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Plattner BJ, Hong S, Li Y, Talavera MJ, Dogan H, Plattner BS, Alavi S. Use of Pea Proteins in High-Moisture Meat Analogs: Physicochemical Properties of Raw Formulations and Their Texturization Using Extrusion. Foods 2024; 13:1195. [PMID: 38672868 PMCID: PMC11049411 DOI: 10.3390/foods13081195] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/18/2023] [Accepted: 01/15/2024] [Indexed: 04/28/2024] Open
Abstract
A new form of plant-based meat, known as 'high-moisture meat analogs' (HMMAs), is captivating the market because of its ability to mimic fresh, animal muscle meat. Utilizing pea protein in the formulation of HMMAs provides unique labeling opportunities, as peas are both "non-GMO" and low allergen. However, many of the commercial pea protein isolate (PPI) types differ in functionality, causing variation in product quality. Additionally, PPI inclusion has a major impact on final product texture. To understand the collective impact of these variables, two studies were completed. The first study compared four PPI types while the second study assessed differences in PPI inclusion amount (30-60%). Both studies were performed on a Wenger TX-52 extruder, equipped with a long-barrel cooling die. Rapid-visco analysis (RVA) and sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) indicated differences in protein solubility among the different PPI types. In general, lower protein solubility led to better product quality, based on visual evaluation. Cutting strength and texture profile analysis showed increasing PPI inclusion from 30-60% led to significantly higher product hardness (14,160-16,885 g) and toughness (36,690-46,195 g. s). PPI4 led to lower product toughness (26,110 and 33,725 g. s), compared to the other PPIs (44,620-60,965 g. s). Heat gelling capacity of PPI4 was also highest among PPI types, by way of least gelation concentration (LGC) and RVA. When compared against animal meat, using more PPI (50-60%) better mimicked the overall texture and firmness of beef steak and pork chops, while less PPI better represented a softer product like chicken breast. In summary, protein content and also functionality such as cold water solubility and heat gelation dictated texturization and final product quality. High cold water solubility and poor heat gelation properties led to excessive protein cross linking and thicker yet less laminated shell or surface layer. This led to lower cutting firmness and toughness, and less than desirable product texture as compared to animal meat benchmarks. On the other hand, pea proteins with less cold water solubility and higher propensity for heat gelation led to products with more laminated surface layer, and higher cutting test and texture profile analysis response. These relationships will be useful for plant-based meat manufacturers to better tailor their products and choice of ingredients.
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Affiliation(s)
| | | | | | | | | | | | - Sajid Alavi
- Department of Grain Science and Industry, Kansas State University, 201 Shellenberger Hall, Manhattan, KS 66506, USA; (B.J.P.); (S.H.); (Y.L.); (M.J.T.); (H.D.); (B.S.P.)
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2
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Rosenberg E, Andersen TI, Samajdar R, Petukhov A, Hoke JC, Abanin D, Bengtsson A, Drozdov IK, Erickson C, Klimov PV, Mi X, Morvan A, Neeley M, Neill C, Acharya R, Allen R, Anderson K, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bilmes A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Campero J, Chang HS, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Barba ADT, Demura S, Di Paolo A, Dunsworth A, Earle C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Garcia G, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hill G, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Mandrà S, Martin O, Martin S, McClean JR, McEwen M, Meeks S, Miao KC, Mieszala A, Montazeri S, Movassagh R, Mruczkiewicz W, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Omonije S, Opremcak A, Potter R, Pryadko LP, Quintana C, Rhodes DM, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Sivak V, Skruzny J, Smith WC, Somma RD, Sterling G, Strain D, Szalay M, Thor D, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Khemani V, Gopalakrishnan S, Prosen T, Roushan P. Dynamics of magnetization at infinite temperature in a Heisenberg spin chain. Science 2024; 384:48-53. [PMID: 38574139 DOI: 10.1126/science.adi7877] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Affiliation(s)
- E Rosenberg
- Google Research, Mountain View, CA, USA
- Department of Physics, Cornell University, Ithaca, NY, USA
| | | | - R Samajdar
- Department of Physics, Princeton University, Princeton, NJ, USA
- Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, USA
| | | | - J C Hoke
- Department of Physics, Stanford University, Stanford, CA, USA
| | - D Abanin
- Google Research, Mountain View, CA, USA
| | | | - I K Drozdov
- Google Research, Mountain View, CA, USA
- Department of Physics, University of Connecticut, Storrs, CT, USA
| | | | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - A Morvan
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - R Allen
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - A Bilmes
- Google Research, Mountain View, CA, USA
| | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - J Campero
- Google Research, Mountain View, CA, USA
| | - H-S Chang
- Google Research, Mountain View, CA, USA
| | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | - C Earle
- Google Research, Mountain View, CA, USA
| | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - G Garcia
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | - G Hill
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- QSI, Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - S Mandrà
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | - S Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | - S Meeks
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - S Omonije
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - V Sivak
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | | | - R D Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - D Thor
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | | | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - V Khemani
- Department of Physics, Stanford University, Stanford, CA, USA
| | | | - T Prosen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - P Roushan
- Google Research, Mountain View, CA, USA
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3
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Mi X, Michailidis AA, Shabani S, Miao KC, Klimov PV, Lloyd J, Rosenberg E, Acharya R, Aleiner I, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Malone FD, Martin O, McClean JR, McEwen M, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Roushan P, Smelyanskiy V, Abanin DA. Stable quantum-correlated many-body states through engineered dissipation. Science 2024; 383:1332-1337. [PMID: 38513021 DOI: 10.1126/science.adh9932] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 02/13/2024] [Indexed: 03/23/2024]
Abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Affiliation(s)
- X Mi
- Google Research, Mountain View, CA, USA
| | - A A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - S Shabani
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | - J Lloyd
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | | | - R Acharya
- Google Research, Mountain View, CA, USA
| | - I Aleiner
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - C Chou
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | - A G Dau
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | | | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Software and Information (QSI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | | | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | | | | | - A Morvan
- Google Research, Mountain View, CA, USA
| | | | | | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | | | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Google Research, Mountain View, CA, USA
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - R Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z J Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | - P Roushan
- Google Research, Mountain View, CA, USA
| | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
- Department of Physics, Princeton University, Princeton, NJ, USA
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4
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Hong S, Sun L, Hao Y, Li P, Zhou Y, Liang X, Hu J, Wei H. From NAFLD to MASLD: When metabolic comorbidity matters. Ann Hepatol 2024; 29:101281. [PMID: 38135250 DOI: 10.1016/j.aohep.2023.101281] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/27/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION AND OBJECTIVES In a recent development, a cohort of hepatologists has proposed altering the nomenclature of non-alcoholic fatty liver disease (NAFLD) to metabolic-associated steatotic liver disease (MASLD), accompanied by modified diagnostic criteria. Our objective was to investigate the effect of the revised definition on identifying significant hepatic fibrosis. PATIENTS AND METHODS From Jan 2009 to Dec 2022, a total of 428 patients with biopsy-proven hepatic steatosis were diagnosed with NAFLD. Patients were classified into subgroups according to MASLD and Cryptogenic-SLD diagnostic criteria. The clinical pathological features were compared between these two groups. Risk factors for significant fibrosis were analysed in the MASLD group. In total, 329 (76.9 %) patients were diagnosed with MASLD, and 99 (23.1 %) were diagnosed with Cryptogenic-SLD. RESULTS Those with MASLD exhibited a higher degree of disease severity regarding histology features than Cryptogenic-SLD. The prevalence of significant fibrosis increased from 13 % to 26.6 % for one and two criteria present to 42.5 % for meeting three or more cardiometabolic risk factor (CMRF) criteria (p = 0.001). ALB (aOR:0.94,95 %CI:0.90-1.00; p = 0.030), lower levels of PLT (aOR:0.99, 95 %CI:0.99-1.00; p < 0.001), and more metabolic comorbidities (aOR:1.42,95 %CI:1.14-1.78; p = 0.012) were independent risk factors of significant fibrosis in MASLD. CONCLUSIONS The new nomenclature of MASLD and SLD is more applicable to identifying significant fibrosis than NAFLD. Patients with three or more cardiometabolic risk factors are at higher risk of fibrosis.
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Affiliation(s)
- Shan Hong
- Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Lei Sun
- Department of Pathology, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Yiwei Hao
- Department of Medical Records and Statistics, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Ping Li
- Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Yuling Zhou
- Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Xiuxia Liang
- Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Julong Hu
- Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Hongshan Wei
- Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
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5
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Hong S, Li D, Wei Y, Zheng Y, Cai J, Zheng H, Zhang X, Deng Y, Han D, Wang J, Chen L, Li S, Qiu W, Ren M, Zou L. Epidemiology of respiratory pathogens in patients with acute respiratory tract infection in Xiamen, China: A retrospective survey from 2020 to 2022. Heliyon 2023; 9:e22302. [PMID: 38053876 PMCID: PMC10694312 DOI: 10.1016/j.heliyon.2023.e22302] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/07/2023] Open
Abstract
Acute respiratory tract infections (ARTI) are caused by respiratory pathogens and range from asymptomatic infections to severe respiratory diseases. These diseases can be life threatening with high morbidity and mortality worldwide. Under the pandemic of coronavirus disease 2019 (COVID-19), little has been reported about the pathogen etiologies and epidemiology of patients suffering from ARTI of all age in Xiamen. Region-specific surveillance in individuals with ARTI of all ages was performed in Xiamen from January 2020 to October 2022. Here, we observed the epidemiological characteristics of thirteen pathogens within ARTI patients and further revealed the difference of that between upper respiratory tract infections (URTI) and lower respiratory tract infections (LRTI). In total 56.36 % (2358/4184) of the ARTI patients were positive for at least one respiratory pathogen. Rhinovirus (RVs, 29.22 %), influenza A (FluA, 19.59 %), respiratory syncytial virus (RSV, 18.36 %), metapneumovirus (MPV, 13.91 %), and adenovirus (ADV, 10.31 %) were the five leading respiratory pathogens. Respiratory pathogens displayed age- and season-specific patterns, even between URTI and LRTI. Compared with other groups, a higher proportion of FluA (52.17 % and 68.75 %, respectively) infection was found in the adult group and the elder group, while the lower proportion of RVs (14.11 % and 11.11 %) infection was also observed in them. Although ARTI cases circulated throughout the year, RVs, FluB, and BoV peaked in autumn, and FluA circulated more in summer. Besides, the co-infectious rate was 8.7 % with the most common for RVs. Logistic regression analyses revealed the correlations between respiratory pathogens and disease types. These results are essential for replenishing epidemiological characteristics of common respiratory pathogens that caused ARTI in Xiamen during the epidemic of COVID-19, and a better understanding of it might optimize the local prevention and clinical control.
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Affiliation(s)
- Shan Hong
- Department of Pediatrics, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Dan Li
- Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang, China
| | - Yanli Wei
- Department of General Practice, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Yilin Zheng
- Department of Pediatrics, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Jiading Cai
- Department of General Practice, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Heping Zheng
- Department of Intensive Care Unit, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Xuan Zhang
- Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang, China
| | - Yulin Deng
- Department of Pediatrics, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Dandan Han
- Department of Pediatrics, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Jia Wang
- Department of Pediatrics, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Linlin Chen
- Department of Pediatrics, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Shujing Li
- Department of Pediatrics, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Weiping Qiu
- Department of General Practice, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Min Ren
- Department of Pediatrics, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
| | - Liangneng Zou
- Department of General Practice, The Fifth Hospital of Xiamen, Xiamen, Fujian, China
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6
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Ward MC, Atlas JL, Carrizosa DR, Milas ZL, Brickman DS, Frenkel CH, Hong S, Heinzerling JH, Prabhu RS, Moeller BJ. Weekly vs. Bolus Cisplatin Concurrent with Definitive Radiotherapy for Squamous Carcinoma of the Head and Neck: A Systematic Review and Network Meta-Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e632-e633. [PMID: 37785889 DOI: 10.1016/j.ijrobp.2023.06.2031] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The optimal schedule for cisplatin delivered concurrently with definitive radiation for squamous carcinoma of the head and neck remains controversial. Randomized data in the postoperative setting is mixed, and definitive studies are ongoing. Meanwhile, multiple trials have already compared cetuximab to cisplatin in the definitive setting. Across these trials, the cetuximab dosing was identical, but cisplatin dosing was variable and can be categorized as weekly (40 mg/m2 q1 week) or bolus (100 mg/m2 q3 weeks). We indirectly compared these two cisplatin schedules by performing a network meta-analysis of cetuximab trials. MATERIALS/METHODS We performed a PRISMA-concordant systematic review to identify randomized controlled trials comparing cisplatin to cetuximab for patients with non-metastatic squamous carcinoma of the head and neck treated with definitive radiation therapy. Trials of primary surgery, incorporating induction therapy, or mixing other therapeutics were excluded. The analysis was pre-registered with the Open Science Foundation. Individual patient survival data was extracted from the published overall survival curves using a digitizer, and outcomes were validated against published point-estimates and hazard ratios. A random effects Cox regression was used to perform the individual-patient analysis using a one-step approach under a frequentist framework. Random effects were applied to model heterogeneity in the baseline hazard function and treatment effect. Models were adjusted by HPV and smoking status, which were trial-level covariates. Alternative endpoints (DFS, LRF, DM, etc.) were analyzed qualitatively. IRB approval was not required. RESULTS Five randomized trials were identified, including 1,678 patients. Bolus cisplatin was delivered to 572 patients in 2 trials, and weekly to 271 in 3 trials. The risk of bias was low. Relative to cetuximab, both bolus and weekly cisplatin reduced the risk of death (adjusted HR 0.63, 95% CI 0.46-0.87, p = 0.004 & HR 0.56, 95% CI 0.37-0.86, p = 0.008 respectively). No interaction was identified between regimen and HPV or smoking status. Between-study heterogeneity (δ2) was 0.148 and treatment effect heterogeneity (τ2) was small (<0.0002). There was no statistical difference in OS between bolus vs. weekly regimens (weekly vs. bolus HR 0.90, 95% CI 0.53-1.52, p = 0.345). This Cox model therefore suggested that on average, the absolute difference in 5-year OS is <1.5% between the two regimens, which was not statistically significant. Secondary endpoints and toxicity were not obviously different by regimen, qualitatively. CONCLUSION Using cetuximab as a common reference, there was no significant difference in survival between weekly and bolus cisplatin schedules.
