules. In consequence, the MPS TMS techology can use semiconductor devices with voltage and current ratings lower than the overall pulse voltage and distribute the overall switching of several hundred kilohertz among multiple transistors. MPS TMS can synthesize practically any pulse shape, including conventional ones, with fine quantization of the induced electric field (⩽17% granularity without modulation and ∼300 kHz bandwidth). Moreover, the technology allows optional symmetric differential coil driving so that the average electric potential of the coil, in contrast to conventional TMS devices, stays constant to prevent capacitive artifacts in sensitive recording amplifiers, such as electroencephalography. MPS TMS can enable the optimization of stimulation paradigms for more sophisticated probing of brain function as well as stronger and more selective neuromodulation, further expanding the parameter space available to users.
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Affiliation(s)
- Z Li
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
| | - J Zhang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
| | - A V Peterchev
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States of America
| | - S M Goetz
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States of America
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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Li Z, Zhang Q, Li Z, Qiao Y, Du K, Tian C, Zhu N, Leng P, Yue Z, Cheng H, Chen G, Li F. Effects of straw mulching and nitrogen application rates on crop yields, fertilizer use efficiency, and greenhouse gas emissions of summer maize. Sci Total Environ 2022; 847:157681. [PMID: 35908708 DOI: 10.1016/j.scitotenv.2022.157681] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/24/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
Although straw mulching and nitrogen applications are extensively practiced in the agriculture sector, large uncertainties remain about their impacts on crop yields and especially the environment. The responses of summer maize yields, fertilizer use efficiency, and greenhouse gas (GHG) emissions including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in the North China Plain (NCP) to two straw management practices (S0: no straw and S1: straw mulching) and two nitrogen application rates (N1: 180 and N2: 210 kg N ha-1) were investigated in field tests in 2018, 2019, and 2020. The highest yields and partial factor productivity (PFP) were obtained by S1N1, followed by S1N2, S0N1, and S0N2. S1N2 had the highest CO2 emissions and greatest CH4 uptake, S0N1 had the lowest CO2 emissions, and S0N2 had the smallest CH4 uptake. The highest and lowest N2O emissions were found in S0N1 and S1N1, respectively. The S1N2 treatment, an extensively applied practice, had the greatest global warming potential (GWP), which was 70.3 % larger than S1N1 and two times more than S0N1 and S0N2. The largest GHG emission intensity (GHGI) of 19.4 was found in the S1N2 treatment, while the other three treatments, S0N1, S0N2, and S1N1, had a GHGI of 10.1, 10.7, and 10.7, respectively according to three tested results. In conclusion, S1N1 treatment achieved a better trade-off between crop yields and GHG emissions of summer maize in NCP.
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Affiliation(s)
- Zhaoxin Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Qiuying Zhang
- Chinese Research Academy of Environmental Sciences, Beijing, China.
| | - Zhao Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China
| | - Yunfeng Qiao
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Kun Du
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Chao Tian
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China
| | - Nong Zhu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China
| | - Peifang Leng
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China
| | - Zewei Yue
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | | | - Gang Chen
- Department of Civil & Environmental Engineering, College of Engineering, Florida A&M University-Florida State University, Tallahassee, USA
| | - Fadong Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Ministry of Science and Technology, Yucheng, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China.
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187
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Fang Y, Zhang Y, Li Z, Sang SW, Yang XR, Zhang TC, Yin XL, Man JY, Lyu M. [Shandong hilly rural natural population cohort study: method and baseline characteristics of survey subjects]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:1789-1795. [PMID: 36444464 DOI: 10.3760/cma.j.cn112338-20220404-00258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To understand the study method and the baseline characteristics of the survey subjects of Shandong hilly rural natural population cohort study, and provide reference for the research of the prevalence and risk factors of common chronic and non-communicable diseases. Methods: Baseline survey, including questionnaire survey, physical examination, biochemical index examination and blood and saliva collection, was conducted in local residents aged 20-79 years in Kongcun and Xiaozhi townships of Pingyin county, Shandong province, from 2017 to 2019. Shandong hilly rural natural population cohort was established and main baseline characteristics of the study subjects were statistically analyzed. Results: A total of 10 296 study subjects aged 54.45 years were included in the study, in whom 40.6% were males. Among the study subjects, 88.3% had education level of junior high school or below, 62.1% were famers, and 90.7% were married. Smokers accounted for 45.6% of men and 0.9% of women, and drinkers accounted for 65.8% of men and 3.0% of women, respectively. The self-reported rates of hypertension, diabetes, coronary heart disease, stroke and tumors were 19.8%, 3.2%, 2.8%, 2.7% and 1.2%, respectively. Conclusion: The Shandong hilly rural cohort natural population study provided important evidence for assessing the risk for common chronic and non-communicable diseases and disease prevention and control in hilly rural areas.
