{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:04:45Z","timestamp":1789535085602,"version":"build-2803163510"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72571040, 42071273"],"award-info":[{"award-number":["72571040, 42071273"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUT24YG147"],"award-info":[{"award-number":["DUT24YG147"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Carbapenem-resistant\n                      <jats:italic>Klebsiella pneumoniae<\/jats:italic>\n                      (CRKP) is a major global health threat. Predicting resistance from electronic medical records (EMRs) may support infection control and antimicrobial stewardship, but class imbalance can impair detection of resistant isolates. This study developed an interpretable machine-learning framework for predicting carbapenem-resistant versus carbapenem-susceptible\n                      <jats:italic>K. pneumoniae<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 5,794 records collected from 2014 to 2024, including 999 carbapenem-resistant and 4,795 carbapenem-susceptible isolates. The prediction anchor was culture-specimen collection (T0), and all predictors were restricted to information available at or before T0. Seven algorithms and 18 sampling strategies were evaluated using patient-grouped five-fold cross-validation with fold-specific preprocessing. The selected resampling pipeline was compared with Platt-calibrated XGBoost using sensitivity-targeted, F2-maximizing, and cost-sensitive thresholds. Feature-restriction analyses, multiplicity-adjusted comparisons, ICU subgroup analysis, reconstructed prevalence scenarios, calibration assessment, and same-center temporal validation were also performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>XGBoost and LightGBM showed comparable discrimination after multiplicity adjustment. ENN\u2013BLSMOTE\u2013XGBoost increased sensitivity and reduced the very-major-error rate (VME) at the default threshold, but slightly reduced ranking discrimination and increased false-positive alerts. Sensitivity-targeted threshold optimization of calibrated XGBoost produced an operating point close to that of the resampled model, indicating that much of the sensitivity gain could be achieved without changing the training distribution. After all duration-based predictors were excluded, ENN\u2013BLSMOTE\u2013XGBoost retained an ROC AUC of 0.940 and a PR AUC of 0.833. In the 2025 temporal cohort, the calibrated ENN\u2013BLSMOTE\u2013XGBoost model achieved an ROC AUC of 0.889, a PR AUC of 0.684, a sensitivity of 0.749, and a VME of 0.251; Platt scaling reduced its Brier score from 0.121 to 0.108. SHAP analysis identified ICU admission, vascular system disease, days of indwelling urinary catheterization, days of carbapenem use, and days of endotracheal intubation as the five highest-attribution predictors. A web calculator was developed to provide calibrated probability estimates referenced to the 17.24% resistance prevalence of the development cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The proposed model effectively addresses class imbalance in predicting carbapenem resistance in\n                      <jats:italic>K. pneumoniae<\/jats:italic>\n                      and may support early identification of patients at risk of harboring carbapenem-resistant isolates, thereby informing infection control and antimicrobial stewardship.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcimb.2026.1906306","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T04:33:40Z","timestamp":1789533220000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An explainable machine learning approach to predicting carbapenem resistance in Klebsiella pneumoniae with imbalanced data and resistance-ratio validation"],"prefix":"10.3389","volume":"16","author":[{"given":"Lili","family":"Geng","sequence":"first","affiliation":[{"name":"Dalian Medical University","place":["Dalian, China"]},{"name":"Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Systems Engineering, Dalian University of Technology","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangfei","family":"Yang","sequence":"additional","affiliation":[{"name":"Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","place":["Dalian, China"]},{"name":"Institute of Systems Engineering, Dalian University of Technology","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Renmin University of China","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingling","family":"Zhao","sequence":"additional","affiliation":[{"name":"Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"The Second Hospital, Dalian Medical University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105751","article-title":"A novel approach to antimicrobial resistance: machine learning predictions for carbapenem-resistant Klebsiella in intensive care units","volume":"195","author":"Alparslan","year":"2025","journal-title":"Int. J. Med. Inf."