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Moon SI, Yim DH, Choi K, Eom SY, Choi BS, Park JD, Kim H, Kim YD. Association Between Multiple Heavy Metal Exposures and Cholesterol Levels in Residents Living Near a Smelter Plant in Korea. J Korean Med Sci 2024; 39:e77. [PMID: 38442720 PMCID: PMC10911942 DOI: 10.3346/jkms.2024.39.e77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/28/2023] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Considering the interactions between heavy metals, a comprehensive evaluation of the effects of exposure to various types of co-interacting heavy metals on health is required. This study assessed the association between dyslipidemia markers and blood mercury, lead, cadmium, iron, zinc, and nickel levels in residents of an abandoned refinery plant. METHODS A total of 972 individuals (exposed group: 567, control group: 405) living near the Janghang refinery plant in the Republic of Korea were included. Blood mercury, lead, cadmium, iron, zinc, nickel, cholesterol, and triglyceride levels were measured. The combined effect of the six heavy metals on dyslipidemia markers was evaluated using a Bayesian kernel machine regression (BKMR) model and compared with the results of a linear regression analysis. The BKMR model results were compared using a stratified analysis of the exposed and control groups. RESULTS In the BKMR model, the combined effect of the six heavy metals was significantly associated with total cholesterol (TC) levels both below the 45th percentile and above the 55th percentile in the total population. The combined effect range between the 25th and 75th percentiles of the six metals on TC levels was larger in the exposed group than that in the total population. In the control group, the combined effects of the changes in concentration of the six heavy metals on the TC concentration were not statistically significant. CONCLUSION These results suggest that the cholesterol levels of residents around the Janghang refinery plant may be elevated owing to exposure to multiple heavy metals.
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Affiliation(s)
- Sun-In Moon
- Chungbuk Environmental Health Center, Chungbuk National University Hospital, Cheongju, Korea
- Department of Preventive Medicine, College of Medicine, Chungbuk National University, Cheongju, Korea
| | - Dong-Hyuk Yim
- Department of Preventive Medicine, College of Medicine, Chungbuk National University, Cheongju, Korea
| | - Kyunghi Choi
- Department of Preventive Medicine, College of Medicine, Chungbuk National University, Cheongju, Korea
| | - Sang-Yong Eom
- Chungbuk Environmental Health Center, Chungbuk National University Hospital, Cheongju, Korea
- Department of Preventive Medicine, College of Medicine, Chungbuk National University, Cheongju, Korea
- Department of Office of Public Healthcare Service, Chungbuk National University Hospital, Cheongju, Korea
| | - Byung-Sun Choi
- Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Jung-Duck Park
- Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Heon Kim
- Chungbuk Environmental Health Center, Chungbuk National University Hospital, Cheongju, Korea
- Department of Preventive Medicine, College of Medicine, Chungbuk National University, Cheongju, Korea
- Department of Occupational and Environmental Medicine, Chungbuk National University Hospital, Cheongju, Korea
| | - Yong-Dae Kim
- Chungbuk Environmental Health Center, Chungbuk National University Hospital, Cheongju, Korea
- Department of Preventive Medicine, College of Medicine, Chungbuk National University, Cheongju, Korea
- Chungbuk Regional Cancer Center, Chungbuk National University Hospital, Cheongju, Korea.
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Lu G, Dong Z, Huang B, Hu S, Cai S, Hu M, Hu R, Wang C. Determination of weight loss effectiveness evaluation indexes and establishment of a nomogram for forecasting the probability of effectiveness of weight loss in bariatric surgery: a retrospective cohort. Int J Surg 2023; 109:850-860. [PMID: 36974733 PMCID: PMC10389379 DOI: 10.1097/js9.0000000000000330] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/22/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND The purpose of this research was to determine the index that contributes the most to assessing the effectiveness of weight loss 1 year following bariatric surgery and to implement it as the clinical outcome to develop and confirm a nomogram to predict whether bariatric surgery would be effective. METHODS Patient information was extracted from the Chinese Obesity and Metabolic Surgery Database for this retrospective study. The most contributing weight loss effectiveness evaluation index was created using canonical correlation analysis (CCA), and the predictors were screened using logistic regression analysis. A nomogram for estimating the likelihood of effectiveness of weight loss was constructed, and its performance was further verified. RESULTS Information was obtained for 540 patients, including 30 variables. According to the CCA, ≥25 percentage total weight loss was found to be the most correlated with patient information and contribute the most as a weight loss effectiveness evaluation index. Logistic regression analysis and nomogram scores identified age, surgical strategy, abdominal circumference, weight loss history, and hyperlipidemia as predictors of effectiveness in weight loss. The prediction model's discrimination, accuracy, and clinical benefit were demonstrated by the consistency index, calibration curve, and decision curve analysis. CONCLUSIONS The authors determined a 25 percentage total weight loss as an index for weight loss effectiveness assessment by CCA and next established and validated a nomogram, which demonstrated promising performance in predicting the probability of effectiveness of weight loss in bariatric surgery. The nomogram might be a valuable tool in clinical practice.
