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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 楊孝友(Hsiao-Yu Yang) | |
dc.contributor.author | Wei-Chi Lin | en |
dc.contributor.author | 林蔚琪 | zh_TW |
dc.date.accessioned | 2021-06-16T09:18:59Z | - |
dc.date.available | 2025-08-14 | |
dc.date.copyright | 2020-08-27 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-14 | |
dc.identifier.citation | 1. Air pollution levels rising in many of the world’s poorest cities. 2016. at http://www.who.int/mediacentre/news/releases/2016/air-pollution-rising/en/.) 2. WHO releases country estimates on air pollution exposure and health impact. 2016. at http://www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/en/.) 3. Kim KH, Jahan SA, Kabir E. A review on human health perspective of air pollution with respect to allergies and asthma. Environ Int 2013;59:41-52. 4. Jiang XQ, Mei XD, Feng D. Air pollution and chronic airway diseases: what should people know and do? J Thorac Dis 2016;8:E31-40. 5. Montero-Montoya R, Lopez-Vargas R, Arellano-Aguilar O. Volatile Organic Compounds in Air: Sources, Distribution, Exposure and Associated Illnesses in Children. Ann Glob Health 2018;84:225-38. 6. Karakatsani A, Analitis A, Perifanou D, et al. Particulate matter air pollution and respiratory symptoms in individuals having either asthma or chronic obstructive pulmonary disease: a European multicentre panel study. Environ Health 2012;11:75. 7. Jayaraj RL, Rodriguez EA, Wang Y, Block ML. Outdoor Ambient Air Pollution and Neurodegenerative Diseases: the Neuroinflammation Hypothesis. Curr Environ Health Rep 2017;4:166-79. 8. Block ML, Calderon-Garciduenas L. Air pollution: mechanisms of neuroinflammation and CNS disease. Trends Neurosci 2009;32:506-16. 9. Peters A, Dockery Douglas W, Muller James E, Mittleman Murray A. Increased Particulate Air Pollution and the Triggering of Myocardial Infarction. Circulation 2001;103:2810-5. 10. Brook RD, Franklin B, Cascio W, et al. Air pollution and cardiovascular disease: a statement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation 2004;109:2655-71. 11. Delfino RJ, Gong H, Jr., Linn WS, Pellizzari ED, Hu Y. Asthma symptoms in Hispanic children and daily ambient exposures to toxic and criteria air pollutants. Environ Health Perspect 2003;111:647-56. 12. Nishimura KK, Galanter JM, Roth LA, et al. Early-life air pollution and asthma risk in minority children. The GALA II and SAGE II studies. American journal of respiratory and critical care medicine 2013;188:309-18. 13. Esposito S, Tenconi R, Lelii M, et al. Possible molecular mechanisms linking air pollution and asthma in children. BMC Pulm Med 2014;14:31. 14. Gauderman WJ, Avol E, Gilliland F, et al. The Effect of Air Pollution on Lung Development from 10 to 18 Years of Age. New England Journal of Medicine 2004;351:1057-67. 15. Bolden AL, Kwiatkowski CF, Colborn T. New Look at BTEX: Are Ambient Levels a Problem? Environ Sci Technol 2015;49:5261-76. 16. Lee SC, Chiu MY, Ho KF, Zou SC, Wang X. Volatile organic compounds (VOCs) in urban atmosphere of Hong Kong. Chemosphere 2002;48:375-82. 17. International Agency for Research on Cancer. Benzene. Benzene. Lyon (FR)2018. 18. International Agency for Research on Cancer. List of Classifications. Agents classified by the IARC Monographs. 2020;1-125. 19. Dales R, Raizenne M. Residential Exposure to Volatile Organic Compounds and Asthma. Journal of Asthma 2004;41:259-70. 20. Nurmatov UB, Tagiyeva N, Semple S, Devereux G, Sheikh A. Volatile organic compounds and risk of asthma and allergy: a systematic review. Eur Respir Rev 2015;24:92-101. 