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Lewis L, Huang HY, Tran VT, Lehner S, Kueng R, Preskill J. Author Correction: Improved machine learning algorithm for predicting ground state properties. Nat Commun 2024; 15:1740. [PMID: 38409126 PMCID: PMC10897443 DOI: 10.1038/s41467-024-46164-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024] Open
Affiliation(s)
- Laura Lewis
- California Institute of Technology, Pasadena, CA, USA
- University of Cambridge, Cambridge, UK
| | - Hsin-Yuan Huang
- California Institute of Technology, Pasadena, CA, USA.
- Massachusetts Institute of Technology, Cambridge, MA, USA.
- Google Quantum AI, Venice, CA, USA.
| | | | | | | | - John Preskill
- California Institute of Technology, Pasadena, CA, USA
- AWS Center for Quantum Computing, Pasadena, CA, USA
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2
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Huang RZ, Wang YW, Huang HY, Jiang RH, Xue NN, Yin SP, Zhao HY. [Application effect of a dual release system of androgen and its antagonist in the repair of full-thickness burn wounds in mice]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2024; 40:180-189. [PMID: 38418180 DOI: 10.3760/cma.j.cn501225-20230802-00033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Objective: To explore the optimal ratio of dihydrotestosterone and hydroxyflutamide (hereinafter referred to as DH), construct a dual release system of androgen and its antagonist, and analyze the application effect of this system in the repair of full-thickness burn wounds in mice. Methods: This study was an experimental study. The HaCaT cells were divided into blank group (without drug culture), low baseline group, medium baseline group, and high baseline group according to the random number table (the same grouping method below), and the last three groups of cells were cultured by adding three different ratios of DH. Under a medium ratio, the mass of dihydrotestosterone in the three baseline groups from low to high was 1.4, 2.8, and 4.0 µg, respectively, and the mass of hydroxyflutamide was 1.2, 1.6, and 2.0 µg, respectively. On this basis, under a small ratio, the mass of dihydrotestosterone was reduced by half and the mass of hydroxyflutamide was increased by half; under a large ratio, the mass of dihydrotestosterone was increased by half and the mass of hydroxyflutamide was reduced by half. After culture of 2 days, the cell proliferation level was detected by cell counting kit 8 (n=4). Sixteen 6-8-week-old male BALB/c mice were used to establish a full-thickness burn wound on the back and divided into blank group, small ratio group, medium ratio group, and large ratio group, with 4 mice in each group. On post injury day (PID) 7, normal saline containing different ratios of DH was locally dropped to the wounds of mice in the last three groups of mice (the total mass of DH in the three ratio groups from small to large was 127.5, 165.0, and 202.5 µg, respectively, and the mass ratios of dihydrotestosterone to hydroxyflutamide (hereinafter referred to as drug mass ratio) were 8∶9, 8∶3, and 8∶1, respectively), afterwards, the administration was repeated every 48 hours until PID 27; normal saline was dropped to the wound of mice in blank group at the aforementioned time points. The wound healing status on PID 0 (immediately), 7, 14, 21, and 28 was observed, and the wound healing rates on PID 7, 14, 21, and 28 were calculated (n=4). On PID 28, the wound tissue was taken, which was stained with hematoxylin and eosin for observing re-epithelialization and with Masson for observing collagen fibers, and the proportion of collagen fibers was analyzed (n=3). Twenty 6-8-week-old male BALB/c mice were used to establish a full-thickness burn wound on the back and divided into ordinary scaffold group, small proportion scaffold group, medium proportion scaffold group, and large proportion scaffold group (with 5 mice in each group). On PID 7, the wound was continuously dressed with a polycaprolactone scaffold without drug and a polycaprolactone scaffold containing DH with a drug mass ratio of 1∶3, 1∶1, or 3∶1 (i.e. the dual release system of androgen and its antagonist, with total mass of DH being about 1.7 mg) prepared by using electrospinning technology until the end of the experiment. Histopathological analyses of tissue (n=3) at the same time points as those in the previous animal experiment were performed. On PID 7 and 14, the wound exudates were collected and the relative abundance of bacterial communities was analyzed using 16S ribosomal RNA high-throughput sequencing (n=3). Results: After culture of 2 days, under a small ratio, the proliferation levels of HaCaT cells in low baseline group and high baseline group were significantly higher than the level in blank group (P<0.05). As the time after injury prolonged, the wounds of all four groups of mice continued to shrink. On PID 14, the wound healing rate of mice in large ratio group was 72.5% (61.7%, 75.1%), which was close to 53.3% (49.5%, 64.4%) in blank group (P>0.05); the wound healing rates of mice in small and medium ratio groups were 74.2% (71.0%, 84.2%) and 70.4% (65.1%, 74.4%), respectively, which were significantly higher than the rate in blank group (with both Z values being -2.31, P<0.05). On PID 21, the wound healing rate of mice in small ratio group was significantly higher than that in blank group (Z=-2.31, P<0.05). On PID 28, the wounds of mice in the three ratio groups were completely re-epithelialized and the epidermis was thicker than that in blank group; compared with that in blank group, the collagen fiber content in the wound tissue of mice in the three ratio groups was higher and arranged more orderly, and the proportions of collagen fibers in the wound tissue of mice in small and large ratio groups were significantly increased (P<0.05). On PID 28, the wounds of mice in ordinary scaffold group were partially epithelialized, while the wounds of mice in the three proportion scaffold groups were almost completely epithelialized. Among them, the wounds of mice in small proportion scaffold group had the thickest epidermis. The proportion of collagen fibers in the wound tissue of mice in small proportion scaffold group was significantly increased compared with that in ordinary scaffold group (P<0.05). On PID 7, the bacterial communities with high relative abundance in the wound exudation of mice in the four groups included bacteria of Corynebacterium, Staphylococcus, and Rhodococcus. On PID 14, the bacterial communities with high relative abundance in the wound exudation of mice in the four groups included bacteria of Stenotrophomonas, Rhodococcus, and Staphylococcus, and the number of bacterial species in the wound exudation of mice in the three proportion scaffold groups was more than that in ordinary scaffold group. Conclusions: When the drug mass ratio is relatively small, DH has the effect of promoting the proliferation of HaCaT cells. The ratio of 8∶9 is the optimal mass ratio of dihydrotestosterone to hydroxyflutamide, and DH with this mass ratio can promote re-epithelialization and collagen deposition of full-thickness burn wounds in mice, and promote wound healing. The constructed dual release system of androgen and its antagonist with DH in a 1∶3 drug mass ratio contributes to the re-epithelialization and collagen deposition of the full-thickness burn wounds in mice, and can improve the diversity of wound microbiota.
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Affiliation(s)
- R Z Huang
- Jiangsu Provincial Research Center for Development and Application of External Medicine of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Y W Wang
- Jiangsu Provincial Research Center for Development and Application of External Medicine of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - H Y Huang
- Jiangsu Provincial Research Center for Development and Application of External Medicine of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - R H Jiang
- Jiangsu Provincial Research Center for Development and Application of External Medicine of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - N N Xue
- Jiangsu Provincial Research Center for Development and Application of External Medicine of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - S P Yin
- Jiangsu Provincial Research Center for Development and Application of External Medicine of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - H Y Zhao
- Clinical Research Center, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing 210003, China
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3
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Chen S, Cotler J, Huang HY, Li J. Publisher Correction: The complexity of NISQ. Nat Commun 2024; 15:1308. [PMID: 38346999 PMCID: PMC10861558 DOI: 10.1038/s41467-024-45799-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024] Open
Affiliation(s)
- Sitan Chen
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, USA.
| | - Jordan Cotler
- Society of Fellows, Harvard University, Cambridge, MA, USA.
| | - Hsin-Yuan Huang
- Institute for Quantum Information and Matter, CAltech, Pasadena, CA, USA.
- Department of Computing and Mathematical Sciences, CAltech, Pasadena, CA, USA.
| | - Jerry Li
- Microsoft Research AI, Redmond, WA, USA.
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4
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Chen YS, Lin IH, Huang HY, Liu SW, Hung WY, Wong KT. Exciplex-forming cohost systems with 2,7-dicyanofluorene acceptors for high efficiency red and deep-red OLEDs. Sci Rep 2024; 14:2458. [PMID: 38291066 PMCID: PMC10827723 DOI: 10.1038/s41598-024-52680-6] [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: 07/11/2023] [Accepted: 01/21/2024] [Indexed: 02/01/2024] Open
Abstract
Two 2,7-dicyaonfluorene-based molecules 27-DCN and 27-tDCN are utilized as acceptors (A) to combine with hexaphenylbenzene-centered donors (D) TATT and DDT-HPB for probing the exciplex formation. The photophysical characteristics reveal that the steric hindered 27-tDCN not only can increase the distance of D and A, resulting in a hypsochromic emission, but also dilute the concentration of triplet excitons to suppress non-radiative process. The 27-tDCN-based exciplex-forming blends exhibit better photoluminescence quantum yield (PLQY) as compared to those of 27-DCN-based pairs. In consequence, among these D:A blends, the device employing DDT-HPB:27-tDCN blend as the emissiom layer (EML) exhibits the best EQE of 3.0% with electroluminescence (EL) λmax of 542 nm. To further utilize the exciton electrically generated in exciplex-forming system, two D-A-D-configurated fluorescence emitter DTPNT and DTPNBT are doped into the DDT-HPB:27-tDCN blend. The nice spectral overlap ensures fast and efficient Förster energy transfer (FRET) process between the exciplex-forming host and the fluorescent quests. The red device adopting DDT-HPB:27-tDCN:10 wt% DTPNT as the EML gives EL λmax of 660 nm and maximum external quantum efficiency (EQEmax) of 5.8%, while EL λmax of 685 nm and EQE of 5.0% for the EML of DDT-HPB:27-tDCN:10 wt% DTPNBT. This work manifests a potential strategy to achieve high efficiency red and deep red OLED devices by incorporating the highly fluorescent emitters to extract the excitons generated by the exciplex-forming blend with bulky acceptor for suppressing non-radiative process.
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Affiliation(s)
- Yi-Sheng Chen
- Organic Electronic Research Center, Ming Chi University of Technology, New Taipei City, 24031, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, 10617, Taiwan
| | - I-Hung Lin
- Department of Optoelectronics and Materials Technology, National Taiwan Ocean University, Keelung, 20224, Taiwan
| | - Hsin-Yuan Huang
- Department of Optoelectronics and Materials Technology, National Taiwan Ocean University, Keelung, 20224, Taiwan
| | - Shun-Wei Liu
- Organic Electronic Research Center, Ming Chi University of Technology, New Taipei City, 24031, Taiwan
| | - Wen-Yi Hung
- Department of Optoelectronics and Materials Technology, National Taiwan Ocean University, Keelung, 20224, Taiwan.
| | - Ken-Tsung Wong
- Department of Chemistry, National Taiwan University, Taipei, 10617, Taiwan.
- Institute of Atomic and Molecular Science Academia Sinica, Taipei, 10617, Taiwan.
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5
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Lewis L, Huang HY, Tran VT, Lehner S, Kueng R, Preskill J. Improved machine learning algorithm for predicting ground state properties. Nat Commun 2024; 15:895. [PMID: 38291046 PMCID: PMC10828424 DOI: 10.1038/s41467-024-45014-7] [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: 03/26/2023] [Accepted: 01/08/2024] [Indexed: 02/01/2024] Open
Abstract
Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only [Formula: see text] data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require [Formula: see text] data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as [Formula: see text] in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.
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Affiliation(s)
- Laura Lewis
- California Institute of Technology, Pasadena, CA, USA
- University of Cambridge, Cambridge, UK
| | - Hsin-Yuan Huang
- California Institute of Technology, Pasadena, CA, USA.
- Massachusetts Institute of Technology, Cambridge, MA, USA.
- Google Quantum AI, Venice, CA, USA.
| | | | | | | | - John Preskill
- California Institute of Technology, Pasadena, CA, USA
- AWS Center for Quantum Computing, Pasadena, CA, USA
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6
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Chen S, Cotler J, Huang HY, Li J. The complexity of NISQ. Nat Commun 2023; 14:6001. [PMID: 37752125 PMCID: PMC10522708 DOI: 10.1038/s41467-023-41217-6] [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: 02/19/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023] Open
Abstract
The recent proliferation of NISQ devices has made it imperative to understand their power. In this work, we define and study the complexity class NISQ, which encapsulates problems that can be efficiently solved by a classical computer with access to noisy quantum circuits. We establish super-polynomial separations in the complexity among classical computation, NISQ, and fault-tolerant quantum computation to solve some problems based on modifications of Simon's problems. We then consider the power of NISQ for three well-studied problems. For unstructured search, we prove that NISQ cannot achieve a Grover-like quadratic speedup over classical computers. For the Bernstein-Vazirani problem, we show that NISQ only needs a number of queries logarithmic in what is required for classical computers. Finally, for a quantum state learning problem, we prove that NISQ is exponentially weaker than classical computers with access to noiseless constant-depth quantum circuits.
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Affiliation(s)
- Sitan Chen
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, USA.
| | - Jordan Cotler
- Society of Fellows, Harvard University, Cambridge, MA, USA.
| | - Hsin-Yuan Huang
- Institute for Quantum Information and Matter, CAltech, Pasadena, CA, USA.
- Department of Computing and Mathematical Sciences, CAltech, Pasadena, CA, USA.
| | - Jerry Li
- Microsoft Research AI, Redmond, WA, USA.
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7
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Fang H, Hou YR, Huang HY, Wu DW, Jia SP, Tang Y, Li N. [International comparison and assessment of the quality of drug clinical trial implementation in China based on scientific regulatory system]. Zhonghua Zhong Liu Za Zhi 2023; 45:1-7. [PMID: 37749051 DOI: 10.3760/cma.j.cn112152-20230805-00060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
With the rapid development of clinical research and the continuous enhancement of innovation capability in China, the quality of clinical research under China's scientific regulatory system has drawn widespread attention. This study evaluated the quality results of China's drug clinical trials implementation, compared the scientific regulatory systems of clinical research quality between China and the United States, analyzed real-world clinical application on the approval of new anti-tumor drugs through clinical trials, in order to analyze China's status and level of clinical trial implementation quality in the international industry, and explore the advantages and value of China's clinical research scientific regulation by collecting clinical trial data inspections disclosed by regulatory agencies in both China and the United States, as well as verifying information on the approval of new anti-tumor drugs.
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Affiliation(s)
- H Fang
- Department of Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y R Hou
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - H Y Huang
- Department of Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - D W Wu
- Department of Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S P Jia
- Department of Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Tang
- Department of Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Department of Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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8
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Caro MC, Huang HY, Ezzell N, Gibbs J, Sornborger AT, Cincio L, Coles PJ, Holmes Z. Out-of-distribution generalization for learning quantum dynamics. Nat Commun 2023; 14:3751. [PMID: 37407571 DOI: 10.1038/s41467-023-39381-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 06/09/2023] [Indexed: 07/07/2023] Open
Abstract
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.
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Affiliation(s)
- Matthias C Caro
- Department of Mathematics, Technical University of Munich, Garching, Germany.
- Munich Center for Quantum Science and Technology (MCQST), Munich, Germany.
- Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Berlin, Germany.
- Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.
| | - Hsin-Yuan Huang
- Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA
| | - Nicholas Ezzell
- Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
- Department of Physics & Astronomy, University of Southern California, Los Angeles, CA, USA
| | - Joe Gibbs
- Department of Physics, University of Surrey, Guildford, GU2 7XH, UK
- AWE, Aldermaston, Reading, RG7 4PR, UK
| | | | - Lukasz Cincio
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Patrick J Coles
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Normal Computing Corporation, New York, NY, USA
| | - Zoë Holmes
- Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
- Institute of Physics, Ecole Polytechnique Fédéderale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
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9
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Huang HY, Tong Y, Fang D, Su Y. Learning Many-Body Hamiltonians with Heisenberg-Limited Scaling. Phys Rev Lett 2023; 130:200403. [PMID: 37267566 DOI: 10.1103/physrevlett.130.200403] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 04/18/2023] [Indexed: 06/04/2023]
Abstract
Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. In this Letter, we propose the first algorithm to achieve the Heisenberg limit for learning an interacting N-qubit local Hamiltonian. After a total evolution time of O(ε^{-1}), the proposed algorithm can efficiently estimate any parameter in the N-qubit Hamiltonian to ε error with high probability. Our algorithm uses ideas from quantum simulation to decouple the unknown N-qubit Hamiltonian H into noninteracting patches and learns H using a quantum-enhanced divide-and-conquer approach. The proposed algorithm is robust against state preparation and measurement error, does not require eigenstates or thermal states, and only uses polylog(ε^{-1}) experiments. In contrast, the best existing algorithms require O(ε^{-2}) experiments and total evolution time. We prove a matching lower bound to establish the asymptotic optimality of our algorithm.
