Agrawal M, Jess T. Implications of the changing epidemiology of inflammatory bowel disease in a changing world. United Eur Gastroenterol J [Internet]. 2022 [cited 2025 Jun 13];10:1113–20. Available from: https://pubmed.ncbi.nlm.nih.gov/36251359/

Pigneur B, Seksik P, Viola S, Viala J, Beaugerie L, Girardet JP et al. Natural history of Crohn’s disease: Comparison between childhood- and adult-onset disease. Inflamm Bowel Dis [Internet]. 2010 [cited 2025 Jun 13];16:953–61. Available from: https://pubmed.ncbi.nlm.nih.gov/19834970/

Kuenzig ME, Fung SG, Marderfeld L, Mak JWY, Kaplan GG, Ng SC et al. Twenty-first Century Trends in the Global Epidemiology of Pediatric-Onset Inflammatory Bowel Disease: Systematic Review. Gastroenterology [Internet]. 2022 [cited 2025 Jun 13];162:1147–1159.e4. Available from: https://pubmed.ncbi.nlm.nih.gov/34995526/

Haberman Y, Tickle TL, Dexheimer PJ, Kim MO, Tang D, Karns R et al. Pediatric Crohn disease patients exhibit specific ileal transcriptome and microbiome signature. J Clin Invest [Internet]. 2014 [cited 2025 Jun 13];124:3617–33. Available from: https://pubmed.ncbi.nlm.nih.gov/25003194/

Wang F, Kaplan JL, Gold BD, Bhasin MK, Ward NL, Kellermayer R et al. Detecting Microbial Dysbiosis Associated with Pediatric Crohn Disease Despite the High Variability of the Gut Microbiota. Cell Rep [Internet]. 2016 [cited 2025 Jun 13];14:945–55. Available from: https://pubmed.ncbi.nlm.nih.gov/26804920/

Wu P, Wu B, Zhuang Z, Liu J, Hong L, Ma B et al. Intestinal mucosal and fecal microbiota profiles in Crohn’s disease in Chinese children. Med Microecol [Internet]. 2023 [cited 2025 Jun 13];15:100071. Available from: https://www.sciencedirect.com/science/article/pii/S2590097822000210

Chauhan N, Khan HH, Kumar S, Lyons H. Clinical Variables as Predictors of First Relapse in Pediatric Crohn’s Disease. Cureus [Internet]. 2019 [cited 2025 Jun 13];11. Available from: https://pubmed.ncbi.nlm.nih.gov/31467814/

Kato J, Yoshida T, Hiraoka S. Prediction of treatment outcome and relapse in inflammatory bowel disease. Expert Rev Clin Immunol [Internet]. 2019 [cited 2025 Jun 13];15:667–77. Available from: https://pubmed.ncbi.nlm.nih.gov/30873890/

Turner D, Otley AR, Mack D, Hyams J, de Bruijne J, Uusoue K et al. Development, Validation, and Evaluation of a Pediatric Ulcerative Colitis Activity Index: A Prospective Multicenter Study. Gastroenterology [Internet]. 2007 [cited 2025 Sep 30];133:423–32. Available from: https://pubmed.ncbi.nlm.nih.gov/17681163/

Turner D, Griffiths AM, Walters TD, Seah T, Markowitz J, Pfefferkorn M et al. Mathematical weighting of the pediatric Crohn’s disease activity index (PCDAI) and comparison with its other short versions. Inflamm Bowel Dis [Internet]. 2012 [cited 2025 Sep 30];18:55–62. Available from: https://pubmed.ncbi.nlm.nih.gov/21351206/

Koninckx CR, Donat E, Benninga MA, Broekaert IJ, Gottrand F, Kolho KL et al. The Use of Fecal Calprotectin Testing in Paediatric Disorders: A Position Paper of the European Society for Paediatric Gastroenterology and Nutrition Gastroenterology Committee. J Pediatr Gastroenterol Nutr [Internet]. 2021 [cited 2025 Sep 30];72:617–40. Available from: https://pubmed.ncbi.nlm.nih.gov/33716293/

Andrews S. (2010) FastQC A Quality Control Tool for High Throughput Sequence Data. - References - Scientific Research Publishing [Internet]. [cited 2025 Jun 13]. Available from: https://www.scirp.org/reference/referencespapers?referenceid=2781642

Wingett SW, Andrews S. FastQ Screen: A tool for multi-genome mapping and quality control. F1000Research [Internet]. 2018 [cited 2024 Jun 21];7:1338.

Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics [Internet]. 2018 [cited 2024 Jun 13];34:i884–90. Available from: https://pubmed.ncbi.nlm.nih.gov/30423086/

Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods [Internet]. 2012 [cited 2025 Jun 13];9:357–9. Available from: https://www.nature.com/articles/nmeth.1923

Pedregosa FABIANPEDREGOSAF, Michel V, Grisel OLIVIERGRISELO, Blondel M, Prettenhofer P, Weiss R et al. Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. J Mach Learn Res [Internet]. 2011 [cited 2025 Jun 13];12:2825–30. Available from: http://scikit-learn.sourceforge.net

Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res [Internet]. 2013 [cited 2020 Oct 6];41. Available from: https://pubmed.ncbi.nlm.nih.gov/23193283/

Davison AC, Hinkley DV. Bootstrap Methods and their Application. Bootstrap Methods their Appl [Internet]. 1997 [cited 2025 Jun 13]; Available from: https://www.cambridge.org/core/books/bootstrap-methods-and-their-application/ED2FD043579F27952363566DC09CBD6A

Canty A, Ripley B. (2022) Boot Bootstrap R (S-Plus) Functions. R Package Version 1.3–28.1. - References - Scientific Research Publishing [Internet]. [cited 2025 Jun 13]. Available from: https://www.scirp.org/reference/referencespapers?referenceid=3500252

Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol [Internet]. 2010 [cited 2025 Jun 13];5:1315–6. Available from: https://pubmed.ncbi.nlm.nih.gov/20736804/

R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna: R Foundation for Statistical Computing. 2022. Available from: https://www.r-project.org/

Rosen MJ, Dhawan A, Saeed SA. Inflammatory bowel disease in children and adolescents. JAMA Pediatr [Internet]. 2015 [cited 2025 Jun 14];169:1053–60. Available from: https://pubmed.ncbi.nlm.nih.gov/26414706/

Cellier C, Sahmoud T, Froguel E, Adenis A, Belaiche J, Bretagne JF et al. Correlations between clinical activity, endoscopic severity, and biological parameters in colonic or ileocolonic Crohn’s disease. A prospective multicentre study of 121 cases. Gut [Internet]. 1994 [cited 2025 Jun 14];35:231–5. Available from: https://pubmed.ncbi.nlm.nih.gov/7508411/

Kittanakom S, Shajib MS, Garvie K, Turner J, Brooks D, Odeh S et al. Comparison of fecal calprotectin methods for predicting relapse of pediatric inflammatory bowel disease. Can J Gastroenterol Hepatol [Internet]. 2017 [cited 2025 Jun 14];2017. Available from: https://pubmed.ncbi.nlm.nih.gov/28491862/

Shaoul R, Day AS. An Overview of Tools to Score Severity in Pediatric Inflammatory Bowel Disease. Front Pediatr [Internet]. 2021 [cited 2025 Jun 14];9. Available from: https://pubmed.ncbi.nlm.nih.gov/33912519/

Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe [Internet]. 2014 [cited 2025 Jun 14];15:382–92. Available from: https://pubmed.ncbi.nlm.nih.gov/24629344/

Douglas GM, Hansen R, Jones CMA, Dunn KA, Comeau AM, Bielawski JP et al. Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease. Microbiome [Internet]. 2018 [cited 2025 Jun 14];6. Available from: https://pubmed.ncbi.nlm.nih.gov/29335008/

Alipour M, Zaidi D, Valcheva R, Jovel J, Martínez I, Sergi C et al. Mucosal barrier depletion and loss of bacterial diversity are primary abnormalities in paediatric ulcerative colitis. J Crohn’s Colitis [Internet]. 2016 [cited 2025 Jun 14];10:462–71. Available from: https://pubmed.ncbi.nlm.nih.gov/26660940/