21. Rumchev K, Spickett J, Bulsara M, Phillips M, Stick S. Association of domestic exposure to volatile organic compounds with asthma in young children. Thorax 2004;59:746-51. 22. Elliott L, Longnecker MP, Kissling GE, London SJ. Volatile organic compounds and pulmonary function in the Third National Health and Nutrition Examination Survey, 1988-1994. Environ Health Perspect 2006;114:1210-4. 23. Sexton K, Adgate JL, Church TR, et al. Children's exposure to volatile organic compounds as determined by longitudinal measurements in blood. Environ Health Perspect 2005;113:342-9. 24. Beauchamp J. Inhaled today, not gone tomorrow: pharmacokinetics and environmental exposure of volatiles in exhaled breath. Journal of Breath Research 2011;5:037103. 25. Španěl P, Dryahina K, Smith D. A quantitative study of the influence of inhaled compounds on their concentrations in exhaled breath. Journal of Breath Research 2013;7:017106. 26. Sun X, Yang X. Volatile organic compounds in normal human exhaled breath: A long neglected pollutant source2013. 27. Meyer N, Dallinga JW, Nuss SJ, et al. Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air. Respiratory research 2014;15:136-. 28. He J, Sun X, Yang X. Human respiratory system as sink for volatile organic compounds: Evidence from field measurements. Indoor Air 2019;29:968-78. 29. Uddin MS, Blount BC, Lewin MD, Potula V, Ragin AD, Dearwent SM. Comparison of blood volatile organic compound levels in residents of Calcasieu and Lafayette Parishes, LA, with US reference ranges. Journal of Exposure Science Environmental Epidemiology 2014;24:602-7. 30. Ashley DL, Prah JD. Time dependence of blood concentrations during and after exposure to a mixture of volatile organic compounds. Arch Environ Health 1997;52:26-33. 31. Delfino RJ, Gong H, Linn WS, Hu Y, Pellizzari ED. Respiratory symptoms and peak expiratory flow in children with asthma in relation to volatile organic compounds in exhaled breath and ambient air. Journal of Exposure Science Environmental Epidemiology 2003;13:348-63. 32. Wallace L, Buckley T, Pellizzari E, Gordon S. Breath measurements as volatile organic compound biomarkers. Environmental health perspectives 1996;104 Suppl 5:861-9. 33. Wallace LA. HUMAN EXPOSURE TO VOLATILE ORGANIC POLLUTANTS: Implications for Indoor Air Studies. Annual Review of Energy and the Environment 2001;26:269-301. 34. van Aalderen WM. Childhood asthma: diagnosis and treatment. Scientifica (Cairo) 2012;2012:674204. 35. The Global Asthma Network. Global Asthma Report 2018. The Global Asthma Report 2018 2018. 36. Chen BY, Chen CH, Chuang YC, Wu YH, Pan SC, Guo YL. Changes in the relationship between childhood asthma and ambient air pollution in Taiwan: Results from a nationwide survey repeated 5 years apart. Pediatr Allergy Immunol 2019;30:188-94. 37. Hwang BF, Lee YL, Lin YC, Jaakkola JJK, Guo YL. Traffic related air pollution as a determinant of asthma among Taiwanese school children. Thorax 2005;60:467-73. 38. Chung H-Y, Hsieh C-J, Tseng C-C, Yiin L-M. Association between the First Occurrence of Allergic Rhinitis in Preschool Children and Air Pollution in Taiwan. Int J Environ Res Public Health 2016;13:268. 39. Johnson JD, Theurer WM. A stepwise approach to the interpretation of pulmonary function tests. Am Fam Physician 2014;89:359-66. 40. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J 2005;26:319-38. 41. Warke TJ, Fitch PS, Brown V, et al. Exhaled nitric oxide correlates with airway eosinophils in childhood asthma. Thorax 2002;57:383-7. 