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Affiliation(s)
- Hsin-Yuan Huang
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, California 91125, USA
| | - Yu Tong
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, California 91125, USA
- Department of Mathematics, University of California, Berkeley, California 94720, USA
| | - Di Fang
- Department of Mathematics, University of California, Berkeley, California 94720, USA
- Simons Institute for the Theory of Computing, University of California, Berkeley, California 94720, USA
| | - Yuan Su
- Microsoft Quantum, Redmond, Washington 98052, USA
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10
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Choi J, Shaw AL, Madjarov IS, Xie X, Finkelstein R, Covey JP, Cotler JS, Mark DK, Huang HY, Kale A, Pichler H, Brandão FGSL, Choi S, Endres M. Preparing random states and benchmarking with many-body quantum chaos. Nature 2023; 613:468-473. [PMID: 36653567 DOI: 10.1038/s41586-022-05442-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/13/2022] [Indexed: 01/19/2023]
Abstract
Producing quantum states at random has become increasingly important in modern quantum science, with applications being both theoretical and practical. In particular, ensembles of such randomly distributed, but pure, quantum states underlie our understanding of complexity in quantum circuits1 and black holes2, and have been used for benchmarking quantum devices3,4 in tests of quantum advantage5,6. However, creating random ensembles has necessitated a high degree of spatio-temporal control7-12 placing such studies out of reach for a wide class of quantum systems. Here we solve this problem by predicting and experimentally observing the emergence of random state ensembles naturally under time-independent Hamiltonian dynamics, which we use to implement an efficient, widely applicable benchmarking protocol. The observed random ensembles emerge from projective measurements and are intimately linked to universal correlations built up between subsystems of a larger quantum system, offering new insights into quantum thermalization13. Predicated on this discovery, we develop a fidelity estimation scheme, which we demonstrate for a Rydberg quantum simulator with up to 25 atoms using fewer than 104 experimental samples. This method has broad applicability, as we demonstrate for Hamiltonian parameter estimation, target-state generation benchmarking, and comparison of analogue and digital quantum devices. Our work has implications for understanding randomness in quantum dynamics14 and enables applications of this concept in a much wider context4,5,9,10,15-20.
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Affiliation(s)
- Joonhee Choi
- California Institute of Technology, Pasadena, CA, USA
| | - Adam L Shaw
- California Institute of Technology, Pasadena, CA, USA
| | | | - Xin Xie
- California Institute of Technology, Pasadena, CA, USA
| | | | - Jacob P Covey
- California Institute of Technology, Pasadena, CA, USA.,Department of Physics, The University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Daniel K Mark
- Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | - Hannes Pichler
- Institute for Theoretical Physics, University of Innsbruck, Innsbruck, Austria.,Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Innsbruck, Austria
| | | | - Soonwon Choi
- Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Department of Physics, University of California, Berkeley, CA, USA.
| | - Manuel Endres
- California Institute of Technology, Pasadena, CA, USA.
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11
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Wang CY, Xu HM, Tian J, Hong SQ, Liu G, Wang SX, Gao F, Liu J, Liu FR, Yu H, Wu X, Chen BQ, Shen FF, Zheng G, Yu J, Shu M, Liu L, Du LJ, Li P, Xu ZW, Zhu MQ, Huang LS, Huang HY, Li HB, Huang YY, Wang D, Wu F, Bai ST, Tang JJ, Shan QW, Lan LC, Zhu CH, Xiong Y, Tian JM, Wu JH, Hao JH, Zhao HY, Lin AW, Song SS, Lin DJ, Zhou QH, Guo YP, Wu JZ, Yang XQ, Zhang XH, Guo Y, Cao Q, Luo LJ, Tao ZB, Yang WK, Zhou YK, Chen Y, Feng LJ, Zhu GL, Zhang YH, Xue P, Li XQ, Tang ZZ, Zhang DH, Su XW, Qu ZH, Zhang Y, Zhao SY, Qi ZZ, Pang L, Wang CY, Deng HL, Liu XL, Chen YH, Shu S. [A multicenter epidemiological study of acute bacterial meningitis in children]. Zhonghua Er Ke Za Zhi 2022; 60:1045-1053. [PMID: 36207852 DOI: 10.3760/cma.j.cn112140-20220608-00522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To analyze the clinical epidemiological characteristics including composition of pathogens , clinical characteristics, and disease prognosis acute bacterial meningitis (ABM) in Chinese children. Methods: A retrospective analysis was performed on the clinical and laboratory data of 1 610 children <15 years of age with ABM in 33 tertiary hospitals in China from January 2019 to December 2020. Patients were divided into different groups according to age,<28 days group, 28 days to <3 months group, 3 months to <1 year group, 1-<5 years of age group, 5-<15 years of age group; etiology confirmed group and clinically diagnosed group according to etiology diagnosis. Non-numeric variables were analyzed with the Chi-square test or Fisher's exact test, while non-normal distrituction numeric variables were compared with nonparametric test. Results: Among 1 610 children with ABM, 955 were male and 650 were female (5 cases were not provided with gender information), and the age of onset was 1.5 (0.5, 5.5) months. There were 588 cases age from <28 days, 462 cases age from 28 days to <3 months, 302 cases age from 3 months to <1 year of age group, 156 cases in the 1-<5 years of age and 101 cases in the 5-<15 years of age. The detection rates were 38.8% (95/245) and 31.5% (70/222) of Escherichia coli and 27.8% (68/245) and 35.1% (78/222) of Streptococcus agalactiae in infants younger than 28 days of age and 28 days to 3 months of age; the detection rates of Streptococcus pneumonia, Escherichia coli, and Streptococcus agalactiae were 34.3% (61/178), 14.0% (25/178) and 13.5% (24/178) in the 3 months of age to <1 year of age group; the dominant pathogens were Streptococcus pneumoniae and the detection rate were 67.9% (74/109) and 44.4% (16/36) in the 1-<5 years of age and 5-<15 years of age . There were 9.7% (19/195) strains of Escherichia coli producing ultra-broad-spectrum β-lactamases. The positive rates of cerebrospinal fluid (CSF) culture and blood culture were 32.2% (515/1 598) and 25.0% (400/1 598), while 38.2% (126/330)and 25.3% (21/83) in CSF metagenomics next generation sequencing and Streptococcus pneumoniae antigen detection. There were 4.3% (32/790) cases of which CSF white blood cell counts were normal in etiology confirmed group. Among 1 610 children with ABM, main intracranial imaging complications were subdural effusion and (or) empyema in 349 cases (21.7%), hydrocephalus in 233 cases (14.5%), brain abscess in 178 cases (11.1%), and other cerebrovascular diseases, including encephalomalacia, cerebral infarction, and encephalatrophy, in 174 cases (10.8%). Among the 166 cases (10.3%) with unfavorable outcome, 32 cases (2.0%) died among whom 24 cases died before 1 year of age, and 37 cases (2.3%) had recurrence among whom 25 cases had recurrence within 3 weeks. The incidences of subdural effusion and (or) empyema, brain abscess and ependymitis in the etiology confirmed group were significantly higher than those in the clinically diagnosed group (26.2% (207/790) vs. 17.3% (142/820), 13.0% (103/790) vs. 9.1% (75/820), 4.6% (36/790) vs. 2.7% (22/820), χ2=18.71, 6.20, 4.07, all P<0.05), but there was no significant difference in the unfavorable outcomes, mortility, and recurrence between these 2 groups (all P>0.05). Conclusions: The onset age of ABM in children is usually within 1 year of age, especially <3 months. The common pathogens in infants <3 months of age are Escherichia coli and Streptococcus agalactiae, and the dominant pathogen in infant ≥3 months is Streptococcus pneumoniae. Subdural effusion and (or) empyema and hydrocephalus are common complications. ABM should not be excluded even if CSF white blood cell counts is within normal range. Standardized bacteriological examination should be paid more attention to increase the pathogenic detection rate. Non-culture CSF detection methods may facilitate the pathogenic diagnosis.
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Affiliation(s)
- C Y Wang
- Department of Infectious Diseases, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - H M Xu
- Department of Infectious Diseases, Children's Hospital of Chongqing Medical University, Chongqing 400014, China
| | - J Tian
- Department of Infectious Diseases, Children's Hospital of Chongqing Medical University, Chongqing 400014, China
| | - S Q Hong
- Department of Infectious Diseases, Children's Hospital of Chongqing Medical University, Chongqing 400014, China
| | - G Liu
- Department of Infectious Diseases, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - S X Wang
- Department of Infectious Diseases, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - F Gao
- Department of Infectious Diseases, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - J Liu
- Department of Infectious Diseases, Hunan Children's Hospital, Changsha 410007, China
| | - F R Liu
- Department of Infectious Diseases, Hunan Children's Hospital, Changsha 410007, China
| | - H Yu
- Department of Infectious Diseases, Children's Hospital of Fudan University, Shanghai 201102, China
| | - X Wu
- Department of Infectious Diseases, Children's Hospital of Fudan University, Shanghai 201102, China
| | - B Q Chen
- Department of Infectious Diseases, Anhui Provincial Children's Hospital, Hefei 230022, China
| | - F F Shen
- Department of Infectious Diseases, Anhui Provincial Children's Hospital, Hefei 230022, China
| | - G Zheng
- Department of Neurology, Children's Hospital of Nanjing Medical University,Nanjing 210008, China
| | - J Yu
- Department of Neurology, Children's Hospital of Nanjing Medical University,Nanjing 210008, China
| | - M Shu
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu 610044, China
| | - L Liu
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu 610044, China
| | - L J Du
- Department of Neurology, Children's Hospital of Shanxi, Taiyuan 030006, China
| | - P Li
- Department of Neurology, Children's Hospital of Shanxi, Taiyuan 030006, China
| | - Z W Xu
- Department of Infectious Diseases, the Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, China
| | - M Q Zhu
- Department of Infectious Diseases, the Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, China
| | - L S Huang
- Department of Infectious Diseases, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - H Y Huang
- Department of Infectious Diseases, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - H B Li
- Department of Pediatrics, the First Hospital of Jilin University, Changchu 130061, China
| | - Y Y Huang
- Department of Pediatrics, the First Hospital of Jilin University, Changchu 130061, China
| | - D Wang
- Department of Neurology, the Affiliated Children's Hospital of Xi'an Jiao Tong University, Xi'an 710002, China
| | - F Wu
- Department of Neurology, the Affiliated Children's Hospital of Xi'an Jiao Tong University, Xi'an 710002, China
| | - S T Bai
- Department of Pediatrics, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - J J Tang
- Department of Pediatrics, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Q W Shan
- Department of Pediatrics, the First Affiliated Hospital of Guangxi Medical University,Nanning 530021, China
| | - L C Lan
- Department of Pediatrics, the First Affiliated Hospital of Guangxi Medical University,Nanning 530021, China
| | - C H Zhu
- Department of Infectious Diseases, Jiangxi Provincial Children's Hospital, Nanchang 330006, China
| | - Y Xiong
- Department of Infectious Diseases, Jiangxi Provincial Children's Hospital, Nanchang 330006, China
| | - J M Tian
- Department of Infectious Diseases, Children's Hospital of Soochow University,Suzhou 215002, China
| | - J H Wu
- Department of Infectious Diseases, Children's Hospital of Soochow University,Suzhou 215002, China
| | - J H Hao
- Department of Infectious Diseases, Kaifeng Children's Hospital, Kaifeng 475000, China
| | - H Y Zhao
- Department of Infectious Diseases, Kaifeng Children's Hospital, Kaifeng 475000, China
| | - A W Lin
- Department of Infectious Diseases, Children's Hospital Affiliated Shandong University, Jinan 250022, China
| | - S S Song
- Department of Infectious Diseases, Children's Hospital Affiliated Shandong University, Jinan 250022, China
| | - D J Lin
- Department of Infectious Diseases, Hainan Women and Children's Medical Center, Haikou 571103, China
| | - Q H Zhou
- Department of Infectious Diseases, Hainan Women and Children's Medical Center, Haikou 571103, China
| | - Y P Guo
- Department of Infectious Diseases, Hainan Women and Children's Medical Center, Haikou 571103, China
| | - J Z Wu
- Department of Pediatrics, Women's and Children's Hospital Affiliated to Xiamen University, Xiamen 361003, China
| | - X Q Yang
- Department of Pediatrics, Women's and Children's Hospital Affiliated to Xiamen University, Xiamen 361003, China
| | - X H Zhang
- Department of Neonatology, Children's Hospital of Shanxi, Taiyuan 030006, China
| | - Y Guo
- Department of Neonatology, Children's Hospital of Shanxi, Taiyuan 030006, China
| | - Q Cao
- Department of Infectious Diseases, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - L J Luo
- Department of Infectious Diseases, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Z B Tao
- Department of Pediatrics, the First Hospital of Lanzhou University, Lanzhou 730013, China
| | - W K Yang
- Department of Pediatrics, the First Hospital of Lanzhou University, Lanzhou 730013, China
| | - Y K Zhou
- Department of Pediatrics, the First Hospital of Lanzhou University, Lanzhou 730013, China
| | - Y Chen
- Department of Pediatrics, the Second Hospital of Hebei Medical University, Shijiazhuang 050004, China
| | - L J Feng
- Department of Pediatrics, the Second Hospital of Hebei Medical University, Shijiazhuang 050004, China
| | - G L Zhu
- Department of Infection and Digestive, Qinghai Province Women and Children's Hospital, Xining 810007, China
| | - Y H Zhang
- Department of Infection and Digestive, Qinghai Province Women and Children's Hospital, Xining 810007, China
| | - P Xue
- Department of Pediatrics, Taiyuan Maternal and Child Health Care Hospital, Taiyuan 030012, China
| | - X Q Li
- Department of Pediatrics, Taiyuan Maternal and Child Health Care Hospital, Taiyuan 030012, China
| | - Z Z Tang
- Department of Pediatrics, the First People's Hospital of Zunyi, Zunyi 563099, China
| | - D H Zhang
- Department of Pediatrics, the First People's Hospital of Zunyi, Zunyi 563099, China
| | - X W Su
- Department of Pediatrics, Inner Mongolia People's Hospital, Inner Mongolia 750306, China
| | - Z H Qu
- Department of Pediatrics, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Y Zhang
- Department of Pediatrics, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - S Y Zhao
- Department of Infectious Diseases, Hangzhou Children's Hospital, Hangzhou 310005, China
| | - Z Z Qi
- Department of Infectious Diseases, Hangzhou Children's Hospital, Hangzhou 310005, China
| | - L Pang
- Department of Pediatrics, Beijing Ditan Hospital, Capital Medical University, Beijing 100102, China
| | - C Y Wang
- Department of Pediatrics, Beijing Ditan Hospital, Capital Medical University, Beijing 100102, China
| | - H L Deng
- Department of Pediatrics, Xi'an Central Hospital, Xi'an 710004, China
| | - X L Liu
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Y H Chen
- Department of Infectious Diseases, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Sainan Shu
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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12
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Abstract
Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML over traditional methods have not been firmly established. In this work, we prove that classical ML algorithms can efficiently predict ground-state properties of gapped Hamiltonians after learning from other Hamiltonians in the same quantum phase of matter. By contrast, under a widely accepted conjecture, classical algorithms that do not learn from data cannot achieve the same guarantee. We also prove that classical ML algorithms can efficiently classify a wide range of quantum phases. Extensive numerical experiments corroborate our theoretical results in a variety of scenarios, including Rydberg atom systems, two-dimensional random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases.