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Affiliation(s)
- Y Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Ji'nan 250012, China
| | - Y Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Ji'nan 250012, China Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Ji'nan 250012, China Clinical Research Center of Shandong University, Ji'nan 250012, China
| | - Z Li
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Ji'nan 250012, China
| | - S W Sang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Ji'nan 250012, China Clinical Research Center of Shandong University, Ji'nan 250012, China
| | - X R Yang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Ji'nan 250012, China Clinical Research Center of Shandong University, Ji'nan 250012, China
| | - T C Zhang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Ji'nan 250012, China Clinical Research Center of Shandong University, Ji'nan 250012, China
| | - X L Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Ji'nan 250012, China
| | - J Y Man
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Ji'nan 250012, China
| | - M Lyu
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Ji'nan 250012, China Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Ji'nan 250012, China Clinical Research Center of Shandong University, Ji'nan 250012, China
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188
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Li Z, Lyu YB, Zhao F, Sun Q, Qu YL, Ji SS, Qiu T, Li YW, Song SX, Zhang M, Liu YC, Cai JY, Song HC, Zheng XL, Wu B, Li DD, Liu Y, Zhu Y, Cao ZJ, Shi XM. [Association of lead exposure with stunting and underweight among children aged 3-5 years in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1597-1603. [PMID: 36372750 DOI: 10.3760/cma.j.cn112150-20211229-01197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To evaluate the association of lead exposure with stunting and underweight among children aged 3-5 years in China. Methods: Data was collected from China National Human Biomonitoring (CNHBM) between January 2017 and December 2018. A total of 3 554 children aged 3-5 years were included. Demographic characteristic, lifestyle and nutritional status were collected through questionnaires. Height and weight were measured by standardized method. Stunting and underweight status were determined by calculating height for age Z-score and weight for age Z-score. Blood and urine samples were collected to detect the concentrations of blood lead, urinary lead and urinary creatinine. Children were stratified into 4 groups (Q1 to Q4) by quartiles of blood lead level and corrected urinary lead level, respectively. Complex sampling logistic regression models were applied to evaluate the association of the blood lead level, urinary lead level with stunting and underweight. Results: Among 3 554 children, the age was (4.09±1.06) years, of which 1 779 (80.64%) were female and 1 948 (55.84%) were urban residents. The prevalence of stunting and wasting was 7.34% and 2.96%, respectively. The M (Q1, Q3) for blood lead levels and urinary lead levels in children was 17.49 (12.80, 24.71) μg/L, 1.20 (0.61, 2.14) μg/g Cr, respectively. After adjusting for confounding factors, compared with the lowest blood lead concentration group Q1, the risk of stunting gradually increased in the Q3 and Q4 group (Ptrend=0.010), with OR (95%CI) values of 1.40 (0.80-2.46) and 1.80 (1.07-3.04), respectively. Compared with the lowest urinary lead concentration group Q1, the risk of stunting still increased in the Q3 and Q4 group (Ptrend=0.012), with OR (95%CI) values of 1.69 (1.01-2.84) and 1.79 (1.05-3.06), respectively. The correlation between the lead exposure and underweight was not statistically significant (P>0.05). Conclusion: Lead exposure is positively associated with the risk of stunting among children aged 3-5 years in China.