42. American Thoracic Society, European Respiratory Society. ATS/ERS recommendations for standardized procedures for the online and offline measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide, 2005. Am J Respir Crit Care Med 2005;171:912-30. 43. Dweik RA, Boggs PB, Erzurum SC, et al. An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FENO) for clinical applications. Am J Respir Crit Care Med 2011;184:602-15. 44. Bofan M, Mores N, Baron M, et al. Within-day and between-day repeatability of measurements with an electronic nose in patients with COPD. J Breath Res 2013;7:017103. 45. EPA US. Air Method, Toxic Organics-15 (TO-15): Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air, Second Edition: Determination of Volatile Organic Compounds (VOCs) in Air Collected in Specially-Prepared Canisters and Analyzed by Gas Chromatography/Mass Spectrometry (GC/MS). 1999;EPA 625/R-96/010b. 46. Ripley WNVaBD. Modern Applied Statistics with S. 2002. 47. Morgan M. BiocManager: Access the Bioconductor Project Package Repository. 2019. 48. Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77. 49. Armstrong RA. When to use the Bonferroni correction. Ophthalmic and Physiological Optics 2014;34:502-8. 50. Kong X, Yang X, Zhou J, et al. Analysis of plasma metabolic biomarkers in the development of 4-nitroquinoline-1-oxide-induced oral carcinogenesis in rats. Oncol Lett 2015;9:283-9. 51. Zhong F, Liu X, Zhou Q, et al. 1 H NMR spectroscopy analysis of metabolites in the kidneys provides new insight into pathophysiological mechanisms: applications for treatment with Cordyceps sinensis. Nephrology Dialysis Transplantation 2011;27:556-65. 52. Jasmine Chong DSW, and Jianguo Xia. Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis. Current Protocols 2019;68. 53. Filipiak W, Ruzsanyi V, Mochalski P, et al. Dependence of exhaled breath composition on exogenous factors, smoking habits and exposure to air pollutants. J Breath Res 2012;6:036008. 54. van de Kant KD, van der Sande LJ, Jobsis Q, van Schayck OC, Dompeling E. Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review. Respir Res 2012;13:117. 55. Lourenço C, Turner C. Breath analysis in disease diagnosis: methodological considerations and applications. Metabolites 2014;4:465-98. 56. Intagliata S RA, Gossman WG. . Physiology, Lung Dead Space. 2019. 57. de Silva G, Beyette FR, Jr. Alveolar air volatile organic compound extractor for clinical breath sampling. Conf Proc IEEE Eng Med Biol Soc 2014;2014:5369-72. 58. Delfino RJ, Gong H, Linn WS, Hu Y, Pellizzari ED. Respiratory symptoms and peak expiratory flow in children with asthma in relation to volatile organic compounds in exhaled breath and ambient air. J Expo Anal Environ Epidemiol 2003;13:348-63. 59. Arif AA, Shah SM. Association between personal exposure to volatile organic compounds and asthma among US adult population. International Archives of Occupational and Environmental Health 2007;80:711-9. 60. Hulin M, Caillaud D, Annesi-Maesano I. Indoor air pollution and childhood asthma: variations between urban and rural areas. Indoor Air 2010;20:502-14. 61. Miller SL, Branoff S, Nazaroff WW. Exposure to toxic air contaminants in environmental tobacco smoke: an assessment for California based on personal monitoring data. J Expo Anal Environ Epidemiol 1998;8:287-311. 62. McNabola A, Broderick B, Johnston P, Gill L. Effects of the Smoking Ban on Benzene and 1,3-Butadiene Levels in Pubs in Dublin. Journal of Environmental Science and Health, Part A 2006;41:799-810. 63. S-C Lee HG, N-H Kwok. EMISSIONS OF AIR POLLUTANTS FROM BURNING OF INCENSE BY USING LARGE ENVIRONMENTAL CHAMBER 2002. 