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Affiliation(s)
- Hsin-Yuan Huang
- Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA
| | - Richard Kueng
- Institute for Integrated Circuits, Johannes Kepler University, Linz, Austria
| | | | - Victor V Albert
- Joint Center for Quantum Information and Computer Science, National Institute of Standards and Technology and University of Maryland, College Park, MD, USA
| | - John Preskill
- Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA.,AWS Center for Quantum Computing, Pasadena, CA, USA
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13
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Li YL, Guan X, Dou LZ, Liu Y, Huang HY, Huang SK, Yang ZX, Wei BJ, Wu Y, Chen ZH, Wang GQ, Wang X, Cui W. [The clinical value of multi-target stool fecal immunochemical test-DNA in early screening and diagnosis for colorectal cancer]. Zhonghua Yi Xue Za Zhi 2022; 102:2607-2613. [PMID: 36058686 DOI: 10.3760/cma.j.cn112137-20220430-00974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To investigate the clinical diagnostic value of multi-target stool fecal immunochemical test-DNA (FIT-DNA) test in colorectal cancer (CRC) and advanced adenoma (AA). Methods: A total of 235 patients who were undergoing colonoscopy or colorectal cancer surgery in the Cancer Hospital, Chinese Academy of Medical Sciences from April 2021 to January 2022 were prospectively enrolled. There were 141 males and 94 females, with an average age of (55±13) years (22-86). The patients were divided into two groups, including 215 patients who were first diagnosed but not treated (86 cases of CRC, 12 cases of AA, 25 cases of non-advanced adenoma, 8 cases of hyperplastic or other polyps and 84 apparently healthy cases) and 20 patients in the intervention group (2 cases with a history of CRC surgery, 6 cases with a history of endoscopic surgery, 4 non-CRC patients with special diseases and 8 cases with a history of neoadjuvant chemoradiotherapy). Fresh stool samples were collected before intestinal preparation or surgery for FIT-DNA test using the matching kit for sample processing and nucleic acid purification. KRAS mutation and methylation of BMP3 and NDRG4 genes were detected by fluorescence probe method, and FIT method was employed to detect fecal occult blood. Colonoscopy or pathological biopsy results were used as the gold standard. And the screening and diagnostic efficacy of FIT-DNA test for colorectal cancer and advanced adenoma were evaluated by receiver operating curve (ROC). Results: The sensitivity of FIT-DNA test for early colorectal cancer and advanced adenoma was 7/7 and 8/12, respectively. And the negative predictive value was 98.1% (104/106) and 93.7% (104/111), respectively. The overall screening sensitivity for both early colorectal cancer and advanced adenoma was 15/19, and the negative predictive value was 96.3% (104/108). Besides, the area under the curves (AUCs) were 0.982 (95%CI: 0.960-1.000, P<0.05), 0.758 (95%CI: 0.592-0.924, P<0.05) and 0.841 (95%CI: 0.724-0.957, P<0.05), respectively. Moreover, the diagnostic sensitivity of FIT-DNA test was 98.8% (85/86) for colorectal cancer, 8/12 for advanced adenoma, and 94.9% (93/98) for both colorectal cancer and advanced adenoma, with a specificity of 88.9% (104/117). The AUCs were 0.968 (95%CI: 0.937-0.997, P<0.05), 0.758 (95%CI: 0.592-0.924, P<0.05) and 0.942 (95%CI: 0.905-0.979, P<0.05), respectively. After the inclusion of intervention group, the overall diagnostic sensitivity and specificity of FIT-DNA test was 91.6% (98/107) and 89.1% (114/128), respectively. Conclusion: FIT-DNA test has a high early screening and diagnostic efficacy for colorectal cancer.
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Affiliation(s)
- Y L Li
- Department of Medical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X Guan
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Z Dou
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H Y Huang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S K Huang
- Department of Medical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Z X Yang
- Department of Medical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - B J Wei
- Department of Medical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Wu
- Department of Medical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Z H Chen
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - G Q Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xishan Wang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wei Cui
- Department of Medical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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14
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Cerezo M, Verdon G, Huang HY, Cincio L, Coles PJ. Challenges and opportunities in quantum machine learning. Nat Comput Sci 2022; 2:567-576. [PMID: 38177473 DOI: 10.1038/s43588-022-00311-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/04/2022] [Indexed: 01/06/2024]
Abstract
At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry and high-energy physics. Nevertheless, challenges remain regarding the trainability of quantum machine learning models. Here we review current methods and applications for quantum machine learning. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with quantum machine learning.
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Affiliation(s)
- M Cerezo
- Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
- Quantum Science Center, Oak Ridge, TN, USA
| | - Guillaume Verdon
- X, Mountain View, CA, USA
- Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario, Canada
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
| | - Hsin-Yuan Huang
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Lukasz Cincio
- Quantum Science Center, Oak Ridge, TN, USA
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Patrick J Coles
- Quantum Science Center, Oak Ridge, TN, USA.
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
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15
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Caro MC, Huang HY, Cerezo M, Sharma K, Sornborger A, Cincio L, Coles PJ. Generalization in quantum machine learning from few training data. Nat Commun 2022; 13:4919. [PMID: 35995777 PMCID: PMC9395350 DOI: 10.1038/s41467-022-32550-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 08/04/2022] [Indexed: 11/19/2022] Open
Abstract
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as [Formula: see text]. When only K ≪ T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to [Formula: see text]. Our results imply that the compiling of unitaries into a polynomial number of native gates, a crucial application for the quantum computing industry that typically uses exponential-size training data, can be sped up significantly. We also show that classification of quantum states across a phase transition with a quantum convolutional neural network requires only a very small training data set. Other potential applications include learning quantum error correcting codes or quantum dynamical simulation. Our work injects new hope into the field of QML, as good generalization is guaranteed from few training data.
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Affiliation(s)
- Matthias C Caro
- Department of Mathematics, Technical University of Munich, Garching, Germany.
- Munich Center for Quantum Science and Technology (MCQST), Munich, Germany.
| | - Hsin-Yuan Huang
- Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA
| | - M Cerezo
- Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Kunal Sharma
- Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, 20742, USA
| | - Andrew Sornborger
- Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
- Quantum Science Center, Oak Ridge, TN, 37931, USA
| | - Lukasz Cincio
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Patrick J Coles
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
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16
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Huang HY, Broughton M, Cotler J, Chen S, Li J, Mohseni M, Neven H, Babbush R, Kueng R, Preskill J, McClean JR. Quantum advantage in learning from experiments. Science 2022; 376:1182-1186. [PMID: 35679419 DOI: 10.1126/science.abn7293] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today's quantum processors.
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Affiliation(s)
- Hsin-Yuan Huang
- Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.,Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA
| | | | - Jordan Cotler
- Harvard Society of Fellows, Cambridge, MA 02138, USA.,Black Hole Initiative, Cambridge, MA 02138, USA
| | - Sitan Chen
- Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, USA.,Simons Institute for the Theory of Computing, Berkeley, CA, USA
| | - Jerry Li
- Microsoft Research AI, Redmond, WA 98052, USA
| | | | | | | | - Richard Kueng
- Institute for Integrated Circuits, Johannes Kepler University Linz, Austria
| | - John Preskill
- Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.,Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA.,AWS Center for Quantum Computing, Pasadena, CA 91125, USA
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17
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Li CY, Huang HY, Liu XJ, Liu XL. [Research progress on the characteristics of artemisia pollen allergens and related pollinosis]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:748-754. [PMID: 35785856 DOI: 10.3760/cma.j.cn112150-20220314-00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In recent years, the morbidity of pollinosis has been increasing year by year. Anemophilous flower pollen is the most important allergen causing pollinosis, among which artemisia pollen is one of the most common airborne allergens. In this paper, based on the immune biology characteristics of major sensitization protein components of artemisia pollen, and from the perspective of immunology, the main pathogenic mechanism of action and clinical characteristics of artemisia pollen are elaborated to provide the reference basis for the development of accurate and effective artemisia pollen disease prevention and control strategy, hoping to provide patients with scientific and effective prevention and control suggestions.
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Affiliation(s)
- C Y Li
- Graduate School of Inner Mongolia Medical University, Hohhot 010059, China
| | - H Y Huang
- Department of Otolaryngology, Inner Mongolia People's Hospital, Hohhot 010017,China
| | - X J Liu
- Department of Otolaryngology, Inner Mongolia People's Hospital, Hohhot 010017,China
| | - X L Liu
- Department of Scientific Research, Inner Mongolia People's Hospital, Hohhot 010017, China
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18
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Huang HY, Wu DW, Zhu Q, Yu Y, Wang HX, Wang J, Ga M, Meng XY, Du JT, Miao SM, Zhao ZX, Wang X, Shang P, Guo MJ, Liu LH, Tang Y, Li N, Cao C, Xu BH, Sun Y, He J. [Progress on clinical trials of common gastrointestinal cancer drugs in China from 2012 to 2021]. Zhonghua Zhong Liu Za Zhi 2022; 44:276-281. [PMID: 35316878 DOI: 10.3760/cma.j.cn112152-20211207-00907] [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] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: Systematically summarize the research progress of clinical trials of gastric cancer oncology drugs and the overview of marketed drugs in China from 2012 to 2021, providing data and decision-making evidence for relevant departments. Methods: Based on the registration database of the drug clinical trial registration and information disclosure platform of Food and Drug Administration of China and the data query system of domestic and imported drugs, the information on gastric cancer drug clinical trials, investigational drugs and marketed drugs from January 1, 2012 to December 31, 2021 was analyzed, and the differences between Chinese and foreign enterprises in terms of trial scope, trial phase, treatment lines and drug type, effect and mechanism studies were compared. Results: A total of 114 drug clinical trials related to gastric tumor were registered in China from 2012 to 2021, accounting for 3.7% (114/3 041) of all anticancer drug clinical trials in the same period, the registration number showed a significant growth rate after 2016 and reached its peak with 32 trials in 2020. Among them, 85 (74.6%, 85/114) trials were initiated by Chinese pharmaceutical enterprise. Compared with foreign pharmaceutical enterprise, Chinese pharmaceutical enterprise had higher rates of phase I trials (35.3% vs 6.9%, P=0.001), but the rate of international multicenter trials (11.9% vs 67.9%, P<0.001) was relatively low. There were 76 different drugs involved in relevant clinical trials, of which 65 (85.5%) were targeted drugs. For targeted drugs, HER2 is the most common one (14 types), followed by PD-1 and multi-target VEGER. In the past ten years, 3 of 4 marketed drugs for gastric cancer treatment were domestic and included in the national medical insurance directory. Conclusions: From 2012 to 2021, China has made some progress in drug research and development for gastric carcinoma. However, compared with the serious disease burden, it is still insufficient. Targeted strengthening of research and development of investment in many aspects of gastric cancer drugs, such as new target discovery, matured target excavating, combination drug development and early line therapy promotion, is the key work in the future, especially for domestic companies.
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Affiliation(s)
- H Y Huang
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - D W Wu
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Q Zhu
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Y Yu
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H X Wang
- National Center for Drug Evaluation, National Medical Products Administration, Beijing 100022, China
| | - J Wang
- National Center for Drug Evaluation, National Medical Products Administration, Beijing 100022, China
| | - M Ga
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X Y Meng
- The University of Melbourne, Faculty of Medicine, Dentistry and Health Sciences, Melbourne 3010, Australia
| | - J T Du
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S M Miao
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Z X Zhao
- Department of Clinical Trial Center, China-Japan Friendship Hospital, Beijing 100029, China
| | - X Wang
- Clinical Trials Research Center, Beijing Hoppital, National Center of Getrontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - P Shang
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - M J Guo
- Department of Health Insurance Information Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - L H Liu
- Department of Clinical Trial Center, China-Japan Friendship Hospital, Beijing 100029, China
| | - Y Tang
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - C Cao
- Zhongguancun Jiutai Good Clinical Practice Union, Beijing 100027, China
| | - B H Xu
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Sun
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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19
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Huang HY, Ma HS, Yang JL. [A case of hepatic encephalopathy induced by hereditary hemorrhagic telangiectasia]. Zhonghua Gan Zang Bing Za Zhi 2022; 30:323-325. [PMID: 35462490 DOI: 10.3760/cma.j.cn501113-20210128-00047] [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] [Indexed: 06/14/2023]
Affiliation(s)
- H Y Huang
- Department of Gastroenterology & Hepatology, Sichuan University-Oxford University Huaxi Gastrointestinal Cancer Centre, West China Hospital, Sichuan University, Chengdu 610041, China
| | - H S Ma
- Department of Day Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - J L Yang
- Department of Gastroenterology & Hepatology, Sichuan University-Oxford University Huaxi Gastrointestinal Cancer Centre, West China Hospital, Sichuan University, Chengdu 610041, China
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Chen YH, Shen ZY, Huang HY, Yu YS, Ye WX, Hua F, Hu YQ, Yang BW, Shen H. [Comparison of early outcome between one-stage hybrid technique and frozen elephant thunk technique in the treatment of Stanford A aortic dissection involving the arch]. Zhonghua Yi Xue Za Zhi 2021; 101:3955-3960. [PMID: 34954998 DOI: 10.3760/cma.j.cn112137-20210531-01246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To analyze the early outcome of one-stage hybrid technique in the treatment of Stanford type-A aortic dissection involving the arch and compare its therapeutic efficacy with the classical frozen elephant trunk technique (FET). Methods: A total of 106 patients with Stanford type-A aortic dissection involving the arch in Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University from October 2015 to October 2019 was collected. All patients in this group were treated with one-stage hybrid technique (modified arch debranching technique) without deep hypothermia circulation. Meanwhile, 30 patients with Stanford type A dissection involving the arch who underwent FET from January 2014 to September 2015 were collected. The therapeutic effects of the two surgical methods were analyzed and compared. Results: The age [M (Q1, Q3)] of 106 patients in hybrid group was 49.0 (40.0, 55.0) years, including 89 males and 17 females. The age [M(Q1, Q3)] of 30 patients in FET group was 49.5 (41.5, 65.3) years, including 24 males and 6 females. The time [M(Q1, Q3)] of using ventilator in hybrid group was 56.0 (38.0, 72.0) h, which was shorter than 127.0 (92.0, 145.0) h in FET group (P<0.001). The incidence of cerebral infarction in hybrid group was 2.8% (3 cases), which was lower than 13.3% (4 cases) in FET group (P=0.042); the incidence of postoperative renal insufficiency in hybrid group was 7.5% (8 cases), which was lower than 23.3% (7 cases) in FET group (P=0.023); the ICU time [M (Q1, Q3)] in hybrid group was 8.0 (6.0, 10.0) d, which was shorter than 14.0 (8.3, 24.0) d in FET group (P<0.001). Conclusion: Compared with FET, one-stage hybrid technology is safer and more effective in the treatment of Stanford type A aortic dissection involving the arch. Its short-term therapeutic efficacy appears good.
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Affiliation(s)
- Y H Chen
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - Z Y Shen
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - H Y Huang
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - Y S Yu
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - W X Ye
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - F Hua
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - Y Q Hu
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - B W Yang
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - H Shen
- Department of Cardiac and Vascular Surgery, 1st Affiliated Hospital of Soochow University, Suzhou 215006, China
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Li SP, Chang QQ, Ren XH, Luo NY, Huang HY, Wu DS, Liu YG, Liu JJ. [Induction of hepatocellular carcinoma in B6C3 (F1) mice chronicly exposed to trichloroethylene with enhanced acetylation of histone H2AK9ac and SET expression in the liver tissue]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2021; 39:910-914. [PMID: 35164419 DOI: 10.3760/cma.j.cn121094-20201009-00562] [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] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To establish an animal model of trichloroethylene (TCE) -induced liver cancer following chronic exposure and to understand the changes in SET expression and histone acetylation, potentially serving as a molecular mechanism for TCE-induced hepatocarcinogenesis. Methods: B6C3 mice at 6 weeks were treated with TCE at a series of doses (500, 1000 and 2000 mg/kg) by gastric gavage, with corn oil used as the negative control and carbon tetrachloride (CCl(4)) as the positive control. The serum and liver were sampled for the determination of biochemical indexes and pathological examination after 56 weeks of chemical exposure. Western blot was used to determine the levels of SET, H2AK9ac and HDAC1 expression. Results: The overall survival rate of the mice in various groups was 90.4% (141/156) , with no statistical difference between groups (P>0.05) . Compared with the negative control, the organ coefficient for the liver in the high dose TCE group and the positive control group were significantly increased (P<0.05) . The levels of ALT, AST, LDH and BUN in the all the three TCE groups and the positive control were significantly higher than those in the negative control (P<0.01) . CREA levels in the 1000 and 2000 mg/kg TCE groups were significantly higher than those in the negative control (P<0.05) . Statistical increases in the incidence of hepatocellular carcinoma and the activities of ALT and AST in various doses of TCE-exposed mice as compared with the control were observed (P<0.01) , in a dose-dependent manner. In the 1000 and 2000 mg/kg of TCE treated mice, levels of SET and H2AK9ac were increased (P<0.05) , while HDAC1 was decreased (P<0.05) , Compared to the tissue adjacent to liver cancer, in the 1000 and 2000 mg/kg TCE groups, the levels of SET were increased (P<0.05) , while HDAC1 was decreased (P<0.05) , and H2AK9ac increased in the 2000 mg/kg group. Conclusion: The hepatocellular carcinoma mouse model induced by chronic exposure to trichloroethylene was successfully established, with enhanced SET protein expression and H2AK9ac in the hepatic tissue.
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Affiliation(s)
- S P Li
- Southern Medical University, School of Public Health Guangzhou, Guangzhou 510515, China Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China
| | - Q Q Chang
- Southern Medical University, School of Public Health Guangzhou, Guangzhou 510515, China Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China
| | - X H Ren
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China
| | - N Y Luo
- Southern Medical University, School of Public Health Guangzhou, Guangzhou 510515, China Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China
| | - H Y Huang
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China
| | - D S Wu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China
| | - Y G Liu
- Southern Medical University, School of Public Health Guangzhou, Guangzhou 510515, China
| | - J J Liu
- Southern Medical University, School of Public Health Guangzhou, Guangzhou 510515, China Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China
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22
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McClean JR, Rubin NC, Lee J, Harrigan MP, O'Brien TE, Babbush R, Huggins WJ, Huang HY. What the foundations of quantum computer science teach us about chemistry. J Chem Phys 2021; 155:150901. [PMID: 34686056 DOI: 10.1063/5.0060367] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in a chemical simulation with any computer. Some of these results even impact our understanding of chemistry in the real world. In this Perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on the one hand, this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution, in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics such as the ergodic hypothesis. However, we argue that this Perspective is not defeatist but rather helps shed light on the success of existing chemical models such as transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results, showing that data-augmented models are a more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can also extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.