},{"key":"B2","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/s10462-023-10652-8","article-title":"Cost-sensitive learning for imbalanced medical data: a review","volume":"57","author":"Araf","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1186\/s12879-024-10282-7","article-title":"Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda","volume":"24","author":"Babirye","year":"2024","journal-title":"BMC Infect. Dis."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1093\/infdis\/jiae348","article-title":"Applications of machine learning on electronic health record data to combat antibiotic resistance","volume":"230","author":"Blechman","year":"2024","journal-title":"J. Infect. Dis."},{"key":"B5","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1038\/s41746-025-01696-x","article-title":"Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections","volume":"8","author":"Bonazzetti","year":"2025","journal-title":"NPJ Digital Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s00354-022-00194-y","article-title":"Variational autoencoder-based imbalanced COVID-19 detection using chest X-ray images","volume":"41","author":"Chatterjee","year":"2023","journal-title":"New Gener. Comput."},{"key":"B7","first-page":"785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016"},{"key":"B8","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s00134-017-5036-1","article-title":"Antimicrobial resistance and antibiotic stewardship programs in the ICU: insistence and persistence in the fight against resistance: a position statement from ESICM\/ESCMID\/WAAAR round table on multi-drug resistance","volume":"44","author":"De Waele","year":"2018","journal-title":"Intensive Care Med."},{"key":"B9","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1186\/s12866-025-04477-4","article-title":"Antimicrobial resistance patterns and molecular characterization of Acinetobacter baumannii isolates in non-COVID-19 patients admitted to intensive care units during the pandemic: a retrospective study at a tertiary hospital in Tehran, Iran","volume":"25","author":"Ghamari","year":"2025","journal-title":"BMC Microbiol."},{"key":"B10","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1038\/s41746-024-01426-9","article-title":"Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection","volume":"8","author":"Goldschmidt","year":"2025","journal-title":"NPJ Digital Med."},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2025.105618","article-title":"Machine learning detection of heteroresistance in Escherichia coli","volume":"113","author":"Guliaev","year":"2025","journal-title":"EBioMedicine"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2022.104932","article-title":"Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort","volume":"170","author":"Hassoun","year":"2023","journal-title":"Int. J. Med. Inf."},{"key":"B13","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1056\/NEJMp1916269","article-title":"Misdiagnosis, mistreatment, and harm-when medical care ignores social forces","volume":"382","author":"Holmes","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-26583-z","article-title":"SMENN-hybrid: an efficient technique combining the synthetic minority oversampling technique with ensemble learning for diabetes prediction","volume":"15","author":"Houssein","year":"2025","journal-title":"Sci. Rep"},{"key":"B15","doi-asserted-by":"publisher","first-page":"194","DOI":"10.12688\/wellcomeopenres.16153.1","article-title":"Predicting the future distribution of antibiotic resistance using time series forecasting and geospatial modelling","volume":"5","author":"Jeffrey","year":"2020","journal-title":"Wellcome Open Res."},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.2196\/58039","article-title":"Pioneering Klebsiella pneumoniae antibiotic resistance prediction with artificial intelligence-clinical decision support system-enhanced MALDI-TOF mass spectrometry: retrospective study","volume":"26","author":"Jian","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"B17","doi-asserted-by":"publisher","first-page":"e848","DOI":"10.1093\/cid\/ciaa1576","article-title":"Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records","volume":"72","author":"Lewin-Epstein","year":"2021","journal-title":"Clin. Infect. Dis."},{"key":"B18","doi-asserted-by":"publisher","first-page":"036011","DOI":"10.1088\/1741-2552\/ac6a7d","article-title":"Decoupling representation learning for imbalanced electroencephalography classification in rapid serial visual presentation task","volume":"19","author":"Li","year":"2022","journal-title":"J. Neural Eng."},{"key":"B19","doi-asserted-by":"publisher","first-page":"106005","DOI":"10.1016\/j.ijmedinf.2025.106005","article-title":"Explainable AI assisted vertebral refracture diagnosis after percutaneous vertebroplasty through effective feature engineering and stacked ensemble learning","volume":"203","author":"Li","year":"2025","journal-title":"Int. J. Med. Inf."},{"key":"B20","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/TMI.2023.3310716","article-title":"Hierarchical-instance contrastive learning for minority detection on imbalanced medical datasets","volume":"43","author":"Li","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.2217\/fmb.14.48","article-title":"Molecular pathogenesis of Klebsiella pneumoniae","volume":"9","author":"Li","year":"2014","journal-title":"Future Microbiol."