64. Schleibinger H, Laussmann D, Bornehag CG, Eis D, Rueden H. Microbial volatile organic compounds in the air of moldy and mold-free indoor environments. Indoor Air 2008;18:113-24. 65. Claeson A-S, Levin J-O, Blomquist G, Sunesson A-L. Volatile metabolites from microorganisms grown on humid building materials and synthetic media. Journal of Environmental Monitoring 2002;4:667-72. 66. Borgie M, Garat A, Cazier F, et al. Traffic-related air pollution. A pilot exposure assessment in Beirut, Lebanon. Chemosphere 2014;96:122-8. 67. Xiaoyong Duan aY. Sources and Fates of BTEX in the General Environment and Its Distribution in Coastal Cities of China Journal of Environmental Science and Public Health 2017;1:86-106. 68. Wangchuk T, Mazaheri M, Clifford S, et al. Children's personal exposure to air pollution in rural villages in Bhutan. Environmental Research 2015;140:691-8. 69. Mazaheri M, Lin W, Clifford S, et al. Characteristics of school children's personal exposure to ultrafine particles in Heshan, Pearl River Delta, China – A pilot study. Environment International 2019;132:105134. 70. Tripepi G, Jager KJ, Dekker FW, Zoccali C. Selection Bias and Information Bias in Clinical Research. Nephron Clinical Practice 2010;115:c94-c9. 71. Hernán MA, Hernández-Díaz S, Robins JM. A Structural Approach to Selection Bias. Epidemiology 2004;15. 72. Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc 2016;9:211-7. 73. Nicholson JK, Lindon JC. Metabonomics. Nature 2008;455:1054-6. 74. Ribbenstedt A, Ziarrusta H, Benskin JP. Development, characterization and comparisons of targeted and non-targeted metabolomics methods. PLOS ONE 2018;13:e0207082. 75. Roberts LD, Souza AL, Gerszten RE, Clish CB. Targeted metabolomics. Curr Protoc Mol Biol 2012;Chapter 30:Unit30.2-.2.24. 76. Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 2016;17:451-9. 77. Takahashi H, Morimoto T, Ogasawara N, Kanaya S. AMDORAP: non-targeted metabolic profiling based on high-resolution LC-MS. BMC bioinformatics 2011;12:259-. 78. Lu H, Liang Y, Dunn WB, Shen H, Kell DB. Comparative evaluation of software for deconvolution of metabolomics data based on GC-TOF-MS. TrAC Trends in Analytical Chemistry 2008;27:215-27. 79. Xu D, Wang Y, Chen Z, et al. Prevalence and risk factors for asthma among children aged 0-14 years in Hangzhou: a cross-sectional survey. Respir Res 2016;17:122. 80. Moonie S, Sterling DA, Figgs LW, Castro M. The relationship between school absence, academic performance, and asthma status. J Sch Health 2008;78:140-8. 81. Hulsmann AR, Raatgeep HR, den Hollander JC, et al. Oxidative epithelial damage produces hyperresponsiveness of human peripheral airways. Am J Respir Crit Care Med 1994;149:519-25. 82. Buszewski B, Kęsy M, Ligor T, Amann A. Human exhaled air analytics: biomarkers of diseases. Biomedical Chromatography 2007;21:553-66. 83. Taylor DR, Bateman ED, Boulet LP, et al. A new perspective on concepts of asthma severity and control. Eur Respir J 2008;32:545-54. 84. Alzahrani YA, Becker EA. Asthma Control Assessment Tools. Respir Care 2016;61:106-16. 85. Dinakar C, Chipps BE, Section On A, Immunology, Section On Pediatric P, Sleep M. Clinical Tools to Assess Asthma Control in Children. Pediatrics 2017;139. 86. Kovats E. Gas‐chromatographische Charakterisierung organischer Verbindungen. Teil 1: Retentionsindices aliphatischer Halogenide, Alkohole, Aldehyde und Ketone. Helv Chim Acta 1958;41:1915-32. 87. Rufo JC, Madureira J, Fernandes EO, Moreira A. Volatile organic compounds in asthma diagnosis: a systematic review and meta-analysis. Allergy 2016;71:175-88. 