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Hao M, Huang X, Liu X, Fang X, Li H, Lv L, Zhou L, Guo T, Yan D. Novel model predicts diastolic cardiac dysfunction in type 2 diabetes. Ann Med 2023; 55:766-777. [PMID: 36908240 PMCID: PMC10798288 DOI: 10.1080/07853890.2023.2180154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/08/2023] [Indexed: 03/14/2023] Open
Abstract
OBJECTIVE Diabetes mellitus complicated with heart failure has high mortality and morbidity, but no reliable diagnoses and treatments are available. This study aimed to develop and verify a new model nomogram based on clinical parameters to predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus (T2DM). METHODS 3030 patients with T2DM underwent Doppler echocardiography at the First Affiliated Hospital of Shenzhen University between January 2014 and December 2021. The patients were divided into the training dataset (n = 1701) and the verification dataset (n = 1329). In this study, a predictive diastolic cardiac dysfunction nomogram is developed using multivariable logical regression analysis, which contains the candidates selected in a minor absolute shrinkage and selection operator regression model. Discrimination in the prediction model was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The calibration curve was applied to evaluate the calibration of the alignment nomogram, and the clinical decision curve was used to determine the clinical practicability of the alignment map. The verification dataset was used to evaluate the prediction model's performance. RESULTS A multivariable model that included age, body mass index (BMI), triglyceride (TG), creatine phosphokinase isoenzyme (CK-MB), serum sodium (Na), and urinary albumin/creatinine ratio (UACR) was presented as the nomogram. We obtained the model for estimating diastolic cardiac dysfunction in patients with T2DM. The AUC-ROC of the training dataset in our model was 0.8307, with 95% CI of 0.8109-0.8505. Similar to the results obtained with the training dataset, the AUC-ROC of the verification dataset in our model was 0.8083, with 95% CI of 0.7843-0.8324, thus demonstrating robust. The function of the predictive model was as follows: Diastolic Dysfunction = -4.41303 + 0.14100*Age(year)+0.10491*BMI (kg/m2) +0.12902*TG (mmol/L) +0.03970*CK-MB (ng/mL) -0.03988*Na(mmol/L) +0.65395 * (UACR > 30 mg/g) + 1.10837 * (UACR > 300 mg/g). The calibration plot diagram of predicted probabilities against observed DCM rates indicated excellent concordance. Decision curve analysis demonstrated that the novel nomogram was clinically useful. CONCLUSION Diastolic cardiac dysfunction in patients with T2DM can be predicted by clinical parameters. Our prediction model may represent an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in T2DM patients and provide a reliable method for early screening of T2DM patients with cardiac complications.KEY MESSAGESThis study used clinical parameters to predict diastolic cardiac dysfunction in patients with T2DM. This study established a nomogram for predicting diastolic cardiac dysfunction by multivariate logical regression analysis. Our predictive model can be used as an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in patients with T2DM and provides a reliable method for early screening of cardiac complications in patients with T2DM.
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Affiliation(s)
- Mingyu Hao
- Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiaohong Huang
- Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China
- Guangzhou Medical University, Guangzhou, China
| | - Xueting Liu
- Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China
| | - Xiaokang Fang
- Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China
| | - Haiyan Li
- Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China
| | - Lingbo Lv
- Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China
| | - Liming Zhou
- Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China
| | - Tiecheng Guo
- Chiwan Community Health Service Centre, Shenzhen, China
| | - Dewen Yan
- Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China
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Tao Z, Yang P, Zhou J, Wang R, Jiang Z, Han H, Wang M. Ideal serum non-ceruloplasmin bound copper prediction for long-term treated patients with Wilson disease: a nomogram model. Front Med (Lausanne) 2023; 10:1275242. [PMID: 38020085 PMCID: PMC10656596 DOI: 10.3389/fmed.2023.1275242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose This study aimed to explore the factors associated with the optimal serum non-ceruloplasmin bound copper (NCBC) level and develop a flexible predictive model to guide lifelong therapy in Wilson disease (WD) and delay disease progression. Methods We retrospectively collected clinical data from 144 patients hospitalized in the Encephalopathy Center of the first affiliated hospital of Anhui University of Chinese Medicine between May 2012 and April 2023. Independent variables were selected using variate COX and LASSO regressions, followed by multivariate COX regression analysis. A predictive nomogram was constructed and validated using the concordance index (C-index), calibration curves, and clinical decision curve analysis, of which nomogram pictures were utilized for model visualization. Results A total of 61 (42.36%) patients were included, with an average treatment duration of 55.0 (range, 28.0, 97.0) months. Multivariate regression analysis identified several independent risk factors for serum NCBC level, including age of diagnosis, clinical classification, laminin liver stiffness measurement, and copper to zinc ratio in 24-h urinary excretion. The C-index indicated moderate discriminative ability (48 months: 0.829, 60 months: 0.811, and 72 months: 0.819). The calibration curves showed good consistency and calibration; clinical decision curve analysis demonstrated clinically beneficial threshold probabilities at different time intervals. Conclusion The predictive nomogram model can predict serum NCBC level; consequently, we recommend its use in clinical practice to delay disease progression and improve the clinical prognosis of WD.