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Affiliation(s)
- M C Ward
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - J L Atlas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D R Carrizosa
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - Z L Milas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D S Brickman
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - C H Frenkel
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - S Hong
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - J H Heinzerling
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - R S Prabhu
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - B J Moeller
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
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7
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Hoke JC, Ippoliti M, Rosenberg E, Abanin D, Acharya R, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Eppens D, Erickson C, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Miao KC, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Mi X, Khemani V, Roushan P. Measurement-induced entanglement and teleportation on a noisy quantum processor. Nature 2023; 622:481-486. [PMID: 37853150 PMCID: PMC10584681 DOI: 10.1038/s41586-023-06505-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/01/2023] [Indexed: 10/20/2023]
Abstract
Measurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the 'arrow of time' that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time3-10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11-13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15-17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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8
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Sud S, Poellmann M, Garg V, King T, Casey DL, Wang AZ, Hong S, Weiner AA. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Localized Lung Cancer Treated with Radiotherapy or Chemoradiotherapy with Definitive Intent. Int J Radiat Oncol Biol Phys 2023; 117:e60. [PMID: 37785811 DOI: 10.1016/j.ijrobp.2023.06.778] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To characterize circulating tumor cell (CTC) kinetics in response to definitive therapy in patients with local or locoregional lung cancer and identify CTC kinetic profiles associated with favorable disease response versus progression. MATERIALS/METHODS In this single-institution prospective correlative biomarker study, we enrolled patients receiving definitive intent radiotherapy (RT) or chemoradiotherapy for non-metastatic lung cancer. Blood specimens were collected prior to RT (baseline), during RT and at follow up visits up to 24 months post RT. Subsequent lines of therapy were administered per standard of care. CTCs were captured and enumerated using a previously reported nanotechnology-based assay functionalized with aEpCAM, aHER-2, and aEGFR to facilitate biomimetic cell rolling and dendrimer-mediated multivalent binding. Disease status was assessed per RECIST 1.1 criteria. CTC kinetics and absolute values were analyzed to identify patterns associated with disease control versus progression. RESULTS We enrolled 24 patients with median follow up of 8 months corresponding to 114 CTC measurements. Seven patients (30%) had biopsy proven disease, while 17 (70%) were diagnosed based on clinical and radiographic features alone. Nineteen patients (79%) received stereotactic body radiation therapy. Median baseline CTC count was 12.6 CTCs/ml (range 0-290) and post RT decreased to median 4 CTCs/ml (0-42.7). For 95% of patients, a favorable kinetic profile (defined as stable CTC count, decreased CTC count or <24 CTCs/ml corresponding to the 80th percentile) during radiotherapy or at the time of first follow up corresponded to local control of the irradiated lesion. Five patients (20%) experienced disease progression within the follow up period. In the two patients with local progression of the irradiated lesion, the CTC count rose >10 fold prior to or at the time of radiographic detection of progression. In the three patients with systemic progression, CTC count rose 1.46-5.8-fold at the time of progression. Notably, four of the five patients with disease progression did not have initial biopsy confirmation of disease but did experience a CTC elevation at the time of progression. CONCLUSION Our data suggests CTCs may serve as a biomarker for response to therapy in patients being treated with RT with definitive intent for early stage or locally advanced lung cancer. This finding is of importance given important limitations in obtaining pathologic confirmation of disease in select patients and challenges distinguishing disease progression versus benign post radiotherapy radiographic changes. Further studies are needed to characterize the predictive and prognostic value of circulating biomarker levels and kinetics in lung cancer.
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Affiliation(s)
- S Sud
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - M Poellmann
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - V Garg
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - T King
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - D L Casey
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - A Z Wang
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC; UT Southwestern Department of Radiation Oncology, Dallas, TX
| | - S Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - A A Weiner
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
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9
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Smitherman EA, Chahine RA, Beukelman T, Lewandowski LB, Rahman AKMF, Wenderfer SE, Curtis JR, Hersh AO, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar‐Smiley F, Barillas‐Arias L, Basiaga M, Baszis K, Becker M, Bell‐Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang‐Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel‐Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie‐Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui‐Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein‐Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PM, McGuire S, McHale I, McMonagle A, McMullen‐Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O'Brien B, O'Brien T, Okeke O, Oliver M, Olson J, O'Neil K, Onel K, Orandi A, Orlando M, Osei‐Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan‐Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas‐Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth‐Wojcicki E, Rouster – Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert‐Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner‐Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Childhood-Onset Lupus Nephritis in the Childhood Arthritis and Rheumatology Research Alliance Registry: Short-Term Kidney Status and Variation in Care. Arthritis Care Res (Hoboken) 2023; 75:1553-1562. [PMID: 36775844 PMCID: PMC10500561 DOI: 10.1002/acr.25002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 07/14/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The goal was to characterize short-term kidney status and describe variation in early care utilization in a multicenter cohort of patients with childhood-onset systemic lupus erythematosus (cSLE) and nephritis. METHODS We analyzed previously collected prospective data from North American patients with cSLE with kidney biopsy-proven nephritis enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry from March 2017 through December 2019. We determined the proportion of patients with abnormal kidney status at the most recent registry visit and applied generalized linear mixed models to identify associated factors. We also calculated frequency of medication use, both during induction and ever recorded. RESULTS We identified 222 patients with kidney biopsy-proven nephritis, with 64% class III/IV nephritis on initial biopsy. At the most recent registry visit at median (interquartile range) of 17 (8-29) months from initial kidney biopsy, 58 of 106 patients (55%) with available data had abnormal kidney status. This finding was associated with male sex (odds ratio [OR] 3.88, 95% confidence interval [95% CI] 1.21-12.46) and age at cSLE diagnosis (OR 1.23, 95% CI 1.01-1.49). Patients with class IV nephritis were more likely than class III to receive cyclophosphamide and rituximab during induction. There was substantial variation in mycophenolate, cyclophosphamide, and rituximab ever use patterns across rheumatology centers. CONCLUSION In this cohort with predominately class III/IV nephritis, male sex and older age at cSLE diagnosis were associated with abnormal short-term kidney status. We also observed substantial variation in contemporary medication use for pediatric lupus nephritis between pediatric rheumatology centers. Additional studies are needed to better understand the impact of this variation on long-term kidney outcomes.
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Xiang W, Hong S, Xue Y, Ma Y. Functional Analysis of Novel alkB Genes Encoding Long-Chain n-Alkane Hydroxylases in Rhodococcus sp. Strain CH91. Microorganisms 2023; 11:1537. [PMID: 37375039 DOI: 10.3390/microorganisms11061537] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Rhodococcus sp. strain CH91 is capable of utilizing long-chain n-alkanes as the sole carbon source. Two new genes (alkB1 and alkB2) encoding AlkB-type alkane hydroxylase were predicted by its whole-genome sequence analysis. The purpose of this study was to elucidate the functional role of alkB1 and alkB2 genes in the n-alkane degradation of strain CH91. RT-qPCR analyses revealed that the two genes were induced by n-alkanes ranging from C16 to C36 and the expression of the alkB2 gene was up-regulated much higher than that of alkB1. The knockout of the alkB1 or alkB2 gene in strain CH91 resulted in the obvious reduction of growth and degradation rates on C16-C36 n-alkanes and the alkB2 knockout mutant exhibited lower growth and degradation rate than the alkB1 knockout mutant. When gene alkB1 or alkB2 was heterologously expressed in Pseudomonas fluorescens KOB2Δ1, the two genes could restore its alkane degradation activity. These results demonstrated that both alkB1 and alkB2 genes were responsible for C16-C36 n-alkanes' degradation of strain CH91, and alkB2 plays a more important role than alkB1. The functional characteristics of the two alkB genes in the degradation of a broad range of n-alkanes make them potential gene candidates for engineering the bacteria used for bioremediation of petroleum hydrocarbon contaminations.
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Affiliation(s)
- Wei Xiang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Shan Hong
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanfen Xue
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanhe Ma
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
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11
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Hong S, Yuan X, Yang J, Yang Y, Jv H, Li R, Jia Z, Ruan Y. Selection of rhizosphere communities of diverse rotation crops reveals unique core microbiome associated with reduced banana Fusarium wilt disease. New Phytol 2023; 238:2194-2209. [PMID: 36797661 DOI: 10.1111/nph.18816] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [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: 10/10/2022] [Accepted: 02/10/2023] [Indexed: 05/04/2023]
Abstract
Crop rotation can assemble distinct core microbiota as functionally specific barriers against the invasion of banana Fusarium oxysporum pathogens. However, the taxonomic identity of rotation-unique core taxa and their legacy effects are poorly understood under field conditions. Pepper and eggplant rotations were employed to reveal rotation crop- and banana-unique antagonistic core taxa by in situ tracking of the soil microbiome assembly patterns for 2 yr. The rotation crop-unique antagonistic taxa were isolated and functionally verified by culture-dependent techniques, high-throughput sequencing, and pot experiments. Pepper and eggplant rotations resulted in eight and one rotation-unique antagonistic core taxa out of 12 507 microbial taxa, respectively. These nine antagonistic taxa were retained the following year and significantly decreased banana wilt disease incidence via legacy effects, although the cultivated strains were exclusively of the genera Bacillus and Pseudomonas. The fermentation broth and volatiles of these two taxa showed strong antagonistic activity, and pot experiments demonstrated high suppression of wilt disease and significant promotion of banana growth. Our study provides a mechanistic understanding of the identification of rotation crop-unique antagonistic taxa and highlights the importance of targeted cultivation of beneficial microorganisms for optimizing crop rotation-based scenarios in support of banana agriculture sustainability.
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Affiliation(s)
- Shan Hong
- Sanya Nanfan Research Institute of Hainan University, Hainan University, Sanya, Hainan Province, 572025, China
- College of Tropical Crops, Hainan University, Haikou, 570228, China
- Hainan Key Laboratory of Vegetable Biology, The Institute of Vegetables, Hainan Academy of Agricultural Sciences, Haikou, 570228, China
| | - Xianfu Yuan
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui Province, 239000, China
| | - Jinming Yang
- College of Tropical Crops, Hainan University, Haikou, 570228, China
| | - Yue Yang
- College of Tropical Crops, Hainan University, Haikou, 570228, China
| | - Hongling Jv
- College of Tropical Crops, Hainan University, Haikou, 570228, China
| | - Rong Li
- Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving Fertilizers, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, China
| | - Zhongjun Jia
- Sanya Nanfan Research Institute of Hainan University, Hainan University, Sanya, Hainan Province, 572025, China
- College of Tropical Crops, Hainan University, Haikou, 570228, China
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing, Jiangsu Province, 210008, China
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin Province, 130000, China
| | - Yunze Ruan
- Sanya Nanfan Research Institute of Hainan University, Hainan University, Sanya, Hainan Province, 572025, China
- College of Tropical Crops, Hainan University, Haikou, 570228, China
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Andersen TI, Lensky YD, Kechedzhi K, Drozdov IK, Bengtsson A, Hong S, Morvan A, Mi X, Opremcak A, Acharya R, Allen R, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Babbush R, Bacon D, Bardin JC, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hilton J, Hoffmann MR, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Lucero E, Malone FD, Martin O, McClean JR, McCourt T, McEwen M, Miao KC, Mieszala A, Mohseni M, Montazeri S, Mount E, Movassagh R, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Kim EA, Aleiner I, Roushan P. Non-Abelian braiding of graph vertices in a superconducting processor. Nature 2023; 618:264-269. [PMID: 37169834 DOI: 10.1038/s41586-023-05954-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023]
Abstract
Indistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date-including fermions, bosons and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4-8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9-22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23 to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
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13
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Nieto-Veloza A, Hong S, Reeder M, Sula MJ, D'Souza DH, Zhong Q, Dia VP. Lunasin reduces the susceptibility of IL-10 deficient mice to inflammatory bowel disease and modulates the activation of the NLRP3 inflammasome. J Nutr Biochem 2023:109383. [PMID: 37209953 DOI: 10.1016/j.jnutbio.2023.109383] [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: 11/21/2022] [Revised: 02/21/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023]
Abstract
Inflammatory bowel disease (IBD) is a chronic inflammatory condition that can cause severe damage to the gastrointestinal tract leading to lower quality of life and productivity. Our goal was to investigate the protective effect of the soy peptide lunasin in an in vivo model of susceptibility to IBD and to identify the potential mechanism of action in vitro. In IL-10 deficient mice, oral administration of lunasin reduced the number and frequency of mice exhibiting macroscopic signs of susceptibility to inflammation and significantly decreased levels of the pro-inflammatory cytokines TNF-α, IL-1β, IL-6, and IL-18 by up to 95%, 90%, 90%, and 47%, respectively, in different sections of the small and large intestines. Dose-dependent decrease of caspase-1, IL-1β, and IL-18 in LPS-primed and ATP-activated THP-1 human macrophages demonstrated the ability of lunasin to modulate the NLRP3 inflammasome. We demonstrated that lunasin can decrease susceptibility to IBD in genetically susceptible mice by exerting anti-inflammatory properties.
