{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:31:29Z","timestamp":1790785889602,"version":"4.1.0"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T00:00:00Z","timestamp":1744934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo (ANID)","award":["FOVI230003"],"award-info":[{"award-number":["FOVI230003"]}]},{"name":"Laboratory of Genetic and Metabolic Disease, INTA, University of Chile","award":["FOVI230003"],"award-info":[{"award-number":["FOVI230003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to detect liver cancer but has limitations in early-stage HCC detection. This study aimed to implement a machine-learning (ML) approach to identify the most relevant laboratory variables to predict AFP alteration using constrained multidimensional data from Chilean and Italian HT-1 cohorts. A longitudinal retrospective study analyzed 219 records from 35 HT-1 patients, including 8 with HCC and 5 diagnosed through newborn screening. The dataset contained biochemical and demographic variables that were analyzed using the eXtreme Gradient Boosting algorithm, which was trained to predict abnormal AFP levels (&gt;5 ng\/mL). Four key variables emerged as significant predictors: alanine transaminase (ALT), alkaline phosphatase, age at diagnosis, and current age. ALT emerged as the most promising indicator of AFP alteration, potentially preceding AFP level changes and improving HCC detection specificity at a cut-off value of 29 UI\/L (AUROC = 0.73). Despite limited data from this rare disease, the ML approach successfully analyzed follow-up biomarkers, identifying ALT as an early predictor of AFP elevation and a potential biomarker for HCC progression.<\/jats:p>","DOI":"10.3390\/ijms26083839","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T05:11:16Z","timestamp":1744953076000},"page":"3839","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8829-7336","authenticated-orcid":false,"given":"Karen","family":"Fuenzalida","sequence":"first","affiliation":[{"name":"Laboratory of Genetic and Metabolic Diseases, Institute of Nutrition and Food Technology INTA, University of Chile, Av. El Libano 5524, Santiago 7830490, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5697-1581","authenticated-orcid":false,"given":"Mar\u00eda Jes\u00fas","family":"Leal-Witt","sequence":"additional","affiliation":[{"name":"Laboratory of Genetic and Metabolic Diseases, Institute of Nutrition and Food Technology INTA, University of Chile, Av. El Libano 5524, Santiago 7830490, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandro","family":"Acevedo","sequence":"additional","affiliation":[{"name":"Laboratory of Genetic and Metabolic Diseases, Institute of Nutrition and Food Technology INTA, University of Chile, Av. El Libano 5524, Santiago 7830490, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Laboratory of Genetic and Metabolic Diseases, Institute of Nutrition and Food Technology INTA, University of Chile, Av. El Libano 5524, Santiago 7830490, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4418-8952","authenticated-orcid":false,"given":"Camila","family":"Gudenschwager","sequence":"additional","affiliation":[{"name":"Laboratory of Genetic and Metabolic Diseases, Institute of Nutrition and Food Technology INTA, University of Chile, Av. El Libano 5524, Santiago 7830490, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolina","family":"Arias","sequence":"additional","affiliation":[{"name":"Laboratory of Genetic and Metabolic Diseases, Institute of Nutrition and Food Technology INTA, University of Chile, Av. El Libano 5524, Santiago 7830490, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Francisco","family":"Cabello","sequence":"additional","affiliation":[{"name":"Laboratory of Genetic and Metabolic Diseases, Institute of Nutrition and Food Technology INTA, University of Chile, Av. El Libano 5524, Santiago 7830490, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giancarlo","family":"La Marca","sequence":"additional","affiliation":[{"name":"Meyer Children\u2019s Hospital IRCCS, Viale Gaetano Pieraccini, 24, 50139 Florence, Italy"},{"name":"Department of Experimental and Clinical Biomedical Sciences, University of Florence, Largo Brambilla, 3, 50134 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristiano","family":"Rizzo","sequence":"additional","affiliation":[{"name":"Division of Metabolic Diseases and Hepatology, Ospedale Pediatrico Bambino Ges\u00f9 IRCCS, 00165 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4426-0395","authenticated-orcid":false,"given":"Andrea","family":"Pietrobattista","sequence":"additional","affiliation":[{"name":"Division