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Affiliation(s)
- Jarrod R McClean
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | - Nicholas C Rubin
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | - Joonho Lee
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | | | - Thomas E O'Brien
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | - Ryan Babbush
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | | | - Hsin-Yuan Huang
- Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences, Caltech, Pasadena, California 91125, USA
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23
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Huang HY, Shah LM, McNally JS, Sant T, Hutchins TA, Goldstein ED, Peckham ME. COVID-19-Associated Myelitis Involving the Dorsal and Lateral White Matter Tracts: A Case Series and Review of the Literature. AJNR Am J Neuroradiol 2021; 42:1912-1917. [PMID: 34413066 DOI: 10.3174/ajnr.a7256] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 06/30/2021] [Indexed: 11/07/2022]
Abstract
Coronavirus disease 2019 (COVID-19) myelitis is a rare condition, most commonly presenting with nonenhancing central expansile cord T2 signal changes. A single case report has also described longitudinal involvement of the dorsal columns. We present 5 cases of COVID-19-associated myelitis with tract-specific involvement of the dorsal and lateral columns and discuss potential pathophysiologic pathways for this unique pattern.
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Affiliation(s)
- H Y Huang
- From the Department of Neurology (H.Y.H., E.D.G.), University of Utah, Salt Lake City, Utah
| | - L M Shah
- Departments of Radiology and Imaging Sciences (L.M.S., J.S.M., T.A.H., M.E.P.), University of Utah, Salt Lake City, Utah
| | - J S McNally
- Departments of Radiology and Imaging Sciences (L.M.S., J.S.M., T.A.H., M.E.P.), University of Utah, Salt Lake City, Utah
| | - T Sant
- School of Medicine (T.S.), University of Utah, Salt Lake City, Utah
| | - T A Hutchins
- Departments of Radiology and Imaging Sciences (L.M.S., J.S.M., T.A.H., M.E.P.), University of Utah, Salt Lake City, Utah
| | - E D Goldstein
- From the Department of Neurology (H.Y.H., E.D.G.), University of Utah, Salt Lake City, Utah
| | - M E Peckham
- Departments of Radiology and Imaging Sciences (L.M.S., J.S.M., T.A.H., M.E.P.), University of Utah, Salt Lake City, Utah
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24
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He JQ, Chen JT, Li JH, Chen WZ, Liang XY, Huang HY, Wei HG, Huang WY, Wang JL, Lin M, Yang PK, Chen XY, Liu XZ. [Drug-resistant gene polymorphisms in Plasmodium falciparum isolated from Bioko Island, Equatorial Guinea in 2018 and 2019]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2021; 33:396-400. [PMID: 34505447 DOI: 10.16250/j.32.1374.2021128] [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] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To investigate the genetic polymorphisms of Plasmodium falciparum multidrug resistance protein 1 (PfMDR1), chloroquine resistance transporter (PfCRT) and Kelch 13 (PfK13) genes in Bioko Island, Equatorial Guinea, so as to provide insights into the development of the malaria control strategy in local areas. METHODS A total of 85 peripheral blood samples were collected from patients with Plasmodium falciparum infections in Bioko Island, Equatorial Guinea in 2018 and 2019, and genomic DNA was extracted. The PfMDR1, PfCRT and PfK13 genes were amplified using a nested PCR assay. The amplification products were sequenced, and the gene sequences were aligned. RESULTS There were no mutations associated with artemisinin resistance in PfK13 gene in Bioko Island, Equatorial Guinea, while drug-resistant mutations were detected in PfMDR1 and PfCRT genes, and the proportions of PfMDR1_N86Y, PfMDR1_Y184F and PfCRT_K76T mutations were 35.29% (30/85), 72.94% (62/85) and 24.71% (21/85), respectively. CONCLUSIONS There are mutations in PfMDR1, PfCRT and PfK13 genes in P. falciparum isolates from Bioko Island, Equatorial Guinea.
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Affiliation(s)
- J Q He
- Department of Laboratory Medicine, Humen Hospital of Dongguan City, Guangdong Province, Dongguan 523000, China
- The Chinese Medical Aid Team to the Republic of Equatorial Guinea, Guangdong Province, China
| | - J T Chen
- The Chinese Medical Aid Team to the Republic of Equatorial Guinea, Guangdong Province, China
- Department of Laboratory Medicine, Huizhou Central Hospital, Guangdong Province, China
| | - J H Li
- The Chinese Medical Aid Team to the Republic of Equatorial Guinea, Guangdong Province, China
- Department of Laboratory Medicine, Shijie Hospital, Dongguan City, Guangdong Province, China
| | - W Z Chen
- Chaozhou People's Hospital Affiliated to Shantou University, China
| | - X Y Liang
- Department of Laboratory Medicine, Huizhou Central Hospital, Guangdong Province, China
- Chaozhou People's Hospital Affiliated to Shantou University, China
| | - H Y Huang
- Chaozhou People's Hospital Affiliated to Shantou University, China
| | - H G Wei
- School of Laboratory Medicine, Youjiang Medical University for Nationalities, China
| | - W Y Huang
- School of Laboratory Medicine, Youjiang Medical University for Nationalities, China
| | - J L Wang
- School of Laboratory Medicine, Youjiang Medical University for Nationalities, China
| | - M Lin
- Chaozhou People's Hospital Affiliated to Shantou University, China
- School of Laboratory Medicine, Youjiang Medical University for Nationalities, China
| | - P K Yang
- Chaozhou People's Hospital Affiliated to Shantou University, China
| | - X Y Chen
- Chaozhou People's Hospital Affiliated to Shantou University, China
| | - X Z Liu
- Chaozhou People's Hospital Affiliated to Shantou University, China
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Li CJ, Chang CL, Huang HY, Soong YK, Wu HM. P–570 Embryos originating from oocytes with smooth endoplasmic reticulum clusters have a lower euploidy rate via PGT-A testing using next-generation sequencing. Hum Reprod 2021. [DOI: 10.1093/humrep/deab130.569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Study question
Does the presence of smooth endoplasmic reticulum clusters (sERCs) in oocytes affect the human embryo ploidy?
Summary answer
The euploidy rate of embryos originating from sERCs + oocytes is lower
What is known already
While an expert panel strongly recommended that sERCs+ oocytes should not be inseminated, some normal healthy babies derived from sERCs+ oocytes have been reported. In previous studies have shown that declined fertilization rate and lower proportions of good quality embryos are found in oocytes showing sERCs. The updating findings of the molecular status of sERC+ oocytes elucidated the sERCs+ oocytes may have impaired chromosomal segregation ability. However, no study reveals the relation between sERCs and embryo ploidy.
Study design, size, duration
A retrospective study enrolled 129 preimplantation genetic testing (PGT) cycles from January 2017 to March 2020 at Chang Gung Memorial Hospital, Lonkou.
Participants/materials, setting, methods
ICSI fertilization rate, Day5 usable blastocyst rate (D5UBR), total usable blastocyst rate (TUBR), euploidy rate, mosaic rate, and aneuploidy rate are investigated between embryo originating from sERCs+ and sERCs- oocytes.
Main results and the role of chance
Although higher TBUR in blastocyst derived from sERCs+ oocytes than sERCs- group (73.7% vs. 62.5%) but accompanied lower euploidy rate (7% vs. 29%) and higher aneuploid rate (79% vs. 54%).
Limitations, reasons for caution
Limited sample size, need a large-scale study to confirm the conclusion. The live-birth rate per embryo transfer cycle was not included for analysis. As we did not perform polar body analysis, we cannot state for sure that embryonic aneuploidy was related to the oocyte.
Wider implications of the findings: This study demonstrates that embryos originating from sERCs+ oocytes have a lower euploidy rate.
Trial registration number
CMRPG3H0751
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Affiliation(s)
- C J Li
- Chang Gung Memorial Hospital- Lonkou, Fertility and Reproductive Genetic Center at Department Obstetrics and Gynecology, Taipei, Taiwan R.O.C
| | - C L Chang
- Chang Gung Memorial Hospital- Lonkou, Department Obstetrics and Gynecology, Taipei, Taiwan R.O.C
| | - H Y Huang
- Chang Gung Memorial Hospital- Lonkou, Department Obstetrics and Gynecology, Taipei, Taiwan R.O.C
| | - Y K Soong
- Chang Gung Memorial Hospital- Lonkou, Department Obstetrics and Gynecology, Taipei, Taiwan R.O.C
| | - H M Wu
- Chang Gung Memorial Hospital- Lonkou, Department Obstetrics and Gynecology, Taipei, Taiwan R.O.C
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Qiao J, Zhang Y, Liang X, Ho T, Huang HY, Kim SH, Goethberg M, Mannaerts B, Arce JC. O-110 A randomised, controlled, assessor-blind trial assessing clinical outcomes of individualised dosing with follitropin delta in Asian IVF/ICSI patients. Hum Reprod 2021. [DOI: 10.1093/humrep/deab126.019] [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: 11/12/2022] Open
Abstract
Abstract
Study question
To evaluate the efficacy and safety of individualised dosing with follitropin delta versus conventional dosing with follitropin alfa in an Asian population undergoing ovarian stimulation.
Summary answer
Individualised dosing with follitropin delta results in significantly higher live birth rate and fewer early OHSS and/or preventive interventions compared to conventional follitropin alfa dosing.
What is known already
Previous randomised controlled trials conducted in Europe, North- and South America mainly including Caucasian IVF/ICSI patients as well as in Japan have demonstrated that ovarian stimulation with the individualised follitropin delta dosing regimen based on serum AMH level and body weight modulated the ovarian response and reduced the risk of OHSS without compromising pregnancy and live birth rates.
Study design, size, duration
Randomised, controlled, assessor-blind trial conducted in 1,009 Asian patients from mainland China, South Korea, Vietnam and Taiwan, undergoing their first IVF/ICSI cycle. Randomisation was stratified by age (<35, 35-37, 38-40 years). The primary endpoint was ongoing pregnancy assessed 10-11 weeks after transfer (non-inferiority limit -10.0%; analysis adjusted for age strata). Patients <35 years underwent single embryo transfer if a good-quality embryo was available, otherwise double embryo transfer. Patients ≥35 years underwent double embryo transfer.
Participants/materials, setting, methods
Follitropin delta (Rekovelle, Ferring Pharmaceuticals) daily treatment consisted of a fixed dose individualised according to each patient’s initial AMH level (<15 pmol/L: 12 μg; ≥15 pmol/L: 0.19 to 0.10 μg/kg; min-max 6-12 μg) and body weight. Follitropin alfa (Gonal-f, Merck Serono) dose was 150 IU/day for the first five days with subsequent potential dose adjustments according to individual response. A GnRH antagonist protocol was applied. OHSS was classified based on Golan’s system.
Main results and the role of chance
The ongoing pregnancy rate was 31.3% with follitropin delta and 25.7% with follitropin alfa (adjusted difference 5.4% [95% CI: -0.2%; 11.0%]). The live birth rate was significantly higher at 31.3% with follitropin delta compared to 24.7% with follitropin alfa (adjusted difference 6.4% [95% CI: 0.9%; 11.9%]; p < 0.05). Live birth rates per age stratum were as follows for follitropin delta and follitropin alfa; <35 years: 31.0% versus 25.0%, 3537 years: 35.3% versus 26.7%, 38-40 years: 20.0% versus 14.3%. Early OHSS risk, evaluated as the incidence of early OHSS and/or preventive interventions, was significantly (p < 0.01) reduced from 9.6% with follitropin alfa to 5.0% with follitropin delta. The number of oocytes was 10.0±6.1 with follitropin delta and 12.4±7.3 with follitropin alfa. Individualised follitropin delta dosing compared to conventional follitropin alfa dosing resulted in 2 more oocytes (9.6±5.3 versus 7.6±3.5) in potential low responders (AMH <15 pmol/L) and 3 fewer oocytes (10.1±6.3 versus 13.8±7.5) in potential high responders (AMH ≥15 pmol/L). Among patients with AMH ≥15 pmol/L, excessive response occurred less frequently with individualised than conventional dosing (≥15 oocytes: 20.2% versus 39.1%; ≥20 oocytes: 6.7% versus 18.5%). Total gonadotropin dose was reduced from 109.9±32.9 μg with follitropin alfa to 77.5±24.4 μg with follitropin delta.
Limitations, reasons for caution
The trial only covered the clinical outcome of one treatment cycle with fresh cleavage-stage embryo transfers.
Wider implications of the findings
The present trial implies that in addition to reducing the early OHSS risk, individualised dosing has the potential to improve the take-home baby rate in fresh cycles across all ages and with a lower gonadotropin consumption. The benefits in outcomes appear to be explained by the modulation of ovarian response.
Trial registration number
NCT03296527
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Affiliation(s)
- J Qiao
- Peking University Third Hospital, Medical Center for Human Reproduction\rDept. of OB/GYN, Beijing, China
| | - Y Zhang
- Tianjin Central Hospital of Obstetrics and Gynecology, Center for Reproductive Medicine, Tianjin, China
| | - X Liang
- The Sixth Affiliated Hospital of Sun Yat-sen University, Center for Reproductive Medicine, Guangzhou, China
| | - T Ho
- My Duc Hospital, IVFMD and HOPE Research Center, Ho Chi Minh City, Vietnam
| | - H Y Huang
- Chang Gung Memorial Hospital, Department of Obstetrics and Gynegology, Tao-Yuan City, Taiwan R.O.C
| | - S H Kim
- Asan Medical Center, Department of Obstetrics and Gynecology, Seoul, Korea- South
| | - M Goethberg
- Ferring Pharmaceuticals, Global Biometrics, Copenhagen, Denmark
| | - B Mannaerts
- Ferring Pharmaceuticals, Reproductive Medicine & Maternal Health, Copenhagen, Denmark
| | - J C Arce
- Ferring Pharmaceuticals, Reproductive Medicine & Maternal Health, Copenhagen, Denmark
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27
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Huang HY, Kueng R, Preskill J. Efficient Estimation of Pauli Observables by Derandomization. Phys Rev Lett 2021; 127:030503. [PMID: 34328776 DOI: 10.1103/physrevlett.127.030503] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
We consider the problem of jointly estimating expectation values of many Pauli observables, a crucial subroutine in variational quantum algorithms. Starting with randomized measurements, we propose an efficient derandomization procedure that iteratively replaces random single-qubit measurements by fixed Pauli measurements; the resulting deterministic measurement procedure is guaranteed to perform at least as well as the randomized one. In particular, for estimating any L low-weight Pauli observables, a deterministic measurement on only of order log(L) copies of a quantum state suffices. In some cases, for example, when some of the Pauli observables have high weight, the derandomized procedure is substantially better than the randomized one. Specifically, numerical experiments highlight the advantages of our derandomized protocol over various previous methods for estimating the ground-state energies of small molecules.
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Affiliation(s)
- Hsin-Yuan Huang
- Institute for Quantum Information and Matter, Caltech, Pasadena, California 91125, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, California 91125, USA
| | - Richard Kueng
- Institute for Integrated Circuits, Johannes Kepler University Linz, A-4040, Austria
| | - John Preskill
- Institute for Quantum Information and Matter, Caltech, Pasadena, California 91125, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, California 91125, USA
- Walter Burke Institute for Theoretical Physics, Caltech, Pasadena, California 91125, USA
- AWS Center for Quantum Computing, Pasadena, California 91125, USA
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28
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Huang HY, Kueng R, Preskill J. Information-Theoretic Bounds on Quantum Advantage in Machine Learning. Phys Rev Lett 2021; 126:190505. [PMID: 34047595 DOI: 10.1103/physrevlett.126.190505] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/17/2021] [Accepted: 04/02/2021] [Indexed: 06/12/2023]
Abstract
We study the performance of classical and quantum machine learning (ML) models in predicting outcomes of physical experiments. The experiments depend on an input parameter x and involve execution of a (possibly unknown) quantum process E. Our figure of merit is the number of runs of E required to achieve a desired prediction performance. We consider classical ML models that perform a measurement and record the classical outcome after each run of E, and quantum ML models that can access E coherently to acquire quantum data; the classical or quantum data are then used to predict the outcomes of future experiments. We prove that for any input distribution D(x), a classical ML model can provide accurate predictions on average by accessing E a number of times comparable to the optimal quantum ML model. In contrast, for achieving an accurate prediction on all inputs, we prove that the exponential quantum advantage is possible. For example, to predict the expectations of all Pauli observables in an n-qubit system ρ, classical ML models require 2^{Ω(n)} copies of ρ, but we present a quantum ML model using only O(n) copies. Our results clarify where the quantum advantage is possible and highlight the potential for classical ML models to address challenging quantum problems in physics and chemistry.
