{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T03:18:58Z","timestamp":1789787938579,"version":"4.0.1"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2017M3D9A1073784"],"award-info":[{"award-number":["2017M3D9A1073784"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2018R1A6A1A03024940"],"award-info":[{"award-number":["2018R1A6A1A03024940"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003598","name":"Supreme Prosecutors\u00b4 Office","doi-asserted-by":"publisher","award":["1333-302-260-01"],"award-info":[{"award-number":["1333-302-260-01"]}],"id":[{"id":"10.13039\/501100003598","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>High-resolution liquid chromatography\u2013tandem mass spectrometry (LC--MS-MS)-based machine learning models are constructed to address the analytical challenge of identifying unknown controlled substances and new psychoactive substances (NPSs). Using a training set composed of 770 LC\u2013MS-MS barcode spectra (with binary entries 0 or 1) obtained generally by high-resolution mass spectrometers, three classification machine learning models were generated and evaluated. The three models are artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbor (k-NN) models. In these models, controlled substances and NPSs were classified into 13 subgroups (benzylpiperazine, opiate, benzodiazepine, amphetamine, cocaine, methcathinone, classical cannabinoid, fentanyl, 2C series, indazole carbonyl compound, indole carbonyl compound, phencyclidine and others). Using 193 LC\u2013MS-MS barcode spectra as an external test set, accuracy of the ANN, SVM and k-NN models were evaluated as 72.5%, 90.0% and 94.3%, respectively. Also, the hybrid similarity search (HSS) algorithm was evaluated to examine whether this algorithm can successfully identify unknown controlled substances and NPSs whose data are unavailable in the database. When only 24 representative LC\u2013MS-MS spectra of controlled substances and NPSs were selectively included in the database, it was found that HSS can successfully identify compounds with high reliability. The machine learning models and HSS algorithms are incorporated into our home-coded artificial intelligence screener for narcotic drugs and psychotropic substances standalone software that is equipped with a graphic user interface. The use of this software allows unknown controlled substances and NPSs to be identified in a convenient manner.<\/jats:p>","DOI":"10.1093\/jat\/bkab098","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T07:18:14Z","timestamp":1631085494000},"page":"732-742","source":"Crossref","is-referenced-by-count":17,"title":["Revealing Unknown Controlled Substances and New Psychoactive Substances Using High-Resolution LC\u2013MS-MS Machine Learning Models and the Hybrid Similarity Search Algorithm"],"prefix":"10.1093","volume":"46","author":[{"given":"So Yeon","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Chemistry, Sogang University , Seoul 04107, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Tak","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Sogang University , Seoul 04107, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungill","family":"Suh","sequence":"additional","affiliation":[{"name":"Forensic Genetics & Chemistry Division, Supreme Prosecutors\u2019 Office , Seoul 06590, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bum Jun","family":"Ko","sequence":"additional","affiliation":[{"name":"Forensic Genetics & Chemistry Division, Supreme Prosecutors\u2019 Office , Seoul 06590, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5900-2623","authenticated-orcid":false,"given":"Han Bin","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Sogang University , Seoul 04107, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"2022081310371149100_R1","article-title":"Fifty Years of the 1961 Single Convention on Narcotic Drugs: A Reinterpretation","volume-title":"Series on Legislative Reform of Drug Policies Nr. 12","author":"Bewley-Taylor","year":"2011"},{"key":"2022081310371149100_R2","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1111\/j.1748-720X.2007.00180.x","article-title":"Addressing the global tragedy of needless pain: rethinking the United Nations single convention on narcotic drugs","volume":"35","author":"Taylor","year":"2007","journal-title":"The Journal of Law, Medicine & Ethics"},{"key":"2022081310371149100_R3","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1002\/cpt1974166989","article-title":"The biochemical pharmacology of abused drugs; III. cannabis, opiates, and synthetic narcotics","volume":"16","author":"Caldwell","year":"1974","journal-title":"Clinical Pharmacology and Therapeutics"},{"key":"2022081310371149100_R4","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195125092.001.0001","volume-title":"The American Disease: Origins of Narcotic Control","author":"Musto","year":"1999","edition":"3rd"},{"key":"2022081310371149100_R5","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1056\/NEJMra1507771","article-title":"Opioid abuse in chronic pain \u2014 misconceptions and mitigation strategies","volume":"374","author":"Volkow","year":"2016","journal-title":"New England Journal of Medicine"},{"key":"2022081310371149100_R6","doi-asserted-by":"crossref","first-page":"837","DOI":"10.15585\/mmwr.mm6533a2","article-title":"Fentanyl