of Metabolic Diseases and Hepatology, Ospedale Pediatrico Bambino Ges\u00f9 IRCCS, 00165 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Spada","sequence":"additional","affiliation":[{"name":"Division of Abdominal Transplantation, Hepato-Bilio-Pancreatic Surgery Unit, Ospedale Pediatrico Bambino Ges\u00f9 IRCCS, 00165 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0007-3379","authenticated-orcid":false,"given":"Carlo","family":"Dionisi-Vici","sequence":"additional","affiliation":[{"name":"Division of Metabolic Diseases and Hepatology, Ospedale Pediatrico Bambino Ges\u00f9 IRCCS, 00165 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8858-8499","authenticated-orcid":false,"given":"Ver\u00f3nica","family":"Cornejo","sequence":"additional","affiliation":[{"name":"Laboratory of Genetic and Metabolic Diseases, Institute of Nutrition and Food Technology INTA, University of Chile, Av. El Libano 5524, Santiago 7830490, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1002\/hep.1840200513","article-title":"Hereditary tyrosinemia type I: A new clinical classification with difference in prognosis on dietary treatment","volume":"20","author":"Thomasse","year":"1994","journal-title":"Hepatol. Baltim. Md."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1750-1172-8-8","article-title":"Recommendations for the management of tyrosinaemia type 1","volume":"8","author":"Leonard","year":"2013","journal-title":"Orphanet J. Rare Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/0014-5793(93)80013-K","article-title":"SC-0051, a 2-benzoyl-cyclohexane-1,3-dione bleaching herbicide, is a potent inhibitor of the enzyme p-hydroxyphenylpyruvate dioxygenase","volume":"318","author":"Schulz","year":"1993","journal-title":"FEBS Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1038\/gim.2017.101","article-title":"Diagnosis and treatment of tyrosinemia type I: A US and Canadian consensus group review and recommendations","volume":"19","author":"Chinsky","year":"2017","journal-title":"Genet. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/0140-6736(92)92685-9","article-title":"Treatment of hereditary tyrosinaemia type I by inhibition of 4-hydroxyphenylpyruvate dioxygenase","volume":"340","author":"Lindstedt","year":"1992","journal-title":"Lancet"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1023\/A:1005410820201","article-title":"Tyrosinaemia type I and NTBC (2-(2-nitro-4-trifluoromethylbenzoyl)-1,3-cyclohexanedione)","volume":"21","author":"Holme","year":"1998","journal-title":"J. Inherit. Metab. Dis."},{"key":"ref_7","first-page":"100933","article-title":"Initial presentation, management and follow-up data of 33 treated patients with hereditary tyrosinemia type 1 in the absence of newborn screening","volume":"33","author":"Hajji","year":"2022","journal-title":"Mol. Genet. Metab. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s40272-019-00364-4","article-title":"Long-Term Outcomes and Practical Considerations in the Pharmacological Management of Tyrosinemia Type 1","volume":"21","author":"Rodenburg","year":"2019","journal-title":"Pediatr. Drugs"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ymgme.2012.05.022","article-title":"Effect of nitisinone (NTBC) treatment on the clinical course of hepatorenal tyrosinemia in Qu\u00e9bec","volume":"107","author":"Larochelle","year":"2012","journal-title":"Mol. Genet. Metab."},{"key":"ref_10","first-page":"281","article-title":"Hereditary tyrosinemia type 1 in Turkey: Twenty-year single-center experience","volume":"57","author":"Zeybek","year":"2015","journal-title":"Pediatr. Int. Off. J. Jpn. Pediatr. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/S2213-8587(21)00092-9","article-title":"Long-term safety and outcomes in hereditary tyrosinaemia type 1 with nitisinone treatment: A 15-year non-interventional, multicentre study","volume":"9","author":"Spiekerkoetter","year":"2021","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.ymgme.2006.07.009","article-title":"Tyrosinemia type I treated by NTBC: How does AFP predict liver cancer?","volume":"89","author":"Koelink","year":"2006","journal-title":"Mol. Genet. Metab."