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Affiliation(s)
- Hsin-Yuan Huang
- Institute for Quantum Information and Matter, Caltech, Pasadena, California 91125, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, California 91125, USA
| | - Richard Kueng
- Institute for Integrated Circuits, Johannes Kepler University Linz, Linz 4040, Austria
| | - John Preskill
- Institute for Quantum Information and Matter, Caltech, Pasadena, California 91125, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, California 91125, USA
- Walter Burke Institute for Theoretical Physics, Caltech, Pasadena, California 91125, USA
- AWS Center for Quantum Computing, Pasadena, California 91125, USA
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Huang HY, Broughton M, Mohseni M, Babbush R, Boixo S, Neven H, McClean JR. Power of data in quantum machine learning. Nat Commun 2021; 12:2631. [PMID: 33976136 PMCID: PMC8113501 DOI: 10.1038/s41467-021-22539-9] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [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: 11/18/2020] [Accepted: 03/16/2021] [Indexed: 11/30/2022] Open
Abstract
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We then propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime. For near-term implementations, we demonstrate a significant prediction advantage over some classical models on engineered data sets designed to demonstrate a maximal quantum advantage in one of the largest numerical tests for gate-based quantum machine learning to date, up to 30 qubits.
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Affiliation(s)
- Hsin-Yuan Huang
- Google Quantum AI, Venice, CA, USA
- Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA
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30
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Mwale PF, Lee CH, Huang PN, Tseng SN, Shih SR, Huang HY, Leu SJ, Huang YJ, Chiang LC, Mao YC, Wang WC, Yang YY. In Vitro Characterization of Neutralizing Hen Antibodies to Coxsackievirus A16. Int J Mol Sci 2021; 22:4146. [PMID: 33923724 PMCID: PMC8074035 DOI: 10.3390/ijms22084146] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/14/2021] [Accepted: 04/14/2021] [Indexed: 11/16/2022] Open
Abstract
Coxsackievirus A16 (CA16) is one of the major causative agents of hand, foot, and mouth disease (HFMD). Children aged <5 years are the most affected by CA16 HFMD globally. Although clinical symptoms of CA16 infections are usually mild, severe complications, such as aseptic meningitis or even death, have been recorded. Currently, no vaccine or antiviral therapy for CA16 infection exists. Single-chain variable fragment (scFv) antibodies significantly inhibit viral infection and could be a potential treatment for controlling the infection. In this study, scFv phage display libraries were constructed from splenocytes of a laying hen immunized with CA16-infected lysate. The pComb3X vector containing the scFv genes was introduced into ER2738 Escherichia coli and rescued by helper phages to express scFv molecules. After screening with five cycles of bio-panning, an effective scFv antibody showing favorable binding activity to proteins in CA16-infected lysate on ELISA plates was selected. Importantly, the selected scFv clone showed a neutralizing capability against the CA16 virus and cross-reacted with viral proteins in EV71-infected lysate. Intriguingly, polyclonal IgY antibody not only showed binding specificity against proteins in CA16-infected lysate but also showed significant neutralization activities. Nevertheless, IgY-binding protein did not cross-react with proteins in EV71-infected lysate. These results suggest that the IgY- and scFv-binding protein antibodies provide protection against CA16 viral infection in in vitro assays and may be potential candidates for treating CA16 infection in vulnerable young children.
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Affiliation(s)
- Pharaoh Fellow Mwale
- Ph.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (P.F.M.); (C.-H.L.)
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (H.-Y.H.); (Y.-J.H.)
| | - Chi-Hsin Lee
- Ph.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (P.F.M.); (C.-H.L.)
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (H.-Y.H.); (Y.-J.H.)
| | - Peng-Nien Huang
- Division of Infectious Diseases, Department of Pediatrics, Linkou Chang Gung Memorial Hospital, Research Center for Emerging Viral Infections, Chang Gung University, Taoyuan 333423, Taiwan;
| | - Sung-Nien Tseng
- Research Center for Emerging Viral Infections, Chang Gung University, Taoyuan 333323, Taiwan;
| | - Shin-Ru Shih
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Research Center for Emerging Viral Infections, Chang Gung University, Taoyuan 333423, Taiwan;
| | - Hsin-Yuan Huang
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (H.-Y.H.); (Y.-J.H.)
| | - Sy-Jye Leu
- Department of Microbiology and Immunology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan;
- Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan;
| | - Yun-Ju Huang
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (H.-Y.H.); (Y.-J.H.)
| | - Liao-Chun Chiang
- Institute of Bioinformatics and Structural Biology, College of Life Sciences, National Tsing Hua University, Hsinchu 300040, Taiwan;
| | - Yan-Chiao Mao
- Division of Clinical Toxicology, Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan;
| | - Wei-Chu Wang
- Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan;
| | - Yi-Yuan Yang
- Ph.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (P.F.M.); (C.-H.L.)
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (H.-Y.H.); (Y.-J.H.)
- Core Laboratory of Antibody Generation and Research, Taipei Medical University, Taipei 110301, Taiwan
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31
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Wu DW, Huang HY, Tang Y, Wang HX, Wang J, Wang SH, Fang H, Yang XY, Li J, Wang X, Liu LJ, Yan Y, Wang Q, Li N, Cao C, Xu BH, Sun Y, He J. [Progress on clinical trials of cancer drugs in China, 2020]. Zhonghua Zhong Liu Za Zhi 2021; 43:218-223. [PMID: 33601488 DOI: 10.3760/cma.j.cn112152-20201221-01089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To explore the latest progress of oncology drug clinical trials in China under COVID-19, as well as to provide decision-making evidence for related stakeholders. Research progress of oncology drug trials and approved cancer drugs in China in 2020 were systematically summarized and compared with 2019. Methods: Information Disclosure Platform for Drug Clinical Studies and China Food and Drug Administration Query System for Domestic and Imported Drug were searched for registered clinical trials and approved oncology drugs, respectively. The trial scope, stage, drug type, effect and mechanism of domestic and global pharmaceutical enterprises were compared between 2019 and 2020. Results: A total of 722 cancer drug trials registered in China in 2020, with an annual growth rate of 52.3%, accounting for 28.3% of all registered trials. Among them, 603 (83.5%) trials were initiated by domestic pharmaceutical enterprises, and 105 (14.5%) were international multicenter trials, phase I trials accounted for 44.5%. For all those trials, there were 458 cancer drug varieties, with an annual growth rate of 36.7%, and 361 (85.8%) were developed by domestic enterprises. Most of the investigational products were therapeutic innovative drugs (77.1%), major in tumor treatment (92.8%). In terms of mechanism, targeted drugs were the most popular, accounting for 76.6%, and programmed cell death-1 (PD-1) and epithelial growth factor receptor (EGFR) were the most common targets. In addition, there were 19 anticancer drugs from 17 companies approved in China in 2019, with 10 drugs from domestic companies. Lung cancer and breast cancer are the most common indications for both registered trials and marketed drugs. No statistically significant differences were found between 2020 and 2019 in terms of the distribution of trial sponsor, scope and stage, as well as the distribution of drug type, effect and mechanism (P>0.05). Conclusions: During the Covid-19 epidemic period, clinical trials of oncology drugs in China progress smoothly and maintain a high growth rate. Series of innovative products obtained by domestic enterprises in 2020 is the main driving force of development of oncology drug clinical trials in China.
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Affiliation(s)
- D W Wu
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H Y Huang
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Tang
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H X Wang
- National Center for Drug Evaluation, National Medical Products Administration, Beijing 100022, China
| | - J Wang
- National Center for Drug Evaluation, National Medical Products Administration, Beijing 100022, China
| | - S H Wang
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H Fang
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X Y Yang
- Hospital Office, Hospital for Skin Diseases, Chinese Academy of Medical Sciences, Nanjing 210042, China
| | - J Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardivascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - X Wang
- National Clinical Research Center for Geriatric Diseases/Clinical Trial Center, Beijing Hospital, Beijing 100730, China
| | - L J Liu
- Department of Clinical Trials Center, National Clinial Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
| | - Y Yan
- Department of Clinical Trials Institution, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100144, China
| | - Q Wang
- Department of Clinical Trials Center, China-Japan Friendship Hospital, Beijing 100029, China
| | - N Li
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - C Cao
- ZhongGuanCun JiuTai Drug Clinical Practice Union, Beijing 100027, China
| | - B H Xu
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Sun
- Department of Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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32
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Chen YF, Li D, Lee YM, Lee CC, Huang HY, Tsou CH, Liang HC. Highly efficient solid-state Raman yellow-orange lasers created by enhancing the cavity reflectivity. Opt Lett 2021; 46:797-800. [PMID: 33577517 DOI: 10.1364/ol.415437] [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] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 01/08/2021] [Indexed: 06/12/2023]
Abstract
A new, to the best of our knowledge, output coupler (OC) with enhancement of the cavity reflectivity is proposed to remarkably elevate the output powers and efficiencies of diode-pumped Nd:GdVO4/KGW Raman yellow-orange lasers. The cavity reflectivity is effectively increased by using the double-sided dichroic coating on the OC. In comparison with the conventional single-sided coating, the conversion efficiency can be boosted from 15% to 26.3% in the experiment of a yellow laser at 578.8 nm, and the maximum output power can be increased from 5.7 to 10.5 W in the quasi-continuous-wave mode with 50% duty cycle and frequency of 500 Hz. Furthermore, in the operation of an orange laser at 588 nm, the maximum output power can be improved from 5.6 to 7.0 W by replacing the conventional OC with the new one.
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33
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Onoufriadis A, Boulouadnine B, Dachy G, Higashino T, Huang HY, Hsu CK, Simpson MA, Bork K, Demoulin JB, McGrath JA. A germline mutation in the platelet-derived growth factor receptor beta gene may be implicated in hereditary progressive mucinous histiocytosis. Br J Dermatol 2021; 184:967-970. [PMID: 33301597 DOI: 10.1111/bjd.19717] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/06/2020] [Accepted: 12/07/2020] [Indexed: 01/29/2023]
Affiliation(s)
- A Onoufriadis
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | | | - G Dachy
- De Duve Institute, UCLouvain, Brussels, Belgium
| | - T Higashino
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - H Y Huang
- Department of Dermatology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - C K Hsu
- Department of Dermatology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - M A Simpson
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
| | - K Bork
- Department of Dermatology, Johannes Gutenberg University, Mainz, Germany
| | | | - J A McGrath
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
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Wang H, Cao MD, Liu CC, Yan XX, Huang HY, Zhang Y, Chen HD, Ren JS, Li N, Chen WQ, Dai M, Shi JF. [Disease burden of colorectal cancer in China: any changes in recent years?]. Zhonghua Liu Xing Bing Xue Za Zhi 2020; 41:1633-1642. [PMID: 33297619 DOI: 10.3760/cma.j.cn112338-20200306-00273] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To update the disease burden of colorectal cancer (CRC) in Chinese population by integrating the latest multi-source evidences. Methods: Groups of data from GLOBOCAN, series of Chinese Cancer Registry Annual Report (annual report), Cancer Incidence in Five Continents (CI5), Global Burden of Disease Project 2017 (GBD), China Death Cause Surveillance Datasets and China Health Statistical Yearbooks (yearbook) were used to extract the information. Data on incidence, mortality, disability-adjusted life year (DALY) and percentage distribution of sub-location of CRC were used to analyze the latest disease burden in China, and age-standardized rates by world standard population were mainly used. Joinpoint Trend Analysis Software 4.7.0.0 was applied for time trend analysis. Data related to the economic burden of CRC in China were gathered by literature review. Results: (1) Current status: according to the latest annual report, the incidence and mortality rates of CRC were 17.1 per 100 000 and 7.9 per 100 000, respectively among the covered registration sites in 2015. The incidence ratios of male to female and that of urban to rural were 1.5 and 1.4, with the mortality ratios were 1.6 and 1.4, respectively. Similar to data from the annual report, the mortality rate was reported as 6.9 per 100 000 in 2017 by the surveillance data sets. Data from the GBD project showed that, the DALYs caused by CRC in China in 2017 was 4.254 million person years (doubled compared with that of 1990), accounting for 22.4% of the global burden of CRC. (2) Time trends: according to the annual reports, from 2009 to 2015, the incidence rate and mortality rate of CRC in China decreased by 10.2% and 9.5%, respectively. The same trend was also observed in urban sites, but was opposite in rural areas (increased 20.0% in incidence and 15.2% in mortality). Results from the Joinpoint analysis showed that the averaged annual percentage change (AAPC) was estimated as -1.6% (P<0.05) in the national mortality rate. Similarly, in the incidence and mortality rates of urban sites appeared as AAPC=-1.5% and -1.4% (all P<0.05), but inversely in the incidence rate from the rural sites as AAPC=3.3% (P<0.05). The yearbook data showed a 9.8% increase in urban and 20.6% increase in rural on the mortality in 2017 when compared with 2004, but the Joinpoint analysis showed no statistical significance (P<0.05). (3) Distribution of sub-location of CRC: the annual report showed that among all the new CRC cases in China in 2015, colon, rectal and anal cancer accounted for 49.6%, 49.2% and 1.2%, respectively, while the proportions were 51.3%, 47.6% and 1.1%, respectively in 2009. The proportion of colon cancer was continuously higher in the urban (>52%) than that in the rural areas (<44%). The CI5 Ⅺ data showed that ascending and sigmoid colons were more commonly seen among all the colon cancers. (4) Economic burden: the average annual growth rate of the medical expenditure per CRC patient in China ranged from 6.9% to 9.2%, and the 1-year out-of-pocket expenditure of a newly diagnosed patient accounted for about 60% of their previous-year household income. Conclusions: In China, the overall disease burden of CRC might have been decreased slightly but generally remained stable in the last several years, however, the rising burden appeared in the rural areas should not be ignored. In consistent with findings from a previous review, men and people from the urban areas are considered the target populations for CRC. The finding of higher proportion of colon cancer in urban areas suggests the impact of development of socioeconomic and medical technologies on CRC development and detection. The economic burden of CRC continued to grow.
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Affiliation(s)
- H Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M D Cao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - C C Liu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X X Yan
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H Y Huang
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Zhang
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100026, China
| | - H D Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J S Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J F Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Chen YF, Huang HY, Lee CC, Hsiao JQ, Tsou CH, Liang HC. High-power diode-pumped Nd:GdVO 4/KGW Raman laser at 578 nm. Opt Lett 2020; 45:5562-5565. [PMID: 33001947 DOI: 10.1364/ol.406173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 09/04/2020] [Indexed: 06/11/2023]
Abstract
A diode-pumped neodymium-doped gadolinium vanadate (Nd:GdVO4) laser is developed as a compact efficient yellow light at 578 nm by means of intracavity stimulated Raman scattering (SRS) in a potassium gadolinium tungstate (KGW) crystal and the second-harmonic generation in a lithium triborate crystal. The SRS process with a shift of 768cm-1 is achieved by setting the polarization of the fundamental wave along the Ng axis of the KGW crystal. The self-Raman effect arising from the Nd:GdVO4 crystal is systematically explored by employing two kinds of coating specification for the output coupler. With a specific coating on the output coupler to suppress the self-Raman effect, the maximum output power at 578 nm can reach 3.1 W at a pump power of 32 W. Moreover, two different lengths for the Nd:GdVO4 crystal are individually used to verify the influence of the self-Raman effect on the lasing efficiency.
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Chen YF, Chen CM, Lee CC, Huang HY, Li D, Hsiao JQ, Tsou CH, Liang HC. Efficient solid-state Raman yellow laser at 579.5 nm. Opt Lett 2020; 45:5612-5615. [PMID: 33001961 DOI: 10.1364/ol.405970] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 09/04/2020] [Indexed: 06/11/2023]
Abstract
A highly efficient diode-pumped Nd:YVO4/KGW Raman yellow laser is developed to produce a 6.8 W yellow light at 579.5 nm accompanied by a 3.2 W Stokes wave at 1159 nm under an incident pump power of 30 W. The intracavity stimulated Raman scattering with the shift of 768cm-1 is generated by setting the polarization of the fundamental wave along the Ng direction of an Np-cut KGW crystal. The Nd:YVO4 gain medium is coated as a cavity mirror to reduce the cavity losses for the fundamental wave. More importantly, the KGW crystal is specially coated to prevent the Stokes wave from propagating through the gain medium to minimize the cavity losses for the Stokes wave.
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Hepting M, Li D, Jia CJ, Lu H, Paris E, Tseng Y, Feng X, Osada M, Been E, Hikita Y, Chuang YD, Hussain Z, Zhou KJ, Nag A, Garcia-Fernandez M, Rossi M, Huang HY, Huang DJ, Shen ZX, Schmitt T, Hwang HY, Moritz B, Zaanen J, Devereaux TP, Lee WS. Publisher Correction: Electronic structure of the parent compound of superconducting infinite-layer nickelates. Nat Mater 2020; 19:1036. [PMID: 32661388 DOI: 10.1038/s41563-020-0761-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Affiliation(s)
- M Hepting
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
- Max Planck Institute for Solid State Research, Stuttgart, Germany
| | - D Li
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - C J Jia
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA.
| | - H Lu
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - E Paris
- Photon Science Division, Swiss Light Source, Paul Scherrer Institut, Villigen, Switzerland
| | - Y Tseng
- Photon Science Division, Swiss Light Source, Paul Scherrer Institut, Villigen, Switzerland
| | - X Feng
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - M Osada
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - E Been
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Y Hikita
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Y-D Chuang
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Z Hussain
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - K J Zhou
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
| | - A Nag
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
| | | | - M Rossi
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - H Y Huang
- NSRRC, Hsinchu Science Park, Hsinchu, Taiwan
| | - D J Huang
- NSRRC, Hsinchu Science Park, Hsinchu, Taiwan
| | - Z X Shen
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
- Geballe Laboratory for Advanced Materials, Departments of Physics and Applied Physics, Stanford University, Stanford, CA, USA
| | - T Schmitt
- Photon Science Division, Swiss Light Source, Paul Scherrer Institut, Villigen, Switzerland
| | - H Y Hwang
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - B Moritz
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - J Zaanen
- Instituut-Lorentz for theoretical Physics, Leiden University, Leiden, the Netherlands
| | - T P Devereaux
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - W S Lee
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA.
