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Grunberger G, Sherr J, Allende M, Blevins T, Bode B, Handelsman Y, Hellman R, Lajara R, Roberts VL, Rodbard D, Stec C, Unger J. American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus. Endocr Pract 2021; 27:505-537. [PMID: 34116789 DOI: 10.1016/j.eprac.2021.04.008] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 02/08/2023]
Abstract
OBJECTIVE To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders. METHODS The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development. MAIN OUTCOME MEASURES Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range. RESULTS This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base. RECOMMENDATIONS Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology. CONCLUSIONS Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.
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Affiliation(s)
| | - Jennifer Sherr
- Yale University School of Medicine, New Haven, Connecticut
| | - Myriam Allende
- University of Puerto Rico School of Medicine, San Juan, Puerto Rico
| | | | - Bruce Bode
- Atlanta Diabetes Associates, Atlanta, Georgia
| | | | - Richard Hellman
- University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | | | | | - David Rodbard
- Biomedical Informatics Consultants, LLC, Potomac, Maryland
| | - Carla Stec
- American Association of Clinical Endocrinology, Jacksonville, Florida
| | - Jeff Unger
- Unger Primary Care Concierge Medical Group, Rancho Cucamonga, California
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Orozco-Beltrán D, Navarro-Pérez J, Cebrián-Cuenca AM, Álvarez-Guisasola F, Caride-Miana E, Mora G, Quesada JA, López-Pineda A, Cardona-Llorens AF, Redón J, Gil-Guillen VF, Fernández A, Carratalá-Munuera C. The influence of hemoglobin A1c levels on cardiovascular events and all-cause mortality in people with diabetes over 70 years of age. A prospective study. Prim Care Diabetes 2020; 14:678-684. [PMID: 32605878 DOI: 10.1016/j.pcd.2020.06.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 05/28/2020] [Accepted: 06/07/2020] [Indexed: 12/31/2022]
Abstract
AIM Glycated hemoglobin A1c (HbA1c) is a reliable risk factor of cardiovascular diseases in diabetic patients, but information about this relationship in elderly patients is scarce. The aim of this study is to analyze, the relationship between HbA1c levels and the risk of mayor adverse cardiovascular events (MACE) in patients with diabetes over 70 years. METHODS Prospective study of subjects with diabetes using electronic health records from the universal public health system in the Valencian Community, Spain, 2008-2012. We included men and women aged≥70 years with diabetes who underwent routine health examinations in primary care. Primary endpoint was the incidence of MACE: all-cause mortality and/or hospital admission due to coronary heart disease or stroke. A standard Cox and Cox-Aalen models were adjusted. RESULTS 5016 subjects were included whit a mean age of 75.1 years (46.7% men). During an average follow-up of 49 months (4.1 years), 807 (16.1%) MACE were recorded. The incidence of MACE was 20.6 per 1000-person-years. Variables significantly associated to the incidence of MACE were male gender (HR: 1.61), heart failure (HR: 2.26), antiplatelet therapy (HR: 1.39), oral antidiabetic treatment (HR: 0.74), antithrombotics (HR: 1.79), while age, creatinine, HbA1c and peripheral arterial disease were time-depend associated variables. CONCLUSION These results highlights the importance of HbA1c level in the incidence of cardiovascular events in older diabetic patients.
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Affiliation(s)
| | - Jorge Navarro-Pérez
- Biomedical Research Institute INCLIVA, Hospital Clinico Universitario de Valencia, University of Valencia, Valencia, Spain; Ciber of Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain.
| | | | | | | | | | - José A Quesada
- Clinical Medicine Department, Miguel Hernandez University, San Juan de Alicante, Spain.
| | - Adriana López-Pineda
- Clinical Medicine Department, Miguel Hernandez University, San Juan de Alicante, Spain.
| | | | - Josep Redón
- Biomedical Research Institute INCLIVA, Hospital Clinico Universitario de Valencia, University of Valencia, Valencia, Spain; Ciber of Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain.
| | - Vicente F Gil-Guillen
- Clinical Medicine Department, Miguel Hernandez University, San Juan de Alicante, Spain.
| | - Antonio Fernández
- Biomedical Research Institute INCLIVA, Hospital Clinico Universitario de Valencia, University of Valencia, Valencia, Spain; Ciber of Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain.
