Polaris T, Hcv O. Global prevalence and genotype distribution of hepatitis C virus infection in 2015: a modelling study. 2015;161–76.

Akinyemiju T, Abera S, Ahmed M, Alam N, Alemayohu MA, Allen C, et al. The Burden of Primary Liver Cancer and Underlying Etiologies From 1990 to 2015 at the Global, Regional, and National Level: Results From the Global Burden of Disease Study 2015. JAMA Oncol. 2017;3(12):1683–91.

Article  Google Scholar 

Global hepatitis report. 2017. WHO. https://www.who.int/publications/i/item/global-hepatitis-report-2017.

Lavanchy D. The global burden of hepatitis C. Liver Int Off J Int Assoc Study Liver. 2009;29(Suppl 1):74–81.

Google Scholar 

Dragonieri S, Schot R, Mertens BJA, Le Cessie S, Gauw SA, Spanevello A, et al. An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol. 2007;120(4):856–62.

Article  Google Scholar 

Moura PC, Raposo M, Vassilenko V. Breath volatile organic compounds (VOCs) as biomarkers for the diagnosis of pathological conditions: A review. Biomed J. 2023;46(4):100623.

Article  Google Scholar 

Herman-Saffar O, Boger Z, Libson S, Lieberman D, Gonen R, Zeiri Y. Early non-invasive detection of breast cancer using exhaled breath and urine analysis. Comput Biol Med. 2018;96:227–32.

Article  Google Scholar 

Filipiak W, Beer R, Sponring A, Filipiak A, Ager C, Schiefecker A, et al. Breath analysis for in vivo detection of pathogens related to ventilator-associated pneumonia in intensive care patients: a prospective pilot study. J Breath Res. 2015;9(1):16004.

Article  Google Scholar 

De Lacy CB, Amann A, Al-Kateb H, Flynn C, Filipiak W, Khalid T, et al. A review of the volatiles from the healthy human body. J Breath Res. 2014;8(1):14001.

Article  Google Scholar 

Pereira J, Porto-Figueira P, Cavaco C, Taunk K, Rapole S, Dhakne R, et al. Breath analysis as a potential and non-invasive frontier in disease diagnosis: an overview. Metabolites. 2015;5(1):3–55.

Article  Google Scholar 

Chen Z, Li M, Wang R, Sun W, Liu J, Li H, et al. Diagnosis of COVID-19 via acoustic analysis and artificial intelligence by monitoring breath sounds on smartphones. J Biomed Inform. 2022;130:104078.

Article  Google Scholar 

Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. Mol Pharm. 2016;13(7):2524–30.

Article  Google Scholar 

Saria S, Goldenberg A. Subtyping: What It is and Its Role in Precision Medicine. IEEE Intell Syst. 2015;30(4):70–5.

Article  Google Scholar 

Kim JH, Choi A, Kim MJ, Hyun H, Kim S, Chang H-J. Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database. Sci Rep. 2022;12(1):21797.

Article  Google Scholar 

McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94.

Article  Google Scholar 

Alkhouri N, Cikach F, Eng K, Moses J, Patel N, Yan C, et al. Analysis of breath volatile organic compounds as a noninvasive tool to diagnose nonalcoholic fatty liver disease in children. Eur J Gastroenterol Hepatol. 2014;26(1):82–7.

Article  Google Scholar 

Patnaik RK, Lin Y-C, Agarwal A, Ho M-C, Yeh JA. A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning. Sci Rep. 2022;12(1):2032.

Article  Google Scholar 

De Vincentis A, Vespasiani-Gentilucci U, Sabatini A, Antonelli-Incalzi RPA. Exhaled breath analysis in hepatology: State-of-the-art and perspectives. World J Gastroenterol. 2019;25(30):4043–50.

Article  Google Scholar 

Smoleńska Ż, Zdrojewski Z. Metabolomics and its potential in diagnosis, prognosis and treatment of rheumatic diseases. Reumatologia. 2015;53(3):152–6.