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Affiliation(s)
- Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - T Qiu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S X Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Liu
- School of Public Health, Jilin University, Changchun 130012, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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189
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Yan R, Li Z, Sun X, Wang BB, He HQ, Zhu Y, Lyu HK, Chen ZP. [Willingness of receiving influenza vaccine and its influencing factors among health care workers in Yangtze River Delta region from 2020 to 2021]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1571-1575. [PMID: 36372746 DOI: 10.3760/cma.j.cn112150-20220727-00761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To analyze the willingness of receiving influenza vaccine and its influencing factors among health care workers (HCWs) in Yangtze River Delta region from 2020 to 2021. Methods: Convenient sampling method was adopted. From July 2020 to March 2021, 76 hospitals in Jiangsu, Zhejiang, Anhui and Shanghai provinces were selected according to the hospital level and job position, and a questionnaire survey was conducted on the willingness of receiving influenza vaccination. Logistic regression model was used to analyze the influencing factors of vaccination intention. Results: A total of 1 332 HCWs were investigated, with a ratio of male to female about 1∶3.2, and the length of working years was (15.07±9.75) years. A total of 614 HCWs had received influenza vaccine in 2019, with a vaccination rate of 46.09%. About 63.21% (842/1 332) of HCWs were willing to be vaccinated with influenza vaccine. The results of binary logistic regression analysis showed that the willingness of receiving influenza vaccine among HCWs in primary hospitals was higher than that in secondary hospitals (OR=0.573) and tertiary hospitals (OR=0.357). The willingness of HCWs who had received influenza vaccine in 2019 was higher than that of HCWs who had not received influenza vaccine (OR=0.226) and had unknown history of influenza vaccination (OR=0.228). The willingness of HCWs in departments of prevention, health care and infection was higher than that in departments of pre-examination, outpatient, emergency, pediatrics and respiratory (OR=1.670). Conclusion: The willingness of receiving influenza vaccination among HCWs in Yangtze River Delta region is high, but it is still lower than that in developed countries. It is necessary to strengthen publicity and education to improve the influenza immunization level of HCWs.
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Affiliation(s)
- R Yan
- Department of Immunization Program, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
| | - Z Li
- Department of Immunization Program, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
| | - X Sun
- Department of Immunization Program, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - B B Wang
- Department of Immunization Program, Anhui Provincial Center for Disease Control and Prevention, Hefei 230601, China
| | - H Q He
- Department of Immunization Program, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
| | - Y Zhu
- Department of Immunization Program, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
| | - H K Lyu
- Department of Immunization Program, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
| | - Z P Chen
- Department of Immunization Program, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
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190
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Qu YL, Zhao F, Ji SS, Hu XJ, Li Z, Zhang M, Li YW, Lu YF, Cai JY, Sun Q, Song HC, Li DD, Zheng XL, Wu B, Lyu YB, Zhu Y, Cao ZJ, Shi XM. [Mediation effect of inflammatory biomarkers on the association between blood lead levels and blood pressure changes in Chinese adults]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1591-1596. [PMID: 36372749 DOI: 10.3760/cma.j.cn112150-20211119-01067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To investigate the role of inflammatory biomarkers in the relationship between blood lead levels and blood pressure changes. Methods: A total of 9 910 people aged 18-79 years who participated in the China National Human Biomonitoring in 2017-2018 were included in this study. A self-made questionnaire was used to collect demographic characteristics, lifestyle and other information, and the data including height, weight and blood pressure were determined through physical examination. Blood and urinary samples were collected for