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Sun B, He F, Gao Y, Zhou J, Sun L, Liu R, Xu H, Chen X, Zhou H, Liu Z, Zhang W. Prognostic impact of visit-to-visit glycemic variability on the risks of major adverse cardiovascular outcomes and hypoglycemia in patients with different glycemic control and type 2 diabetes. Endocrine 2019; 64:536-543. [PMID: 30868413 DOI: 10.1007/s12020-019-01893-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 03/05/2019] [Indexed: 02/05/2023]
Abstract
PURPOSE The prognostic impact of visit-to-visit glycemic variability on clinical outcomes in patients with different glycemic control and type 2 diabetes remains obscure. We investigated glucose variability and clinical outcomes for patients in the groups of Good glycemic control (GC), Insufficient glycemic control (IC), and Poor glycemic control (PC) in a prospective cohort study. METHODS By using data from Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation (ADVANCE), 930 patients were enrolled from 61 centers in China and grouped into GC, IC, and PC according to their glycated hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG). Visit-to-visit glycemic variability was defined using the coefficient of variation (CV) of five measurements of HbA1c and FPG taken 3-24 months after treatment. Multivariable Cox proportional hazards models were employed to estimate adjusted hazard ratio (aHR). RESULTS Among 930 patients in the intensive glucose control, 82, 538, and 310 patients were assigned to GC, IC, and PC, respectively. During the median of 4.8 years of follow-up, 322 patients were observed hypoglycemia and 244 patients experienced major adverse cardiovascular events (MACE). The CV of HbA1c and FPG was significantly lower for GC (6.0 ± 3.8, 11.2 ± 6.2) than IC (8.3 ± 5.6, 17.9 ± 10.6) and PC (9.5 ± 6.3, 19.3 ± 10.8). High glycemic variability was associated with a greater risk of MACE (aHR: 2.21; 95% confidence interval (CI): 1.61-3.03; p < 0.001) and hypoglycemia (aHR: 1.36; 95% CI: 1.04-1.79; p = 0.025) than low glycemic variability in total patients. The consistent trend was also found in subgroups of GC, IC, and PC. CONCLUSIONS This prospective cohort study showed that glycemic variability was significantly lower for GC than IC and PC. Furthermore, glycemic variability was associated with the risk of MACE and hypoglycemia in total patients and subgroups of different glycemic control.
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Affiliation(s)
- Bao Sun
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China
| | - Fazhong He
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China
| | - Yongchao Gao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China
| | - Jiecan Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China
| | - Lei Sun
- Data Analysis Technology Lab, School of Mathematics and Statistics, Henan University, 475004, Kaifeng, People's Republic of China
| | - Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China
| | - Heng Xu
- Department of Laboratory Medicine, National Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Xiaoping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China
| | - Honghao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China
| | - Zhaoqian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 410078, Changsha, People's Republic of China.
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical Pharmacology, Central South University, 410078, Changsha, People's Republic of China.
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Zhou J, He F, Sun B, Liu R, Gao Y, Ren H, Shu Y, Chen X, Liu Z, Zhou H, Deng S, Xu H, Li J, Xu L, Zhang W. Polytropic Influence of TRIB3 rs2295490 Genetic Polymorphism on Response to Antihypertensive Agents in Patients With Essential Hypertension. Front Pharmacol 2019; 10:236. [PMID: 30971918 PMCID: PMC6445854 DOI: 10.3389/fphar.2019.00236] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 02/26/2019] [Indexed: 02/05/2023] Open
Abstract
Tribbles homolog 3 (TRIB3) mediating signaling pathways are closely related to blood pressure regulation. Our previous findings suggested a greater benefit on vascular outcomes in patients carrying TRIB3 (251, A > G, rs2295490) G allele with good glucose and blood pressure control. And TRIB3 (rs2295490) AG/GG genotypes were found to reduce primary vascular events in type 2 diabetic patients who received intensive glucose treatment as compared to those receiving standard glucose treatment. However, the effect of TRIB3 genetic variation on antihypertensives was not clear in essential hypertension patients. A total of 368 patients treated with conventional dosage of antihypertensives (6 groups, grouped by atenolol/bisoprolol, celiprolol, doxazosin, azelnidipine/nitrendipine, imidapril, and candesartan/irbesartan) were enrolled in our study. Genetic variations were successfully identified by sanger sequencing. A linear mixed model analysis was performed to evaluate blood pressures among TRIB3 (251, A > G) genotypes and adjusted for baseline age, gender, body mass index, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol and other biochemical factors appropriately. Our data suggested that TRIB3 (251, A > G) AA genotype carriers showed better antihypertensive effect than the AG/GG genotype carriers [P = 0.014 for DBP and P = 0.042 for mean arterial pressure (MAP)], with a maximal reduction of DBP by 4.2 mmHg and MAP by 3.56 mmHg after azelnidipine or nitrendipine treatment at the 4th week. Similar tendency of DBP-change and MAP-change was found for imidapril (ACEI) treatment, in which marginally significances were achieved (P = 0.073 and 0.075, respectively). Against that, we found that TRIB3 (251, A > G) AG/GG genotype carriers benefited from antihypertensive therapy of ARBs with a larger DBP-change during the period of observation (P = 0.036). Additionally, stratified analysis revealed an obvious difference of the maximal blood pressure change (13 mmHg for the MAP between male and female patients with AA genotype who took ARBs). Although no significant difference in antihypertensive effect between TRIB3 (251, A > G) genotypes in patients treated with α, β-ADRs was observed, we found significant difference in age-, sex-dependent manner related to α, β-ADRs. In conclusion, our data supported that TRIB3 (251, A > G) genetic polymorphism may serve as a useful biomarker in the treatment of hypertension.