88. Dragonieri S, Schot R, Mertens BJA, et al. An electronic nose in the discrimination of patients with asthma and controls. Journal of Allergy and Clinical Immunology 2007;120:856-62. 89. Smolinska A, Klaassen EMM, Dallinga JW, et al. Profiling of volatile organic compounds in exhaled breath as a strategy to find early predictive signatures of asthma in children. PloS one 2014;9:e95668-e. 90. Cakmak S, Dales RE, Liu L, et al. Residential exposure to volatile organic compounds and lung function: Results from a population-based cross-sectional survey. Environmental Pollution 2014;194:145-51. 91. Khan A, Staimer N, Tjoa T, Galassetti P, Blake DR, Delfino RJ. Relations between isoprene and nitric oxide in exhaled breath and the potential influence of outdoor ozone: a pilot study. Journal of breath research 2013;7:036007-. 92. Longo V, Forleo A, Capone S, et al. In vitro profiling of endothelial volatile organic compounds under resting and pro-inflammatory conditions. Metabolomics 2019;15:132. 93. Sahiner UM, Birben E, Erzurum S, Sackesen C, Kalayci O. Oxidative stress in asthma. World Allergy Organ J 2011;4:151-8. 94. Alkhouri N, Eng K, Cikach F, et al. Breathprints of childhood obesity: changes in volatile organic compounds in obese children compared with lean controls. Pediatr Obes 2015;10:23-9. 95. Cottrell L, Neal WA, Ice C, Perez MK, Piedimonte G. Metabolic abnormalities in children with asthma. American journal of respiratory and critical care medicine 2011;183:441-8. 96. Shore SA. Obesity and asthma: Possible mechanisms. Journal of Allergy and Clinical Immunology 2008;121:1087-93. 97. 兒童與青少年生長身體質量指數(BMI)建議值. 2018. at https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=542 pid=9547.) 98. Kelly FJ. Oxidative stress: its role in air pollution and adverse health effects. Occup Environ Med 2003;60:612-6. 99. Miekisch W, Schubert JK, Noeldge-Schomburg GF. Diagnostic potential of breath analysis--focus on volatile organic compounds. Clin Chim Acta 2004;347:25-39. 100. Liu L, Poon R, Chen L, et al. Acute effects of air pollution on pulmonary function, airway inflammation, and oxidative stress in asthmatic children. Environmental health perspectives 2009;117:668-74. 101. Bacharier LB, Guilbert TW. Diagnosis and management of early asthma in preschool-aged children. Journal of Allergy and Clinical Immunology 2012;130:287-96. 102. Global Strategy for Asthma Management and Prevention. Global Initiative for Asthma, 2020. at www.ginasthma.org.) 103. Robroeks CM, van Berkel JJ, Jöbsis Q, et al. Exhaled volatile organic compounds predict exacerbations of childhood asthma in a 1-year prospective study. European Respiratory Journal 2013;42:98. 104. Caldeira M, Barros AS, Bilelo MJ, Parada A, Câmara JS, Rocha SM. Profiling allergic asthma volatile metabolic patterns using a headspace-solid phase microextraction/gas chromatography based methodology. Journal of Chromatography A 2011;1218:3771-80. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59261 | - |
dc.description.abstract | 空氣污染是環境中對健康的一個主要危害來源。高濃度的空氣污染物會危害人們的健康,並造成許多疾病。尤其對於肺功能差的氣喘兒童而言更為危險。但是目前空氣污染成分(揮發性有機化合物(VOC))對於健康的危害仍需要採取進一步研究。藉由人體呼出的VOC作為代謝體學的生物標誌物可以評估暴露汙染或呼吸道病理與生理變化,作為協助診斷的工具。本研究包括兩個研究目的:(1)透過氣喘兒童進行人體吐氣分析來評估VOC的暴露情形,(2)探討可能的氣喘生物標誌物,以提供吐氣中目標代謝物特徵的氣喘兒童的診斷及分型參考。 我們在臺灣的兩個研究地點對氣喘學生和健康學生進行了橫斷面研究和病例對照研究。我們收集了空氣污染調查問卷並提供了肺功能與吐氣一氧化氮的測量。接著收集受試者的呼氣,透過氣相層析質譜儀(GC/MS)以目標定量和半定量的方法進行氣體成分及濃度分析。 本研究的吐氣分析可以檢測出暴露於低濃度的空氣污染VOC。我們發現在人體呼出的揮發性有機化合物濃度高低與氣喘的肺功能數值相關症狀之間呈現負相關。在人體吐氣中發現VOC大多來自於室內空氣污染物的暴露。我們發現3,3-二甲基己烷作為氧化壓力的指標,在氣喘兒童的吐氣中有較高的濃度表現(t-test p = 0.000; Fold Change = 3.171, p = 0.001),且超過0.6的曲線下面積具有良好的氣喘預測準確與區辨性(AUC = 0.69, p = 0.000)。我們同時發現3,3-二甲基己烷和異戊二烯在有氣道發炎的較高呼氣一氧化氮濃度和肥胖之氣喘病童呈現正向劑量反應效應(AUC = 0.69, 線性趨勢p value = 0.000; AUC = 0.69, 線性趨勢p value = 0.005)。在控制了空氣污染物引起的汙染物干擾因子後,我們發現2,4-二甲基庚烷在氣喘兒童的吐氣中有較高的濃度表現(t-test p value = 0.001; Fold Change = 7.117, p value = 0.001),可能是氣喘的生物標誌物。 我們提供了低濃度的VOC暴露會影響兒童肺功能的證據,並且透過定量和半定量來分析人體吐氣,評估了氣喘兒童的潛在呼氣生物標誌物,以應用於未來氣喘評估和診斷分型的參考。未來可透過定量環境監測器,進一步評估環境空氣汙染濃度與呼出氣體汙染物之間的直接相關性,並進一步探討氣喘的潛在生物標誌物。 | zh_TW |