the detection of blood lead and cadmium levels, urinary arsenic levels, white blood cells, neutrophils, lymphocytes, and hypersensitive C-reactive protein (hs-CRP). Weighted linear regression models were used to evaluate the associations between blood lead, inflammatory biomarkers and blood pressure. Mediation analysis was performed to investigate the role of inflammation in the relationship between blood lead levels and blood pressure changes. Results: The median (Q1, Q3) age of all participants was 45.4 (33.8, 58.4)years, including 4 984 males accounting for 50.3%. Multivariate logistic regression model analysis showed that after adjusting for age, gender, residence area, BMI, education level, smoking and drinking status, family history of hypertension, consumption frequency of rice, vegetables, and red meat, fasting blood glucose, total cholesterol, triglycerides, blood cadmium and urinary arsenic levels, there was a positive association between blood lead levels, inflammatory biomarkers and blood pressure (P<0.05). Each 2.71 μg/L (log-transformed) increase of the lead was associated with a 2.05 (95%CI: 0.58, 3.53) mmHg elevation in systolic blood pressure (SBP), 2.24 (95%CI: 1.34, 3.14) mmHg elevation in diastolic blood pressure (DBP), 0.25 (95%CI: 0.05, 0.46) mg/L elevation in hs-CRP, 0.16 (95%CI: 0.03, 0.29)×109/L elevation in white blood cells, and 0.11 (95%CI: 0.02, 0.21)×109/L elevation in lymphocytes, respectively. Mediation analysis showed that the levels of hs-CRP significantly mediated the association of blood lead with SBP, with a proportion about 3.88% (95%CI: 0.45%, 7.32%). The analysis also found that the levels of hs-CRP and neutrophils significantly mediated the association of blood lead with SBP, with a proportion about 4.10% (95%CI: 1.11%, 7.10%) and 2.42% (95%CI: 0.07%, 4.76%), respectively. Conclusion: This study suggests that inflammatory biomarkers could significantly mediate the association of blood lead levels and blood pressure changes.
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Affiliation(s)
- Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X J Hu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y F Lu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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191
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Song SX, Sun L, He YJ, Wu JL, Sun WK, Zhang S, Li Z, Kou ZQ, Liu T. [Analysis of the epidemiological characteristics and genetic characteristics of influenza in the surveillance-year of 2021 to 2022 in Shandong Province, China]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1554-1559. [PMID: 36372743 DOI: 10.3760/cma.j.cn112150-20220812-00807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To analyze the epidemiological characteristics, etiology and hemagglutinin (HA) gene characteristics of prevalent strains in Shandong Province from 2021 to 2022. Methods: The sentinel surveillance data of influenza-like illness (ILI) were collected in Shandong Province from 2021 to 2022. ILI specimens were detected with Real-Time PCR and virus isolation to explore the distribution of influenza viruses in different months. Three virus strains of each city were selected for gene sequencing, and the HA phylogenetic analysis was carried out. Results: In the surveillance-year from 2021 to 2022, 528 263 ILI cases were totally reported in 54 sentinel hospitals for influenza surveillance in Shandong Province. ILI visiting ratio (ILI%) was 4.07%, with the largest number in 0-4 age group (45.86%). The highly frequent season for ILI was in winter and spring, with a peak in the 52nd week, 2021 (6.62%). Totally, nucleic acid was detected in 26 754 specimens, with a positive rate of 27.10%, all of which were type B Victoria influenza. The positive rate reached a peak in the 49th week, 2021 (63.78%). A total of 295 outbreaks of ILI had been reported, in which 269 were positive for influenza virus. Most of outbreaks occurred in the primary school, with a peak in December. Gene evolution analysis showed that the HA gene in Shandong possessed high homology, 98.6% to 99.5%, with the recommended vaccine strains in 2020-2023, which was divided into two branches, V1A.3a.1 and V1A.3a.2. Conclusion: In the surveillance-year of 2021-2022, influenza is prevalent in December in Shandong Province, with a single circulating strain type. The positive rate of influenza virus and outbreak are higher than those in the previous surveillance-year. The circulating strain possesses high HA gene homology with those of the WHO vaccine recommended strains. However, the overall immune barrier of influenza virus is weak.