law enforcement submissions and increases in synthetic opioid\u2013involved overdose deaths \u2014 27 States, 2013\u20132014","volume":"65","author":"Gladden","year":"2016","journal-title":"Morbidity and Mortality Weekly Report"},{"key":"2022081310371149100_R7","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1056\/NEJMp1011512","article-title":"A flood of opioids, a rising tide of deaths","volume":"363","author":"Okie","year":"2010","journal-title":"New England Journal of Medicine"},{"key":"2022081310371149100_R8","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.mcna.2018.02.010","article-title":"New and emerging illicit psychoactive substances","volume":"102","author":"Graddy","year":"2018","journal-title":"Medical Clinics of North America"},{"key":"2022081310371149100_R9","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1093\/jat\/bkaa066","article-title":"Report on a new opioid NPS: chemical and in vitro functional characterization of a structural isomer of the MT-45 derivative diphenpipenol","volume":"45","author":"Cannaert","year":"2021","journal-title":"Journal of Analytical Toxicology"},{"key":"2022081310371149100_R10","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s13361-019-02164-z","article-title":"The role of mass spectrometry in the cannabis industry","volume":"30","author":"Nie","year":"2019","journal-title":"Journal of the American Society for Mass Spectrometry"},{"key":"2022081310371149100_R11","first-page":"36","article-title":"Liquid chromatography\u2019s complementary role to gas chromatography in cannabis testing","volume":"36","author":"Kahler","year":"2018","journal-title":"LC GC North America"},{"key":"2022081310371149100_R12","doi-asserted-by":"crossref","first-page":"2803","DOI":"10.4155\/bio.13.244","article-title":"The challenges of LC-MS-MS analysis of opiates and opioids in urine","volume":"5","author":"French","year":"2013","journal-title":"Bioanalysis"},{"key":"2022081310371149100_R13","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1093\/jat\/bkx065","article-title":"Identification of novel opioid interferences using high-resolution mass spectrometry","volume":"42","author":"Mu\u00f1oz-Mu\u00f1oz","year":"2018","journal-title":"Journal of Analytical Toxicology"},{"key":"2022081310371149100_R14","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1097\/FTD.0b013e318283e29a","article-title":"Therapeutic monitoring of opioids: a sensitive LC-MS-MS method for quantitation of several opioids including hydrocodone and its metabolites","volume":"35","author":"Langman","year":"2013","journal-title":"Therapeutic Drug Monitoring"},{"key":"2022081310371149100_R15","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.talanta.2018.02.077","article-title":"Analysis of isomeric opioids in urine using LC-TIMS-TOF MS","volume":"183","author":"Adams","year":"2018","journal-title":"Talanta"},{"key":"2022081310371149100_R16","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1039\/D1AN00082A","article-title":"Chemometrics in forensic science: approaches and applications","volume":"146","author":"Sauzier","year":"2021","journal-title":"Analyst"},{"key":"2022081310371149100_R17","first-page":"929","article-title":"Artificial neural networks built for the recognition of illicit amphetamines using a concatenated database","volume":"54","author":"Gosav","year":"2009","journal-title":"Romanian Journal of Physics"},{"key":"2022081310371149100_R18","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.jpba.2015.08.008","article-title":"Multicriteria wavenumber selection in cocaine classification","volume":"115","author":"Anzanello","year":"2015","journal-title":"Journal of Pharmaceutical and Biomedical Analysis"},{"key":"2022081310371149100_R19","doi-asserted-by":"crossref","DOI":"10.1016\/j.forc.2020.100287","article-title":"Classification of fentanyl analogues through principal component analysis (PCA) and hierarchical clustering of GC \u2013 MS data","volume":"21","author":"Gilbert","year":"2020","journal-title":"Forensic Chemistry"},{"key":"2022081310371149100_R20","doi-asserted-by":"crossref","DOI":"10.1016\/j.forc.2020.100237","article-title":"Mass spectral similarity mapping applied to fentanyl analogs","volume":"19","author":"Moorthy","year":"2020","journal-title":"Forensic Chemistry"},{"key":"2022081310371149100_R21","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.forsciint.2010.03.034","article-title":"The differentiation of fibre- and drug type Cannabis seedlings by gas chromatography\/mass spectrometry and chemometric tools","volume":"200","author":"Esseiva","year":"2010","journal-title":"Forensic Science International"},{"key":"2022081310371149100_R22","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.forsciint.2007.11.013","article-title":"Drug intelligence based on organic impurities in illicit MA samples","volume":"177","author":"Dujourdy","year":"2008","journal-title":"Forensic Science International"},{"key":"2022081310371149100_R23","doi-asserted-by":"crossref","DOI":"10.1016\/j.trac.2021.116188","article-title":"Data processing strategies for non-targeted analysis of foods using liquid chromatography\/high-resolution mass spectrometry","volume":"136","author":"Fisher","year":"2021","journal-title":"Trends in Analytical