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Affiliation(s)
- Guanhua Lu
- Departments of Metabolic and Bariatric Surgery
- Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, The University of Hong Kong and Jinan University, Guangzhou, Guangdong Province, China
| | - Zhiyong Dong
- Departments of Metabolic and Bariatric Surgery
- Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, The University of Hong Kong and Jinan University, Guangzhou, Guangdong Province, China
| | - Biao Huang
- Departments of Metabolic and Bariatric Surgery
- Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, The University of Hong Kong and Jinan University, Guangzhou, Guangdong Province, China
| | - Songhao Hu
- Departments of Metabolic and Bariatric Surgery
- Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, The University of Hong Kong and Jinan University, Guangzhou, Guangdong Province, China
| | - Shenhua Cai
- Department of Thyroid, Mammary and Vascular Surgery, The First Affiliated Hospital of Sun Yat-sen University
| | - Min Hu
- Hepatobiliary Surgery, The First Affiliated Hospital of Jinan University
| | - Ruixiang Hu
- Departments of Metabolic and Bariatric Surgery
- Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, The University of Hong Kong and Jinan University, Guangzhou, Guangdong Province, China
| | - Cunchuan Wang
- Departments of Metabolic and Bariatric Surgery
- Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, The University of Hong Kong and Jinan University, Guangzhou, Guangdong Province, China
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Zhang Y, Huang B, Jin J, Xiao Y, Ying H. Recent advances in the application of ionomics in metabolic diseases. Front Nutr 2023; 9:1111933. [PMID: 36726817 PMCID: PMC9884710 DOI: 10.3389/fnut.2022.1111933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Trace elements and minerals play a significant role in human health and diseases. In recent years, ionomics has been rapidly and widely applied to explore the distribution, regulation, and crosstalk of different elements in various physiological and pathological processes. On the basis of multi-elemental analytical techniques and bioinformatics methods, it is possible to elucidate the relationship between the metabolism and homeostasis of diverse elements and common diseases. The current review aims to provide an overview of recent advances in the application of ionomics in metabolic disease research. We mainly focuses on the studies about ionomic or multi-elemental profiling of different biological samples for several major types of metabolic diseases, such as diabetes mellitus, obesity, and metabolic syndrome, which reveal distinct and dynamic patterns of ion contents and their potential benefits in the detection and prognosis of these illnesses. Accumulation of copper, selenium, and environmental toxic metals as well as deficiency of zinc and magnesium appear to be the most significant risk factors for the majority of metabolic diseases, suggesting that imbalance of these elements may be involved in the pathogenesis of these diseases. Moreover, each type of metabolic diseases has shown a relatively unique distribution of ions in biofluids and hair/nails from patients, which might serve as potential indicators for the respective disease. Overall, ionomics not only improves our understanding of the association between elemental dyshomeostasis and the development of metabolic disease but also assists in the identification of new potential diagnostic and prognostic markers in translational medicine.
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Affiliation(s)
- Yan Zhang
- Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China,*Correspondence: Yan Zhang ✉
| | - Biyan Huang
- Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Jiao Jin
- Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Yao Xiao
- Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Huimin Ying
- Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China,Huimin Ying ✉
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Zhao M, Yin G, Xu J, Ge X, Li A, Mei Y, Wu J, Liu X, Wei L, Xu Q. Independent, combine and interactive effects of heavy metal exposure on dyslipidemia biomarkers: A cross-sectional study in northeastern China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 250:114494. [PMID: 36608569 DOI: 10.1016/j.ecoenv.2022.114494] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Dyslipidemia is a common disease in the older population and represents a considerable disease burden worldwide. Epidemiological and experimental studies have indicated associations between heavy metal exposure and dyslipidemia; few studies have investigated the effects of heavy metal mixture and interactions between metals on dyslipidemia. We recruited 1121 participants living in heavy metal-contaminated and control areas in northeast China from a cross-sectional survey (2017-2019). Urinary metals including chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn) and dyslipidemia biomarkers, namely triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels, were measured. The generalized linear model (GLM) was used to explore the association of a single metal with dyslipidemia biomarkers. Bayesian kernel machine regression (BKMR) and multivariable linear regression were performed to explore the overall effect of metal mixture and the interaction between metals on dyslipidemia. Heavy metal mixture was positively associated with LDL-C, TC, and TG and negatively with HDL-C. In multivariable linear regression, Pb and Cd exhibited a synergistic association with LDL-C in the participants without hyperlipemia. Mn-Cd and Pb-Cr also showed a synergistic association with increasing the level of LDL-C in subjects without hyperlipemia. Cd-Cr showed an antagonistic association with HDL-C, respectively. Cr-Mn exhibited an antagonistic association with decreased HDL-C and TG levels. No significant interaction was noted among the three metals. Our study indicated that exposure to heavy metals is associated with dyslipidemia biomarkers and the presence of potential synergistic or antagonistic interactions between the heavy metals.