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Chen YF, Lee CC, Hsiao JQ, Huang HY, Tsou CH, Liang HC, Huang KF. Exploiting a monolithic passively Q-switched Nd:YAG laser to mimic a single neuron cell under periodic stimulation. Opt Lett 2020; 45:4032-4035. [PMID: 32667347 DOI: 10.1364/ol.399253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 06/20/2020] [Indexed: 06/11/2023]
Abstract
A monolithic passively Q-switched Nd:YAG laser under periodic pulse pumping is originally exploited to emulate the response of a single neuron cell stimulated by periodic pulse inputs. Experimental results reveal that the output characteristics of the monolithic passively Q-switched laser can analogously manifest not only the firing patterns but also the frequency-locked plateaus of the single neuron cell. Moreover, the sine circle map is innovatively used to generate the output pulse sequences that can exactly correspond to experimental firing patterns. The present exploration indicates that a monolithic passively Q-switched solid-state laser is highly feasible to be developed as a compact artificial neuron cell.
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Wang H, Huang HY, Liu CC, Bai FZ, Zhu J, Wang L, Yan XX, Chen YS, Chen HD, Zhang YM, Ren JS, Zou SM, Li N, Zheng ZX, Feng H, Bai HJ, Zhang J, Chen WQ, Dai M, Shi JF. [Health economic evidence for colorectal cancer screening programs in China: an update from 2009-2018]. Zhonghua Liu Xing Bing Xue Za Zhi 2020; 41:429-435. [PMID: 32294848 DOI: 10.3760/cma.j.issn.0254-6450.2020.03.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: This study was to systematically update the economic evaluation evidence of colorectal cancer screening in mainland China. Methods: Based on a systematic review published in 2015, we expanded the scope of retrieval database (PubMed, EMbase, The Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP, CBM) and extended it to December 2018. Focusing on the evidence for nearly 10 years (2009-2018), basic characteristics and main results were extracted. Costs were discounted to 2017 using the consumer price index of medical and health care being provided to the residents, and the ratio of incremental cost-effectiveness ratio (ICER) to per capita GDP in corresponding years were calculated. Results: A total of 12 articles (8 new ones) were included, of which 9 were population-based (all cross-sectional studies) and 3 were model-based. Most of the initial screening age was 40 years (7 articles), and most of the frequency was once in a lifetime (11 articles). Technologies used for primary screening included: questionnaire assessment, immunological fecal occult blood test (iFOBT) and endoscopy. The most commonly used indicator was the cost per colorectal cancer detected, and the median (range) of the 20 screening schemes was 52 307 Chinese Yuan (12 967-3 769 801, n=20). The cost per adenoma detected was 9 220 Yuan (1 859-40 535, n=10). In 3 articles, the cost per life year saved (compared with noscreening) was mentioned and the ratio of ICER to GDP was 0.673 (-0.013-2.459, n=11), which was considered by WHO as "very cost-effective" ; The range of ratios overlapped greatly among different technologies and screening frequencies, but the initial age for screening seemed more cost-effective at the age of 50 years (0.002, -0.013-0.015, n=3), than at the 40 year-olds (0.781, 0.321-2.459, n=8). Conclusions: Results from the population-based studies showed that the cost per adenoma detected was only 1/6 of the cost per colorectal cancer detected, and limited ICER evidence suggested that screening for colorectal cancer was generally cost-effective in Chinese population. Despite the inconclusiveness of the optimal screening technology, the findings suggested that the initial screening might be more cost-effective at older age. No high-level evidence such as randomized controlled trial evaluation was found.
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Affiliation(s)
- H Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H Y Huang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - C C Liu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - F Z Bai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J Zhu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X X Yan
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y S Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H D Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J S Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S M Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Z X Zheng
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H Feng
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - H J Bai
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - J Zhang
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - W Q Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J F Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Hepting M, Li D, Jia CJ, Lu H, Paris E, Tseng Y, Feng X, Osada M, Been E, Hikita Y, Chuang YD, Hussain Z, Zhou KJ, Nag A, Garcia-Fernandez M, Rossi M, Huang HY, Huang DJ, Shen ZX, Schmitt T, Hwang HY, Moritz B, Zaanen J, Devereaux TP, Lee WS. Electronic structure of the parent compound of superconducting infinite-layer nickelates. Nat Mater 2020; 19:381-385. [PMID: 31959951 DOI: 10.1038/s41563-019-0585-z] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 12/11/2019] [Indexed: 05/21/2023]
Abstract
The search continues for nickel oxide-based materials with electronic properties similar to cuprate high-temperature superconductors1-10. The recent discovery of superconductivity in the doped infinite-layer nickelate NdNiO2 (refs. 11,12) has strengthened these efforts. Here, we use X-ray spectroscopy and density functional theory to show that the electronic structure of LaNiO2 and NdNiO2, while similar to the cuprates, includes significant distinctions. Unlike cuprates, the rare-earth spacer layer in the infinite-layer nickelate supports a weakly interacting three-dimensional 5d metallic state, which hybridizes with a quasi-two-dimensional, strongly correlated state with [Formula: see text] symmetry in the NiO2 layers. Thus, the infinite-layer nickelate can be regarded as a sibling of the rare-earth intermetallics13-15, which are well known for heavy fermion behaviour, where the NiO2 correlated layers play an analogous role to the 4f states in rare-earth heavy fermion compounds. This Kondo- or Anderson-lattice-like 'oxide-intermetallic' replaces the Mott insulator as the reference state from which superconductivity emerges upon doping.
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Affiliation(s)
- M Hepting
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
- Max Planck Institute for Solid State Research, Stuttgart, Germany
| | - D Li
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - C J Jia
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA.
| | - H Lu
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - E Paris
- Photon Science Division, Swiss Light Source, Paul Scherrer Institut, Villigen, Switzerland
| | - Y Tseng
- Photon Science Division, Swiss Light Source, Paul Scherrer Institut, Villigen, Switzerland
| | - X Feng
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - M Osada
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - E Been
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Y Hikita
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Y-D Chuang
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Z Hussain
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - K J Zhou
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
| | - A Nag
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
| | | | - M Rossi
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - H Y Huang
- NSRRC, Hsinchu Science Park, Hsinchu, Taiwan
| | - D J Huang
- NSRRC, Hsinchu Science Park, Hsinchu, Taiwan
| | - Z X Shen
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
- Geballe Laboratory for Advanced Materials, Departments of Physics and Applied Physics, Stanford University, Stanford, CA, USA
| | - T Schmitt
- Photon Science Division, Swiss Light Source, Paul Scherrer Institut, Villigen, Switzerland
| | - H Y Hwang
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - B Moritz
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - J Zaanen
- Instituut-Lorentz for theoretical Physics, Leiden University, Leiden, the Netherlands
| | - T P Devereaux
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - W S Lee
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA.
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Xu K, Cai LJ, Chen H, Li YY, Wang ZB, Huang HY, Chu HQ, Cui YH, Liu Z, Lu X. [Safety and effectiveness of transoral robotic surgery for oropharyngeal cancer: a pilot study]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2020; 55:109-115. [PMID: 32074748 DOI: 10.3760/cma.j.issn.1673-0860.2020.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To evaluate the indication, safety and effectiveness of transoral robotic surgery (TORS) for oropharyngeal cancer based on our preliminary experience. Methods: Twelve patients, including six with tonsil cancer, five with tongue base cancer and one with posterior pharyngeal wall cancer, who underwent TORS with Da Vinci Si surgical system from March 2017 to October 2018 at Tongji Hospital of Huazhong University of Science Technology were respectively analyzed. And the surgical time, intraoperative blood loss, postoperative local bleeding, dyspnea, nerve function injury, oral intake time, whether or not to receive chemoradiotherapy were analyzed. Results: All tumors in the 12 patients were en bloc removed by TORS. Surgical time ranged from 25 to 80 min with an average of 34.2 min. The blood loss ranged from 10 ml to 50 ml with an average of 20.8 ml. The recovery time for oral intake ranged from 1 day to 30 days with an average of 8.4 days. No patient underwent tracheostomy after TORS. Also, no patient manifested with airway obstruction, bleeding or nerve injury symptoms after operation. All 12 patients reached pathologically negative surgical margins. The patients were followed up for 4 to 22 months, with a median of 12 months. All patients who combined with more advanced than T3 stage, or more advanced than N2 stage were recommended to oncologist, then, followed with radiotherapy or chemoradiotherapy if no relevant contradictions occurred. No local recurrence or distant metastasis case was found. Conclusion: With proper indications, the application of TORS in oropharyngeal cancer is a relatively safe, effective and minimal invasive therapy, which merits more clinical applications.
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Affiliation(s)
- K Xu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - L J Cai
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - H Chen
- Department of Operation, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Y Y Li
- Department of Operation, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Z B Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - H Y Huang
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - H Q Chu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Y H Cui
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Z Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - X Lu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Fang Y, Yu Y, Wu DW, Fang H, Huang HY, Wang SH, Yu AQ, Sun C, Bai Y, Wang H, Li N. [A review of immune-related adverse events associated with immunotherapy]. Zhonghua Zhong Liu Za Zhi 2020; 42:17-21. [PMID: 32023764 DOI: 10.3760/cma.j.issn.0253-3766.2020.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Immune checkpoint inhibitors have been approved for clinical application in China. However, the increased immune-related adverse event (irAE) needs more attention. This review summarized the incidence, characteristic clinical manifestation and treatment of irAEs associated with programmed cell death protein-1(PD-1) and programmed cell death ligand-1(PD-L1) inhibitors. To have a deep insight into irAE, the potential mechanisms, the different incidences of cancer types, influencing factors and the direction of future research were also discussed here to provide guidance for clinical oncologist to identify and monitor irAE.
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Affiliation(s)
- Y Fang
- GCP center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Mao AY, Shi JF, Qiu WQ, Liu CC, Dong P, Huang HY, Wang K, Wang DB, Liu GX, Liao XZ, Bai YN, Sun XJ, Ren JS, Yang L, Wei DH, Song BB, Lei HK, Liu YQ, Zhang YZ, Ren SY, Zhou JY, Wang JL, Gong JY, Yu LZ, Liu YY, Zhu L, Guo LW, Wang YQ, He YT, Lou PA, Cai B, Sun XH, Wu SL, Qi X, Zhang K, Li N, Dai M, Chen WQ. [Analysis on the consciousness of the cancer early detection and its influencing factors among urban residents in China from 2015 to 2017]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 54:54-61. [PMID: 31914570 DOI: 10.3760/cma.j.issn.0253-9624.2020.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To understand the consciousness of the cancer early detection among urban residents and identify the influencing factors from 2015 to 2017. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. Self-designed questionnaires were used to collect population, socioeconomic indicators, self-cancer risk assessment, regular participation in physical examination and other information. The multivariate logistic regression model was used to identify the factors of people who had not regularly participated in the regular physical examination in the past five years. Results: The self-assessment results of 32 357 residents showed that there were 27.54% (8 882) of total study population with self-reported cancer risk, 45.48% (14 671) without cancer risk and 26.98% (8 704) with unclear judgement on their own cancer risk. Among population with cancer risk, 79.84% (7 091) considered physical examination accounted. In the past five years, there were 21 105 (65.43%) residents participated in regular physical examination and 11 148 (34.56%) participated in non-scheduled one, respectively. The multivariate logistic regression analysis showed that compared with unmarried and western region residents, divorced, middle and eastern region residents had a stronger consciousness to participate in the regular physical examination (P<0.05). Compare with residents with annual household income less than 20 000 CNY in 2014, cancer risk assessment/screening intervention population, and self-assessment with cancer risk, residents with annual household income between 20 000 CNY and 59 000 CNY in 2014, occupational population, community residents, cancer patients, self-reported cancer-free risk, and self-assessment with unclear judgement of cancer risk were less likely to participate in the regular physical examination (all P values <0.05). Conclusion: From 2015 to 2017, the Chinese urban residents had a acceptable consciousness of the cancer early detection. The marital status, annual household income, population group and self-assessment of cancer risk were related to the consciousness of the cancer early detection of people who had not participated in the regular physical examination in the past five years.
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Affiliation(s)
- A Y Mao
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - J F Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Qiu
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - C C Liu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - P Dong
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - H Y Huang
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - K Wang
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - D B Wang
- Health Management College, Anhui Medical University, Hefei 230032, China
| | - G X Liu
- School of Public Health, Harbin Medical University, Harbin 150081, China
| | - X Z Liao
- The Department of Cancer Prevention and Control, Hunan Provincial Cancer Hospital, Changsha 410006, China
| | - Y N Bai
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - X J Sun
- School of Health Care Management, Shandong University, Jinan 250012, China
| | - J S Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Yang
- School of Public Health, Guangxi Medical University, Nanning 530021, China
| | - D H Wei
- Department of Medical Examination for Cancer Prevention, Anhui Provincial Cancer Hospital, Hefei 230032, China
| | - B B Song
- The Department of Cancer Prevention and Control, Affiliated Cancer Hospital of Harbin Medical University, Harbin 150081, China
| | - H K Lei
- Department of Cancer Research and Control, Chongqing University Cancer Hospital/Chongqing Cancer Institute/Chongqing Cancer Hospital, Chongqing 400030, China
| | - Y Q Liu
- Department of Cancer Epidemiology, Gansu Provincial Cancer Hospital, Lanzhou 730050, China
| | - Y Z Zhang
- Department of Epidemiology, Shanxi Provincial Center Hospital, Taiyuan 030013, China
| | - S Y Ren
- Institute for Chronic and Non-communicable Disease Prevention and Control, Yunnan Center for Disease Prevention and Control,Kunming 650118, China
| | - J Y Zhou
- Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J L Wang
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - J Y Gong
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - L Z Yu
- Institute for Chronic and Non-communicable Disease Prevention and Control, Liaoning Provincial Center for Disease Control and Prevention, Shenyang 110005, China
| | - Y Y Liu
- The Department of Cancer Prevention and Control, Liaoning Cancer Hospital & Institute, Shenyang 110042, China
| | - L Zhu
- Cancer Research Institute, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - L W Guo
- Office for Cancer Control and Research, Henan Cancer Hospital/The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China
| | - Y Q Wang
- Department of Cancer Prevention, Cancer Hospital of University of Chinese Academy of Sciences/Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Y T He
- The Department of Cancer Prevention and Control, Cancer Institute, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - P A Lou
- Department of Control and Prevention of Chronic Non-communicable Diseases, Xuzhou Center for Disease Control and Prevention, Xuzhou 221006, China
| | - B Cai
- Department of Health Education and Chronic Disease Control, Nantong Center for Disease Control and Prevention, Nantong 226000, China
| | - X H Sun
- Endocrine Department, Ningbo NO.2 Hospital, Ningbo 315010,China
| | - S L Wu
- Department of Cardiovascular Diseases, Kailuan General Hospital, Tangshan 063000, China
| | - X Qi
- Office of Cancer Screening, Tangshan People's Hospital, Tangshan 063001, China
| | - K Zhang
- Department of Medical Examination for Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Wang K, Liu CC, Mao AY, Shi JF, Dong P, Huang HY, Wang DB, Liu GX, Liao XZ, Bai YN, Sun XJ, Ren JS, Yang L, Wei DH, Song BB, Lei HK, Liu YQ, Zhang YZ, Ren SY, Zhou JY, Wang JL, Gong JY, Yu LZ, Liu YY, Zhu L, Guo LW, Wang YQ, He YT, Lou PA, Cai B, Sun XH, Wu SL, Qi X, Zhang K, Li N, Chen WQ, Qiu WQ, Dai M. [Analysis on the demand, access and related factors of cancer prevention and treatment knowledge among urban residents in China from 2015 to 2017]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 54:84-91. [PMID: 31914574 DOI: 10.3760/cma.j.issn.0253-9624.2020.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the demand and access to the cancer prevention and treatment knowledge and related factors among urban residents in China from 2015 to 2017. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. The self-designed questionnaire was used to collect the information of general demographic characteristics, the demand and access to cancer prevention and treatment knowledge, and the influencing factors of the attitude. The Chi-square test was used to analyze the difference of the demand of the cancer prevention knowledge among different groups and the corresponding factors of the cancer prevention and treatment knowledge were analyzed by using the logistic regression model. Results: The proportion of residents who need the cancer prevention and treatment knowledge was 79.5%. The demand rate of the inducement, symptom and diagnosis methods of cancer in the occupational population was highest, about 66.8%, 71.0% and 20.8%, respectively. The demand rate of treatment methods and cost in current cancer patients was the highest, about the 45.9% and 21.9%, respectively. The top three sources to acquire the cancer prevention and treatment knowledge were "broadcast or television" (69.5%), "books, newspapers, posters or brochures" (44.7%) and "family and friends" (33.8%). The multivariate analysis showed that compared with public institution personnel/civil servants, unmarried/cohabiting/divorced/widowed and others, annual household income less than 20 000 CNY, from the eastern region, people without cancer diagnosis and people with self-assessment of cancer risk, the demand rate of cancer prevention and treatment knowledge was higher in enterprise personnel/workers, married, annual household income between 60 000 CNY and 150 000 CNY, from the central region, people with cancer and people with unclear cancer risk (all P values <0.05). Conclusion: There was a high demand for the cancer prevention and treatment knowledge among urban residents in China from 2015 to 2017. The main access to the knowledge is from the radio or television. The occupation, marital status, annual household income, residential region, health status and risk of disease were the main factors of the demand of the cancer prevention and treatment knowledge.