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Affiliation(s)
- S X Song
- Shandong Provincial Leading Group Office for the Prevention and Control of Major Diseases and Infectious Diseases, Jinan 250014, China Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
| | - L Sun
- Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
| | - Y J He
- Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
| | - J L Wu
- Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
| | - W K Sun
- Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
| | - S Zhang
- Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
| | - Z Li
- Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
| | - Z Q Kou
- Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
| | - T Liu
- Department of Infectious Disease Prevention and Control, Shandong Center for Disease Control and Prevention/Academy of Preventive Medicine, Shandong University/Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Jinan 250014, China
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192
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Wang X, Li Z, Yin Y. An Essential Treatment Pattern of Lung Cancer: Magnetic Resonance-Guided Stereotactic Body Radiotherapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.2218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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193
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Song DJ, Li Z, Zhang Y. [Free anterolateral thigh myocutaneous flap combined with pedicled latissimus dorsi myocutaneous flap transfer for functional reconstruction after resection of huge shoulder tumor]. Zhonghua Wai Ke Za Zhi 2022; 60:1011-1017. [PMID: 36323584 DOI: 10.3760/cma.j.cn112139-20220405-00140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To investigate the method and effect of free anterolateral thigh myocutaneous flap combined with pedicled latissimus dorsi myocutaneous flap transfer for functional reconstruction after resection of huge shoulder tumor. Methods: The clinical data of 6 patients who were treated with pedicled latissimus dorsi myocutaneous flap combined with free anterolateral thigh myocutaneous flap to repair large-area complex defects after shoulder tumor resection at Department of Oncology Plastic Surgery, Hunan Province Cancer Hospital from December 2015 to December 2020 were retrospectively analyzed. There were 2 males and 4 females, with an average age of 41.7 years (range:29 to 56 years). There were 2 cases of synovial sarcoma,2 cases of phylloid cell sarcoma,1 case of liposarcoma and 1 case of fibrosarcoma. Before this operation, tumor resection had been performed for 1 to 5 times on each case,and the course of disease was 6 to 24 months. Pedicled latissimus dorsi myocutaneous flap combined with free anterolateral thigh myocutaneous flap were used to repair soft tissue defects and reconstruct deltoid function. Postoperative flap status, complications, appearance and function of upper limbs and tumor recurrence were recorded. Results: Six patients were followed up for an average of 21.6 months (range: 12 to 36 months). There were no serious complications after operation,and all flaps survived. No tumor recurrence was found. The appearance of shoulder contour reconstructed by flaps was satisfactory. The reinnervation effect of lateral femoral muscle was confirmed recovered smoothly by neuroelectromyography 3 months after operation. Shoulder function was mildly limited in 3 patients,moderately limited in 2 patients and severely limited in 1 patient. All patients reported significant improvement in shoulder discomfort.The overall functional results of all patients were satisfactory. Conclusion: Combined myocutaneous flaps transplantation can perfectly repair the wound left after the resection of huge shoulder tumor,minimize the recurrence of tumor,reconstruct the function of shoulder joint and greatly improve the quality of life of patients.
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Affiliation(s)
- D J Song
- Department of Oncology Plastic Surgery,Hunan Province Cancer Hospital,Changsha 410008,China
| | - Z Li
- Department of Oncology Plastic Surgery,Hunan Province Cancer Hospital,Changsha 410008,China
| | - Yixin Zhang
- Department of Plastic and Reconstructive Surgery,Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200011,China
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194
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Li Z, Zhang Y, Hong W, Zeng Z, Du S. Gut Microbiota Modulates Radiotherapy-Based Antitumor Immune Responses against Hepatocellular Carcinoma through STING Signaling. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.2083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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195
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Hill-Kayser C, Yorke E, Gracia C, Keene K, Ronckers C, van Dulmen-den Broe E, Kremer L, Ginsberg J, Metzger M, Li Z, Jackson A, Constine L, Hua C. Acute Ovarian Failure and Premature Ovarian Insufficiency in Childhood Cancer Survivors Who Received Radiotherapy: A PENTEC Report. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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196