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Affiliation(s)
- Meiduo Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Guohuan Yin
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China
| | - Jing Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Xiaoyu Ge
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Ang Li
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Yayuan Mei
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Jingtao Wu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Xiaolin Liu
- Department of Epidemiology and Biostatistics, Jinzhou Medical University, Jinzhou 121001, Liaoning, China
| | - Lanping Wei
- Jinzhou Central Hospital, Jinzhou 121001, Liaoning, China
| | - Qun Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
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Luo T, Chen S, Cai J, Liu Q, Gou R, Mo X, Tang X, He K, Xiao S, Wei Y, Lin Y, Huang S, Li T, Chen Z, Li R, Li Y, Zhang Z. Association between combined exposure to plasma heavy metals and dyslipidemia in a chinese population. Lipids Health Dis 2022; 21:131. [PMID: 36474262 PMCID: PMC9724421 DOI: 10.1186/s12944-022-01743-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Exposure to heavy metals in the environment is widespread, while the relationship between combined exposure to heavy metals and dyslipidemia is unclear. METHODS A cross-sectional study was performed, and 3544 participants aged 30 years or older were included in the analyses. Heavy metal concentrations in plasma were based on inductively coupled plasma‒mass spectrometry. The relationship between heavy metals and dyslipidemia was estimated by logistic regression. BKMR was used to evaluate metal mixtures and their potential interactions. RESULTS In logistic regression analysis, participants in the fourth quartile of Fe and Zn (Fe > 1352.38 µg/L; Zn > 4401.42 µg/L) had a relatively higher risk of dyslipidemia (Fe, OR = 1.13, 95% CI: 0.92,1.38; Zn, OR = 1.30, 95% CI: 1.03,1.64). After sex stratification, females in the third quartile of plasma Zn (1062.05-4401.42 µg/L) had a higher relative risk of dyslipidemia (OR = 1.75, 95% CI: 1.28, 2.38). In BKMR analysis, metal mixtures were negatively associated with dyslipidemia in females when all metal concentrations were above the 50th percentile. In the total population (estimated from 0.030 to 0.031), As was positively associated with dyslipidemia when other metals were controlled at the 25th, 50th, or 75th percentile, respectively, and As was below the 75th percentile. In females (estimated from - 0.037 to -0.031), Zn was negatively associated with dyslipidemia when it was above the 50th percentile. CONCLUSION This study indicated that As was positively associated with dyslipidemia and that Zn may be negatively associated with dyslipidemia in females. Combined metal exposure was negatively associated with dyslipidemia in females. Females with low plasma Zn levels are more likely to develop dyslipidemia and should receive more clinical attention in this population.
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Affiliation(s)
- Tingyu Luo
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
| | - Shiyi Chen
- grid.411858.10000 0004 1759 3543School of Public Health and Management, Guangxi University of Chinese Medicine, Guangxi 530200 Nanning, China
| | - Jiansheng Cai
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.256607.00000 0004 1798 2653Department of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021 Nanning, Guangxi China
| | - Qiumei Liu
- grid.256607.00000 0004 1798 2653Department of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021 Nanning, Guangxi China
| | - Ruoyu Gou
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
| | - Xiaoting Mo
- grid.256607.00000 0004 1798 2653Department of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021 Nanning, Guangxi China
| | - Xu Tang
- grid.256607.00000 0004 1798 2653Department of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021 Nanning, Guangxi China
| | - Kailian He
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
| | - Song Xiao
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
| | - Yanfei Wei
- grid.256607.00000 0004 1798 2653Department of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021 Nanning, Guangxi China
| | - Yinxia Lin
- grid.256607.00000 0004 1798 2653Department of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021 Nanning, Guangxi China
| | - Shenxiang Huang
- grid.256607.00000 0004 1798 2653Department of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021 Nanning, Guangxi China
| | - Tingjun Li
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
| | - Ziqi Chen
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
| | - Ruiying Li
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
| | - You Li
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
| | - Zhiyong Zhang
- grid.443385.d0000 0004 1798 9548Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi 541199 Guilin, China ,grid.443385.d0000 0004 1798 9548Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, Guilin Medical University, Guangxi 541199 Guilin, China
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