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Affiliation(s)
- K Wang
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - C C Liu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - A Y Mao
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - J F Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - P Dong
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - H Y Huang
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - D B Wang
- Health Management College, Anhui Medical University, Hefei 230032, China
| | - G X Liu
- School of Public Health, Harbin Medical University, Harbin 150081, China
| | - X Z Liao
- The Department of Cancer Prevention and Control, Hunan Provincial Cancer Hospital, Changsha 410006, China
| | - Y N Bai
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - X J Sun
- School of Health Care Management, Shandong University, Jinan 250012, China
| | - J S Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Yang
- School of Public Health, Guangxi Medical University, Nanning 530021, China
| | - D H Wei
- Department of Medical Examination for Cancer Prevention, Anhui Provincial Cancer Hospital, Hefei 230032, China
| | - B B Song
- The Department of Cancer Prevention and Control, Affiliated Cancer Hospital of Harbin Medical University, Harbin 150081, China
| | - H K Lei
- Department of Cancer Research and Control, Chongqing University Cancer Hospital/Chongqing Cancer Institute/Chongqing Cancer Hospital, Chongqing 400030, China
| | - Y Q Liu
- Department of Cancer Epidemiology, Gansu Provincial Cancer Hospital, Lanzhou 730050, China
| | - Y Z Zhang
- Department of Epidemiology, Shanxi Provincial Center Hospital, Taiyuan 030013, China
| | - S Y Ren
- Institute for Chronic and Non-communicable Disease Prevention and Control, Yunnan Center for Disease Prevention and Control, Kunming 650118, China
| | - J Y Zhou
- Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J L Wang
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - J Y Gong
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - L Z Yu
- Institute for Chronic and Non-communicable Disease Prevention and Control, Liaoning Provincial Center for Disease Control and Prevention, Shenyang 110005, China
| | - Y Y Liu
- The Department of Cancer Prevention and Control, Liaoning Cancer Hospital & Institute, Shenyang 110042, China
| | - L Zhu
- Cancer Research Institute, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - L W Guo
- Office for Cancer Control and Research, Henan Cancer Hospital/The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450008, China
| | - Y Q Wang
- Department of Cancer Prevention, Cancer Hospital of University of Chinese Academy of Sciences/Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Y T He
- The Department of Cancer Prevention and Control, Cancer Institute, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - P A Lou
- Department of Control and Prevention of Chronic Non-communicable Diseases, Xuzhou Center for Disease Control and Prevention, Xuzhou221006, China
| | - B Cai
- Department of Health Education and Chronic Disease Control, Nantong Center for Disease Control and Prevention, Nantong 226000, China
| | - X H Sun
- Endocrine Department, Ningbo NO.2 Hospital, Ningbo 315010, China
| | - S L Wu
- Department of Cardiovascular Diseases, Kailuan General Hospital, Tangshan 063000, China
| | - X Qi
- Office of Cancer Screening, Tangshan People's Hospital, Tangshan 063001, China
| | - K Zhang
- Department of Medical Examination for Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Qiu
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Liu CC, Shi CL, Shi JF, Mao AY, Huang HY, Dong P, Bai FZ, Chen YS, Wang DB, Liu GX, Liao XZ, Bai YN, Sun XJ, Ren JS, Yang L, Wei DH, Song BB, Lei HK, Liu YQ, Zhang YZ, Ren SY, Zhou JY, Wang JL, Gong JY, Yu LZ, Liu YY, Zhu L, Guo LW, Wang YQ, He YT, Lou PA, Cai B, Sun XH, Wu SL, Qi X, Zhang K, Li N, Xu WH, Qiu WQ, Dai M, Chen WQ. [Study on the health literacy and related factors of the cancer prevention consciousness among urban residents in China from 2015 to 2017]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 54:47-53. [PMID: 31914569 DOI: 10.3760/cma.j.issn.0253-9624.2020.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To understand the health literacy and relevant factors of cancer prevention consciousness in Chinese urban residents from 2015 to 2017. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. The self-designed questionnaire was used to collect the information of demographic characteristics and cancer prevention consciousness focusing on nine common risk factors, including smoking, alcohol, fiber food, food in hot temperature or pickled food, chewing betel nut, helicobacter pylori, moldy food, hepatitis B infection, estrogen, and exercise. The logistic regression model was adopted to identify the influencing factors. Results: The overall health literacy of the cancer prevention consciousness was 77.4% (24 980 participants), with 77.4% (12 018 participants), 79.9% (6 406 participants), 77.2% (1 766 participants) and 74.5% (4 709 participants) in each group (P<0.001). The correct response rates for nine risk factors ranged from 55.2% to 93.0%. The multivariate logistic regression analysis showed that compared with community residents, people with primary school level education or below, and the number of people living together in the family <3, the cancer risk assessment/screening intervention population, cancer patients, those with junior high school level educationor above and the number of people living in the family ≥3 had better health literacy of the cancer prevention consciousness (all P values <0.05). Compared with females, 39 years old and below, government-affiliated institutions or civil servants, from the eastern region, males, older than 40 years, company or enterprise employees, and from the middle or western region had worse health literacy of the cancer prevention consciousness (all P values <0.05). Conclusion: The health literacy of the cancer prevention consciousness in Chinese urban residents should be improved. The cancer screening intervention, gender, age, education, occupation, the number of people co-living in the family, and residential region were associated with the health literacy of the cancer prevention consciousness.
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Affiliation(s)
- C C Liu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - C L Shi
- Department of Disease Control and Prevention, Xuzhou Center for Disease Control and Prevention, Xuzhou 221006, China
| | - J F Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - A Y Mao
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - H Y Huang
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100020, China
| | - P Dong
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - F Z Bai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y S Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - D B Wang
- Health Management College, Anhui Medical University, Hefei 230032, China
| | - G X Liu
- School of Public Health, Harbin Medical University, Harbin 150081, China
| | - X Z Liao
- The Department of Cancer Prevention and Control, Hunan Provincial Cancer Hospital, Changsha 410006, China
| | - Y N Bai
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - X J Sun
- Scholl of Health Care Management, Shandong University, Jinan 250012, China
| | - J S Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Yang
- School of Public Health, Guangxi Medical University, Nanning 530021, China
| | - D H Wei
- Department of Medical Examination for Cancer Prevention, Anhui Provincial Cancer Hospital, Hefei 230032, China
| | - B B Song
- The department of Cancer Prevention and Control, Affiliated Cancer Hospital of Harbin Medical University, Harbin 150081, China
| | - H K Lei
- Department of Cancer Research and Control, Chongqing University Cancer Hospital/Chongqing Cancer Institute/Chongqing Cancer Hospital, Chongqing 400030, China
| | - Y Q Liu
- Department of Cancer Epidemiology, Gansu Provincial Cancer Hospital, Lanzhou 730050, China
| | - Y Z Zhang
- Department of Epidemiology, Shanxi Provincial Center Hospital, Taiyuan 030013, China
| | - S Y Ren
- Institute for Chronic and Non-communicable Disease Prevention and Control, Yunnan Center for Disease Prevention and Control, Kunming 650118, China
| | - J Y Zhou
- Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J L Wang
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - J Y Gong
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - L Z Yu
- Institute for Chronic and Non-communicable Disease Prevention and Control, Liaoning Provincial Center for Disease Control and Prevention, Shenyang 110005, China
| | - Y Y Liu
- The Department of Cancer Prevention and Control, Liaoning Cancer Hospital/Institute, Shenyang 110042, China
| | - L Zhu
- Cancer Research Institute, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - L W Guo
- Office for Cancer Control and Research, Henan Cancer Hospital/The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China
| | - Y Q Wang
- Department of Cancer Prevention, Cancer hospital of University of Chinese Academy of Sciences/Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Y T He
- The Department of Cancer Prevention and Control, Cancer Institute, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - P A Lou
- Department of Control and Prevention of Chronic Non-communicable Diseases, Xuzhou Center for Disease Control and Prevention, Xuzhou221006, China
| | - B Cai
- Department of Health Education and Chronic Disease Control, Nantong Center for Disease Control and Prevention, Nantong 226000, China
| | - X H Sun
- Endocrine Department, Ningbo NO.2 Hospital, Ningbo 315010, China
| | - S L Wu
- Department of Cardiovascular Diseases, Kailuan General Hospital, Tangshan 063000, China
| | - X Qi
- Office of Cancer Screening, Tangshan People's Hospital, Tangshan 063001, China
| | - K Zhang
- Department of Medical Examination for Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W H Xu
- Key Lab of Health Technology Assessment of Ministry of Health, School of Public Health, Fudan University, Shanghai 200032, China
| | - W Q Qiu
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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46
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Dong P, Shi JF, Qiu WQ, Liu CC, Wang K, Huang HY, Wang DB, Liu GX, Liao XZ, Bai YN, Sun XJ, Ren JS, Yang L, Wei DH, Song BB, Lei HK, Liu YQ, Zhang YZ, Ren SY, Zhou JY, Wang JL, Gong JY, Yu LZ, Liu YY, Zhu L, Guo LW, Wang YQ, He YT, Lou PA, Cai B, Sun XH, Wu SL, Qi X, Zhang K, Li N, Dai M, Chen WQ, Mao AY, He J. [Analysis on the health literacy of the cancer prevention and treatment and its related factors among urban residents in China from 2015 to 2017]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 54:76-83. [PMID: 31914573 DOI: 10.3760/cma.j.issn.0253-9624.2020.01.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To understand the health literacy of the cancer prevention and treatment among urban residents of China, and explore the related factors. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China (CanSPUC) from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. The health literacy of the cancer prevention, early discovery, early diagnosis, early treatment and the demands of cancer prevention and treatment knowledge was analyzed. The level of health literacy among different groups were calculated and compared. The binary logistic regression model was used to analyze the influencing factors of the health literacy of the cancer prevention and treatment. Results: The level of health literacy of the cancer prevention and treatment was 56.97% among all study population; in each group it was 55.01% for community residents, 59.08% for cancer risk assessment/screening population, 61.99% for cancer patients and 57.31% for occupational population, respectively (P<0.001). The level of health literacy of the cancer prevention and treatment of residents aged 50 to 69 years old, other occupational groups, unmarried, the central and western region residents and the group with unclear self-assessment of cancer risk was significantly lower than that of residents younger than 40 years old, personnel of public institutions/civil servants, married, the eastern region residents and the group whose self-assessment without cancer risk (P<0.05) . The level of health literacy of cancer prevention and treatment of females, people who went to high school or over, cancer risk assessment/screening population, cancer patients and occupational population was significantly higher than that of males, people who had an education level of primary school or below and community residents (P<0.05) . Conclusion: The health literacy of the cancer prevention and treatment of urban residents in China was relatively high, but there was still room for improvement. Gender, age, educational level, occupation, region, marital status, self-assessment of cancer risk, and type of respondents were the key influencing factors of the health literacy of the cancer prevention and treatment. Male, 50-69 years old, lower educational level, central and western regions, unclear cancer risk self-assessment, and without specific environmental exposure to cancer prevention and treatment knowledge or related risk factors were the characteristics of the key intervention group of the health literacy of the cancer prevention and treatment.
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Affiliation(s)
- P Dong
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - J F Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Qiu
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - C C Liu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - K Wang
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - H Y Huang
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - D B Wang
- Health Management College, Anhui Medical University, Hefei 230032, China
| | - G X Liu
- School of Public Health, Harbin Medical University, Harbin 150081, China
| | - X Z Liao
- The Department of Cancer Prevention and Control, Hunan Provincial Cancer Hospital, Changsha 410006, China
| | - Y N Bai
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - X J Sun
- School of Health Care Management, Shandong University, Jinan 250012, China
| | - J S Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Yang
- School of Public Health, Guangxi Medical University, Nanning 530021, China
| | - D H Wei
- Department of Medical Examination for Cancer Prevention, Anhui Provincial Cancer Hospital, Hefei 230032, China
| | - B B Song
- The Department of Cancer Prevention and Control, Affiliated Cancer Hospital of Harbin Medical University, Harbin 150081, China
| | - H K Lei
- Department of Cancer Research and Control, Chongqing University Cancer Hospital/Chongqing Cancer Institute/Chongqing Cancer Hospital, Chongqing 400030, China
| | - Y Q Liu
- Department of Cancer Epidemiology, Gansu Provincial Cancer Hospital, Lanzhou 730050, China
| | - Y Z Zhang
- Department of Epidemiology, Shanxi Provincial Center Hospital, Taiyuan 030013, China
| | - S Y Ren
- Institute for Chronic and Non-communicable Disease Prevention and Control, Yunnan Center for Disease Prevention and Control, Kunming 650118, China
| | - J Y Zhou
- Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J L Wang
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - J Y Gong
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - L Z Yu
- Institute for Chronic and Non-communicable Disease Prevention and Control, Liaoning Provincial Center for Disease Control and Prevention, Shenyang 110005, China
| | - Y Y Liu
- The Department of Cancer Prevention and Control, Liaoning Cancer Hospital & Institute, Shenyang 110042, China
| | - L Zhu
- Cancer Research Institute, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - L W Guo
- Office for Cancer Control and Research, Henan Cancer Hospital/The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China
| | - Y Q Wang
- Department of Cancer Prevention, Cancer hospital of University of Chinese Academy of Sciences/Zhejiang cancer hospital, Hangzhou 310022, China
| | - Y T He
- The Department of Cancer Prevention and Control, Cancer Institute, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - P A Lou
- Department of Control and Prevention of Chronic Non-communicable Diseases, Xuzhou Center for Disease Control and Prevention, Xuzhou 221006, China
| | - B Cai
- Department of Health Education and Chronic Disease Control, Nantong Center for Disease Control and Prevention, Nantong 226000, China
| | - X H Sun
- Endocrine Department, Ningbo NO.2 Hospital, Ningbo 315010, China
| | - S L Wu
- Department of Cardiovascular Diseases, Kailuan General Hospital, Tangshan 063000, China
| | - X Qi
- Office of Cancer Screening, Tangshan People's Hospital, Tangshan 063001, China
| | - K Zhang
- Department of Medical Examination for Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - A Y Mao
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - J He
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Li HC, Wang K, Yuan YN, Mao AY, Liu CC, Liu S, Yang L, Huang HY, Dong P, Wang DB, Liu GX, Liao XZ, Bai YN, Sun XJ, Ren JS, Yang L, Wei DH, Song BB, Lei HK, Liu YQ, Zhang YZ, Ren SY, Zhou JY, Wang JL, Gong JY, Yu LZ, Liu YY, Zhu L, Guo LW, Wang YQ, He YT, Lou PA, Cai B, Sun XH, Wu SL, Qi X, Zhang K, Li N, Dai M, Chen WQ, Wang N, Qiu WQ, Shi JF. [Analysis on the consciousness of the early cancer treatment and its influencing factors among urban residents in China from 2015 to 2017]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 54:69-75. [PMID: 31914572 DOI: 10.3760/cma.j.issn.0253-9624.2020.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To understand the consciousness of the cancer early treatment and its demographic and socioeconomic factors. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China (CanSPUC) from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. The questionnaire collected personal information, the consciousness of the cancer early treatment and relevant factors. The Chi square test was used to compare the difference between the consciousness of the cancer early treatment and relevant factors among the four groups. The logistic regression model was used to analyze the influencing factors related to the consciousness of the cancer early treatment. Results: With the assumption of being diagnosed as precancer or cancer, 89.97% of community residents, 91.84% of cancer risk assessment/screening population, 93.00% of cancer patients and 91.52% of occupational population would accept active treatments (P<0.001). If the immediate family members were diagnosed as precancer or cancer, people who would encourage their family members to receive early treatment in the four groups accounted for 91.96%, 91.94%, 92.44% and 91.55%, respectively (P<0.001). The company employees, annual household income with 40 000 yuan and more and other three groups had a relatively better consciousness of the cancer early treatment (P<0.05). Male, widowed, unemployed and from the central and western regions had a relatively worse consciousness of the cancer early treatment (P<0.05). Conclusion: Residents in urban China participants had a good consciousness of the cancer early treatment. The marital status, occupation, annual household income and residential regions were major factors related to the consciousness of the cancer early treatment.