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e749","DOI":"10.1542\/peds.2014-1913","article-title":"Hepatocellular carcinoma in tyrosinemia type 1 without clear increase of AFP","volume":"135","author":"Gouw","year":"2015","journal-title":"Pediatrics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1002\/hep.30233","article-title":"Longitudinal Assessment of Three Serum Biomarkers to Detect Very Early-Stage Hepatocellular Carcinoma","volume":"69","author":"Choi","year":"2019","journal-title":"Hepatology"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Debes, J.D., Romagnoli, P.A., Prieto, J., Arrese, M., Mattos, A.Z., and Boonstra, A. (2021). Serum Biomarkers for the Prediction of Hepatocellular Carcinoma. Cancers, 13.","DOI":"10.3390\/cancers13071681"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e73","DOI":"10.1097\/MPG.0000000000002698","article-title":"Late Development of Hepatocellular Carcinoma in Tyrosinemia Type 1 Despite Nitisinone (NTBC) Treatment","volume":"71","author":"Almuqbil","year":"2020","journal-title":"J. Pediatr. Gastroenterol. Nutr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e14334","DOI":"10.1111\/petr.14334","article-title":"Hepatocellular carcinoma requiring liver transplantation in hereditary tyrosinemia type 1 despite nitisinone therapy and \u03b11-fetoprotein normalization","volume":"26","author":"Bhushan","year":"2022","journal-title":"Pediatr. Transpl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e13498","DOI":"10.1111\/petr.13498","article-title":"Live donor liver transplantation for type 1 tyrosinemia: An analysis of 15 patients","volume":"23","author":"Karaca","year":"2019","journal-title":"Pediatr. Transpl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.jbi.2010.12.001","article-title":"Data mining methods for classification of Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data","volume":"44","author":"Wouters","year":"2011","journal-title":"J. Biomed. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Peng, G., Tang, Y., Cowan, T.M., Enns, G.M., Zhao, H., and Scharfe, C. (2020). Reducing False-Positive Results in Newborn Screening Using Machine Learning. Int. J. Neonatal Screen., 6.","DOI":"10.3390\/ijns6010016"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Gu, J., Genchev, G.Z., Cai, X., Wang, Y., Guo, J., Tian, G., and Lu, H. (2020). Improving the Diagnosis of Phenylketonuria by Using a Machine Learning-Based Screening Model of Neonatal MRM Data. Front. Mol. Biosci., 7.","DOI":"10.3389\/fmolb.2020.00115"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1093\/jb\/mvy085","article-title":"Application of machine learning algorithms for the differential diagnosis of peroxisomal disorders","volume":"165","author":"Subhashini","year":"2019","journal-title":"J. Biochem."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2985","DOI":"10.1093\/bioinformatics\/bth343","article-title":"Supervised machine learning techniques for the classification of metabolic disorders in newborns","volume":"20","author":"Baumgartner","year":"2004","journal-title":"Bioinforma"},{"key":"ref_24","first-page":"1534","article-title":"Severity modeling of propionic acidemia using clinical and laboratory biomarkers","volume":"23","author":"Shchelochkov","year":"2021","journal-title":"Genet. Med. Off. J. Am. Coll. Med. Genet."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Leal-Witt, M.J., Rojas-Agurto, E., Mu\u00f1oz-Gonz\u00e1lez, M., Pe\u00f1aloza, F., Arias, C., Fuenzalida, K., Bunout, D., Cornejo, V., and Acevedo, A. (2023). Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots. Metabolites, 13.","DOI":"10.3390\/metabo13060677"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e4759","DOI":"10.1136\/bmj.e4759","article-title":"Cardiovascular disease risk in healthy children and its association with body mass index: Systematic review and meta-analysis","volume":"345","author":"Friedemann","year":"2012","journal-title":"BMJ"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1016\/j.metabol.2010.06.009","article-title":"Overweight, obesity, and their associations with insulin resistance and \u03b2-cell function among Chinese: A cross-sectional study in China","volume":"59","author":"Chen","year":"2010","journal-title":"Metabolism"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1002\/cncr.28706","article-title":"Determinants of alpha-fetoprotein levels in patients with hepatocellular carcinoma: Implications for its clinical use","volume":"120","author":"Giannini","year":"2014","journal-title":"Cancer"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1111\/hpb.12146","article-title":"Performance of serum \u03b1-fetoprotein levels in the diagnosis of hepatocellular carcinoma in patients with a hepatic mass","volume":"16","author":"Chan","year":"2014","journal-title":"HPB"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1002\/hep.1840090119","article-title":"alpha-Fetoprotein monitoring in Chinese patients with chronic hepatitis B virus infection: Role in the early detection of hepatocellular