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Affiliation(s)
- Zhuang Tao
- Encephalopathy Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Pingping Yang
- Graduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Jiafeng Zhou
- Graduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Rui Wang
- Graduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Zhenzhen Jiang
- Graduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Hui Han
- Graduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Meixia Wang
- Encephalopathy Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China
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Collis B, Vogrin S, Trubiano JA, Reynolds G. Validation Study of a Clinical Predictive Model for Fluconazole Resistance in Patients With Candida Bloodstream Infection. Open Forum Infect Dis 2023; 10:ofad323. [PMID: 37496611 PMCID: PMC10368446 DOI: 10.1093/ofid/ofad323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/09/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- Brennan Collis
- Department of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia
| | - Sara Vogrin
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
| | - Jason A Trubiano
- Department of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
- National Centre for Infections in Cancer, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Department of Infectious Diseases, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | - Gemma Reynolds
- Correspondence: Gemma Reynolds, BArts (Hons), MBBS (Hons), MIDI (Dist), FRACP, Department of Infectious Diseases, Austin Health, 145 Studley Road, Heidelberg, Victoria 3084, Australia ()
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Rauseo AM, Spec A. Reply to Collis et al. Open Forum Infect Dis 2023; 10:ofad322. [PMID: 37496605 PMCID: PMC10368197 DOI: 10.1093/ofid/ofad322] [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] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 06/21/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- Adriana M Rauseo
- Correspondence: Adriana M. Rauseo, MD, Division of Infectious Diseases, Washington University School of Medicine in St Louis, 4523 Clayton Ave, Campus Box 8051, St Louis, MO 63110 (); Andrej Spec, MD, MSCI, Division of Infectious Diseases, Washington University School of Medicine in St Louis, 4523 Clayton Ave, Campus Box 8051, St Louis, MO 63110 ()
| | - Andrej Spec
- Correspondence: Adriana M. Rauseo, MD, Division of Infectious Diseases, Washington University School of Medicine in St Louis, 4523 Clayton Ave, Campus Box 8051, St Louis, MO 63110 (); Andrej Spec, MD, MSCI, Division of Infectious Diseases, Washington University School of Medicine in St Louis, 4523 Clayton Ave, Campus Box 8051, St Louis, MO 63110 ()
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Huang X, Yang S, Chen X, Zhao Q, Pan J, Lai S, Ouyang F, Deng L, Du Y, Chen J, Hu Q, Guo B, Liu J. Development and validation of a clinical predictive model for 1-year prognosis in coronary heart disease patients combine with acute heart failure. Front Cardiovasc Med 2022; 9:976844. [PMID: 36312262 PMCID: PMC9609152 DOI: 10.3389/fcvm.2022.976844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/22/2022] [Indexed: 11/26/2022] Open
Abstract
Background The risk factors for acute heart failure (AHF) vary, reducing the accuracy and convenience of AHF prediction. The most common causes of AHF are coronary heart disease (CHD). A short-term clinical predictive model is needed to predict the outcome of AHF, which can help guide early therapeutic intervention. This study aimed to develop a clinical predictive model for 1-year prognosis in CHD patients combined with AHF. Materials and methods A retrospective analysis was performed on data of 692 patients CHD combined with AHF admitted between January 2020 and December 2020 at a single center. After systemic treatment, patients were discharged and followed up for 1-year for major adverse cardiovascular events (MACE). The clinical characteristics of all patients were collected. Patients were randomly divided into the training (n = 484) and validation cohort (n = 208). Step-wise regression using the Akaike information criterion was performed to select predictors associated with 1-year MACE prognosis. A clinical predictive model was constructed based on the selected predictors. The predictive performance and discriminative ability of the predictive model were determined using the area under the curve, calibration curve, and clinical usefulness. Results On step-wise regression analysis of the training cohort, predictors for MACE of CHD patients combined with AHF were diabetes, NYHA ≥ 3, HF history, Hcy, Lp-PLA2, and NT-proBNP, which were incorporated into the predictive model. The AUC of the predictive model was 0.847 [95% confidence interval (CI): 0.811–0.882] in the training cohort and 0.839 (95% CI: 0.780–0.893) in the validation cohort. The calibration curve indicated good agreement between prediction by nomogram and actual observation. Decision curve analysis showed that the nomogram was clinically useful. Conclusion The proposed clinical prediction model we have established is effective, which can accurately predict the occurrence of early MACE in CHD patients combined with AHF.