dc.description.abstract | Air pollution is a major environmental hazard to respiratory health. High levels of air pollutants can harm people’s health and cause poor lung function in asthmatic children. However, the component of air pollution level (volatile organic compounds (VOCs)) effect on respiratory health still needs further approach. Exhaled VOCs as a metabolomics biomarker can provide a diagnostic opportunity to assess air pollution exposure and pathophysiological changes in the respiratory system. The aims of our study include two objectives: (1) To assess the respiratory function and the effect of VOC exposure by using exhaled breath analysis in asthmatic children, (2) To develop exhaled breath metabolites as possible biomarkers for the diagnosis and phenotypes classification in asthmatic children. We conducted a cross-sectional study and case-control study between asthmatic students and healthy students in two study sites in Taiwan. We collected air pollution and the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire, lung function measurement, and Fractional Exhaled Nitric Oxide (FeNO). The exhaled breath of the subjects was collected then analyzed with the targeted quantitative and semi-quantitative method by gas chromatography /mass spectrometer (GC/MS). A low level of VOC concentrations of air pollutants can be detected by using exhaled breath analysis. We found negative correlations between exhaled VOCs and pulmonary function values. Indoor air pollution plays a role in exhaled-VOC exposure. We found 3,3-dimethylhexane as an oxidative stress marker and correlated asthma severity, which found higher concentration in asthma patients (t-test p = 0.000; Fold Change = 3.171, p = 0.001). The Youden index with area under the curve (AUC) over 0.6 was used to determine the cutoff 3,3-dimethylhexane values (AUC = 0.69, p = 0.000) to predict asthma patients and normal healthy subjects. We found 3,3-dimethylhexane (AUC = 0.69, p for trend = 0.000) and Isoprene (AUC = 0.69, p for trend = 0.005) were positive dose-response association in having higher FeNO and obesity of the asthmatic children. After avoiding the effect of air pollutants-induced VOCs, we found 2,4-dimethylheptane have a higher concentration in the exhaled breath of the asthmatic children (t-test p value = 0.001; Fold Change = 7.117, p value = 0.001), which could be indicated as a potential asthmatic biomarker. We provided evidence of VOC exposure can affect pulmonary function in children. Potential biomarkers of asthmatic children were estimated for asthma assessment and asthma phenotypes classification by conducted exhaled breath with targeted quantitative analysis. In the future, a quantitative environmental monitor can incorporate personal evaluation with exhaled pollutant exposure in asthmatic biomarkers are needed. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T09:18:59Z (GMT). No. of bitstreams: 1 U0001-1408202014225700.pdf: 5535947 bytes, checksum: 9c583c9287eef781ee1b058bcef6c38b (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 中文摘要 I Abstract III Chapter 1: To estimate the effect of air pollution with exhaled volatile metabolites: asthmatic students in Taiwan. 