Chemistry"},{"key":"2022081310371149100_R24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.forsciint.2016.04.008","article-title":"Chemical profiling: a tool to decipher the structure and organisation of illicit drug markets: an 8-year study in Western Switzerland","volume":"266","author":"Bros\u00e9us","year":"2016","journal-title":"Forensic Science International"},{"key":"2022081310371149100_R25","doi-asserted-by":"crossref","DOI":"10.1016\/j.aca.2021.338403","article-title":"Taking the leap between analytical chemistry and artificial intelligence: a tutorial review","volume":"1161","author":"Ayres","year":"2021","journal-title":"Analytica Chimica Acta"},{"key":"2022081310371149100_R26","doi-asserted-by":"crossref","first-page":"9119","DOI":"10.1021\/acs.analchem.9b01643","article-title":"LC-MS-MS software for screening unknown erectile dysfunction drugs and analogues: artificial neural network classification, peak-count scoring, simple similarity search, and hybrid similarity search algorithms","volume":"91","author":"Jang","year":"2019","journal-title":"Analytical Chemistry"},{"key":"2022081310371149100_R27","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.chroma.2017.02.032","article-title":"Reliable screening and confirmation of 156 multi-class illegal adulterants in dietary supplements based on extracted common ion chromatograms by ultra-high-performance liquid chromatography-quadrupole\/time of flight-mass spectrometry","volume":"1491","author":"Kim","year":"2017","journal-title":"Journal of Chromatography. A"},{"key":"2022081310371149100_R28","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1080\/10826076.2017.1364264","article-title":"Prediction of liquid chromatography retention times of erectile dysfunction drugs and analogues using chemometric approaches","volume":"40","author":"Park","year":"2017","journal-title":"Journal of Liquid Chromatography & Related Technologies"},{"key":"2022081310371149100_R29","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1002\/bkcs.11365","article-title":"Fragmentation pathways of tadalafil and its analogues in electrospray ionization tandem mass spectrometry","volume":"39","author":"Lee","year":"2018","journal-title":"Bulletin of the Korean Chemical Society"},{"key":"2022081310371149100_R30","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.jfda.2018.08.006","article-title":"Rapid screening of sulfonamides in dietary supplements based on extracted common ion chromatogram and neutral loss scan by LC-Q\/TOF-mass spectrometry","volume":"27","author":"Ki","year":"2019","journal-title":"Journal of Food and Drug Analysis"},{"key":"2022081310371149100_R31","doi-asserted-by":"crossref","first-page":"5260","DOI":"10.1039\/C9AY01517E","article-title":"Comprehensive screening of multiclass illegal adulterants in herbal supplements and Spice-type drugs using specific MS-MS fragmentations by UHPLC-Q\/TOF-MS","volume":"11","author":"Hur","year":"2019","journal-title":"Analytical Methods"},{"key":"2022081310371149100_R32","doi-asserted-by":"crossref","first-page":"13261","DOI":"10.1021\/acs.analchem.7b03320","article-title":"Combining fragment-ion and neutral-loss matching during mass spectral library searching: a new general purpose algorithm applicable to illicit drug identification","volume":"89","author":"Moorthy","year":"2017","journal-title":"Analytical Chemistry"},{"key":"2022081310371149100_R33","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1016\/1044-0305(94)87009-8","article-title":"Optimization and testing of mass spectral library search algorithms for compound identification","volume":"5","author":"Stein","year":"1994","journal-title":"Journal of the American Society for Mass Spectrometry"},{"key":"2022081310371149100_R34","doi-asserted-by":"crossref","first-page":"1924","DOI":"10.1021\/acs.jproteome.6b00988","article-title":"The hybrid search: a mass spectral library search method for discovery of modifications in proteomics","volume":"16","author":"Burke","year":"2017","journal-title":"Journal of Proteome Research"},{"key":"2022081310371149100_R35","doi-asserted-by":"crossref","first-page":"10316","DOI":"10.1021\/acs.analchem.0c00342","article-title":"Increasing the coverage of a mass spectral library of milk oligosaccharides using a hybrid-search-based bootstrapping method and milks from a wide variety of mammals","volume":"92","author":"Remoroza","year":"2020","journal-title":"Analytical Chemistry"},{"key":"2022081310371149100_R36","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1021\/acs.jproteome.8b00863","article-title":"False discovery rate estimation for hybrid mass spectral library search identifications in bottom-up proteomics","volume":"18","author":"Burke","year":"2019","journal-title":"Journal of Proteome Research"},{"key":"2022081310371149100_R37","doi-asserted-by":"crossref","first-page":"13924","DOI":"10.1021\/acs.analchem.9b03415","article-title":"Hybrid search: a method for identifying metabolites absent from tandem mass spectrometry libraries","volume":"91","author":"Cooper","year":"2019","journal-title":"Analytical Chemistry"},{"key":"2022081310371149100_R38","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1002\/widm.53","article-title":"Algorithms for hierarchical clustering: an