},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101723","article-title":"A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset","volume":"101","author":"Liu","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"B23","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1038\/s41746-025-01875-w","article-title":"GRACE-ICU: a multimodal nomogram-based approach for illness severity assessment of older adults in the ICU","volume":"8","author":"Liu","year":"2025","journal-title":"NPJ Digital Med."},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2025.117670","article-title":"Marine natural products as potential anti-Pseudomonas aeruginosa agents: challenges and advances","volume":"2025","author":"Lv","year":"2025","journal-title":"Eur. J. Med. Chem."},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106362","article-title":"Practical use of a multicenter clinical research support system connected to electronic medical records","volume":"210","author":"Manabe","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"B26","doi-asserted-by":"publisher","first-page":"23752","DOI":"10.1038\/s41598-025-07460-1","article-title":"Explainable artificial intelligence for predicting medical students' performance in comprehensive assessments","volume":"15","author":"Mastour","year":"2025","journal-title":"Sci. Rep."},{"key":"B27","doi-asserted-by":"publisher","first-page":"e00063","DOI":"10.1128\/aac.00063-21","article-title":"A pragmatic machine learning model to predict carbapenem resistance","volume":"65","author":"McGuire","year":"2021","journal-title":"Antimicrob. Agents Chemother."},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2025.1629422","article-title":"Utilization of machine learning to predict antibiotic-resistant event outcomes in acute myeloid leukemia patients undergoing induction chemotherapy","volume":"15","author":"McMahon","year":"2025","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"B29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13054-025-05653-8","article-title":"Optimal antibiotic use in the intensive care unit","volume":"29","author":"Micek","year":"2025","journal-title":"Crit. Care"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16223782","article-title":"Novelty classification model use in reinforcement learning for cervical cancer","volume":"16","author":"Muksimova","year":"2024","journal-title":"Cancers"},{"key":"B31","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1016\/s1473-3099(13)70190-7","article-title":"Clinical epidemiology of the global expansion of Klebsiella pneumoniae carbapenemases","volume":"13","author":"Munoz-Price","year":"2013","journal-title":"Lancet Infect. Dis."},{"key":"B32","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1007\/s00521-018-3471-8","article-title":"Similarity-based attribute weighting methods via clustering algorithms in the classification of imbalanced medical datasets","volume":"30","author":"Polat","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"B33","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1186\/s12931-024-02753-x","article-title":"Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights","volume":"25","author":"Prithula","year":"2024","journal-title":"Respir. Res."},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2025.105809","article-title":"Tackling the small imbalanced horizontal dataset regressions by stability selection and SMOGN: a case study of ventilation-free days prediction in the pediatric intensive care unit and the importance of PRISM","volume":"196","author":"Rad","year":"2025","journal-title":"Int. J. Med. Inf."},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1093\/eurpub\/ckaf161.459","article-title":"Predicting antimicrobial resistance at the emergency department for urinary tract infection","volume":"35","author":"Reiner-Benaim","year":"2025","journal-title":"Eur. J. Public Health"},{"key":"B36","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10462-024-10884-2","article-title":"Handling imbalanced medical datasets: review of a decade of research","volume":"57","author":"Salmi","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.3390\/app12073673","article-title":"Exploring early prediction of chronic kidney disease using machine learning algorithms for small and imbalanced datasets","volume":"12","author":"Silveira","year":"2022","journal-title":"Appl. Sci."},{"key":"B38","doi-asserted-by":"publisher","first-page":"106093","DOI":"10.1016\/j.ijmedinf.2025.106093","article-title":"Explainable machine learning models for early Alzheimer's disease detection using multimodal clinical data","volume":"204","author":"Soladoye","year":"2025","journal-title":"Int. J. Med. Inf."},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1093\/ofid\/ofy091","article-title":"The rapid prediction of carbapenem resistance in patients with Klebsiella pneumoniae bacteremia using electronic medical record data","volume":"5","author":"Sullivan","year":"2018","journal-title":"Open Forum Infect. Dis."