1 1. Introduction 1 2. Method 3 2.1 Study design 3 2.2 Participants 4 2.3 Measurement 5 2.3.1 Questionnaire 5 2.3.2 Clinical examinations 6 2.3.3 Collection and analysis of breath air 7 2.4 VOC sample and data preprocessing 10 2.4.1 Quality assurance and quality control 10 2.4.2 Data preprocessing 12 2.5 Statistics 13 2.5.1 Pulmonary function test and FeNO test 13 2.5.2 Machine learning 13 2.5.3 63-VOCs and each VOC analysis 13 3. Results 14 3.1 Characteristics of the population 14 3.2 E-nose analysis 15 3.3 63-VOCs analysis 16 4. Discussion 17 5. Conclusion 22 Ⅱ Chapter 2: Application of metabolomic analysis with untargeted techniques 23 1. Introduction 23 2. Method 26 2.1 Study design/ Setting 26 2.2 Participants 26 2.3 Measurement 28 2.3.1 Collection and analysis of breath air 28 2.4 Data preprocessing 29 2.4.1 Targeted analysis 29 2.4.2 Validation Analysis for volatile compounds quantitation 30 2.5 Statistics 31 3. Results 32 3.1 Characteristics of the population 32 3.2 Quantitative VOCs analysis 32 4. Discussion 33 5. Conclusion 39 Reference 40 Figures 47 Figure 1. The flow chart represents the inclusion and exclusion of the study subjects in CCH. 47 Figure 2. The flow chart represents the inclusion and exclusion of the study subjects in Long-Pu elementary school. 48 Figure 3. Setting and procedure of breath analysis. 49 Figure 4. AUCs for the machine learning model of LDA and SVM. 50 Figure 5. The 2D plot of PLS-DA for E-nose. 51 Figure 6. The permutation test for E-nose. 52 Figure 7. The volcano plot for 63-VOCs. (The CCH group/the LPES group). 53 Figure 8. The 2D plot of PLS-DA for the 63-VOCs. 54 Figure 9. The permutation test for 63-VOCs between the CCH group and the LPES group. 55 Figure 10. The heat map of the Pearson correlation test between 63-VOCs and PFT and FeNO. 56 Figure 11. The flow chart represents Chapter 2 inclusion and grouping criteria of the study subjects in two study sites (CC and LPES). 57 Figure 12. The volcano plot for the quantitative analysis VOCs. 58 Figure 13. The representative full scan chromatograms (total ion chromatogram, TIC). The TIC of the case group and the control group samples showed differential regulation of some of the statistically significant VOCs. 59 Figure 14. The canonical discrimination analysis between FeNO and Obesity groups. 60 Figure 15. The dose-response analysis of Isoprene. 61 Figure 16. The dose-response analysis of 3,3-dimethylhexane. 62 Tables 63 Table 1. Characteristics of the population in the CCH group and the LPES group. 63 Table 2. Asthmatic history in the questionnaire. 64 Table 3. Indoor air pollution in the questionnaire. 66 Table 4. Outdoor air pollution in the questionnaire. 68 Table 5. Prediction accuracy of LDA and SVM in the E-nose analysis. 70 Table 6. The geometric mean concentration of the VOC exposure concentration between the CCH group and the LPES group and the p-values. 71 Table 7. The fold change of the 63-VOCs concentration difference (The CCH group/the LPES group). 75 Table 8. Pearson correlation coefficients between exhaled VOCs and the PFT and FeNO in all subjects. 78 Table 9. The geometric mean of the questionnaire indoor air pollution exposure and VOC concentration (geometric mean ± geometric SD) (ppb) and the p-value of independent t-test. 80 Table 10. The geometric mean of the questionnaire outdoor air pollution exposure and VOC concentration (geometric mean ± geometric SD) (ppb) and the p-value. 100 Table 11. Characteristics of the population in the case group and the control group in quantitative analysis. 109 Table 12. 