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Affiliation(s)
- Andrea Nieto-Veloza
- Department of Food Science, University of Tennessee Institute of Agriculture, 2510 River Dr., Knoxville, TN, 37996, USA.
| | - Shan Hong
- Department of Food Science, University of Tennessee Institute of Agriculture, 2510 River Dr., Knoxville, TN, 37996, USA.
| | - Matthew Reeder
- Department of Food Science, University of Tennessee Institute of Agriculture, 2510 River Dr., Knoxville, TN, 37996, USA.
| | - Mee-Ja Sula
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, 2407 River Dr., Knoxville, TN, 37996, USA.
| | - Doris H D'Souza
- Department of Food Science, University of Tennessee Institute of Agriculture, 2510 River Dr., Knoxville, TN, 37996, USA.
| | - Qixin Zhong
- Department of Food Science, University of Tennessee Institute of Agriculture, 2510 River Dr., Knoxville, TN, 37996, USA.
| | - Vermont P Dia
- Department of Food Science, University of Tennessee Institute of Agriculture, 2510 River Dr., Knoxville, TN, 37996, USA.
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Li N, Chang M, Zhou Q, Zhang L, Wang Y, Guan Y, Li H, Zhao Y, Ding C, Hong S, Yao S. Activation of AMPK signalling by Metformin: Implication an important molecular mechanism for protecting against mice silicosis via inhibited endothelial cell-to-mesenchymal transition by regulating oxidative stress and apoptosis. Int Immunopharmacol 2023; 120:110321. [PMID: 37192555 DOI: 10.1016/j.intimp.2023.110321] [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: 02/21/2023] [Revised: 04/30/2023] [Accepted: 05/08/2023] [Indexed: 05/18/2023]
Abstract
Inhalation of silica particles (SiO2) causes oxidative stress-induced inflammation and cell apoptosis, ultimately resulting in irreversible pulmonary fibrosis, Unfortunately, effective treatment or preventative measures have yet to be fully established. Metformin (Met), a relatively safe and effective medication for treating diabetes, may hold promise as protective agent against early-stage pulmonary fibrosis in mice through the activation of autophagy and inhibition of endothelial cell to mesenchymal transition (EndoMT). Here, we investigated whether Met could reduce silicosis in mice by regulating inflammation, oxidative stress, and apoptosis, and to identify the underlying protective effect on endothelial cells. First, through pathological observation, we found that 21 consecutive days of Met (100 mg/kg) administration is optimal against silicosis. Next, using haematoxylin-eosin and Masson's trichrome staining and immunoblotting, we found that Met effectively blunted the inflammatory response and collagen deposition at 56 days after exposure to SiO2. We also demonstrated that Met effectively activates AMPK signalling and markedly relieves oxidative stress, the mitochondrial apoptotic pathway and EndoMT induced by SiO2 exposure both in vivo and in vitro. Overall, Met can alleviate SiO2-induced pulmonary fibrosis by regulating oxidative stress and the mitochondrial apoptotic pathway. The current study provides a rationale for the clinical treatment of SiO2-induced pulmonary fibrosis.
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Affiliation(s)
- Ning Li
- School of Public Health, North China University of Science of Technology, Tangshan 062310, China; School of Public Health, Xinxiang Medical University, Xinxiang 453003, China
| | - Meiyu Chang
- School of Public Health, North China University of Science of Technology, Tangshan 062310, China
| | - Qiang Zhou
- School of Public Health, North China University of Science of Technology, Tangshan 062310, China
| | - Lin Zhang
- Clinical Medical Research Center for Women and Children Diseases, Key Laboratory of Birth Defect Prevention and Genetic Medicine of Shandong Health Commission, Maternal and Child Health Care Hospital of Shandong Province, Jinan, China
| | - Yongheng Wang
- School of Public Health, North China University of Science of Technology, Tangshan 062310, China
| | - Yi Guan
- School of Public Health, North China University of Science of Technology, Tangshan 062310, China
| | - Haibin Li
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, China
| | - Yingzheng Zhao
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, China
| | - Chunjie Ding
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, China
| | - Shan Hong
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, China
| | - Sanqiao Yao
- School of Public Health, North China University of Science of Technology, Tangshan 062310, China; School of Public Health, Xinxiang Medical University, Xinxiang 453003, China.
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15
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Hong S, Ding CJ, Zhou Q, Sun YZ, Zhang M, Li N, Dong XW, Guan Y, Zhang L, Tian LQ, Cao J, Yao W, Ren WJ, Yao SQ. Proteomic Analysis Revealed the Involvement of Autophagy in Rat Acute Lung Injuries Caused by Gas Explosion Based on a Data-Independent Acquisition Strategy. Biomed Environ Sci 2023; 36:206-212. [PMID: 36861200 DOI: 10.3967/bes2023.024] [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] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/02/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Shan Hong
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan, China;Department of Nephrology, Xinxiang Second People's Hospital, Xinxiang 453003, Henan, China
| | - Chun Jie Ding
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan, China
| | - Qiang Zhou
- School of Public Health, North China University of Science and Technology, Tangshan 063210, Hebei, China
| | - Yun Zhe Sun
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan, China
| | - Miao Zhang
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan, China
| | - Ning Li
- School of Public Health, North China University of Science and Technology, Tangshan 063210, Hebei, China
| | - Xin Wen Dong
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan, China
| | - Yi Guan
- School of Public Health, North China University of Science and Technology, Tangshan 063210, Hebei, China
| | - Lin Zhang
- Clinical Medical Research Center for Women and Children Diseases, Maternal and Child Health Care Hospital of Shandong Province, Shandong University, Jinan 250001, Shandong, China
| | - Lin Qiang Tian
- Institute of Trauma and Orthopedics, Xinxiang Medical University, Xinxiang 453003, Henan, China
| | - Jia Cao
- Toxicology Research Institute, PLA Army Medical University, Chongqing 400038, China
| | - Wu Yao
- School of Public Health, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Wen Jie Ren
- Institute of Trauma and Orthopedics, Xinxiang Medical University, Xinxiang 453003, Henan, China
| | - San Qiao Yao
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan, China
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Akhtar M, Bonus F, Lebrun-Gallagher FR, Johnson NI, Siegele-Brown M, Hong S, Hile SJ, Kulmiya SA, Weidt S, Hensinger WK. A high-fidelity quantum matter-link between ion-trap microchip modules. Nat Commun 2023; 14:531. [PMID: 36754957 PMCID: PMC9908934 DOI: 10.1038/s41467-022-35285-3] [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: 06/30/2022] [Accepted: 11/25/2022] [Indexed: 02/10/2023] Open
Abstract
System scalability is fundamental for large-scale quantum computers (QCs) and is being pursued over a variety of hardware platforms. For QCs based on trapped ions, architectures such as the quantum charge-coupled device (QCCD) are used to scale the number of qubits on a single device. However, the number of ions that can be hosted on a single quantum computing module is limited by the size of the chip being used. Therefore, a modular approach is of critical importance and requires quantum connections between individual modules. Here, we present the demonstration of a quantum matter-link in which ion qubits are transferred between adjacent QC modules. Ion transport between adjacent modules is realised at a rate of 2424 s-1 and with an infidelity associated with ion loss during transport below 7 × 10-8. Furthermore, we show that the link does not measurably impact the phase coherence of the qubit. The quantum matter-link constitutes a practical mechanism for the interconnection of QCCD devices. Our work will facilitate the implementation of modular QCs capable of fault-tolerant utility-scale quantum computation.
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Affiliation(s)
- M. Akhtar
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - F. Bonus
- Universal Quantum Ltd, Brighton, BN1 6SB UK ,grid.83440.3b0000000121901201Department of Physics and Astronomy, University College London, London, WC1E 6BT UK
| | - F. R. Lebrun-Gallagher
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - N. I. Johnson
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - M. Siegele-Brown
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. Hong
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. J. Hile
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. A. Kulmiya
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,grid.5337.20000 0004 1936 7603Quantum Engineering Centre for Doctoral Training, University of Bristol, Bristol, BS8 1TH UK
| | - S. Weidt
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - W. K. Hensinger
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
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Bang S, Kwon H, Yoon C, Rhew S, Shin D, Moon H, Cho H, Ha U, Lee J, Hong S. Development and validation of a machine learning-based CT radiomics model for differentiation of benign and malignant solid renal tumors. Eur Urol 2023. [DOI: 10.1016/s0302-2838(23)01313-1] [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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18
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Ding C, Hong S, Zhang M, Sun Y, Li N, Zhang J, Ma L, Tian L, Ren W, Zhang L, Yao S. Establishment and evaluation of an in vitro blast lung injury model using alveolar epithelial cells. Front Public Health 2022; 10:994670. [PMID: 36620304 PMCID: PMC9816474 DOI: 10.3389/fpubh.2022.994670] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 07/15/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
Background Gas explosion is a fatal disaster commonly occurred in coal mining and often causes systematic physical injuries, of which blast lung injury is the primary one and has not yet been fully investigated due to the absence of disease models. To facilitate studies of this field, we constructed an in vitro blast lung injury model using alveolar epithelial cells. Methods We randomly divided the alveolar epithelial cells into the control group and blast wave group, cells in the blast wave group were stimulated with different strengths of blast wave, and cells in the control group received sham intervention. Based on the standards we set up for a successful blast injury model, the optimal modeling conditions were studied on different frequencies of blast wave, modeling volume, cell incubation duration, and cell density. The changes of cell viability, apoptosis, intracellular oxidative stress, and inflammation were measured. Results We found that cell viability decreased by approximately 50% at 6 h after exposing to 8 bar energy of blast wave, then increased with the extension of culture time and reached to (74.33 ± 9.44) % at 12 h. By applying 1000 ~ 2500 times of shock wave to 1 ~ 5 × 105 cells /ml, the changes of cell viability could well meet the modeling criteria. In parallel, the content of reactive oxide species (ROS), malonaldehyde (MDA), interleukin 18 (IL-18), tumor necrosis factor alpha (TNF-α), and transforming growth factor beta (TGF-β) increased in the blast wave group, while superoxide dismutase (SOD) and Glutathione -S- transferase (GST) decreased, which were highly consistent with that of human beings with gas explosion-induced pulmonary injury. Conclusion An in vitro blast lung injury model is set up using a blast wave physiotherapy under 8 bar, 10 Hz blast wave on (1 ~ 5) ×105 alveolar epithelial cells for 1 000 times. This model is flexible, safe, and stable, and can be used for studies of lung injury caused by gas explosion and blast-associated other external forces.
