{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:34:48Z","timestamp":1788413688579,"version":"build-2803163510"},"reference-count":60,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&D Program of Jining","award":["2023YXNS151"],"award-info":[{"award-number":["2023YXNS151"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Candidemia often poses a diagnostic challenge due to the lack of specific clinical features, and delayed antifungal therapy can significantly increase mortality rates, particularly in the intensive care unit (ICU). This study aims to develop a machine learning predictive model for early candidemia diagnosis in ICU patients, leveraging their clinical information and findings. We conducted this study with a cohort of 334 patients admitted to the ICU unit at Ji Ning NO.1 people\u2019s hospital in China from Jan. 2015 to Dec. 2022. To ensure the model\u2019s reliability, we validated this model with an external group consisting of 77 patients from other sources. The candidemia to bacteremia ratio is 1:1. We collected relevant clinical procedures and eighteen key examinations or tests features to support the recursive feature elimination (RFE) algorithm. These features included total bilirubin, age, platelet count, hemoglobin, CVC, lymphocyte, Duration of stay in ICU and so on. To construct the candidemia diagnosis model, we employed random forest (RF) algorithm alongside other machine learning methods and conducted internal and external validation with training and testing sets allocated in a 7:3 ratio. The RF model demonstrated the highest area under the receiver operating characteristic (AUC) with values of 0.87 and 0.83 for internal and external validation, respectively. To evaluate the importance of features in predicting candidemia, Shapley additive explanation (SHAP) values were calculated and results revealed that total bilirubin and age were the most important factors in the prediction model. This advancement in candidemia prediction holds significant promise for early intervention and improved patient outcomes in the ICU setting, where timely diagnosis is of paramount crucial.<\/jats:p>","DOI":"10.1371\/journal.pone.0309748","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T17:27:48Z","timestamp":1725902868000},"page":"e0309748","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":13,"title":["A machine learning model for early candidemia prediction in the intensive care unit: Clinical application"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0132-0001","authenticated-orcid":true,"given":"Qiang","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingyuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ranran","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6043-7665","authenticated-orcid":true,"given":"Feng","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"issue":"3","key":"pone.0309748.ref001","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1086\/421946","article-title":"Nosocomial bloodstream infections in US hospitals: analysis of 24,179 cases from a prospective nationwide surveillance study","volume":"39","author":"H Wisplinghoff","year":"2004","journal-title":"Clin Infect Dis"},{"issue":"1","key":"pone.0309748.ref002","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1186\/s13054-020-2766-1","article-title":"Risk factors for candidemia: a prospective matched case-control study.","volume":"24","author":"J Poissy","year":"2020","journal-title":"Crit Care."},{"issue":"9","key":"pone.0309748.ref003","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1086\/496922","article-title":"The epidemiology and attributable outcomes of candidemia in adults and children hospitalized in the United States: a propensity analysis","volume":"41","author":"TE Zaoutis","year":"2005","journal-title":"Clin Infect Dis"},{"issue":"2","key":"pone.0309748.ref004","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1093\/cid\/ciab928","article-title":"Multicenter Prospective Study of Biomarkers for Diagnosis of Invasive Candidiasis in Children and Adolescents","volume":"75","author":"BT Fisher","year":"2022","journal-title":"Clin Infect Dis"},{"key":"pone.0309748.ref005","doi-asserted-by":"crossref","first-page":"18026","DOI":"10.1038\/nrdp.2018.26","article-title":"Invasive candidiasis.","volume":"4","author":"PG Pappas","year":"2018","journal-title":"Nat Rev Dis Primers"},{"issue":"4","key":"pone.0309748.ref006","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1055\/s-0039-1693704","article-title":"Diagnosis and Treatment of Candidemia in the Intensive Care Unit.","volume":"40","author":"M Bassetti","year":"2019","journal-title":"Semin Respir Crit Care Med."