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Xing J, Fu YH, Song Z, Wang Q, Ma T, Li M, Zhuang Y, Li Z, Zhu YJ, Tang W, Wang SG, Yang N, Wang PF, Zhang K. Predictive model for deep venous thrombosis caused by closed lower limb fracture after thromboprophylactic treatment. Eur Rev Med Pharmacol Sci 2022; 26:8508-8522. [PMID: 36459032 DOI: 10.26355/eurrev_202211_30387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
OBJECTIVE Currently, there are still no convincing clinical models predicting closed lower extremity fracture-associated deep vein thrombosis in patients treated through thromboprophylactic methods. We aimed at using two retrospective cohorts to develop and externally verify a clinical prediction model for deep vein thrombosis in patients treated with anticoagulants after suffering closed lower extremity fractures. PATIENTS AND METHODS We evaluated the patients' pre- and post-operatively, to accurately determine the predictive power of the biomarkers and clinical risk factors. Two retrospective cohorts were used for the development and external verification of a pre-operative clinical prediction model (development: n = 2,253; verification: n = 833) and post-operative clinical prediction model (development: n = 1,422; verification: n = 449), respectively. RESULTS The C-indices were used to show the predicted incidence of objective thrombosis at the pre- and post-operative stage, which were then compared with the observed incidence of thrombosis in both cohorts. Biomarkers and clinical indicators were included in pre- and post-operative nomograms, which were adequately calibrated in both cohorts. The cross-validated C-indices of the pre- and post-operative clinical prediction models in the verification cohort were 0.706 (95% Cl, 0.67-0.74) and 0.875 (95% Cl, 0.84-0.91), respectively. CONCLUSIONS We present our findings of novel pre- and post-operative nomograms for the prediction of deep venous thrombosis in patients who received thromboprophylaxis after suffering closed lower extremity fractures.
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Affiliation(s)
- J Xing
- Department of Orthopedics and Traumatology, Honghui Hospital, Xi'an Jiaotong University, Shaanxi, China.
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Chen S, Xing X, Li Z, Zhang W. Scoping review on the role of social media in oral health promotion. Eur Rev Med Pharmacol Sci 2022; 26:8256-8264. [PMID: 36459009 DOI: 10.26355/eurrev_202211_30357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
OBJECTIVE This review was conducted to assess the role of social media in oral health promotion by reviewing the perspectives and evaluation methods of previous related studies. MATERIALS AND METHODS The preferred reporting items PRISMA checklist was used to structure this review. Key search terms were identified to examine databases including PubMed, Web of Science and Embase. Manual searches in relevant journals and materials were also conducted in the meantime. RESULTS A total of 640 articles were identified after multi-source screening and duplicates removing, and finally 19 original studies published before April 2020 met the inclusion criteria. These studies mainly cover the fields of dentistry education and research, clinical treatment, and preventive dentistry. Both traditional and new-type social media have advantages and focuses, as well as biased information. Detailed assessment methods and indicators are classified into several groups, which could be selected to use in future research. CONCLUSIONS The application of social media in oral health promotion is becoming popular with the development of information technology. The broader use in the future, covering dentistry, mass health education, both long-term and short-term treatments of additional clinical content, requires further evaluation and supervision in online information sharing process. The reasonable selection of methods and indicators according to different topics and preference is of great importance.
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Affiliation(s)
- S Chen
- The Hubei-MOST and Key Lab For Oral Biomedical Engineering of the Ministry of Education, School and Hospital of Stomatology, Wuhan University, China.
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Kim J, Karp J, Hu K, Vaezi A, Liu C, Rybstein M, Li Z, Jacobson A, Persky M, Givi B, Tam M. Disease Characteristics, Patterns of Care and Survival Outcomes in Patients with Synovial Cell Sarcoma of the Head and Neck (HNSCS). Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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199
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Ye X, Guo D, Liu J, Ge J, Yu H, Wang F, LU Z, Sun X, Yuan S, Zhao L, Jin X, Li J, He C, Zhang Q, Meng Y, Yang X, Liang J, Liu R, Ding S, Zhao J, Li Z, Zhong W, Zhu B, Zhou S, Yuan T, Yan L, Hua X, Lu L, Yan S, Jin D, Kong S. AI Model of Using Stratified Deep Learning to Delineate the Organs at Risk (OARs) for Thoracic Radiation Therapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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200
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Zhang W, Li Z, Peng Y, Yin Y, Zhou Q. Patient-Specific Daily Updated Deep Learning Auto-Segmentation for MRI-Guided Adaptive Radiotherapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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