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Affiliation(s)
- Chunjie Ding
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Shan Hong
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Miao Zhang
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Yunzhe Sun
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Ning Li
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Jing Zhang
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Lan Ma
- School of Public Health, Weifang Medical University, Weifang, China
| | - Linqiang Tian
- Institute of Trauma and Orthopedics, Xinxiang Medical University, Xinxiang, China
| | - Wenjie Ren
- Institute of Trauma and Orthopedics, Xinxiang Medical University, Xinxiang, China
| | - Lin Zhang
- Clinical Medical Research Center for Women and Children Diseases, Maternal and Child Health Care Hospital of Shandong Province Affiliated to Qingdao University, Jinan, China,*Correspondence: Lin Zhang ✉
| | - Sanqiao Yao
- School of Public Health, Xinxiang Medical University, Xinxiang, China,Sanqiao Yao ✉
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Choi H, Pyo KH, Lim S, Cho B, Hong S. PP223 Single-cell RNA sequencing in metastatic lung cancer uncovers the efficacy of PD-1/PD-L1 inhibitors on immune cell population. ESMO Open 2022. [DOI: 10.1016/j.esmoop.2022.100719] [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: 12/31/2022] Open
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Knight, Imwattana K, Lim SC, Hong S, Putsathit P, Collins DA, Riley TV. WS1.6: GENOMIC EPIDEMIOLOGY OF RECURRENT CLOSTRIDIOIDES DIFFICILE INFECTION IN WESTERN AUSTRALIA. J Glob Antimicrob Resist 2022. [DOI: 10.1016/s2213-7165(22)00274-0] [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: 12/23/2022] Open
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21
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Morvan A, Andersen TI, Mi X, Neill C, Petukhov A, Kechedzhi K, Abanin DA, Michailidis A, Acharya R, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores Burgos L, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Grajales Dau A, Gross JA, Habegger S, Hamilton MC, Harrigan MP, Harrington SD, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Malone F, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Nersisyan A, Newman M, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Olenewa R, Opremcak A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shvarts V, Skruzny J, Smith WC, Strain D, Sterling G, Su Y, Szalay M, Torres A, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Xing C, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Aleiner I, Ioffe LB, Roushan P. Formation of robust bound states of interacting microwave photons. Nature 2022; 612:240-245. [PMID: 36477133 PMCID: PMC9729104 DOI: 10.1038/s41586-022-05348-y] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/14/2022] [Indexed: 12/12/2022]
Abstract
Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2-9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.
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Affiliation(s)
- A Morvan
- Google Research, Mountain View, CA, USA
| | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J Basso
- Google Research, Mountain View, CA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | - D Eppens
- Google Research, Mountain View, CA, USA
| | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Computation and Communication Technology, Centre for Quantum Software and Information, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Y Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - F Malone
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | - K C Miao
- Google Research, Mountain View, CA, USA
| | - M Mohseni
- Google Research, Mountain View, CA, USA
| | | | - E Mount
- Google Research, Mountain View, CA, USA
| | | | - O Naaman
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - R Olenewa
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | | | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - D Strain
- Google Research, Mountain View, CA, USA
| | | | - Y Su
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - I Aleiner
- Google Research, Mountain View, CA, USA.
| | - L B Ioffe
- Google Research, Mountain View, CA, USA.
| | - P Roushan
- Google Research, Mountain View, CA, USA.
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Hahn T, Daymont C, Beukelman T, Groh B, Hays K, Bingham CA, Scalzi L, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Intraarticular steroids as DMARD-sparing agents for juvenile idiopathic arthritis flares: Analysis of the Childhood Arthritis and Rheumatology Research Alliance Registry. Pediatr Rheumatol Online J 2022; 20:107. [PMID: 36434731 PMCID: PMC9701017 DOI: 10.1186/s12969-022-00770-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/08/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Children with juvenile idiopathic arthritis (JIA) who achieve a drug free remission often experience a flare of their disease requiring either intraarticular steroids (IAS) or systemic treatment with disease modifying anti-rheumatic drugs (DMARDs). IAS offer an opportunity to recapture disease control and avoid exposure to side effects from systemic immunosuppression. We examined a cohort of patients treated with IAS after drug free remission and report the probability of restarting systemic treatment within 12 months. METHODS We analyzed a cohort of patients from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry who received IAS for a flare after a period of drug free remission. Historical factors and clinical characteristics and of the patients including data obtained at the time of treatment were analyzed. RESULTS We identified 46 patients who met the inclusion criteria. Of those with follow up data available 49% had restarted systemic treatment 6 months after IAS injection and 70% had restarted systemic treatment at 12 months. The proportion of patients with prior use of a biologic DMARD was the only factor that differed between patients who restarted systemic treatment those who did not, both at 6 months (79% vs 35%, p < 0.01) and 12 months (81% vs 33%, p < 0.05). CONCLUSION While IAS are an option for all patients who flare after drug free remission, it may not prevent the need to restart systemic treatment. Prior use of a biologic DMARD may predict lack of success for IAS. Those who previously received methotrexate only, on the other hand, are excellent candidates for IAS.
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Affiliation(s)
- Timothy Hahn
- Department of Pediatrics, Penn State Children's Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA, 17033-0855, USA.
| | - Carrie Daymont
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Timothy Beukelman
- grid.265892.20000000106344187Department of Pediatrics, University of Alabama at Birmingham, CPPN G10, 1600 7th Ave South, Birmingham, AL 35233 USA
| | - Brandt Groh
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | | | - Catherine April Bingham
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Lisabeth Scalzi
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
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23
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He LN, Fu S, Ma H, Chen C, Zhang X, Li H, Du W, Chen T, Jiang Y, Wang Y, Wang Y, Zhou Y, Lin Z, Yang Y, Huang Y, Zhao H, Fang W, Zhang H, Zhang L, Hong S. Early on-treatment tumor growth rate (EOT-TGR) determines treatment outcomes of advanced non-small-cell lung cancer patients treated with programmed cell death protein 1 axis inhibitor. ESMO Open 2022; 7:100630. [PMID: 36442353 PMCID: PMC9808481 DOI: 10.1016/j.esmoop.2022.100630] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/02/2022] [Accepted: 10/09/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Tumor growth rate (TGR), denoted as percentage change in tumor size per month, is a well-established indicator of tumor growth kinetics. The predictive value of early on-treatment TGR (EOT-TGR) for immunotherapy remains unclear. We sought to establish and validate the association of EOT-TGR with treatment outcomes in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 (programmed cell death protein 1/programmed death-ligand 1) therapy. PATIENTS AND METHODS This bicenter retrospective cohort study included a training cohort, a contemporaneously treated internal validation cohort, and an external validation cohort. Computed tomography images were retrieved to calculate EOT-TGR, denoted as tumor burden change per month during a period between baseline and the first imaging evaluation after immunotherapy. Kaplan-Meier methodology and Cox regression analysis were conducted for survival analyses. RESULTS In the pooled cohort (n = 172), 125 patients (72.7%) were males; median age at diagnosis was 58 (range 28-79) years. Based on the training cohort, we determined the optimal cut-off value for EOT-TGR as 10.4%/month. Higher EOT-TGR was significantly associated with inferior overall survival [OS; hazard ratio (HR) 2.93, 95% confidence interval (CI) 1.47-5.83; P = 0.002], worse progression-free survival (PFS; HR 2.44, 95% CI 1.46-4.08; P = 0.001), and lower objective response rate (3.3% versus 20.9%; P = 0.040) and durable clinical benefit rate (6.7% versus 41.9%; P = 0.001). Results were reproducible in the two validation cohorts for OS and PFS. Among 43 patients who had a best response of progressive disease in the training cohort, those with high EOT-TGR had worse OS (HR 2.64; P = 0.041) and were more likely to progress due to target lesions at the first tumor evaluation (85.2% versus 0.0%; P <0.001). CONCLUSIONS Higher EOT-TGR was associated with inferior OS and immunotherapeutic response in patients with aNSCLC undergoing anti-PD-1/PD-L1 therapy. This easy-to-calculate radiologic biomarker may help evaluate the abilities of immunotherapy to prolong survival and assist in tailoring patients' management. TRIAL REGISTRATION ClinicalTrials.govNCT04722406; https://clinicaltrials.gov/ct2/show/NCT04722406.
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Affiliation(s)
- L.-N. He
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - S. Fu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation of Sun Yat-Sen University; Department of Cellular & Molecular Diagnostics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - H. Ma
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - C. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Departments of Radiation Oncology, Guangzhou, China
| | - X. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Li
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Du
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - T. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Endoscopy, Guangzhou, China
| | - Y. Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,VIP Region, Guangzhou, China
| | - Z. Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhang
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China,Prof. Haibo Zhang, Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, 111 Dade Road, Guangzhou, Guangdong 510120, People’s Republic of China. Tel: +86-20-81887233-34830
| | - L. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Prof. Li Zhang, MD, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87343458
| | - S. Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Correspondence to: Prof. Shaodong Hong, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87342480
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24
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Mi X, Sonner M, Niu MY, Lee KW, Foxen B, Acharya R, Aleiner I, Andersen TI, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Brill L, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Debroy DM, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores L, Forati E, Fowler AG, Giang W, Gidney C, Gilboa D, Giustina M, Dau AG, Gross JA, Habegger S, Harrigan MP, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Kafri D, Kechedzhi K, Khattar T, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Lee J, Laws L, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Morvan A, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Newman M, O’Brien TE, Opremcak A, Petukhov A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schuster C, Shearn MJ, Shvarts V, Strain D, Su Y, Szalay M, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Abanin DA, Roushan P. Noise-resilient edge modes on a chain of superconducting qubits. Science 2022; 378:785-790. [DOI: 10.1126/science.abq5769] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the one-dimensional kicked Ising model, which exhibits nonlocal Majorana edge modes (MEMs) with
ℤ
2
parity symmetry. We find that any multiqubit Pauli operator overlapping with the MEMs exhibits a uniform late-time decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This characteristic allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Furthermore, the MEMs are found to be resilient against certain symmetry-breaking noise owing to a prethermalization mechanism. Our work elucidates the complex interplay between noise and symmetry-protected edge modes in a solid-state environment.