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Affiliation(s)
- Jiecan Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China.,Pharmacy Department, The First Affiliated Hospital, University of South China, Hengyang, China
| | - Fazhong He
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Bao Sun
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Yongchao Gao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Huan Ren
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Yan Shu
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, United States
| | - Xiaoping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Zhaoqian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Honghao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Sheng Deng
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
| | - Heng Xu
- Department of Laboratory Medicine, Precision Medicine Center, and Precision Medicine Key Laboratory of Sichuan Province, Collaborative Innovation Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jianmin Li
- Department of Respiratory Medicine, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Linyong Xu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,National Clinical Research Center for Geriatrics, Xiangya Hospital, Central South University, Changsha, China
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5
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Zheng D, Zhang Y, Hu Y, Guan J, Xu L, Xiao W, Zhong Q, Ren C, Lu J, Liang J, Hou J. Long noncoding RNA Crnde attenuates cardiac fibrosis via Smad3-Crnde negative feedback in diabetic cardiomyopathy. FEBS J 2019; 286:1645-1655. [PMID: 30748104 PMCID: PMC6849551 DOI: 10.1111/febs.14780] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 01/02/2019] [Accepted: 02/10/2019] [Indexed: 01/08/2023]
Abstract
Diabetic cardiomyopathy (DCM)-ventricular dysfunction in the absence of underlying heart disease-is a common complication of diabetes and a leading cause of mortality associated with the disease. In DCM, cardiac fibrosis is the main cause of heart failure. Although it is well-established that the transforming growth factor-beta signaling pathway plays a part in inducing cardiac fibrosis in DCM, details of the molecular mechanism involved remain elusive. Therefore, it is crucial to study the gene reg;ulation of key signaling effectors in DCM-associated cardiac fibrosis. A recently emerged hotspot in the field of gene regulation is the role of long noncoding RNAs (lncRNAs). Recent evidence indicates that lncRNAs play a critical role in cardiac fibrosis; however, in DCM, the function of these regulatory RNAs have not been studied in depth. In this study, we identified a conserved cardiac-specific lncRNA named colorectal neoplasia differentially expressed (Crnde). By analyzing 376 human heart tissues, it was found that Crnde expression is negatively correlated with that of cardiac fibrosis marker genes. Moreover, Crnde expression was shown to be enriched in cardiac fibroblasts (CFs). Overexpression of Crnde attenuated cardiac fibrosis and enhanced cardiac function in mice with DCM. Further, in vitro experiments showed that Crnde negatively regulates the myofibroblast differentiation of CFs. The expression of Crnde was activated by SMAD family member 3 (Smad3), shedding light on the underlying molecular mechanism. Interestingly, Crnde also inhibited the transcriptional activation of Smad3 on target genes, thereby inhibiting the expression of myofibroblastic marker genes in CFs. Overall, our data provide valuable insights into the development of potential anti-cardiac fibrosis strategies centered on lncRNAs, for the treatment of DCM.
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Affiliation(s)
- Dezhi Zheng
- Department of Cardiovascular Surgery, The 960th Hospital of the PLA Joint Logistic Support Force, Jinan, China
| | - Yong Zhang
- Department of Cardiovascular Surgery, The 960th Hospital of the PLA Joint Logistic Support Force, Jinan, China
| | - Yonghe Hu
- Department of Pharmacy, The General Hospital of Western Theater Command, Chengdu, China
| | - Jing Guan
- Department of Radiology, The General Hospital of Western Theater Command, Chengdu, China
| | - Lianbin Xu
- Department of Cardiovascular Surgery, The 960th Hospital of the PLA Joint Logistic Support Force, Jinan, China
| | - Wenjing Xiao
- Department of Pharmacy, The General Hospital of Western Theater Command, Chengdu, China
| | - Qinyue Zhong
- Department of Pharmacy, The General Hospital of Western Theater Command, Chengdu, China
| | - Chao Ren
- Department of Cardiovascular Surgery, The 960th Hospital of the PLA Joint Logistic Support Force, Jinan, China
| | - Jinfeng Lu
- Department of Cardiovascular Surgery, The 960th Hospital of the PLA Joint Logistic Support Force, Jinan, China
| | - Jiali Liang
- Department of Cardiovascular Surgery, The 960th Hospital of the PLA Joint Logistic Support Force, Jinan, China
| | - Jun Hou
- Department of Pharmacy, The General Hospital of Western Theater Command, Chengdu, China
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