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Affiliation(s)
- H C Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - K Wang
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - Y N Yuan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - A Y Mao
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - C C Liu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - L Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - H Y Huang
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - P Dong
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - D B Wang
- Health Management College, Anhui Medical University, Hefei 230032, China
| | - G X Liu
- School of Public Health, Harbin Medical University, Harbin 150081, China
| | - X Z Liao
- The Department of Cancer Prevention and Control, Hunan Provincial Cancer Hospital, Changsha 410006, China
| | - Y N Bai
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - X J Sun
- School of Health Care Management, Shandong University, Jinan 250012, China
| | - J S Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Yang
- School of Public Health, Guangxi Medical University, Nanning 530021, China
| | - D H Wei
- Department of Medical Examination for Cancer Prevention, Anhui Provincial Cancer Hospital, Hefei 230032, China
| | - B B Song
- The Department of Cancer Prevention and Control, Affiliated Cancer Hospital of Harbin Medical University, Harbin 150081, China
| | - H K Lei
- Department of Cancer Research and Control, Chongqing University Cancer Hospital/Chongqing Cancer Institute/Chongqing Cancer Hospital, Chongqing 400030, China
| | - Y Q Liu
- Department of Cancer Epidemiology, Gansu Provincial Cancer Hospital, Lanzhou 730050, China
| | - Y Z Zhang
- Department of Epidemiology, Shanxi Provincial Center Hospital, Taiyuan 030013, China
| | - S Y Ren
- Institute for Chronic and Non-communicable Disease Prevention and Control, Yunnan Center for Disease Prevention and Control, Kunming 650118, China
| | - J Y Zhou
- Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J L Wang
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - J Y Gong
- The Department of Cancer Prevention and Control, Shandong Tumor Hospital, Jinan 250117, China
| | - L Z Yu
- Institute for Chronic and Non-communicable Disease Prevention and Control, Liaoning Provincial Center for Disease Control and Prevention, Shenyang 110005, China
| | - Y Y Liu
- The Department of Cancer Prevention and Control, Liaoning Cancer Hospital & Institute, Shenyang 110042, China
| | - L Zhu
- Cancer Research Institute, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - L W Guo
- Office for Cancer Control and Research, Henan Cancer Hospital/The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China
| | - Y Q Wang
- Department of Cancer Prevention, Cancer hospital of University of Chinese Academy of Sciences/Zhejiang cancer hospital, Hangzhou 310022, China
| | - Y T He
- The Department of Cancer Prevention and Control, Cancer Institute, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - P A Lou
- Department of Control and Prevention of Chronic Non-communicable Diseases, Xuzhou Center for Disease Control and Prevention, Xuzhou 221006, China
| | - B Cai
- Department of Health Education and Chronic Disease Control, Nantong Center for Disease Control and Prevention, Nantong 226000, China
| | - X H Sun
- Endocrine Department, Ningbo NO.2 Hospital, Ningbo 315010, China
| | - S L Wu
- Department of Cardiovascular Diseases, Kailuan General Hospital, Tangshan 063000, China
| | - X Qi
- Officeof Cancer Screening, Tangshan People's Hospital, Tangshan 063001, China
| | - K Zhang
- Department of Medical Examination for Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Q Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - W Q Qiu
- Department of Public Health Strategy Research, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
| | - J F Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Huang HY, Zhang LZ, Zhang QX, Peng L, Xu B, Jiang GF, Zhong J, Fu L, Jiang LY, Song YQ, He HS, Wu XJ, Tan YS. [Analysis of mental state of allergic rhinitis patients in Chengdu city by symptom check list 90 (SCL-90) scale]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2019; 54:576-583. [PMID: 31434370 DOI: 10.3760/cma.j.issn.1673-0860.2019.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Objective: To analyse the mental state of patients with allergic rhinitis (AR) in Chengdu. Methods: One thousand five hundred and thirty-six AR patients from Sichuan Provincial Integrated Traditional Chinese and Western Medicine Hospital, West China Hospital of Sichuan University, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan People's Hospital, Sichuan Second Hospital of Traditional Chinese Medicine were selected from July 2013 to January 2018. Eight hundred and twenty-seven patients were screened into study group by inclusion and exclusion standards. The symptom check list 90 (SCL-90) was used to group and score the mental state of these patients according to nine classification criteria: gender, BMI, age, marital status, monthly salary, disease duration, living environment, education level and working environment. Then, the scores were compared within groups. Inter-group comparison was made between the study group and the Chinese norm, and the positive factors for psychological disorders were extracted. Four symptoms in the study group, i.e. nasal itching, sneezing, clear discharge and nasal congestion, were scored on the visual analogue scale (VAS). SPSS 19.0 software was used to carry out statistical analysis. Partial correlation analysis was performed between the positive factors and the symptom scores by multiple regression statistical method. Results: The total score of SCL-90 in the study group was 2.64±0.25, which was accorded with mild to moderate mental health impairment. There were 124 (15.0%) without mental health damage, 176 (21.3%) with mild damage, 474 (57.3%) with mild to moderate damage, 41 (5.0%) with moderate to severe damage and 12 (1.4%) with severe damage. The in-group comparison showed that the top three categories of different items were the living environment, gender and working environment. The scores of somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, psychosis, other (sleep, diet) and total average score of urban residents were higher than that of country residents (3.29±0.61 vs 2.65±0.50, 2.81±0.77 vs 2.05±0.38, 3.10±0.19 vs 2.49±0.67, 3.40±0.84 vs 2.49±0.70, 3.04±0.64 vs 2.33±0.51, 3.02±0.55 vs 2.40±0.77, 3.40±0.41 vs 2.52±0.77, 2.91±0.11 vs 2.29±0.40, Z value was 4.88, 5.25, 4.57, 5.91, 5.09, 4.63, 5.55, -4.55, respectively, all P<0.05). Women scored higher than man for somatization, interpersonal sensitivity, depression and others (2.66±0.51 vs 2.00±0.45, 3.37±0.47 vs 2.63±0.51, 3.44±0.57 vs 2.85±0.52, 3.47±0.36 vs 2.76±0.45, Z value was -5.10, -5.51, -4.86, -5.28, respectively, all P<0.05). The scores of somatization, interpersonal sensitivity, psychosis and other (sleep, diet) were higher in the indoor group than those in the outdoor group (3.49±0.64 vs 2.78±0.46, 3.33±0.30 vs 2.56±0.68, 3.28±0.60 vs 2.67±0.31, 3.50±0.85 vs 2.85±0.37, Z value was 5.31, 5.79, 4.89, 5.00, respectively, all P<0.05). The outdoor group scored higher on obsessive-compulsive symptoms, anxiety and hostility (3.44±0.40 vs 2.83±0.35, 3.40±0.50 vs 2.57±0.93, 3.34±0.88 vs 2.69±0.56, Z value was 4.96, 6.22, 5.08, respectively, all P<0.05). The inter-group comparison found that depression, anxiety, psychosis and other (sleep, diet) could be partially correlated with VAS scores as 4 positive factors. The results of partial correlation analysis showed that depression was positively correlated with sneezing and nasal runny discharge, anxiety was positively correlated with nasal itching and nasal obstruction, psychosis was positively correlated with nasal itching and sneezing, and other (sleep, diet) was positively correlated with nasal runny discharge and nasal obstruction. Conclusion: AR patients have mild to moderate mental health impairments, which are correlated with AR symptoms.
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Affiliation(s)
- H Y Huang
- Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China; Department of Otorhinolaryngology, Sichuan Provincial Integrated Traditional Chinese and Western Medicine Hospital, Chengdu 610000, China
| | - L Z Zhang
- Department of Aesthetic and Plastic Surgery, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China
| | - Q X Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China
| | - L Peng
- Department of Subhealth Center, Sichuan Provincial Integrated Traditional Chinese and Western Medicine Hospital, Chengdu 610000, China
| | - B Xu
- Psychological Counseling Room, Dazhou Central Hospital, Dazhou 635000, China
| | - G F Jiang
- Department of Psychosomatic Medicine, Dazhou Central Hospital, Dazhou 635000, China
| | - J Zhong
- Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China; Department of Otorhinolaryngology Head and Neck Surgery, West China Hospital of Sichuan University, Chengdu 610000, China
| | - L Fu
- Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China; Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China
| | - L Y Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China
| | - Y Q Song
- Department of Operation Room, Sichuan Second Hospital of Traditional Chinese Medicine, Chengdu 610000, China
| | - H S He
- Department of Otorhinolaryngology, Sichuan Provincial Integrated Traditional Chinese and Western Medicine Hospital, Chengdu 610000, China
| | - X J Wu
- Department of Otorhinolaryngology, Sichuan Provincial Integrated Traditional Chinese and Western Medicine Hospital, Chengdu 610000, China
| | - Y S Tan
- Department of Otorhinolaryngology Head and Neck Surgery, Sichuan People's Hospital, Chengdu 610000, China
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Huang HY, Zhu SL, Zhou TH, Li ZF, Liu CC, Wang H, Yan SP, Song SM, Zou SM, Zhang YM, Li N, Zhu L, Liao XZ, Shi JF, Dai M. [Natural history of colorectal cancer: a Meta-analysis on global prospective cohort studies]. Zhonghua Liu Xing Bing Xue Za Zhi 2019; 40:821-831. [PMID: 31357806 DOI: 10.3760/cma.j.issn.0254-6450.2019.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To acknowledge the availability and rates of annual transition of outcomes during the progression and regression stages of colorectal cancer (CRC) and related diseases, by pooling global follow-up studies on the natural history of CRC. Methods: Till March, 2017, data was collected through systematic literature review over multiple databases, including PubMed, Embase, Cochrane and Chinese Biology Medicine (CBM) disc. Information regarding the characteristics, classification system of health states, related outcomes and incidence rates on CRC or high-risk adenoma for the surveillance cohorts of the studies, were extracted and summarized. Both Meta and sensitivity analyses were performed on those outcomes if they appeared in more than 3 studies, using the random effects model. Annual transition rate with 95%CI was used to estimate each of the outcomes, Quality of the studies was assessed, using the Newcastle-Ottawa Scale. Results: A total of 29 cohort studies were included, with the mean follow-up period as 5.7 years. All studies except one, focused on adenoma-carcinoma pathway and reported the outcome parameters of adenomas by different risk, and some reported the findings on different sizes (n=6) of adenomas. These cohorts were divided into three groups (normal status, with low-risk or high-risk adenoma) according to the status of baseline endoscopic pathologic findings. Their available outcome parameters, corresponding number of involved articles, aggregated sample size and pooled annual transition rates were presented. Six parameters were obtained in the normal cohorts, including those from normal to low-risk adenoma (16 articles, 58 235, 0.030: 0.024-0.037), to high-risk adenoma (17 articles, 62 089, 0.003: 0.002-0.004), to diminutive adenoma (<5 mm, 4 articles, 1 277, 0.021: 0.013-0.029), to small adenoma (6-9 mm, 4 articles, 1 277, 0.006: 0.001-0.010), to large adenoma (≥10 mm, 7 articles, 3 531, 0.002: 0.000-0.003) and to CRC (19 articles, 104 836, 0.000 3: 0.000 2-0.000 5). Three parameters were obtained in low-risk adenoma in cohorts with polypectomy findings, including recurrence (9 articles, 4 788, 0.109: 0.062-0.157) from low-risk adenoma after polypectomy to high-risk adenoma (10 articles, 5 736, 0.009: 0.004-0.013) and to CRC (12 articles, 11 347, 0.000 6: 0.000 4-0.000 8). Three parameters were obtained on high-risk adenoma from cohorts with polypectomy findings, including recurrence (12 articles, 7 030, 0.038: 0.028-0.048) from high-risk adenoma after polypectomy to low-risk adenoma (8 articles, 2 489, 0.133: 0.081-0.185) and CRC (14 articles, 14 899, 0.002: 0.001-0.003). Except for normal to low-risk adenomas, results from the sensitivity analysis for the other parameters showed stable. Of the included studies, two presented incidence rates of CRC in different clinical stages and the another two were focusing on the parameters related to serrated pathway. Conclusions: Globally, follow-up studies reported data on natural history of colorectal cancer is of paucity. Compared to the "adenoma-carcinoma" pathway, transition parameters of the serrated lesion pathway are more limited. This Meta-analysis provided convincing evidence for optimizing the strategies regarding follow-up program on the disease, using the baseline endoscopic findings from global CRC Screening Program. These results also offered strong data-related support for Chinese population- specific interventional model on colorectal cancer.
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Affiliation(s)
- H Y Huang
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S L Zhu
- Office for Cancer Control and Research, Hunan Cancer Hospital, Changsha 410006, China
| | - T H Zhou
- Teaching and Research Department, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - Z F Li
- Medical Oncology, Health Center for Staff in Kailuan Hospital, Tangshan 063000, China
| | - C C Liu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S P Yan
- Office for Cancer Control and Research, Hunan Cancer Hospital, Changsha 410006, China
| | - S M Song
- Teaching and Research Department, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - S M Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Li
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Zhu
- Teaching and Research Department, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - X Z Liao
- Office for Cancer Control and Research, Hunan Cancer Hospital, Changsha 410006, China
| | - J F Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Huang HY, Chen P, Liang XF, Wu XF, Gu X, Xue M. Dietary N-Carbamylglutamate (NCG) alleviates liver metabolic disease and hepatocyte apoptosis by suppressing ERK1/2-mTOR-S6K1 signal pathway via promoting endogenous arginine synthesis in Japanese seabass (Lateolabrax japonicus). Fish Shellfish Immunol 2019; 90:338-348. [PMID: 31075404 DOI: 10.1016/j.fsi.2019.04.294] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [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: 01/18/2019] [Revised: 04/23/2019] [Accepted: 04/26/2019] [Indexed: 06/09/2023]
Abstract
N-Carbamylglutamate (NCG), an analogue of N-acetylglutamate (NAG), can promote the synthesis of endogenous Arginine (Arg) in mammals, but not well studied in fish. This study was conducted to investigate the capacity of Arg endogenous synthesis by NCG, and the effects of various dietary NCG doses on growth performance, hepatic health and underlying nutrient regulation metabolism on ERK1/2-mTOR-S6K1 signaling pathway in Japanese seabass (Lateolabrax japonicus). Four experimental diets were prepared with NCG supplement levels of 0 (N0), 360 (N360), 720 (N720) and 3600 (N3600) mg/kg, in which N360 was at the maximum recommended level authorized by MOA, China in fish feed, and the N720 and N3600 levels were 2 and 10-fold of N360, respectively. Each diet was fed to 6 replicates with 30 Japanese seabass (initial body weight, IBW = 11.67 ± 0.02 g) in each tank. The results showed that the dietary NCG supplementation had no significant effects on the SGR and morphometric parameters of Japanese seabass, but 360-720 mg/kg NCG inclusion promoted PPV, while the 10-fold (3600 mg/kg) overdose of NCG had remarkably negative effects with significantly reduced feed efficiency, PPV and LPV. We found that Japanese seabass can utilize 360-720 mg/kg NCG to synthesis Arg to improve the amino acid metabolism by increasing plasma Arg and up-regulating intestinal ASL gene expression. Increased plasma GST and decreased MDA indicated the improved antioxidant response. Dietary NCG inclusion decreased plasma IgM and down-regulated the mRNA levels of inflammation (TNF-α and IL8), apoptosis (caspase family) and fibrosis (TGF-β1) related genes in the liver. The immunofluorescence examination revealed significantly decreased hepatic apoptosis and necrosis signals in the NCG groups. The ameliorated liver function and histological structure were closely related to the improved lipid metabolism parameters with decreased plasma VLDL and hepatic TG and NEFA accumulation, down-regulated fatty acid and cholesterol synthesis and simultaneously increased lipolysis gene mRNA levels, which regulated by inhibiting phosphorylation of ERK1/2-mTOR-S6K1 signaling pathway. Consuming 3600 mg/kg of dietary NCG is not safe for Japanese seabass culturing with the significantly increased FCR and decreased protein and lipid retention, and reduced plasma ALB. Accordingly, the observed efficacy and safety level of dietary NCG in the diet of Japanese seabass is 720 mg/kg.
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Affiliation(s)
- H Y Huang
- National Aquafeed Safety Assessment Center, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - P Chen
- National Aquafeed Safety Assessment Center, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - X F Liang
- National Aquafeed Safety Assessment Center, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - X F Wu
- National Aquafeed Safety Assessment Center, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - X Gu
- National Aquafeed Safety Assessment Center, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - M Xue
- National Aquafeed Safety Assessment Center, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; Key Laboratory of Feed Biotechnology of Ministry of Agriculture, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; National Engineering Research Center of Biological Feed, 100081, China.
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