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Affiliation(s)
- X. Mi
- Google Research, Mountain View, CA, USA
| | - M. Sonner
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - M. Y. Niu
- Google Research, Mountain View, CA, USA
| | - K. W. Lee
- Google Research, Mountain View, CA, USA
| | - B. Foxen
- Google Research, Mountain View, CA, USA
| | | | | | | | - F. Arute
- Google Research, Mountain View, CA, USA
| | - K. Arya
- Google Research, Mountain View, CA, USA
| | - A. Asfaw
- Google Research, Mountain View, CA, USA
| | | | - J. C. Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J. Basso
- Google Research, Mountain View, CA, USA
| | | | | | | | - L. Brill
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Chen
- Google Research, Mountain View, CA, USA
| | - B. Chiaro
- Google Research, Mountain View, CA, USA
| | | | - P. Conner
- Google Research, Mountain View, CA, USA
| | | | | | | | - S. Demura
- Google Research, Mountain View, CA, USA
| | | | - D. Eppens
- Google Research, Mountain View, CA, USA
| | | | - L. Faoro
- Google Research, Mountain View, CA, USA
| | - E. Farhi
- Google Research, Mountain View, CA, USA
| | - R. Fatemi
- Google Research, Mountain View, CA, USA
| | - L. Flores
- Google Research, Mountain View, CA, USA
| | - E. Forati
- Google Research, Mountain View, CA, USA
| | | | - W. Giang
- Google Research, Mountain View, CA, USA
| | - C. Gidney
- Google Research, Mountain View, CA, USA
| | - D. Gilboa
- Google Research, Mountain View, CA, USA
| | | | - A. G. Dau
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - S. Hong
- Google Research, Mountain View, CA, USA
| | - T. Huang
- Google Research, Mountain View, CA, USA
| | - A. Huff
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Jiang
- Google Research, Mountain View, CA, USA
| | - C. Jones
- Google Research, Mountain View, CA, USA
| | - D. Kafri
- Google Research, Mountain View, CA, USA
| | | | | | - S. Kim
- Google Research, Mountain View, CA, USA
| | - A. Y. Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | | | - A. N. Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P. Laptev
- Google Research, Mountain View, CA, USA
| | - K.-M. Lau
- Google Research, Mountain View, CA, USA
| | - J. Lee
- Google Research, Mountain View, CA, USA
| | - L. Laws
- Google Research, Mountain View, CA, USA
| | - W. Liu
- Google Research, Mountain View, CA, USA
| | | | - O. Martin
- Google Research, Mountain View, CA, USA
| | | | - M. McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | | | | | | | - A. Morvan
- Google Research, Mountain View, CA, USA
| | - E. Mount
- Google Research, Mountain View, CA, USA
| | | | - O. Naaman
- Google Research, Mountain View, CA, USA
| | - M. Neeley
- Google Research, Mountain View, CA, USA
| | - C. Neill
- Google Research, Mountain View, CA, USA
| | - M. Newman
- Google Research, Mountain View, CA, USA
| | | | | | | | - R. Potter
- Google Research, Mountain View, CA, USA
| | | | | | - N. Saei
- Google Research, Mountain View, CA, USA
| | - D. Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - D. Strain
- Google Research, Mountain View, CA, USA
| | - Y. Su
- Google Research, Mountain View, CA, USA
| | - M. Szalay
- Google Research, Mountain View, CA, USA
| | - G. Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T. White
- Google Research, Mountain View, CA, USA
| | - Z. Yao
- Google Research, Mountain View, CA, USA
| | - P. Yeh
- Google Research, Mountain View, CA, USA
| | - J. Yoo
- Google Research, Mountain View, CA, USA
| | | | - Y. Zhang
- Google Research, Mountain View, CA, USA
| | - N. Zhu
- Google Research, Mountain View, CA, USA
| | - H. Neven
- Google Research, Mountain View, CA, USA
| | - D. Bacon
- Google Research, Mountain View, CA, USA
| | - J. Hilton
- Google Research, Mountain View, CA, USA
| | - E. Lucero
- Google Research, Mountain View, CA, USA
| | | | - S. Boixo
- Google Research, Mountain View, CA, USA
| | | | - Y. Chen
- Google Research, Mountain View, CA, USA
| | - J. Kelly
- Google Research, Mountain View, CA, USA
| | | | - D. A. Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
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25
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Hong S, Lee J, Heo J, Suh K, Kim S, Kim Y, Kim J, Lee JS. 413P Association of concomitant medications on survival outcomes in cancer patients treated with immune checkpoint inhibitors: Analysis of health insurance review and assessment database. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.444] [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: 12/07/2022] Open
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26
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Hall J, Sud S, Casey D, Poellmann M, Bu J, Wang A, Hong S, Shen C. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Locoregional Head and Neck Cancer Receiving Definitive Therapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1318] [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: 11/16/2022]
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27
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Song S, Kim J, Nam J, Ko Y, Kim J, Jung S, Kang S, Park J, Seo H, Kim H, Jeong B, Kim T, Choi S, Nam J, Ku J, Joo K, Jang W, Yoon Y, Yun S, Hong S, Oh J. Stage matched head-to-head comparison between urachal carcinoma and urothelial bladder cancer: TNM-stage based analysis from a national multicenter database. EUR UROL SUPPL 2022. [DOI: 10.1016/s2666-1683(22)02591-5] [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: 11/11/2022] Open
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28
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Yu M, Yun M, Lee S, Rajasekaran N, Park K, Kim N, Hong S, Oh S, Lee Y, Lee E, Kim C, Lim S, Choi J, Cho B. 1174P The MET inhibitor ABN401 in combination with the third-generation EGFR-TKI is effective MET-amplified and EGFR-mutant NSCLC with acquired resistance to third-generation EGFR-TKI in preclinical models. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.1297] [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: 11/26/2022] Open
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29
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Hong S, Shen Y, Li Y. Physicochemical and Functional Properties of Texturized Vegetable Proteins and Cooked Patty Textures: Comprehensive Characterization and Correlation Analysis. Foods 2022; 11:foods11172619. [PMID: 36076805 PMCID: PMC9455741 DOI: 10.3390/foods11172619] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/03/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Rising concerns of environment and health from animal-based proteins have driven a massive demand for plant proteins. Textured vegetable protein (TVP) is a plant-protein-based product with fibrous textures serving as a promising meat analog. This study aimed to establish possible correlations between the properties of raw TVPs and the corresponding meatless patties. Twenty-eight commercial TVPs based on different protein types and from different manufacturers were compared in proximate compositions, physicochemical and functional properties, as well as cooking and textural attributes in meatless patties. Significant differences were observed in the compositions and properties of the raw TVPs (p < 0.05) and were well reflected in the final patties. Of all the TVP attributes, rehydration capacity (RHC) was the most dominant factor affecting cooking loss (r = 0.679) and textures of hardness (r = −0.791), shear force (r = −0.621) and compressed juiciness (r = 0.812) in meatless patties, as evidenced by the significant correlations (p < 0.01). The current study may advance the knowledge for TVP-based meat development.
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30
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Ramesh P, Jaishankar D, Cosgrove C, Kosche C, Li A, Hong S, Shivde R, Munir S, Zhang H, Choi J, Le Poole I. 318 Skin rash composition after checkpoint inhibitor therapy varies by therapeutic regimen. J Invest Dermatol 2022. [DOI: 10.1016/j.jid.2022.05.326] [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: 10/17/2022]
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31
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Zhang M, Sun Y, Ding C, Hong S, Li N, Guan Y, Zhang L, Dong X, Cao J, Yao W, Ren W, Yao S. Metformin mitigates gas explosion‑induced blast lung injuries through AMPK‑mediated energy metabolism and NOX2‑related oxidation pathway in rats. Exp Ther Med 2022; 24:529. [PMID: 35837050 PMCID: PMC9257965 DOI: 10.3892/etm.2022.11456] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/31/2022] [Indexed: 11/17/2022] Open
Abstract
Gas explosions are a recurrent event in coal mining that cause severe pulmonary damage due to shock waves, and there is currently no effective targeted treatment. To illustrate the mechanism of gas explosion-induced lung injury and to explore strategies for blast lung injury (BLI) treatment, the present study used a BLI rat model and supplementation with metformin (MET), an AMP-activated protein kinase (AMPK) activator, at a dose of 10 mg/kg body weight by intraperitoneal injection. Protein expression levels were detected by western blotting. Significantly decreased expression of phosphorylated (p)-AMPK, peroxisome proliferator-activated receptor-γ coactivator-1α (PGC1α) and metabolic activity were observed in the BLI group compared with those in the control group. However, the mitochondrial stability, metabolic activity and expression of p-AMPK and PGC1α were elevated following MET treatment. These results suggested that MET could attenuate gas explosion-induced BLI by improving mitochondrial homeostasis. Meanwhile, high expression of nicotinamide adenine dinucleotide phosphate oxidase (NOX2) and low expression of catalase (CAT) were observed in the BLI group. The expression levels of NOX2 and CAT were restored in the BLI + MET group relative to changes in the BLI group, and the accumulation of oxidative stress was successfully reversed following MET treatment. Overall, these findings revealed that MET could alleviate BLI by activating the AMPK/PGC1α pathway and inhibiting oxidative stress caused by NOX2 activation.
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Affiliation(s)
- Miao Zhang
- Research Center for Precision Prevention and Control of Occupational Hazards, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Yunzhe Sun
- Research Center for Precision Prevention and Control of Occupational Hazards, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Chunjie Ding
- Research Center for Precision Prevention and Control of Occupational Hazards, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Shan Hong
- Research Center for Precision Prevention and Control of Occupational Hazards, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Ning Li
- Department of Occupational and Environmental Health, School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063000, P.R. China
| | - Yi Guan
- Department of Occupational and Environmental Health, School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063000, P.R. China
| | - Lin Zhang
- Key Laboratory of Birth Regulation and Control Technology, National Health Commission of China, Maternal and Child Care Hospital of Shandong Province, Shandong University, Jinan, Shandong 250001, P.R. China
| | - Xinwen Dong
- Research Center for Precision Prevention and Control of Occupational Hazards, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Jia Cao
- Institute of Toxicology, College of Preventive Medicine, Army Medical University, Chongqing 400038, P.R. China
| | - Wu Yao
- Department of Occupational and Environmental Health, School of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, P.R. China
| | - Wenjie Ren
- Research Center for Precision Prevention and Control of Occupational Hazards, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Sanqiao Yao
- Research Center for Precision Prevention and Control of Occupational Hazards, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
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Kang E, Kim YG, Oh JS, Hong S, Lee CK, Yoo B, Ahn SM. POS1247 THE EFFECT OF IMMUNOSUPPRESSIVE AGENTS ON ANTIBODY FORMATION AFTER COVID-19 VACCINATION IN RHEUMATOID ARTHRITIS PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3412] [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: 11/04/2022]
Abstract
BackgroundThere is still controversy about the efficacy of COVID-19 vaccination and its extent in lowering immunogenicity of Rheumatoid Arthritis (RA) patients. The guideline in whether immunosuppressive agents need to be discontinued before the vaccination is continuously updated because it is considered to lower immunogenicity. Furthermore, there is great discussion on the effectiveness of the COVID-19 booster vaccine and interest in antibody generation in different types of vaccine, as in South Korea there are many patients who were prescribed the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine.ObjectivesThus, we investigated the differences of antibody production between patients who received only two doses of ChAdOx1-S nCoV-19 and those who received the mRNA booster vaccine. Also, antibody production under different types of immunosuppressive agents was analyzed.MethodsFrom October 14, 2021 to January 21, 2022 at a tertiary referral center, two patient groups diagnosed with RA were studied prospectively; one group that completed 1st and 2nd doses of ChAdOx1-S nCoV-19 vaccine, second group that completed mRNA booster vaccine as well as two doses of ChAdOx1-S nCoV-19 vaccine. SARS-CoV-2 antibody testing on the semiquantitative anti-SARS-CoV-2 S enzyme immunoassay was done, and differences in antibody titers were analyzed in patients who received different immunosuppressive agents such as csDMARD, TNF inhibitor, JAK inhibitor, Tocilizumab, Abatacept and Corticosteroid. Statistical analysis with a multivariate logistic regression model was performed.ResultsIn a total of 261 patients, 153 patients had completed two doses of ChAdOx1-S nCoV-19, 108 patients had completed third mRNA booster vaccine. Anti-SARS-CoV-2 RBD antibody positive rate (titer>0.8U/mL) was 97%(149/153) and 99%(107/108) respectively, and only 5 patients showed negative result. In the aspect of high antibody titer(>250U/mL), which is the upper limit of the RBD antibody immunoassay, the result showed rate of 31% (47/153) in the non-booster group and 94%(102/108) in the booster group respectively.Among the different immunosuppressive agents and other clinical aspects, multivariate analysis revealed that corticosteroid use (OR 0.91; 95% CI: 0.86-0.98), older age(OR 4.33; 95% CI: 1.34-13.91), and male gender(OR 0.35; 95% CI 0.16-0.75) were significantly associated with low rate of high antibody titer.Furthermore, out of 14 patients who underwent antibody test twice before and after the mRNA booster vaccine, other than four patients who already showed high titer of >250U/mL before the mRNA booster vaccine, 10 patients showed an increase in titer after the booster vaccine and 7 patients were acquired high titer of >250U/mL.Figure 1.Anti-SARS-CoV RBD antibody titer of two groupsTable 1.Analysis of immunosuppressive agents and other clinical aspects for high antibody titer(>250U/mL) after two doses of ChAdOx1-S nCoV-19Univariate analysisMultivariate analysisParameterOR95% CIp valueOR95% CIp valueClinical features Age0.9170.860-0.9780.0080.9170.857-0.9810.012 Sex3.6741.206-11.1910.0224.3301.348-13.9120.014 DAS 281.1440.670-1.9500.622 Duration0.9300.830-1.0430.214Medications csDMARD1.2730.639-2.5331.273 TNF inhibitor2.2110.795-6.1450.128 JAK inhibitor0.6650.275-1.6070.365 Abatacept0.3680.038-3.6020.391 Tocilizumab1.2640.438-3.6480.665 Corticosteroid0.4720.235-0.9490.0350.3490.163-0.7480.007Medication dose Methotrexate0.9930.919-1.0720.855 Corticosteroid0.8490.719-1.0030.054ConclusionAnti-SARS-CoV-2 RBD antibody positive rate was 97% or more regardless of the mRNA booster vaccination. However, patients who received the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine showed high antibody titer (>250U/mL) three times more than those who did not receive the booster shot.Our findings also showed that corticosteroid use, old age, and male gender is significantly associated with low rate of acquiring high antibody titer.Disclosure of InterestsNone declared