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Affiliation(s)
- Xiyi Huang
- Department of Clinical Laboratory, The Affiliated Shunde Hospital of Guangzhou Medical University, Foshan, China
| | - Shaomin Yang
- Department of Radiology, The Affiliated Shunde Hospital of Guangzhou Medical University, Foshan, China
| | - Xinjie Chen
- Department of Radiology, Shunde Hospital, Southern Medical University, Foshan, Guangdong, China
| | - Qiang Zhao
- Department of Cardiovascular Medicine, The Affiliated Shunde Hospital of Guangzhou Medical University, Foshan, China
| | - Jialing Pan
- Department of Radiology, Shunde Hospital, Southern Medical University, Foshan, Guangdong, China
| | - Shaofen Lai
- Department of Clinical Laboratory, The Affiliated Shunde Hospital of Guangzhou Medical University, Foshan, China
| | - Fusheng Ouyang
- Department of Radiology, Shunde Hospital, Southern Medical University, Foshan, Guangdong, China
| | - Lingda Deng
- Department of Radiology, Shunde Hospital, Southern Medical University, Foshan, Guangdong, China
| | - Yongxing Du
- Department of Radiology, Shunde Hospital, Southern Medical University, Foshan, Guangdong, China
| | - Jiacheng Chen
- Department of Clinical Laboratory, The Affiliated Shunde Hospital of Guangzhou Medical University, Foshan, China
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University, Foshan, Guangdong, China
| | - Baoliang Guo
- Department of Radiology, Shunde Hospital, Southern Medical University, Foshan, Guangdong, China,*Correspondence: Baoliang Guo,
| | - Jiemei Liu
- Department of Rehabilitation Medicine, Shunde Hospital, Southern Medical University, Foshan, Guangdong, China,Jiemei Liu,
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Rauseo AM, Olsen MA, Stwalley D, Mazi PB, Larson L, Powderly WG, Spec A. Creation and Internal Validation of a Clinical Predictive Model for Fluconazole Resistance in Patients With Candida Bloodstream Infection. Open Forum Infect Dis 2022; 9:ofac447. [PMID: 36119958 PMCID: PMC9472663 DOI: 10.1093/ofid/ofac447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/27/2022] [Indexed: 11/20/2022] Open
Abstract
Background Fluconazole is recommended as first-line therapy for candidemia when risk of fluconazole resistance (fluc-R) is low. Lack of methods to estimate resistance risk results in extended use of echinocandins and prolonged hospitalization. This study aimed to develop a clinical predictive model to identify patients at low risk for fluc-R where initial or early step-down fluconazole would be appropriate. Methods Retrospective analysis of hospitalized adult patients with positive blood culture for Candida spp from 2013 to 2019. Multivariable logistic regression model was performed to identify factors associated with fluc-R. Stepwise regression was performed on bootstrapped samples to test individual variable stability and estimate confidence intervals (CIs). We used receiver operating characteristic curves to assess performance across the probability spectrum. Results We identified 539 adults with candidemia and 72 Candida isolates (13.4%) were fluc-R. Increased risk of fluc-R was associated with older age, prior bacterial bloodstream infection (odds ratio [OR], 2.02 [95% CI, 1.13-3.63]), myelodysplastic syndrome (OR, 3.09 [95% CI, 1.13-8.44]), receipt of azole therapy (OR, 5.42 [95% CI, 2.90-10.1]) within 1 year of index blood culture, and history of bone marrow or stem cell transplant (OR, 2.81 [95% CI, 1.41-5.63]). The model had good discrimination (optimism-corrected c-statistic 0.771), and all of the selected variables were stable. The prediction model had a negative predictive value of 95.7% for the selected sensitivity cutoff of 90.3%. Conclusions This model is a potential tool for identifying patients at low risk for fluc-R candidemia to receive first-line or early step-down fluconazole.