overview","volume":"2","author":"Murtagh","year":"2011","journal-title":"Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery"},{"key":"2022081310371149100_R39","article-title":"RDKit: Cheminformatics and Machine Learning Software, Open-source","year":"2014"},{"key":"2022081310371149100_R40","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ymeth.2014.08.005","article-title":"Molecular fingerprint similarity search in virtual screening","volume":"71","author":"Cereto-Massagu\u00e9","year":"2015","journal-title":"Methods"},{"key":"2022081310371149100_R41","doi-asserted-by":"crossref","DOI":"10.1186\/s13321-020-00443-6","article-title":"QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping","volume":"12","author":"\u0160kuta","year":"2020","journal-title":"Journal of Cheminformatics"},{"key":"2022081310371149100_R42","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1021\/ci300261r","article-title":"Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets","volume":"52","author":"Todeschini","year":"2012","journal-title":"Journal of Chemical Information and Modeling"},{"key":"2022081310371149100_R43","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/0021-9681(66)90032-4","article-title":"A proposed index for measuring agreement in test-retest studies","volume":"19","author":"Rogot","year":"1966","journal-title":"Journal of Chronic Diseases"},{"key":"2022081310371149100_R44","article-title":"Similarity Encoder: A Neural Network Architecture for Learning Similarity Preserving Embeddings","author":"Horn","year":"2020"},{"key":"2022081310371149100_R45","article-title":"SciPy: Open Source Scientific Tools for Python","year":"2015"},{"key":"2022081310371149100_R46","first-page":"1","article-title":"Matplotlib in Python","author":"Ari","year":"2014"},{"key":"2022081310371149100_R47","article-title":"MoNA \u2013 MassBank of North America","year":"2007"},{"key":"2022081310371149100_R48","first-page":"9","volume-title":"Fundamentals of Data Mining in Genomics and Proteomics","author":"Coombes","year":"2007"},{"key":"2022081310371149100_R49","first-page":"327","article-title":"Introduction to artificial neural network (ANN) methods: what they are and how to use them","volume":"41","author":"Zupan","year":"1994","journal-title":"Acta Chimica Slovenica"},{"key":"2022081310371149100_R50","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Saitta","year":"1995","journal-title":"Machine Learning"},{"key":"2022081310371149100_R51","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Mining and Knowledge Discovery"},{"key":"2022081310371149100_R52","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Transactions on Information Theory"},{"key":"2022081310371149100_R53","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1007\/978-3-540-39964-3_62","article-title":"KNN model-based approach in classification","volume":"2888","author":"Guo","year":"2003","journal-title":"Lecture Notes in Computer Science"},{"key":"2022081310371149100_R54","article-title":"ROC Graphs : Notes and Practical Considerations for Researchers","volume-title":"Machine Learning","author":"Fawcett","year":"2004"},{"key":"2022081310371149100_R55","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 Recognition Letters"},{"key":"2022081310371149100_R56","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1016\/j.patcog.2014.10.032","article-title":"Classifying imbalanced data sets using similarity based hierarchical decomposition","volume":"48","author":"Beyan","year":"2015","journal-title":"Pattern Recognition"},{"key":"2022081310371149100_R57","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2105-11-523","article-title":"Class prediction for high-dimensional class-imbalanced data","volume":"11","author":"Blagus","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2022081310371149100_R58","first-page":"375","volume-title":"Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science","author":"Waske","year":"2009"},{"key":"2022081310371149100_R59","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: an applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"International Journal of Remote Sensing"},{"key":"2022081310371149100_R60","first-page":"2074","article-title":"Classification with class imbalance problem: a review","volume":"5","author":"Ali","year":"2013","journal-title":"International Journal of Advances in Soft Computing and Its Applications"}],"container-title":["Journal of Analytical Toxicology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jat\/advance-article-pdf\/doi\/10.1093\/jat\/bkab098\/40435616\/bkab098.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jat\/article-pdf\/46\/7\/732\/45413986\/bkab098.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jat\/article-pdf\/46\/7\/732\/45413986\/bkab098.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T09:22:51Z","timestamp":1699435371000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jat\/article\/46\/7\/732\/6366874"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"references-count":60,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,9,8]]},"published-print":{"date-parts":[[2022,8,13]]}},"URL":"https:\/\/doi.org\/10.1093\/jat\/bkab098","relation":{},"ISSN":["0146-4760","1945-2403"],"issn-type":[{"value":"0146-4760","type":"print"},{"value":"1945-2403","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,9,1]]},"published":{"date-parts":[[2021,9,8]]}}}