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Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0476 PREDICTIVE FACTORS FOR THE DEVELOPMENT OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH IMMUNE THROMBOCYTOPENIA. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.1436] [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: 11/04/2022]
Abstract
BackgroundPatients with immune thrombocytopenia (ITP) have a risk of developing systemic lupus erythematosus (SLE). We sought to examine the clinical characteristics of patients with primary ITP who later developed SLE, and identified the risk factors for the development of SLE.ObjectivesWe retrospectively examined patients who were diagnosed with primary ITP at a tertiary hospital between August 2001 and November 2019. We compared the clinical characteristics according to the development of SLE. Logistic regression analysis was performed to identify the factors associated with the development of SLE.MethodsOf 130 patients with primary ITP, 10 (7.7%) were later diagnosed with SLE during follow-up (median, 30 months [IQR, 15.5–105]). The presence of skin bleeding, organ bleeding, lymphopenia, anemia, and positive antinuclear antibody (ANA) titer (> 1:160) were more common among patients who later developed SLE than did those who did not develop SLE. Multivariate analysis showed that young age (< 40 years; odds ratio [OR], 8.359 [95% confidence interval (CI), 1.230–56.793]; p = 0.033), organ bleeding (OR, 18.349 [95% CI, 2.771–121.517]; p = 0.003), and ANA positivity (>1:160; OR, 7.692 [95% CI, 1.482–39.910]; p = 0.015) were significantly associated with the development of SLE.ResultsYoung age (< 40 years), organ bleeding, and ANA positivity (> 1:160) were risk factors for the development of SLE in patients with primary ITP.ConclusionThese results suggest that continued follow-up for the detection of SLE development is needed for patients with ITP, particularly those with young age, ANA positivity, or organ bleeding.References[1]Zhu, Fang-Xiao, et al. “Risk of systemic lupus erythematosus in patients with idiopathic thrombocytopenic purpura: a population-based cohort study.” Annals of the rheumatic diseases 79.6 (2020): 793-799.Table 1.Factors associated with the development of SLE in patients with primary ITPUnivariateMultivariateOR95% CIP valueOR95% CIP valueYoung agea5.4441.332–22.2500.0188.3591.230–56.7930.033Female4.3330.530–35.4220.17BMI0.8730.717–1.0700.20Skin bleeding8.4191.034–68.5330.046Mucosa bleeding1.2500.247–6.3300.79Organ bleeding14.8643.633–60.815< 0.00118.3492.771–121.5170.003Platelet counts0.9110.828–1.0020.06ANA positivityb16.5003.984–68.341< 0.0017.6921.482–39.9100.015Neutropeniac2.1110.229–19.4990.51Lymphopeniad4.8461.189–19.7590.028Anemiae10.1182.044–50.0910.005SLE: systemic lupus erythematosus, ITP: immune thrombocytopenia, BMI: body mass index, ANA: antinuclear antibody, OR: odds ratio, CI: confidence interval.aYoung age = age < 40 yearsbANA positivity ≥ 1:160cNeutropenia = Absolute neutrophil count < 1500 μLdLymphopenia = Absolute lymphocyte count < 1500 μLeAnemia = Hemoglobin < 12 g/dLDisclosure of InterestsNone declared
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Kim YE, Choi SJ, Lim DH, Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0456 DISEASE FLARE OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH END-STAGE RENAL DISEASE ON DIALYSIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.696] [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: 11/03/2022]
Abstract
BackgroundThe systemic lupus erythematosus (SLE) disease activity in patients with lupus nephritis (LN) generally declines after the initiation of renal replacement therapy (RRT); this is known as the “burn out” phenomenon that possibly occurs due to the suppression of cellular and humoral immunity in the end-stage renal disease (ESRD) state and elimination of disease pathogenic factor by dialysis [1-4]. However, several studies showed that SLE flares could occur even during RRT [5-8]. Nevertheless, the details of disease flares of SLE in patients under dialysis have not been studied yet.ObjectivesThis study aimed to investigate the clinical features, risk factors, and treatment details of SLE patients experiencing disease flare under RRT.MethodsThe medical records of SLE patients who received dialysis at two tertiary referral hospitals in Seoul and Ulsan, South Korea were reviewed. All patients in this study were either clinically or histologically diagnosed with LNResultsOf a total of 121 patients with SLE on dialysis, 96 (79.3%) were on hemodialysis (HD) and 25 (20.6%) were on peritoneal dialysis (PD). During a median follow-up of 45 months (IQR, 23–120) after the initiation of dialysis, 32 (26.4%) patients experienced SLE flare (HD, n = 25; PD, n = 7). The most common features of SLE flare were hematologic (40.6%) and constitutional manifestations (40.6%). Treatments for disease flares were based on corticosteroids, and 11 (34.3%) patients required additional immunosuppressants including cyclophosphamide and mycophenolate mofetil. There was no case of severe adverse events related to medication. non-renal SLE Disease Activity Index (SLEDAI) score before dialysis initiation (HR 1.235; 95% CI, 1.122–1.359; P = 0.001) was a significant risk factor for disease flare during dialysis.Table 1.Multivariable analysis of factors associated with SLE flare under dialysisHazard ratio95% CIP-valueNon-renal SLEDAI at the initiation of dialysis1.2351.122–1.3590.001Hematologic manifestation prior to dialysis1.2560.690–2.8260.150Cumulative amount of steroid during 1 year prior to the initiation of dialysis1.0400.995–1.0870.086Dialysis modality: hemodialysis0.7660.262–2.2430.630ConclusionMore than one-quarter of SLE patients experienced disease flare during dialysis, which most commonly had hematologic manifestations. Continued follow-up and appropriate treatments including immunosuppressants should be considered for patients with SLE under dialysis.References[1]Coplon NS, Diskin CJ, Petersen J, Swenson RS. The Long-Term Clinical Course of Systemic Lupus Erythematosus in End-Stage Renal Disease. New England Journal of Medicine 1983;308:186-90.[2]Lee P-T, Fang H-C, Chen C-L, Chiou Y-H, Chou K-J, Chung H-M. Poor prognosis of end-stage renal disease in systemic lupus erythematosus: a cohort of Chinese patients. Lupus 2003;12:827-32.[3]Pahl MV, Gollapudi S, Sepassi L, Gollapudi P, Elahimehr R, Vaziri ND. Effect of end-stage renal disease on B-lymphocyte subpopulations, IL-7, BAFF and BAFF receptor expression. Nephrology Dialysis Transplantation 2010;25:205-12.[4]Ribeiro FM, Fabris CL, Bendet I, Lugon JR. Survival of lupus patients on dialysis: a Brazilian cohort. Rheumatology 2013;52:494-500.[5]Okano K, Yumura W, Nitta K et al. Analysis of Lupus Activity in End-Stage Renal Disease Treated by Hemodialysis. Internal Medicine 2001;40:598-602.[6]Barrera-Vargas A, Quintanar-Martínez M, Merayo-Chalico J, Alcocer-Varela J, Gómez-Martín D. Risk factors for systemic lupus erythematosus flares in patients with end-stage renal disease: a case–control study. Rheumatology 2015:kev349.[7]Cucchiari D, Graziani G, Ponticelli C. The dialysis scenario in patients with systemic lupus erythematosus. Nephrology Dialysis Transplantation 2014;29:1507-13.[8]Kang S-H, Chung B-H, Choi S-R et al. Comparison of Clinical Outcomes by Different Renal Replacement Therapy in Patients with End-Stage Renal Disease Secondary to Lupus Nephritis. The Korean Journal of Internal Medicine 2011;26:60.Disclosure of InterestsNone declared
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Nam SH, Ahn SM, Oh JS, Hong S, Lee CK, Yoo B, Kim YG. AB1273 MACROPHAGE ACTIVATION SYNDROME IN RHEUMATIC DISEASE: CLINICAL CHARACTERISTICS AND PROGNOSIS OF 20 PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.461] [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: 11/04/2022]
Abstract
BackgroundMacrophage activation syndrome (MAS) is a hyperinflammatory condition that is known to be secondary hemophagocytic lymphohistiocytosis (HLH) in patients with rheumatic disease.ObjectivesThe aim of study was to evaluate the clinical manifestations and outcomes in patients with MAS with rheumatic disease.MethodsWe performed a retrospective study of 20 adult patients who were diagnosed with MAS from 2012 to 2020. MAS was classified according to the HLH-2004 criteria. Patients’ information, including clinical features, laboratory findings, and treatment regimens, was collected, and the overall survival rate was estimated by the Kaplan–Meier method.ResultsTwenty patients (18 women, 35.6 ± 18.3 years) who met the HLH-2004 criteria also fulfilled the 2016 EULAR/ACR/PRINTO classification criteria for MAS, and HScore was higher than 169 (median, 238.5). Fourteen patients with systemic lupus erythematosus and 6 patients with adult-onset Still’s disease were included. All patients were treated initially with corticosteroids, and 16 patients required additional immunosuppressants. The overall survival at 3 and 6 months was 75.2% and 64.3%. In survivors, renal impairment was less common (23.1% versus 42.9%, p = 0.007), the levels of AST (202.0 versus 72.0 IU/L, p = 0.006) and LDH (1144.0 versus 343.0IU/L, p = 0.001), and platelet count (90.0 versus 46.0 × 109/L, p = 0.016) were higher in compared to non-survivors. Nine patients had opportunistic infections, five of whom died during admission.ConclusionThe mortality of patients with MAS remains high. Renal impairment, levels of AST and LDH, and platelet count might be associated with prognosis.Table 1.Treatments and management characteristics of patients with MASNo.Age/sexDiseaseDisease duration (months)1st Treatment (corticosteroids)2nd Treatment3rd TreatmentCombined infectionAlive/dead119/FSLE11 mg/kgIVIG + PPTCZ, RTXBacteremiaDead220/MSLE01 mg/kg---Alive320/FAOSD11 mg/kgVP16--Alive422/FSLE1100 mgIVIG + PP-PneumoniaDead522/FAOSD0500 mgIVIG--Alive623/FSLE1821 mg/kg---Alive723/FSLE411 mg/kg---Alive830/FSLE1461 mg/kgIVIGCsA-Alive932/FSLE1271 mg/kgIVIG + PPCsA, TCZPneumoniaAlive1035/FAOSD01 mg/kgCsA-Viral infectionAlive1137/FSLE651 mg/kgCsA, VP16-BacteremiaAlive1238/FSLE01 mg/kgIVIG + PPRTX-Dead1340/FAOSD00.5 mg/kgCsA--Alive1443/FSLE601 mg/kgIVIG + PPTCZ, RTX, CsA,PCP,DeadVP16, IFXViral infection1549/FSLE01 mg/kgCYC-BacteremiaAlive1651/FAOSD01 mg/kg---Alive1757/FSLE01 mg/kgIVIG + PPCsA, VP16Fungal infectionDead1861/FSLE21 mg/kgIVIG + PPTCZ-Dead1968/FSLE21 mg/kgIVIG + PPCsAFungal infectionAlive2070/MAOSD01 mg/kgIVIG + PPCsA, VP16Fungal infectionDeadSLE: Systemic lupus erythematosus, IVIG: Intravenous immunoglobulin, PP: Plasmapheresis, TCZ: Tocilizumab, RTX: Rituximab, AOSD: Adult-onset still’s disease, VP16: Etoposide, PCP: Pneumocystis pneumonia, CsA: Cyclosporin, IFX: Infliximab, MCTD: Mixed connective tissue disease.Disclosure of InterestsNone declared
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Kang E, Hong S, Kim YG, Lee CK, Oh JS, Yoo B, Ahn SM. POS0762 LONG-TERM RENAL OUTCOMES OF PATIENTS WITH NON-PROLIFERATIVE LUPUS NEPHRITIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3393] [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: 11/04/2022]
Abstract
BackgroundAlthough proliferative (class III or IV) lupus nephritis (LN) is the most common finding in the classification of LN, pure membranous (class V) or mesangial (class I or II) LN can occur as a form of LN. Even though non-proliferative LN (class I, II, or V) is a less severe form with good outcomes, data on long-term renal prognosis are limited.ObjectivesThis study investigated the long-term outcomes and prognostic factors in non-proliferative LN.MethodsWe retrospectively reviewed the medical records of patients with systemic lupus erythematosus who were diagnosed with LN class I, II, V or II+IV by kidney biopsy between 1997 and 2021 at a tertiary referral center. Clinical and laboratory data were compared between patients with and without poor renal outcomes. Poor renal outcome was defined as an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 or death due to renal cause. Univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the factors associated with poor renal outcomes.ResultsWe included 71 patients with non-proliferative LN (4: class I; 17: class II; 48: class V, 17; 2: class II+V). Median follow-up duration was 103 months (interquartile range 27–185) and the overall rate of poor renal outcomes at last follow-up was 29% (21/71), including end-stage renal disease (n=2) and renal death (n=1).Univariate analysis indicated that older age (HR 1.05; 95% CI: 1.00–1.09), low eGFR (HR 0.97; 95% CI: 0.95–0.99) and failure to reach complete remission at 6 months (HR 0.332; 95% CI: 0.12–0.92) were significantly associated with poor renal outcomes. Multivariate analysis revealed that low eGFR at 6 months (HR 0.97; 95% CI: 0.95–0.99) was significantly associated with poor renal outcomes.Figure 1.Renal outcomes at last follow upeGFR, estimated glomerular filtration rate (ml/min/1.73m2)Table 1.Univariate and multivariate Cox proportional hazard regression analyses of the factor associated with poor renal outcomesParameterUnivariate analysisMultivariate analysisHR95% CIp valueHR95% CIp valueClinical features Age1.0461.003-1.0910.0361.0020.960-1.0470.921 Sex1.6540.375-7.2980.506 SLEDAI1.0360.965-1.1120.327 Extra renal SLEDAI1.0380.971-1.110.272Renal profiles eGFR at LN diagnosis0.9930.976-1.0110.456 Proteinuria at LN diagnosis1.0001.000-1.0000.444 > 1g/24 hours0.6690.243-1.8410.437 > 3g/24 hours0.6240.229-1.6990.356 eGFR at 6M0.9670.948-0.9860.0010.9680.948-0.9880.002 eGFR at 12M0.9640.947-0.9810.000 Complete remission at 6M0.3320.119-0.9240.0350.5530.179-1.7070.303 Complete remission at 12M0.6670.232-1.9140.451 Transformation1.2460.423-0.7010.692Laboratory data Anti-dsDNA1.0010.999-1.0030.196 C31.0201.000-1.0410.051 C41.0270.969-1.0890.367 Albumin1.1800.661-2.1090.576ClassificationaClass I0.8020.102-6.3030.834Class II1.2980.412-4.0880.656Class V0.8870.308-2.5570.824Class II+V0.0480.000-16850.837Medicationsb ACEi/ARB1.6520.603-4.5280.329 Hydroxychloroquine1.3260.414-4.2420.635 Corticosteroid1.1860.154-9.1080.870 CNI2.4390.464-12.8240.292 MMF3.7880.959-14.9650.057 AZA0.5890.133-2.6110.486a LN classifications were based on the International Society of Pathology/Renal Pathology Society (ISN/RPS) classification.b Medications maintained at least one year since Lupus Nephritis diagnosis.HR, hazard ratio; 95% CI, 95% confidence interval; SLEDAI, systemic lupus erythematosus disease activity index; eGFR, estimated glomerular filtration rate; LN, lupus nephritis; anti-dsDNA, anti-double strand DNA; C3/C4; complement 3/4; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CNI, carcineurin inhibitor; MMF, mycophenolate mofetil; AZA, azathioprine.ConclusionPoor renal outcomes occurred in approximately 30% of patients with non-proliferative LN (class I, II or V) after long-term follow-up.Our findings suggest that more active management may be needed for non-proliferative LN, particularly in patients with low eGFR at 6 months.Disclosure of InterestsNone declared
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Qiu L, Huang X, Luo J, Zhao Y, Hong S, Wang X, Feng K, Pan Y, Sun Q. Secondary cross infection with dengue virus serotype 2/3 aggravates vascular leakage in BALB/c mice. J Med Virol 2022; 94:4338-4347. [PMID: 35510565 DOI: 10.1002/jmv.27826] [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: 12/29/2021] [Revised: 04/21/2022] [Accepted: 05/02/2022] [Indexed: 11/09/2022]