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Affiliation(s)
- Adriana M Rauseo
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
| | - Margaret A Olsen
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
| | - Dustin Stwalley
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
| | - Patrick B Mazi
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
| | - Lindsey Larson
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
| | - William G Powderly
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
| | - Andrej Spec
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
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Fu R, Yang M, Li Z, Kang Z, Xun M, Wang Y, Wang M, Wang X. Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis. Front Pediatr 2022; 10:967249. [PMID: 36061380 PMCID: PMC9428464 DOI: 10.3389/fped.2022.967249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/01/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES To explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction. METHODS We retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram. RESULTS Age, persistent cutaneous purpura, erythrocyte distribution width, complement C3, immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15-82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively. CONCLUSION The prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability. CLINICAL TRIAL REGISTRATION www.chictr.org.cn, identifier ChiCTR2000033435.
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Affiliation(s)
- Ruqian Fu
- Academy of Pediatrics of University of South China, Changsha, China.,Department of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, China
| | - Manqiong Yang
- Department of Pediatrics, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Zhihui Li
- Academy of Pediatrics of University of South China, Changsha, China.,Department of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, China
| | - Zhijuan Kang
- Academy of Pediatrics of University of South China, Changsha, China.,Department of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, China
| | - Mai Xun
- Department of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, China
| | - Ying Wang
- Department of Pediatrics of Changsha Central Hospital, Changsha, China
| | - Manzhi Wang
- Department of Pediatrics of Changsha Central Hospital, Changsha, China
| | - Xiangyun Wang
- Department of Pediatrics of Changsha First People's Hospital, Changsha, China
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Rauseo AM, Aljorayid A, Olsen MA, Larson L, Lipsey KL, Powderly WG, Spec A. Clinical predictive models of invasive Candida infection: a systematic literature review. Med Mycol 2021; 59:1053-1067. [PMID: 34302351 DOI: 10.1093/mmy/myab043] [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] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/30/2021] [Accepted: 07/22/2021] [Indexed: 12/23/2022] Open
Abstract
Clinical predictive models (CPM) serve to identify and categorize patients into risk categories to assist in treatment and intervention recommendations. Predictive accuracy and practicality of models varies depending on methods used for their development, and should be evaluated.The aim of this study was to summarize currently available CPM for invasive candidiasis, analyze their performance, and assess their suitability for use in clinical decision making.We identified studies that described the construction of a CPM for invasive candidiasis from PubMed/MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library databases and Clinicaltrials.gov. Data extracted included: author, data source, study design, recruitment period, characteristics of study population, outcome types, predictor types, number of study participants and outcome events, modelling method and list of predictors used in the final model. Calibration and discrimination in the derivative datasets were used to assess the performance of each model.Ten articles were identified in our search and included for full text review. Five models were developed using data from ICUs, and five models included all hospitalized patients. The findings of this review highlight the limitations of currently available models to predict invasive candidiasis, including lack of generalizability, difficulty in everyday clinical use, and overly optimistic performance.There are significant concerns regarding predictive performance and usability in every day practice of existing CPM to predict invasive candidiasis. LAY SUMMARY Clinical predictive models may assist in early identification of patients at risk for invasive candidiasis to initiate appropriate treatment. The findings of this systematic review highlight the limitations of currently available models to predict invasive candidiasis.