},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1016\/j.idnow.2025.105052","article-title":"Risk factors for clinical treatment failure regarding carbapenem-resistant Enterobacterales in the Southwestern United States","volume":"55","author":"Ting","year":"2025","journal-title":"Infect. Dis. Now"},{"key":"B41","doi-asserted-by":"publisher","first-page":"5535","DOI":"10.2147\/idr.s415885","article-title":"Predicting antibiotic resistance in ICU patients by applying machine learning in Vietnam","volume":"16","author":"Tran Quoc","year":"2023","journal-title":"Infection Drug Resistance"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanmic.2024.100990","article-title":"Global epidemiological trend of Klebsiella pneumoniae ST340: emergence of subclade KL15 co-producing KPC-2 and NDM-7 in the Americas","volume":"6","author":"Vasquez-Ponce","year":"2025","journal-title":"Lancet Microbe"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2025.105905","article-title":"Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record data strategies","volume":"200","author":"Velez","year":"2025","journal-title":"Int. J. Med. Inf."},{"key":"B44","doi-asserted-by":"publisher","first-page":"24039","DOI":"10.1038\/s41598-021-03430-5","article-title":"Research on expansion and classification of imbalanced data based on SMOTE algorithm","volume":"11","author":"Wang","year":"2021","journal-title":"Sci. Rep."},{"key":"B45","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.2147\/rmhp.s310295","article-title":"Improving risk identification of adverse outcomes in chronic heart failure using SMOTE+ENN and machine learning","volume":"14","author":"Wang","year":"2021","journal-title":"Risk Manage. Healthcare Policy"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0327569","article-title":"Reducing bias in coronary heart disease prediction using SMOTE-ENN and PCA","volume":"20","author":"Wei","year":"2025","journal-title":"PLoS One"},{"key":"B47","volume-title":"WHO List of Critically Important Antimicrobials for Human Medicine","year":"2017"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105609","article-title":"Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery","volume":"192","author":"Wu","year":"2024","journal-title":"Int. J. Med. Inf."},{"key":"B49","doi-asserted-by":"publisher","first-page":"501","DOI":"10.3892\/br.2016.752","article-title":"A model for predicting nosocomial carbapenem-resistant Klebsiella pneumoniae infection","volume":"5","author":"Yang","year":"2016","journal-title":"Biomed. Rep."},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102971","article-title":"MAPRS: an intelligent approach for post-prescription review based on multi-label learning","volume":"157","author":"Yang","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.3390\/metabo14050258","article-title":"Prediction of myocardial infarction using a combined generative adversarial network model and feature-enhanced loss function","volume":"14","author":"Yu","year":"2024","journal-title":"Metabolites"},{"key":"B52","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1186\/s12866-023-03147-7","article-title":"Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in Mycobacterium tuberculosis","volume":"23","author":"Yurtseven","year":"2023","journal-title":"BMC Microbiol."},{"key":"B53","doi-asserted-by":"publisher","first-page":"105875","DOI":"10.1016\/j.ijmedinf.2025.105875","article-title":"Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: a systematic review and meta-analysis","volume":"198","author":"Zhang","year":"2025","journal-title":"Int. J. Med. Inf."},{"key":"B54","doi-asserted-by":"publisher","first-page":"173","DOI":"10.5582\/bst.2025.01013","article-title":"Investigating perioperative pressure injuries and factors influencing them with imbalanced samples using a synthetic minority over-sampling technique","volume":"19","author":"Zhou","year":"2025","journal-title":"BioScience Trends"},{"key":"B55","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s13040-024-00384-y","article-title":"Processing imbalanced medical data at the data level with assisted-reproduction data as an example","volume":"17","author":"Zhu","year":"2024","journal-title":"Biodata Min."},{"key":"B56","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1186\/s12879-025-11900-8","article-title":"Evaluating antimicrobial resistance and clinical outcomes in surgical ICU using a machine learning perspective: a retrospective observational study","volume":"25","author":"Ziaian","year":"2025","journal-title":"BMC Infect. Dis."}],"container-title":["Frontiers in Cellular and Infection Microbiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcimb.2026.1906306\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T04:33:44Z","timestamp":1789533224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcimb.2026.1906306\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":56,"alternative-id":["10.3389\/fcimb.2026.1906306"],"URL":"https:\/\/doi.org\/10.3389\/fcimb.2026.1906306","relation":{},"ISSN":["2235-2988"],"issn-type":[{"value":"2235-2988","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]},"article-number":"1906306"}}