Article  Google Scholar 

Pradhan S, Gautam K, Pant V. Variation in Laboratory Reports: Causes other than Laboratory Error. JNMA J Nepal Med Assoc. 2022;60(246):222–4.

Article  Google Scholar 

Gutiérrez-Gómez L, Vohryzek J, Chiêm B, Baumann PS, Conus P, Cuenod K Do, et al. Stable biomarker identification for predicting schizophrenia in the human connectome. NeuroImage Clin. 2020;27:102316.

Ho CM, Huang YM, Hu RH, Wu YM, Ho MC, Lee PH. Revisiting donor risk over two decades of single-center experience: More attention on the impact of overweight. Asian J Surg. 2019;42(1):172–9.

Article  Google Scholar 

Chu S, Haffner GD, Letcher RJ. Simultaneous determination of tetrabromobisphenol A, tetrachlorobisphenol A, bisphenol A and other halogenated analogues in sediment and sludge by high performance liquid chromatography-electrospray tandem mass spectrometry. J Chromatogr A. 2005;1097(1):25–32.

Article  Google Scholar 

Busardò FP, Carlier J, Giorgetti R, Tagliabracci A, Pacifici R, Gottardi M, et al. Ultra-high-performance liquid chromatography-tandem mass spectrometry assay for quantifying fentanyl and 22 analogs and metabolites in whole blood, urine, and hair. Front Chem. 2019;7:1–13.

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. https://nlp.stanford.edu/IR-book/html/htmledition/frequency-based-feature-selection-1.html.

Kuhn M, Johnson K. Applied Predictive Modeling. Springer New York; 2013. https://books.google.com.tw/books?id=xYRDAAAAQBAJ.

Girden ER. ANOVA: Repeated Measures,. SAGE Publications; 1992. (ANOVA: Repeated Measures). https://books.google.com.tw/books?id=JomGKpjnfPcC.

Feng P-M, Ding H, Chen W, Lin H. Naïve Bayes classifier with feature selection to identify phage virion proteins. Comput Math Methods Med. 2013;2013:530696.

Article  MATH  Google Scholar 

VanderPlas J. Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media 2016. https://books.google.com.tw/books?id=6omNDQAAQBAJ.

Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu I-C, Oberije C, et al. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys. 2018;45(7):3449–59.

Article  Google Scholar 

Mantas J, Hasman A, Househ MS. The Importance of Health Informatics in Public Health during a Pandemic. IOS Press; 2020. (Studies in Health Technology and Informatics). https://books.google.com.tw/books?id=GpP-DwAAQBAJ.

Clark JS. Model Assessment and Selection. Models for Ecol Data. 2020;143–160.

Ma Y, He H. Imbalanced Learning: Foundations, Algorithms, and Applications. Wiley; 2013. https://books.google.com.tw/books?id=CVHx-Gp9jzUC.

Al-Khelaifi F, Diboun I, Donati F, Botrè F, Alsayrafi M, Georgakopoulos C, et al. A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines. Sport Med - Open. 2018;4(1).

Aghdassi E, Allard JP. Breath alkanes as a marker of oxidative stress in different clinical conditions. Free Radic Biol Med. 2000;28(6):880–6.

Article  Google Scholar 

Koureas M, Kirgou P, Amoutzias G, Hadjichristodoulou C, Gourgoulianis K, Tsakalof A. Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons. Metabolites. 2020;10(8).

Lu SC. Glutathione synthesis. Biochim Biophys Acta. 2013;1830(5):3143–53.

Article  Google Scholar 

Dludla P V, Nkambule BB, Mazibuko-Mbeje SE, Nyambuya TM, Marcheggiani F, Cirilli I, et al. N-Acetyl Cysteine Targets Hepatic Lipid Accumulation to Curb Oxidative Stress and Inflammation in NAFLD: A Comprehensive Analysis of the Literature. Antioxidants (Basel, Switzerland). 2020;9(12).