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Affiliation(s)
- Adriana M Rauseo
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Abdullah Aljorayid
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.,Department of Medicine, College of Medicine, Qassim University, Buraydah, Saudi Arabia
| | - Margaret A Olsen
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Lindsey Larson
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Kim L Lipsey
- Bernard Becker Medical Library, Washington University School of Medicine, St. Louis, MO, USA
| | - William G Powderly
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrej Spec
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
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Wang Z, Chen Y, Lv S, Sun Z, Lu X, Huang L, Li L. Predictive Value of Limb Artery Indices and Endothelial Functional Tests for the Degree of Coronary Artery Stenosis in a Diabetic Population. Int J Gen Med 2021; 14:2343-2349. [PMID: 34113164 PMCID: PMC8184229 DOI: 10.2147/ijgm.s316297] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 05/19/2021] [Indexed: 12/27/2022] Open
Abstract
Objective To investigate the correlation between limb artery indices (brachial-ankle pulse wave velocity and ankle-brachial index), endothelial function index (FMD value), and the degree of coronary artery stenosis in diabetic patients and analyze their values in predicting the degree of coronary artery stenosis. Methods The study included 151 patients with type 2 diabetes mellitus and suspected coronary atherosclerotic heart disease. The patients were divided into “coronary atherosclerotic heart disease” (N=94) and “non-coronary atherosclerotic heart disease” (N=57) groups based on the coronary angiographic findings. Within the coronary atherosclerotic heart disease group, the patients were further divided into “low stenosis” (N=47) and “high stenosis” (N=47) subgroups according to their Gensini score. Indicators such as brachial-ankle pulse wave velocity, ankle-brachial index, and FMD value were measured and correlated with the degree of coronary artery stenosis. Logistic regression models were constructed and receiver operating characteristic curves plotted to assess the predictive ability of limb artery and endothelial functional indices for the degree of coronary artery stenosis. Results In a diabetic population, FMD value (P=0.003), ankle-brachial index (P=0.004), and brachial-ankle pulse wave velocity (P=0.003) were different in patients with and without coronary atherosclerotic heart disease. In the population with both diabetes mellitus and coronary atherosclerotic heart disease, the ankle-brachial index and FMD value were both independently associated with the degree of coronary artery stenosis (P=0.003). The area under the receiver operating characteristic curve plotted from the combined coefficients of ankle-brachial index and FMD value was 0.773, which is predictive of coronary artery stenosis in diabetic patients. Conclusion Ankle-brachial index and FMD value are indicative of the degree of coronary artery stenosis in diabetic patients, and predictive efficacy can be improved by combining the two tests.
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Affiliation(s)
- Zihan Wang
- Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Ying Chen
- Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Shuying Lv
- Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Ziyi Sun
- Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Xiaoyan Lu
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Li Huang
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Lin Li
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, People's Republic of China
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Hu B, Tan HY, Rao XW, Jiang JY, Yang K. A Scoring System for Surgical Site Infection after Pancreaticoduodenectomy Using Clinical Data. Surg Infect (Larchmt) 2020; 22:240-244. [PMID: 32543287 DOI: 10.1089/sur.2020.082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Object: To analyze the factors influencing surgical site infection (SSI) after pancreaticoduodenectomy and to establish a scoring system for predicting such infections. Methods: Patients who underwent pancreaticoduodenectomy in the Department of Hepatobiliary Surgery of the Second Affiliated Hospital of Chongqing Medical University from January 2015 to March 2019 were divided randomly into a model group and a test group in a proportion of 3:1. According to whether an SSI occurred after operation, the model group was divided into an incision-infection group and a non-infection group. Univariable analysis and multivariable regression analysis were used to analyze factors related to post-operative incision infection and to establish a clinical predictive scoring system. The scoring system was evaluated for the test group. Results: A total of 236 patients, 177 in the model group and 59 in the test group, were included. In the model group, univariable and logistic regression analysis showed that tumor nature (benign versus malignant), post-operative albumin concentration, pancreatic fistula formation, post-operative cough, and peri-operative blood transfusion were the independent risk factors for incision infection. Then we established a clinical predictive scoring system. In the test group, the area under the receiver operator characteristic curve of the system was 0.768 (p < 0.001, with sensitivity = 59.1% and specificity = 94.6%). Conclusion: The scoring system had good clinical prediction ability and high specificity, so it was worth using in the clinic.