},{"issue":"4","key":"pone.0309748.ref007","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/S0891-5520(05)70184-7","article-title":"Laboratory diagnosis of bacteremia and fungemia.","volume":"15","author":"RR Magadia","year":"2001","journal-title":"Infect Dis Clin North Am"},{"issue":"11","key":"pone.0309748.ref008","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1093\/cid\/ciw610","article-title":"Problematic Dichotomization of Risk for Intensive Care Unit (ICU)-Acquired Invasive Candidiasis: Results Using a Risk-Predictive Model to Categorize 3 Levels of Risk From a Multicenter Prospective Cohort of Australian ICU Patients.","volume":"63","author":"EG Playford","year":"2016","journal-title":"Clin Infect Dis"},{"issue":"9","key":"pone.0309748.ref009","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1128\/AAC.49.9.3640-3645.2005","article-title":"Delaying the empiric treatment of candida bloodstream infection until positive blood culture results are obtained: a potential risk factor for hospital mortality","volume":"49","author":"M Morrell","year":"2005","journal-title":"Antimicrobial agents and chemotherapy"},{"key":"pone.0309748.ref010","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1186\/1471-2334-10-150","article-title":"Inappropriate empiric antifungal therapy for candidemia in the ICU and hospital resource utilization: a retrospective cohort study.","volume":"10","author":"MD Zilberberg","year":"2010","journal-title":"BMC Infect Dis"},{"issue":"1","key":"pone.0309748.ref011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1556\/EuJMI.4.2014.1.1","article-title":"Molecular diagnosis of sepsis: New aspects and recent developments","volume":"4","author":"O Liesenfeld","year":"2014","journal-title":"Eur J Microbiol Immunol (Bp)."},{"key":"pone.0309748.ref012","doi-asserted-by":"crossref","first-page":"829066","DOI":"10.3389\/fcimb.2022.829066","article-title":"Development and Validation of a Risk Score for Predicting Invasive Candidiasis in Intensive Care Unit Patients by Incorporating Clinical Risk Factors and Lymphocyte Subtyping.","volume":"12","author":"J Zhang","year":"2022","journal-title":"Front Cell Infect Microbiol"},{"issue":"12","key":"pone.0309748.ref013","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1007\/s00134-017-4990-y","article-title":"Biomarker-guided antifungal therapy in patients with suspected invasive candidiasis: Ready for prime time?","volume":"43","author":"JA Paiva","year":"2017","journal-title":"Intensive Care Med"},{"issue":"8","key":"pone.0309748.ref014","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1093\/jac\/dkw112","article-title":"(1,3)-\u03b2-d-Glucan-based antifungal treatment in critically ill adults at high risk of candidaemia: an observational study.","volume":"71","author":"B Posteraro","year":"2016","journal-title":"J Antimicrob Chemother"},{"issue":"7","key":"pone.0309748.ref015","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1164\/rccm.201703-0657LE","article-title":"The Timing of Early Antibiotics and Hospital Mortality in Sepsis: Playing Devil\u2019s Advocate","volume":"196","author":"J Chertoff","year":"2017","journal-title":"Am J Respir Crit Care Med"},{"issue":"11","key":"pone.0309748.ref016","doi-asserted-by":"crossref","first-page":"e1020","DOI":"10.1097\/CCM.0000000000004556","article-title":"The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.","volume":"48","author":"SV Bhavani","year":"2020","journal-title":"Crit Care Med"},{"issue":"1","key":"pone.0309748.ref017","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s12968-019-0575-y","article-title":"Machine learning in cardiovascular magnetic resonance: basic concepts and applications.","volume":"21","author":"T Leiner","year":"2019","journal-title":"J Cardiovasc Magn Reson"},{"issue":"7","key":"pone.0309748.ref018","doi-asserted-by":"crossref","first-page":"e24651","DOI":"10.2196\/24651","article-title":"Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study.","volume":"9","author":"J Yoo","year":"2021","journal-title":"JMIR Med Inform"},{"issue":"1","key":"pone.0309748.ref019","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.diagmicrobio.2019.04.007","article-title":"Predicting candidemia in the internal medicine wards: a comparison with gram-negative bacteremia-a retrospectives study","volume":"95","author":"A Atamna","year":"2019","journal-title":"Diagnostic microbiology and infectious disease"},{"key":"pone.0309748.ref020","article-title":"Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis.","volume":"10","author":"Y Gao","year":"2022","journal-title":"PeerJ."},{"issue":"1","key":"pone.0309748.ref021","doi-asserted-by":"crossref","first-page":"e13667","DOI":"10.1111\/myc.13667","article-title":"Application of machine learning for mortality prediction in patients with candidemia: Feasibility verification and comparison with clinical severity scores.","volume":"67","author":"WH Hu","year":"2024","journal-title":"Mycoses"},{"issue":"1","key":"pone.0309748.ref022","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.","volume":"162","author":"KG Moons","year":"2015","journal-title":"Ann Intern Med"},{"issue":"5","key":"pone.0309748.ref023","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s15010-020-01488-3","article-title":"Personalized machine learning approach to predict candidemia in medical wards.","volume":"48","author":"A Ripoli","year":"2020","journal-title":"Infection"},{"key":"pone.0309748.ref024","doi-asserted-by":"crossref","first-page":"103375","DOI":"10.1016\/j.compbiomed.2019.103375","article-title":"A review of feature selection methods in medical applications","volume":"112","author":"B Remeseiro","year":"2019","journal-title":"Comput Biol Med"},{"issue":"11","key":"pone.0309748.ref025","doi-asserted-by":"crossref","first-page":"e0278217","DOI":"10.1371\/journal.pone.0278217","article-title":"Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study.","volume":"17","author":"H Lin","year":"2022","journal-title":"PLoS One."