47 volatile organic compounds were conducted in our semi quantitation analysis. 110 Table 13. The geometric mean concentration of the quantitative VOC concentrations between the case group and the control group and the p-values. 113 Table 14. The fold change of the quantitative VOC concentration difference (The case group / The control group). 119 Table 15. Receiver operating characteristic (ROC) analysis of the 110 VOCs for case group prediction and the performance of the classification. 123 Table 16. Discriminant analysis of dose-response analysis by exhaled breath metabolites. 128 Table 17. The geometric mean concentration of the quantitative VOC concentrations for the dose-response analysis and the p-values. 129 Supplementary 136 Supplementary Ⅰ The Institutional Review Board Approval Certificate 136 The Institutional Review Board of National Taiwan University Hospital 136 Changhua Christian Hospital Institutional Review Board 139 Supplementary Ⅱ. The questionnaire of clinical history measurement. 141 Supplementary Ⅲ Figures and Tables 155 Supplementary Figures 155 Supplementary Figure 1. The volcano plot of the results of CCH case-control subgroup analysis for the quantitative analysis VOC concentrations. 155 Supplementary Figure 2. The heat map of the Pearson correlation test between 63-VOCs and AQI. 156 Supplementary Figure 3. The Metabolic pathway analysis of untargeted analysis. 157 Supplementary Tables 158 Supplementary Table 1. The geometric mean of the questionnaire indoor air pollution exposure and VOC concentration (geometric mean ± geometric SD) (ppb) and the p-value. 158 Supplementary Table 2. The geometric mean of the questionnaire outdoor air pollution exposure and VOC concentration (geometric mean ± geometric SD) (ppb) and the p-value. 206 Supplementary Table 3. Characteristic of the population of subgroup analysis of the subgroup analysis between the case group and the control group in the CCH group in the quantitave analysis. 209 Supplementary Table 4. The geometric mean concentration of the quantitative and semi-quantitative VOC concentrations and the p-values of the subgroup analysis between the case group and the control group in the CCH group. 210 Supplementary Table 5. The fold change of the quantitative analysis VOC concentrations difference between the case group and control group in the CCH group. (The Case group/the Control group). 217 Supplementary Table 6. The initial calibration report from the ITRI. 221 Supplementary Table 7. The method detection limit (MDL) report from the ITRI 225 Supplementary Table 8. The replicate precision(%RP) report from the ITRI 227 Supplementary Table 9. MZmine 2 parameters for GC/MS untargeted analysis. 229 Supplementary Table 10. MetaboAnalyst parameters for GC/MS untargeted analysis. 230 Supplementary Table 11. The fold change of the duplicated VOCs in untargeted analysis and targeted analysis. 231 | |
dc.language.iso | en | |
dc.title | 以吐氣代謝體學偵測氣喘學童之呼吸危害研究 | zh_TW |
dc.title | Respiratory Hazard of Asthmatic Students by Exhaled Metabolism | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳保中(Pau-Chung Chen),郭錦樺(Ching-Hua Kuo),謝瑞豪(Ruei-Hao Shie),陳志道(Chih-Dao Chen) | |
dc.subject.keyword | 代謝體,氣喘,空氣汙染,吐氣分析, | zh_TW |
dc.subject.keyword | Metabolites,Asthma,Air pollution,Exhaled breath analysis, | en |
dc.relation.page | 231 | |
dc.identifier.doi | 10.6342/NTU202003411 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-15 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 環境與職業健康科學研究所 | zh_TW |
顯示於系所單位: | 環境與職業健康科學研究所 |
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