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Affiliation(s)
- Bo Hu
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hao-Yang Tan
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin-Wen Rao
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jia-Yi Jiang
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Kang Yang
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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11
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Liu Y, Wang L, Liu H, Li C, He J. The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma. Front Oncol 2020; 10:61. [PMID: 32133283 PMCID: PMC7040247 DOI: 10.3389/fonc.2020.00061] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [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: 07/25/2019] [Accepted: 01/14/2020] [Indexed: 12/14/2022] Open
Abstract
Background: Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC. Methods: Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these patients. The status of the MetS-related tumor microenvironment was analyzed with the CIBERSORT and ESTIMATE algorithms. A lncRNA signature was established with univariate and multivariate Cox proportional hazards regression (PHR) analysis and verified using the Kaplan–Meier survival curve analysis and time-dependent receiver operating characteristic (ROC) curves. A clinical predictive model was constructed based on multiple risk factors, evaluated using C-indexes and calibration curves, and verified using data from the GEO and TCGA databases. Results: The results showed that MetS was an independent risk factor for ESCC patients conferring low OS and RFS. Tumor microenvironment analysis indicated that patients with MetS have high stromal scores and M2 macrophage infiltration. A six-lncRNA signature was established by 60 ESCC patients randomly selected from GSE53624 and identified with an effective predictive ability in validation cohorts (59 patients from GSE53624 and 60 patients from GSE53622), subgroup analysis, and ESCC patients from TCGA. MetS and the six-lncRNA signature could be regarded as independent risk factors and enhanced predictive ability in the clinical predictive model. Conclusions: Our results indicated that MetS was associated with poor prognosis in ESCC patients, and the possible mechanism was related to changes in the tumor microenvironment. MetS and the six-lncRNA signature could also serve as independent risk factors with available clinical application value.
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Affiliation(s)
- Yu Liu
- 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, China
| | - Liyu Wang
- 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, China
| | - Hengchang Liu
- 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, China
| | - Chunxiang Li
- 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, China
| | - Jie 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, China
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12
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Kronen R, Hsueh K, Lin C, Powderly WG, Spec A. Creation and Assessment of a Clinical Predictive Calculator and Mortality Associated With Candida krusei Bloodstream Infections. Open Forum Infect Dis 2018; 5:ofx253. [PMID: 29450209 PMCID: PMC5808796 DOI: 10.1093/ofid/ofx253] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [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: 08/08/2017] [Accepted: 02/05/2018] [Indexed: 11/23/2022] Open
Abstract
Background Candida krusei bloodstream infection (CK BSI) is associated with high mortality, but whether this is due to underlying comorbidities in affected patients or the organism itself is unknown. Identifying patient characteristics that are associated with CK BSI is crucial for clinical decision-making and prognosis. Methods We conducted a retrospective analysis of hospitalized patients with Candida BSI at our institution between 2002 and 2015. Data were collected on demographics, comorbidities, medications, procedures, central lines, vital signs, and laboratory values. Multivariable logistic and Cox regression were used to identify risk factors associated with CK and mortality, respectively. Results We identified 1873 individual patients who developed Candida BSI within the study period, 59 of whom had CK BSI. CK BSI was predicted by hematologic malignancy, gastric malignancy, neutropenia, and the use of prophylactic azole antifungals, monoclonal antibodies, and β-lactam/β-lactamase inhibitor combinations. The C-statistic was 0.86 (95% confidence interval, 0.81–0.91). The crude mortality rates were 64.4% for CK BSI and 41.4% for non-CK BSI. Although CK was associated with higher mortality in univariable Cox regression, this relationship was no longer significant with the addition of the following confounders: lymphoma, neutropenia, glucocorticoid use, chronic liver disease, and elevated creatinine. Conclusions Six patient comorbidities predicted the development of CK BSI with high accuracy. Although patients with CK BSI have higher crude mortality rates than patients with non-CK BSI, this difference is not significant when accounting for other patient characteristics.
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Affiliation(s)
- Ryan Kronen
- Washington University School of Medicine, St Louis, Missouri
| | - Kevin Hsueh
- Division of Infectious Diseases, St Louis, Missouri
| | - Charlotte Lin
- Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | | | - Andrej Spec
- Division of Infectious Diseases, St Louis, Missouri
- Correspondence: A. Spec, MD, MSCI, Infectious Disease Clinical Research Unit, 4523 Clayton Ave., Campus Box 8051 St Louis, MO, 63110-0193 ()
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13
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Wessler BS, Ruthazer R, Udelson JE, Gheorghiade M, Zannad F, Maggioni A, Konstam MA, Kent DM. Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure. J Am Heart Assoc 2017; 6:JAHA.117.006121. [PMID: 29151026 PMCID: PMC5721739 DOI: 10.1161/jaha.117.006121] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background Heart failure clinical practice guidelines recommend applying validated clinical predictive models (CPMs) to support decision making. While CPMs are now widely available, the generalizability of heart failure CPMs is largely unknown. Methods and Results We identified CPMs derived in North America that predict mortality for patients with acute heart failure and validated these models in different world regions to assess performance in a contemporary international clinical trial (N=4133) of patients with acute heart failure treated with guideline‐directed medical therapy. We performed independent external validations of 3 CPMs predicting in‐hospital mortality, 60‐day mortality, and 1‐year mortality, respectively. CPM discrimination decreased in all regional validation cohorts. The median change in area under the receiver operating curve was −0.09 (range −0.05 to −0.23). Regional calibration was highly variable (90th percentile of absolute difference between smoothed observed and predicted values range <1% to >50%). Calibration remained poor after global recalibrations; however, region‐specific recalibration procedures significantly improved regional performance (recalibrated 90th percentile of absolute difference range <1% to 5% across all regions and all models). Conclusions Acute heart failure CPM discrimination and calibration vary substantially across different world regions; region‐specific (as opposed to global) recalibration techniques are needed to improve CPM calibration.