},{"issue":"3","key":"pone.0309748.ref026","first-page":"1157","article-title":"An Introduction to Variable and Feature Selection","author":"I Guyon","year":"2003","journal-title":"Journal of machine learning research"},{"issue":"1","key":"pone.0309748.ref027","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1186\/s12879-021-06503-y","article-title":"Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.","volume":"21","author":"CX Lv","year":"2021","journal-title":"BMC Infect Dis."},{"issue":"2","key":"pone.0309748.ref028","first-page":"215","volume":"20","author":"Cox DRJJotRSSSBSM","year":"1958","journal-title":"The regression analysis of binary sequences."},{"key":"pone.0309748.ref029","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks.","volume":"20","author":"C Cortes","year":"1995","journal-title":"Machine Learning."},{"issue":"1","key":"pone.0309748.ref030","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s13040-022-00309-7","article-title":"Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit.","volume":"15","author":"Z Zeng","year":"2022","journal-title":"BioData Min."},{"key":"pone.0309748.ref031","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests.","volume":"45","author":"L. Breiman","year":"2001","journal-title":"Machine learning."},{"issue":"14","key":"pone.0309748.ref032","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1001\/jama.2017.12126","article-title":"Discrimination and Calibration of Clinical Prediction Models: Users\u2019 Guides to the Medical Literature","volume":"318","author":"AC Alba","year":"2017","journal-title":"JAMA"},{"key":"pone.0309748.ref033","doi-asserted-by":"crossref","first-page":"728521","DOI":"10.3389\/fmed.2021.728521","article-title":"Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery.","volume":"8","author":"J Li","year":"2021","journal-title":"Frontiers in medicine"},{"issue":"5","key":"pone.0309748.ref034","article-title":"The R Language: An Engine for Bioinformatics and Data Science.","volume":"12","author":"FM Giorgi","year":"2022","journal-title":"Life (Basel)."},{"issue":"6","key":"pone.0309748.ref035","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph18063317","article-title":"Prediction of Type 2 Diabetes Based on Machine Learning Algorithm.","volume":"18","author":"HM Deberneh","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"pone.0309748.ref036","first-page":"1","article-title":"Prediction of Prognostic Risk Factors in Patients with Invasive Candidiasis and Cancer: A Single-Centre Retrospective Study.","volume":"2022","author":"J Li","year":"2022","journal-title":"BioMed Research International"},{"issue":"1","key":"pone.0309748.ref037","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1186\/s12879-022-07125-8","article-title":"Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study.","volume":"22","author":"Y Li","year":"2022","journal-title":"BMC Infect Dis"},{"issue":"2","key":"pone.0309748.ref038","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1515\/CCLM.2010.045","article-title":"Artificial intelligence for diagnostic purposes: principles, procedures and limitations.","volume":"48","author":"TJ Cleophas","year":"2010","journal-title":"cclm"},{"key":"pone.0309748.ref039","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artmed.2017.06.011","article-title":"Artificial Intelligence in Medicine AIME 2015","volume":"81","author":"JH Holmes","year":"2017","journal-title":"Artif Intell Med"},{"key":"pone.0309748.ref040","unstructured":"<embc44109.2020.9175287.pdf>."},{"issue":"1","key":"pone.0309748.ref041","doi-asserted-by":"crossref","DOI":"10.1186\/s13054-022-04163-1","article-title":"Critical care hepatology: definitions, incidence, prognosis and role of liver failure in critically ill patients.","volume":"26","author":"A Perez Ruiz de Garibay","year":"2022","journal-title":"Critical Care."},{"issue":"3","key":"pone.0309748.ref042","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.cgh.2007.12.039","article-title":"Prognostic implications of lactate, bilirubin, and etiology in German patients with acute liver failure","volume":"6","author":"J Hadem","year":"2008","journal-title":"Clin Gastroenterol Hepatol"},{"issue":"3","key":"pone.0309748.ref043","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1097\/JCMA.0000000000000695","article-title":"Candidemia in hospitalized cirrhotic patients with bloodstream infection: A retrospective analysis and brief summary of published studies","volume":"85","author":"Y-C Chang","year":"2022","journal-title":"Journal of the Chinese Medical Association"},{"issue":"2","key":"pone.0309748.ref044","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/jpen.1451","article-title":"Hypoalbuminemia: Pathogenesis and Clinical Significance.","volume":"43","author":"PB Soeters","year":"2019","journal-title":"JPEN J Parenter Enteral Nutr."