carcinoma","volume":"9","author":"Lok","year":"1989","journal-title":"Hepatology"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fuenzalida, K., Leal-Witt, M.J., Guerrero, P., Hamilton, V., Salazar, M.F., Pe\u00f1aloza, F., Arias, C., and Cornejo, V. (2021). NTBC Treatment Monitoring in Chilean Patients with Tyrosinemia Type 1 and Its Association with Biochemical Parameters and Liver Biomarkers. J. Clin. Med., 10.","DOI":"10.3390\/jcm10245832"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e17303","DOI":"10.1097\/MD.0000000000017303","article-title":"Evolution of tyrosinemia type 1 disease in patients treated with nitisinone in Spain","volume":"98","author":"Couce","year":"2019","journal-title":"Medicine"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1186\/s13023-014-0107-7","article-title":"Cross-sectional study of 168 patients with hepatorenal tyrosinaemia and implications for clinical practice","volume":"9","author":"Mayorandan","year":"2014","journal-title":"Orphanet J. Rare Dis."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/nmeth.4642","article-title":"Statistics versus machine learning","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat. Methods."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yan, P., Liu, Y., Jia, Y., and Zhao, T. (2024). Deep Learning and Machine Learning Applications in Biomedicine. Appl. Sci., 14.","DOI":"10.3390\/app14010307"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Stahlschmidt, S.R., Ulfenborg, B., and Synnergren, J. (2022). Multimodal deep learning for biomedical data fusion: A review. Brief Bioinform., 23.","DOI":"10.1093\/bib\/bbab569"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1515\/cclm-2016-0318","article-title":"Pediatric reference intervals for alkaline phosphatase","volume":"55","author":"Zierk","year":"2017","journal-title":"Clin. Chem. Lab. Med. CCLM"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7815","DOI":"10.1007\/s11033-023-08666-0","article-title":"Biomarkers of liver diseases","volume":"50","author":"Tamber","year":"2023","journal-title":"Mol. Biol. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/ajg.2016.517","article-title":"ACG Clinical Guideline: Evaluation of Abnormal Liver Chemistries","volume":"112","author":"Kwo","year":"2017","journal-title":"Am. J. Gastroenterol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1373\/clinchem.2011.177741","article-title":"Closing the gaps in pediatric laboratory reference intervals: A CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children","volume":"58","author":"Colantonio","year":"2012","journal-title":"Clin. Chem."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1097\/MPG.0000000000001482","article-title":"NASPGHAN Clinical Practice Guideline for the Diagnosis and Treatment of Nonalcoholic Fatty Liver Disease in Children","volume":"64","author":"Vos","year":"2017","journal-title":"J. Pediatr. Gastroenterol. Nutr."},{"key":"ref_42","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python. Mach Learn PYTHON","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","first-page":"1","article-title":"mice: Multivariate Imputation by Chained Equations in R","volume":"45","year":"2011","journal-title":"J. Stat. Softw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"UMAP: Uniform Manifold Approximation and Projection","volume":"3","author":"McInnes","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA. Available online: https:\/\/dl.acm.org\/doi\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Feng, T., Wang, S., Dong, R., Yang, J., Su, J., and Wang, B. (2020). A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations. Front. Genet., 11.","DOI":"10.3389\/fgene.2020.585029"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Panagiotopoulos, K., Korfiati, A., Theofilatos, K., Hurwitz, P., Deriu, M.A., and Mavroudi, S. (2023). MEvA-X: A hybrid multiobjective evolutionary tool using an XGBoost classifier for biomarkers discovery on biomedical datasets. Bioinformatics, 39.","DOI":"10.1093\/bioinformatics\/btad384"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_49","unstructured":"Lundberg, S.M., and Lee, S.I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), Long Beach, CA, USA."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/8\/3839\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:17:07Z","timestamp":1760030227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/8\/3839"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,18]]},"references-count":49,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["ijms26083839"],"URL":"https:\/\/doi.org\/10.3390\/ijms26083839","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,18]]}}}