Abstract
Dengue virus (DV) has occasionally emerged at epidemic levels in Yunnan, China. Vaccine development is limited by antibody dependent enhancement (ADE) and a lack of good animal models. Thus study investigated cross infection based on maternal immunity in BALB/c mice and assessed the risk of cross infection by DV2-D13113 and DV3-YNWS2 epidemic virus strains. DV replicated within the organs of the BALB/c infant mice, even causing death. Particularly, DV3-infected infant mice were at higher risk of severe disease if their mothers were infected with DV2. Although BALB/c adults and pups survived DV2/DV3 infection and produced anti-DV antibodies after 5-8 days, extensive subcutaneous vascular leakage was observed after secondary DV infection. Further, vascular permeability in the lung and kidney significantly increased in offspring born to heterotypic virus-infected mothers. Thus, vascular leakage indicates severe DV infection. The results indicate that maternal immunity increases the severity of subsequent heterotypic infection. Additionally, secondary cross infection by D13113 and YNWS2 represents a risk of serious disease. This study has implications for studies of DV cross infection and vaccine development. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Lijuan Qiu
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,Kunming Children's Hospital, Yunnan Key Laboratory of Children's Major Disease Research, Yunnan Institute of Pediatrics, Kunming, China.,Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious Diseases, Kunming, China
| | - Xinwei Huang
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jia Luo
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,Kunming Medical University Haiyuan College, Kunming, China
| | - Yujiao Zhao
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious Diseases, Kunming, China
| | - Shan Hong
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious Diseases, Kunming, China
| | - Xiaodan Wang
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious Diseases, Kunming, China
| | - Kai Feng
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious Diseases, Kunming, China
| | - Yue Pan
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious Diseases, Kunming, China
| | - Qiangming Sun
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.,Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious Diseases, Kunming, China.,Current postal address: Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, Yunnan, Province 650118, P.R, China
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Chidambaram S, Hong S, Simpson M, Osazuwa-Peters N, Ward G, Massa S. Temporal Trends in Oropharyngeal Cancer Incidence, Survival, and Cancer-Directed Surgery Among Elderly Americans. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2021.12.067] [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: 10/18/2022]
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Hong S, Niu M, Meng D, Li A, Dong Q, Zhang J, Tian X, Lu S, Wang Y. High-density lipoprotein reduces microglia activation and protects against experimental autoimmune encephalomyelitis in mice. Int Immunopharmacol 2022; 105:108566. [DOI: 10.1016/j.intimp.2022.108566] [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] [Received: 09/03/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 11/05/2022]
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Abstract
The demand for proteins continues to increase due to their nutritional benefits, the growing world population, and rising protein deficiency. Plant-based proteins represent a sustainable source to supplement costly animal proteins. Pea (Pisum sativum L.) is one of the most produced plant legume crops in the world and contributes to 26% of the total pulse production. The average protein content of pea is about 20%-25%. The commercial utilization of pea proteins is limited, partially due to its less desirable functionalities and beany off-flavor. Protein modification may change these properties and broaden the application of pea proteins in the food industry. Functional properties such as protein solubility, water and oil holding capacity, emulsifying/foaming capacity and stability, and gelation can be altered and improved by enzymatic, chemical, and physical modifications. These modifications work by affecting protein chemical structures, hydrophobicity/hydrophilicity balance, and interactions with other food constituents. Modifiers, reaction conditions, and degree of modifications are critical variables for protein modifications and can be controlled to achieve desirable functional attributes that may meet applications in meat analogs, baking products, dressings, beverages, dairy mimics, encapsulation, and emulsions. Understanding pea protein characteristics will allow us to design better functional ingredients for food applications.
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Affiliation(s)
- Yanting Shen
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS, United States
| | - Shan Hong
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS, United States
| | - Yonghui Li
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS, United States.
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Shen Y, Hong S, Singh G, Koppel K, Li Y. Improving functional properties of pea protein through "green" modifications using enzymes and polysaccharides. Food Chem 2022; 385:132687. [PMID: 35299020 DOI: 10.1016/j.foodchem.2022.132687] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [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: 12/06/2021] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 12/22/2022]
Abstract
Pea proteins have gained significant interest in recent years. The objective of this study was to enhance pea protein functional properties through enzymatic and/or conjugation modifications and understand the physicochemical properties of the modified proteins. Molecular changes of the proteins were characterized, and protein functionality, in vitro digestibility, and sensory properties were analyzed. The proteins crosslinked with transglutaminase showed significantly improved water holding capacity (5.2-5.6 g/g protein) compared with the control pea protein isolate (2.8 g/g). The pea proteins conjugated with guar gum showed exceptional emulsifying capacity (EC) and stability (ES) of up to 100% compared with the control protein (EC of 58% and ES of 48%). Some sequentially modified pea proteins, such as transglutaminase crosslinking followed by guar gum conjugation had multiple functional enhancement (water holding, oil holding, emulsifying, and gelation). The functionally enhanced pea proteins had comparable sensory scores as the control protein.
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Affiliation(s)
- Yanting Shen
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS, 66506, United States
| | - Shan Hong
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS, 66506, United States
| | - Gaganpreet Singh
- Department of Food, Nutrition, Dietetics and Health, Kansas State University, Manhattan, KS 66506, United States
| | - Kadri Koppel
- Department of Food, Nutrition, Dietetics and Health, Kansas State University, Manhattan, KS 66506, United States
| | - Yonghui Li
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS, 66506, United States.
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Soulsby WD, Balmuri N, Cooley V, Gerber LM, Lawson E, Goodman S, Onel K, Mehta B, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Social determinants of health influence disease activity and functional disability in Polyarticular Juvenile Idiopathic Arthritis. Pediatr Rheumatol Online J 2022; 20:18. [PMID: 35255941 PMCID: PMC8903717 DOI: 10.1186/s12969-022-00676-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/07/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Social determinants of health (SDH) greatly influence outcomes during the first year of treatment in rheumatoid arthritis, a disease similar to polyarticular juvenile idiopathic arthritis (pJIA). We investigated the correlation of community poverty level and other SDH with the persistence of moderate to severe disease activity and functional disability over the first year of treatment in pJIA patients enrolled in the Childhood Arthritis and Rheumatology Research Alliance Registry. METHODS In this cohort study, unadjusted and adjusted generalized linear mixed effects models analyzed the effect of community poverty and other SDH on disease activity, using the clinical Juvenile Arthritis Disease Activity Score-10, and disability, using the Child Health Assessment Questionnaire, measured at baseline, 6, and 12 months. RESULTS One thousand six hundred eighty-four patients were identified. High community poverty (≥20% living below the federal poverty level) was associated with increased odds of functional disability (OR 1.82, 95% CI 1.28-2.60) but was not statistically significant after adjustment (aOR 1.23, 95% CI 0.81-1.86) and was not associated with increased disease activity. Non-white race/ethnicity was associated with higher disease activity (aOR 2.48, 95% CI: 1.41-4.36). Lower self-reported household income was associated with higher disease activity and persistent functional disability. Public insurance (aOR 1.56, 95% CI 1.06-2.29) and low family education (aOR 1.89, 95% CI 1.14-3.12) was associated with persistent functional disability. CONCLUSION High community poverty level was associated with persistent functional disability in unadjusted analysis but not with persistent moderate to high disease activity. Race/ethnicity and other SDH were associated with persistent disease activity and functional disability.
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Affiliation(s)
- William Daniel Soulsby
- University of California, San Francisco, 550 16th Street, 4th Floor, Box #0632, San Francisco, CA, 94158, USA.
| | - Nayimisha Balmuri
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Victoria Cooley
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Linda M. Gerber
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Erica Lawson
- grid.266102.10000 0001 2297 6811University of California, San Francisco, 550 16th Street, 4th Floor, Box #0632, San Francisco, CA 94158 USA
| | - Susan Goodman
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Karen Onel
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Bella Mehta
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
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Hong S, Dia VP, Baek SJ, Zhong Q. Nanoencapsulation of apigenin with whey protein isolate: physicochemical properties, in vitro activity against colorectal cancer cells, and bioavailability. Lebensm Wiss Technol 2022; 154:112751. [PMID: 34840350 PMCID: PMC8612601 DOI: 10.1016/j.lwt.2021.112751] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [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: 01/17/2023]
Abstract
Incorporating lipophilic phytochemicals with anti-cancer activities in functional beverages requires an appropriate nanoencapsulation technology. The present objective was to encapsulate apigenin with whey protein isolate (WPI) utilizing a pH-cycle method and subsequently characterize physicochemical properties, the in vitro anticancer activities against human colorectal HCT-116 and HT-29 cancer cells, and the in vivo bioavailability. Up to 2.0 mg/mL of apigenin was nanoencapsulated with 1.0 mg/mL WPI, with an encapsulation efficiency of up to 98.15% and loading capacity of up to 196.21 mg/g-WPI. Nanodispersions were stable during storage, and apigenin became amorphous after encapsulation. Nanoencapsulation and in vitro digestion did not reduce the anti-proliferative activity of apigenin. Nanoencapsulation of apigenin enhanced the cellular uptake, the pro-apoptotic effects, and the bioavailability in the mice's blood and colon mucosa when comparing to the unencapsulated apigenin. Therefore, the present work may be significant to incorporate lipophilic phytochemicals in functional beverages for disease prevention.
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Affiliation(s)
- Shan Hong
- Department of Food Science, The University of Tennessee, Knoxville, TN, USA
| | - Vermont P Dia
- Department of Food Science, The University of Tennessee, Knoxville, TN, USA
| | - Seung Joon Baek
- College of Veterinary Medicine, Seoul National University, Seoul 08826, South Korea
| | - Qixin Zhong
- Department of Food Science, The University of Tennessee, Knoxville, TN, USA,Corresponding Author: Department of Food Science, The University of Tennessee, 2510 River Drive, Knoxville, TN 37996, United States,
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Shen Y, Hong S, Du Z, Chao M, O'Quinn T, Li Y. Effect of adding modified pea protein as functional extender on the physical and sensory properties of beef patties. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112774] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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45
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Hong S, L R, Mclellan L, Dabney J, Gerds TA, Rotz S, Kalaycio M, Hanna R, Hamilton BK, Majhail N, Sobecks RM. Comparison of Quality of Life and Outcomes between Haploidentical and Matched Related/Unrelated Donor Allogeneic Hematopoietic Cell Transplantation. Transplant Cell Ther 2022; 28:217.e1-217.e6. [DOI: 10.1016/j.jtct.2022.01.012] [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] [Received: 11/17/2021] [Revised: 01/04/2022] [Accepted: 01/12/2022] [Indexed: 11/17/2022]
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46
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Zhao J, Weiss T, Du Z, Hong S, Bean SR, Li Y, Wang D. Comparative evaluation of physicochemical and fermentative responses of three sorghum varieties from dryland and irrigated land and the properties of proteins from distillers’ grains. J Cereal Sci 2022. [DOI: 10.1016/j.jcs.2022.103432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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47
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Hong S, Dia VP, Zhong Q. Synergistic anti-inflammatory activity of apigenin and curcumin co-encapsulated in caseins assessed with lipopolysaccharide-stimulated RAW 264.7 macrophages. Int J Biol Macromol 2021; 193:702-712. [PMID: 34717976 DOI: 10.1016/j.ijbiomac.2021.10.153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 09/15/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 12/14/2022]
Abstract
Dietary polyphenols are potential anti-inflammatory agents, and their combinations with enhanced biological activities may lower toxicity and side effects. The objective of this work was to investigate the potential synergistic anti-inflammatory activities of apigenin and curcumin co-nanoencapsulated in sodium caseinate, with comparison to unencapsulated polyphenol combinations. Non-toxic concentrations of apigenin, curcumin, and their combinations in the free and co-encapsulated forms were studied in lipopolysaccharide-stimulated RAW 264.7 macrophage cells. Combinations of free polyphenols produced stronger inhibition of nitric oxide (NO) production, more significant at a higher proportion of curcumin, which was further enhanced after co-encapsulation. The enhanced reduction of NO was concomitant with the decreased expression of iNOS, the enhanced inhibition of pro-inflammatory cytokines of IL-6 and TNF-α, and the reduced production of intracellular reactive oxygen species. The potential multi-target effects and the enhanced solubility, proximity, and bioavailability of AP and CUR after co-encapsulation contributed to the synergistic activities. These results demonstrated that co-nanoencapsulation of apigenin and curcumin may enable the practical application utilizing the synergistic anti-inflammation effects to improve health.