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Affiliation(s)
- Benjamin S Wessler
- Tufts Cardiovascular Center, Tufts Medical Center, Boston, MA .,Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
| | - Robin Ruthazer
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
| | - James E Udelson
- Tufts Cardiovascular Center, Tufts Medical Center, Boston, MA
| | | | - Faiez Zannad
- Institut National de la Santé et de la Recherche Médicale (INSERM), Nancy, France
| | - Aldo Maggioni
- Associazione Nazionale Medici Cardioligi Ospedalieri Research Center, Florence, Italy
| | | | - David M Kent
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
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14
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Zhou W, Ma Y, Zhang J, Hu J, Zhang M, Wang Y, Li Y, Wu L, Pan Y, Zhang Y, Zhang X, Zhang X, Zhang Z, Zhang J, Li H, Lu L, Jin L, Wang J, Yuan Z, Liu J. Predictive model for inflammation grades of chronic hepatitis B: Large-scale analysis of clinical parameters and gene expressions. Liver Int 2017; 37:1632-1641. [PMID: 28328162 DOI: 10.1111/liv.13427] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 03/14/2017] [Indexed: 02/07/2023]
Abstract
BACKGROUND Liver biopsy is the gold standard to assess pathological features (eg inflammation grades) for hepatitis B virus-infected patients although it is invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small hepatitis B virus (HBV)-infected samples. We aimed to analyse correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase and HBV-DNA) in large-scale CHB samples and to predict inflammation grades by using clinical parameters and/or gene expressions. METHODS We analysed gene expressions with three clinical parameters in 122 CHB samples by an improved regression model. Principal component analysis and machine-learning methods including Random Forest, K-nearest neighbour and support vector machine were used for analysis and further diagnosis models. Six normal samples were conducted to validate the predictive model. RESULTS Significant genes related to clinical parameters were found enriching in the immune system, interferon-stimulated, regulation of cytokine production, anti-apoptosis, and etc. A panel of these genes with clinical parameters can effectively predict binary classifications of inflammation grade (area under the ROC curve [AUC]: 0.88, 95% confidence interval [CI]: 0.77-0.93), validated by normal samples. A panel with only clinical parameters was also valuable (AUC: 0.78, 95% CI: 0.65-0.86), indicating that liquid biopsy method for detecting the pathology of CHB is possible. CONCLUSIONS This is the first study to systematically elucidate the relationships among gene expressions, clinical parameters and pathological inflammation grades in CHB, and to build models predicting inflammation grades by gene expressions and/or clinical parameters as well.
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Affiliation(s)
- Weichen Zhou
- Department of Digestive Diseases of Huashan Hospital, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yanyun Ma
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Jun Zhang
- Department of Digestive Diseases of Huashan Hospital, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Jingyi Hu
- Department of Digestive Diseases of Huashan Hospital, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Menghan Zhang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yi Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yi Li
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Lijun Wu
- Department of Digestive Diseases of Huashan Hospital, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Yida Pan
- Department of Digestive Diseases of Huashan Hospital, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Yitong Zhang
- Department of Digestive Diseases of Huashan Hospital, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xiaonan Zhang
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Xinxin Zhang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhanqing Zhang
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Jiming Zhang
- Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Hai Li
- Department of Gastroenterology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Lungen Lu
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Zhenghong Yuan
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Key Laboratory of Medical Molecular Virology of MOE/MOH, Department of Immunology, Institutes of Biomedical Sciences, Shanghai Medical School, Fudan University, Shanghai, China
| | - Jie Liu
- Department of Digestive Diseases of Huashan Hospital, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.,Key Laboratory of Medical Molecular Virology of MOE/MOH, Department of Immunology, Institutes of Biomedical Sciences, Shanghai Medical School, Fudan University, Shanghai, China
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