Baniasadi S, Eftekhari P, Tabarsi P, Fahimi F, Raoufy MR, Masjedi MR, et al. Protective effect of N-acetylcysteine on antituberculosis drug-induced hepatotoxicity. Eur J Gastroenterol Hepatol. 2010;22(10):1235–8.

Article  Google Scholar 

De Andrade KQ, Moura FA, dos Santos JM, de Araújo ORP, de Farias Santos JC, Goulart MOF. Oxidative Stress and Inflammation in Hepatic Diseases: Therapeutic Possibilities of N-Acetylcysteine. Int J Mol Sci. 2015;16(12):30269–308.

Article  Google Scholar 

Gao B, Jeong W-I, Tian Z. Liver: An organ with predominant innate immunity. Hepatology. 2008;47(2):729–36.

Article  Google Scholar 

Berg JM, Stryer L, Tymoczko JL, Gatto GJ. Biochemistry. Macmillan Learning; 2015. https://books.google.com.tw/books?id=5bjzrQEACAAJ.

Lehninger AL, Cox MM, Nelson DL, Lehninger Principles of Biochemistry. W. H. Freeman; 2005. https://books.google.com.tw/books?id=7chAN0UY0LYC.

Leonardi R, Jackowski S. Biosynthesis of Pantothenic Acid and Coenzyme A. EcoSal Plus. 2007;2(2).

Theodoulou FL, Sibon OCM, Jackowski S, Gout I. Coenzyme A and its derivatives: renaissance of a textbook classic. Biochem Soc Trans. 2014;42(4):1025–32.

Article  Google Scholar 

Wagner GR, Payne RM. Widespread and enzyme-independent Nε-acetylation and Nε-succinylation of proteins in the chemical conditions of the mitochondrial matrix. J Biol Chem. 2013;288(40):29036–45.

Article  Google Scholar 

Leonardi R, Zhang Y-M, Rock CO, Jackowski S. Coenzyme A: back in action. Prog Lipid Res. 2005;44(2–3):125–53.

Article  Google Scholar 

Buglak AA, Kapitonova MA, Vechtomova YL, Telegina TA. Insights into Molecular Structure of Pterins Suitable for Biomedical Applications. Int J Mol Sci. 2022;23(23).

Kośliński P, Pluskota R, Mądra-Gackowska K, Gackowski M, Markuszewski MJ, Kędziora-Kornatowska K, et al. Comparison of Pteridine Normalization Methods in Urine for Detection of Bladder Cancer. Diagnostics. 2020;10(9).

Zvarik M, Martinicky D, Hunakova L, Sikurova L. Differences in pteridine urinary levels in patients with malignant and benign ovarian tumors in comparison with healthy individuals. J Photochem Photobiol B Biol. 2015;153:191–7.

Article  Google Scholar 

Zeitler HJ, Andondonskaja-Renz B. Evaluation of pteridines in patients with different tumors. Cancer Detect Prev. 1987;10(1–2):71–9.

Google Scholar 

Zhao Z-H, Wang Z-X, Zhou D, Han Y, Ma F, Hu Z, et al. Sodium Butyrate Supplementation Inhibits Hepatic Steatosis by Stimulating Liver Kinase B1 and Insulin-Induced Gene. Cell Mol Gastroenterol Hepatol. 2021;12(3):857–71.

Article  Google Scholar 

Zheng Z, Wang B. The Gut-Liver Axis in Health and Disease: The Role of Gut Microbiota-Derived Signals in Liver Injury and Regeneration. Front Immunol. 2021;12:775526.

Article  Google Scholar 

Al Mardini H, Bartlett K, Record CO. Blood and brain concentrations of mercaptans in hepatic and methanethiol induced coma. Gut. 1984;25(3):284–90.

Article  Google Scholar 

Chen S, Zieve L, Mahadevan V. Mercaptans and dimethyl sulfide in the breath of patients with cirrhosis of the liver. Effect of feeding methionine. J Lab Clin Med. 1970;75(4):628–35.