Shah R, Cope JL, Nagy-Szakal D, Dowd S, Versalovic J, Hollister EB et al. Composition and function of the pediatric colonic mucosal microbiome in untreated patients with ulcerative colitis. Gut Microbes [Internet]. 2016 [cited 2025 Jun 14];7:384–96. Available from: https://pubmed.ncbi.nlm.nih.gov/27217061/

Kowalska-Duplaga K, Gosiewski T, Kapusta P, Sroka-Oleksiak A, Wędrychowicz A, Pieczarkowski S et al. Differences in the intestinal microbiome of healthy children and patients with newly diagnosed Crohn’s disease. Sci Rep [Internet]. 2019 [cited 2025 Jun 14];9. Available from: https://pubmed.ncbi.nlm.nih.gov/31827191/

Golińska E, Tomusiak A, Gosiewski T, Wiȩcek G, Machul A, Mikołajczyk D et al. Virulence factors of Enterococcus strains isolated from patients with inflammatory bowel disease. World J Gastroenterol [Internet]. 2013 [cited 2025 Jun 14];19:3562–72. Available from: https://pubmed.ncbi.nlm.nih.gov/23801857/

De Meij TGJ, De Groot EFJ, Peeters CFW, De Boer NKH, Kneepkens CMF, Eck A et al. Variability of core microbiota in newly diagnosed treatment-naïve paediatric inflammatory bowel disease patients. PLoS One [Internet]. 2018 [cited 2025 Jun 14];13. Available from: https://pubmed.ncbi.nlm.nih.gov/30102706/

Wang X, Xiao Y, Xu X, Guo L, Yu Y, Li N et al. Characteristics of Fecal Microbiota and Machine Learning Strategy for Fecal Invasive Biomarkers in Pediatric Inflammatory Bowel Disease. Front Cell Infect Microbiol [Internet]. 2021 [cited 2025 Jun 14];11. Available from: https://pubmed.ncbi.nlm.nih.gov/34950604/

Öhman L, Lasson A, Strömbeck A, Isaksson S, Hesselmar M, Simrén M et al. Fecal microbiota dynamics during disease activity and remission in newly diagnosed and established ulcerative colitis. Sci Rep [Internet]. 2021 [cited 2025 Sep 30];11. Available from: https://pubmed.ncbi.nlm.nih.gov/33883600/

Chen L, Lu Z, Kang D, Feng Z, Li G, Sun M et al. Distinct alterations of fecal microbiota refer to the efficacy of adalimumab in Crohn’s disease. Front Pharmacol [Internet]. 2022 [cited 2025 Jun 14];13. Available from: https://pubmed.ncbi.nlm.nih.gov/36034848/

Vestergaard MV, Allin KH, Eriksen C, Zakerska-Banaszak O, Arasaradnam RP, Alam MT et al. Gut microbiota signatures in inflammatory bowel disease. United Eur Gastroenterol J [Internet]. 2024 [cited 2025 Jun 14];12:22–33. Available from: https://pubmed.ncbi.nlm.nih.gov/38041519/

Mancabelli L, Milani C, Lugli GA, Turroni F, Cocconi D, van Sinderen D et al. Identification of universal gut microbial biomarkers of common human intestinal diseases by meta-analysis. FEMS Microbiol Ecol [Internet]. 2017 [cited 2025 Jun 14];93. Available from: https://pubmed.ncbi.nlm.nih.gov/29126267/

Sabater C, Calvete-Torre I, Ruiz L, Margolles A. Arabinoxylan and Pectin Metabolism in Crohn’s Disease Microbiota: An In Silico Study. Int J Mol Sci [Internet]. 2022 [cited 2025 Jun 14];23. Available from: https://pubmed.ncbi.nlm.nih.gov/35806099/

Wang F, Kaplan JL, Gold BD, Bhasin MK, Ward NL, Kellermayer R et al. Detecting Microbial Dysbiosis Associated with Pediatric Crohn Disease Despite the High Variability of the Gut Microbiota. Cell Rep [Internet]. 2016 [cited 2025 Jun 14];14:945–55. Available from: https://pubmed.ncbi.nlm.nih.gov/26804920/