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Affiliation(s)
- Shan Hong
- Department of Food Science, The University of Tennessee, Knoxville, TN, USA.
| | - Vermont P Dia
- Department of Food Science, The University of Tennessee, Knoxville, TN, USA
| | - Qixin Zhong
- Department of Food Science, The University of Tennessee, Knoxville, TN, USA.
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Satzinger KJ, Liu YJ, Smith A, Knapp C, Newman M, Jones C, Chen Z, Quintana C, Mi X, Dunsworth A, Gidney C, Aleiner I, Arute F, Arya K, Atalaya J, Babbush R, Bardin JC, Barends R, Basso J, Bengtsson A, Bilmes A, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chiaro B, Collins R, Courtney W, Demura S, Derk AR, Eppens D, Erickson C, Faoro L, Farhi E, Fowler AG, Foxen B, Giustina M, Greene A, Gross JA, Harrigan MP, Harrington SD, Hilton J, Hong S, Huang T, Huggins WJ, Ioffe LB, Isakov SV, Jeffrey E, Jiang Z, Kafri D, Kechedzhi K, Khattar T, Kim S, Klimov PV, Korotkov AN, Kostritsa F, Landhuis D, Laptev P, Locharla A, Lucero E, Martin O, McClean JR, McEwen M, Miao KC, Mohseni M, Montazeri S, Mruczkiewicz W, Mutus J, Naaman O, Neeley M, Neill C, Niu MY, O'Brien TE, Opremcak A, Pató B, Petukhov A, Rubin NC, Sank D, Shvarts V, Strain D, Szalay M, Villalonga B, White TC, Yao Z, Yeh P, Yoo J, Zalcman A, Neven H, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Kitaev A, Knap M, Pollmann F, Roushan P. Realizing topologically ordered states on a quantum processor. Science 2021; 374:1237-1241. [PMID: 34855491 DOI: 10.1126/science.abi8378] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
[Figure: see text].
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Affiliation(s)
| | - Y-J Liu
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany
| | - A Smith
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK.,Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems, University of Nottingham, Nottingham NG7 2RD, UK
| | - C Knapp
- Department of Physics and Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.,Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
| | - M Newman
- Google Quantum AI, Mountain View, CA, USA
| | - C Jones
- Google Quantum AI, Mountain View, CA, USA
| | - Z Chen
- Google Quantum AI, Mountain View, CA, USA
| | - C Quintana
- Google Quantum AI, Mountain View, CA, USA
| | - X Mi
- Google Quantum AI, Mountain View, CA, USA
| | | | - C Gidney
- Google Quantum AI, Mountain View, CA, USA
| | - I Aleiner
- Google Quantum AI, Mountain View, CA, USA
| | - F Arute
- Google Quantum AI, Mountain View, CA, USA
| | - K Arya
- Google Quantum AI, Mountain View, CA, USA
| | - J Atalaya
- Google Quantum AI, Mountain View, CA, USA
| | - R Babbush
- Google Quantum AI, Mountain View, CA, USA
| | - J C Bardin
- Google Quantum AI, Mountain View, CA, USA.,Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - R Barends
- Google Quantum AI, Mountain View, CA, USA
| | - J Basso
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Bilmes
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Quantum AI, Mountain View, CA, USA
| | - B Burkett
- Google Quantum AI, Mountain View, CA, USA
| | - N Bushnell
- Google Quantum AI, Mountain View, CA, USA
| | - B Chiaro
- Google Quantum AI, Mountain View, CA, USA
| | - R Collins
- Google Quantum AI, Mountain View, CA, USA
| | - W Courtney
- Google Quantum AI, Mountain View, CA, USA
| | - S Demura
- Google Quantum AI, Mountain View, CA, USA
| | - A R Derk
- Google Quantum AI, Mountain View, CA, USA
| | - D Eppens
- Google Quantum AI, Mountain View, CA, USA
| | - C Erickson
- Google Quantum AI, Mountain View, CA, USA
| | - L Faoro
- Laboratoire de Physique Theorique et Hautes Energies, Sorbonne Université, 75005 Paris, France
| | - E Farhi
- Google Quantum AI, Mountain View, CA, USA
| | - A G Fowler
- Google Quantum AI, Mountain View, CA, USA
| | - B Foxen
- Google Quantum AI, Mountain View, CA, USA
| | - M Giustina
- Google Quantum AI, Mountain View, CA, USA
| | - A Greene
- Google Quantum AI, Mountain View, CA, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - J A Gross
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - J Hilton
- Google Quantum AI, Mountain View, CA, USA
| | - S Hong
- Google Quantum AI, Mountain View, CA, USA
| | - T Huang
- Google Quantum AI, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Quantum AI, Mountain View, CA, USA
| | - S V Isakov
- Google Quantum AI, Mountain View, CA, USA
| | - E Jeffrey
- Google Quantum AI, Mountain View, CA, USA
| | - Z Jiang
- Google Quantum AI, Mountain View, CA, USA
| | - D Kafri
- Google Quantum AI, Mountain View, CA, USA
| | | | - T Khattar
- Google Quantum AI, Mountain View, CA, USA
| | - S Kim
- Google Quantum AI, Mountain View, CA, USA
| | - P V Klimov
- Google Quantum AI, Mountain View, CA, USA
| | - A N Korotkov
- Google Quantum AI, Mountain View, CA, USA.,Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | - D Landhuis
- Google Quantum AI, Mountain View, CA, USA
| | - P Laptev
- Google Quantum AI, Mountain View, CA, USA
| | - A Locharla
- Google Quantum AI, Mountain View, CA, USA
| | - E Lucero
- Google Quantum AI, Mountain View, CA, USA
| | - O Martin
- Google Quantum AI, Mountain View, CA, USA
| | | | - M McEwen
- Google Quantum AI, Mountain View, CA, USA.,Department of Physics, University of California, Santa Barbara, CA, USA
| | - K C Miao
- Google Quantum AI, Mountain View, CA, USA
| | - M Mohseni
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - J Mutus
- Google Quantum AI, Mountain View, CA, USA
| | - O Naaman
- Google Quantum AI, Mountain View, CA, USA
| | - M Neeley
- Google Quantum AI, Mountain View, CA, USA
| | - C Neill
- Google Quantum AI, Mountain View, CA, USA
| | - M Y Niu
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Opremcak
- Google Quantum AI, Mountain View, CA, USA
| | - B Pató
- Google Quantum AI, Mountain View, CA, USA
| | - A Petukhov
- Google Quantum AI, Mountain View, CA, USA
| | - N C Rubin
- Google Quantum AI, Mountain View, CA, USA
| | - D Sank
- Google Quantum AI, Mountain View, CA, USA
| | - V Shvarts
- Google Quantum AI, Mountain View, CA, USA
| | - D Strain
- Google Quantum AI, Mountain View, CA, USA
| | - M Szalay
- Google Quantum AI, Mountain View, CA, USA
| | | | - T C White
- Google Quantum AI, Mountain View, CA, USA
| | - Z Yao
- Google Quantum AI, Mountain View, CA, USA
| | - P Yeh
- Google Quantum AI, Mountain View, CA, USA
| | - J Yoo
- Google Quantum AI, Mountain View, CA, USA
| | - A Zalcman
- Google Quantum AI, Mountain View, CA, USA
| | - H Neven
- Google Quantum AI, Mountain View, CA, USA
| | - S Boixo
- Google Quantum AI, Mountain View, CA, USA
| | - A Megrant
- Google Quantum AI, Mountain View, CA, USA
| | - Y Chen
- Google Quantum AI, Mountain View, CA, USA
| | - J Kelly
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Kitaev
- Google Quantum AI, Mountain View, CA, USA.,Department of Physics and Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.,Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
| | - M Knap
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany.,Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
| | - F Pollmann
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany
| | - P Roushan
- Google Quantum AI, Mountain View, CA, USA
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49
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Sud S, Hall J, Tan X, Roberts O, Green R, Park S, Poellmann M, Bu J, Hong S, Wang A, Casey D. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients With Oligometastatic Disease Receiving Definitive Radiation Therapy. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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50
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Yuan X, Hong S, Xiong W, Raza W, Shen Z, Wang B, Li R, Ruan Y, Shen Q, Dini-Andreote F. Development of fungal-mediated soil suppressiveness against Fusarium wilt disease via plant residue manipulation. Microbiome 2021; 9:200. [PMID: 34635164 PMCID: PMC8507339 DOI: 10.1186/s40168-021-01133-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 07/13/2021] [Indexed: 05/27/2023]
Abstract
BACKGROUND The development of suppressive soils is a promising strategy to protect plants against soil-borne diseases in a sustainable and viable manner. The use of crop rotation and the incorporation of plant residues into the soil are known to alleviate the stress imposed by soil pathogens through dynamics changes in soil biological and physicochemical properties. However, relatively little is known about the extent to which specific soil amendments of plant residues trigger the development of plant-protective microbiomes. Here, we investigated how the incorporation of pineapple residues in soils highly infested with the banana Fusarium wilt disease alleviates the pathogen pressure via changes in soil microbiomes. RESULTS The addition of above- and below-ground pineapple residues in highly infested soils significantly reduced the number of pathogens in the soil, thus resulting in a lower disease incidence. The development of suppressive soils was mostly related to trackable changes in specific fungal taxa affiliated with Aspergillus fumigatus and Fusarium solani, both of which displayed inhibitory effects against the pathogen. These antagonistic effects were further validated using an in vitro assay in which the pathogen control was related to growth inhibition via directly secreted antimicrobial substances and indirect interspecific competition for nutrients. The disease suppressive potential of these fungal strains was later validated using microbial inoculation in a well-controlled pot experiment. CONCLUSIONS These results mechanistically demonstrated how the incorporation of specific plant residues into the soil induces trackable changes in the soil microbiome with direct implications for disease suppression. The incorporation of pineapple residues in the soil alleviated the pathogen pressure by increasing the relative abundance of antagonistic fungal taxa causing a negative effect on pathogen growth and disease incidence. Taken together, this study provides a successful example of how specific agricultural management strategies can be used to manipulate the soil microbiome towards the development of suppressive soils against economically important soil-borne diseases. Video Abstract.
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Affiliation(s)
- Xianfu Yuan
- Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving fertilizers, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
- The Key Laboratory of Plant Immunity, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Shan Hong
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bio-resources, College of Tropical Crops, Hainan University, Haikou, 570228, People's Republic of China
| | - Wu Xiong
- Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving fertilizers, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
- The Key Laboratory of Plant Immunity, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Waseem Raza
- Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving fertilizers, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
- The Key Laboratory of Plant Immunity, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
- Ecology and Biodiversity Group, Department of Biology, Institute of Environmental Biology, Utrecht University, 3584 CH, Utrecht, the Netherlands
| | - Zongzhuan Shen
- Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving fertilizers, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
- The Key Laboratory of Plant Immunity, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Beibei Wang
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bio-resources, College of Tropical Crops, Hainan University, Haikou, 570228, People's Republic of China
| | - Rong Li
- Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving fertilizers, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China.
- The Key Laboratory of Plant Immunity, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China.
| | - Yunze Ruan
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bio-resources, College of Tropical Crops, Hainan University, Haikou, 570228, People's Republic of China
| | - Qirong Shen
- Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving fertilizers, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
- The Key Laboratory of Plant Immunity, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Francisco Dini-Andreote
- Department of Plant Science, The Pennsylvania State University, University Park, PA, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
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