},{"issue":"3","key":"pone.0309748.ref045","author":"MJ Rafferty","year":"2016","journal-title":"Reprioritisation of Liver Export Protein Synthesis in Patients with Decompensated Alcoholic Liver Disease"},{"issue":"9","key":"pone.0309748.ref046","doi-asserted-by":"crossref","DOI":"10.3390\/ijms22094496","article-title":"Hypoalbuminemia as Surrogate and Culprit of Infections.","volume":"22","author":"CJ Wiedermann","year":"2021","journal-title":"Int J Mol Sci."},{"issue":"3","key":"pone.0309748.ref047","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1111\/jdi.13183","article-title":"Opportunistic invasive fungal disease in patients with type 2 diabetes mellitus from Southern China: Clinical features and associated factors.","volume":"11","author":"M Lao","year":"2020","journal-title":"Journal of diabetes investigation"},{"key":"pone.0309748.ref048","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/1471-2466-14-128","article-title":"Pneumococcal pneumonia: differences according to blood culture results","volume":"14","author":"A Capelastegui","year":"2014","journal-title":"BMC Pulm Med"},{"key":"pone.0309748.ref049","doi-asserted-by":"crossref","first-page":"105","DOI":"10.3389\/fcimb.2013.00105","article-title":"Intestinal epithelial cell apoptosis and loss of barrier function in the setting of altered microbiota with enteral nutrient deprivation","volume":"3","author":"FR Demehri","year":"2013","journal-title":"Front Cell Infect Microbiol"},{"issue":"10","key":"pone.0309748.ref050","doi-asserted-by":"crossref","first-page":"e110396","DOI":"10.1371\/journal.pone.0110396","article-title":"Specific microbiome changes in a mouse model of parenteral nutrition associated liver injury and intestinal inflammation","volume":"9","author":"JK Harris","year":"2014","journal-title":"PLoS One"},{"issue":"7","key":"pone.0309748.ref051","doi-asserted-by":"crossref","first-page":"e036452","DOI":"10.1136\/bmjopen-2019-036452","article-title":"Risk of invasive candidiasis with prolonged duration of ICU stay: a systematic review and meta-analysis","volume":"10","author":"Z Zhang","year":"2020","journal-title":"BMJ Open"},{"issue":"6","key":"pone.0309748.ref052","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.7150\/ijbs.58695","article-title":"Glucocorticoids are double-edged sword in the treatment of COVID-19 and cancers","volume":"17","author":"R Yang","year":"2021","journal-title":"Int J Biol Sci"},{"issue":"9398","key":"pone.0309748.ref053","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1016\/S0140-6736(03)14904-5","article-title":"Glucocorticoids and invasive fungal infections","volume":"362","author":"MS Lionakis","year":"2003","journal-title":"Lancet"},{"issue":"1","key":"pone.0309748.ref054","doi-asserted-by":"crossref","DOI":"10.3390\/pathogens5010006","article-title":"How Chemotherapy Increases the Risk of Systemic Candidiasis in Cancer Patients: Current Paradigm and Future Directions.","volume":"5","author":"F Teoh","year":"2016","journal-title":"Pathogens"},{"issue":"5","key":"pone.0309748.ref055","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1086\/338714","article-title":"Should vascular catheters be removed from all patients with candidemia? An evidence-based review","volume":"34","author":"M Nucci","year":"2002","journal-title":"Clin Infect Dis"},{"issue":"8","key":"pone.0309748.ref056","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1128\/AAC.48.8.3051-3056.2004","article-title":"Susceptibility to thrombin-induced platelet microbicidal protein is associated with increased fluconazole efficacy against experimental endocarditis due to Candida albicans","volume":"48","author":"MR Yeaman","year":"2004","journal-title":"Antimicrobial agents and chemotherapy"},{"key":"pone.0309748.ref057","doi-asserted-by":"crossref","first-page":"155081","DOI":"10.1016\/j.cyto.2020.155081","article-title":"Diagnostic efficacy of serum cytokines and chemokines in patients with candidemia and bacteremia","volume":"130","author":"Q Wang","year":"2020","journal-title":"Cytokine"},{"key":"pone.0309748.ref058","first-page":"187","article-title":"PAMPs of the Fungal Cell Wall and Mammalian PRRs","volume":"425","author":"R Hatinguais","year":"2020","journal-title":"Curr Top Microbiol Immunol"},{"issue":"9","key":"pone.0309748.ref059","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1177\/0885066618800690","article-title":"Invasive Fungal Disease in Critically Ill Patients at High Risk: Usefulness of Lymphocyte Subtyping.","volume":"35","author":"J Zhang","year":"2020","journal-title":"J Intensive Care Med."},{"key":"pone.0309748.ref060","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.mib.2019.05.006","article-title":"Heme-iron acquisition in fungi","volume":"52","author":"U Roy","year":"2019","journal-title":"Current opinion in microbiology"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0309748","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T17:28:34Z","timestamp":1725902914000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0309748"}},"subtitle":[],"editor":[{"given":"Giovanni","family":"Giordano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":60,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9,9]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0309748","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,9]]}}}