{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:33:14Z","timestamp":1769387594671,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps\/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the \u201cLassosum\u201d penalty used to fit polygenic risk scores and integrated risk models using either summary statistics or raw data. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section on both Alzheimer\u2019s disease and COPD (chronic obstructive pulmonary disease) demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm (for the datasets under consideration), allowing it to draw equal with state-of-the-art methodology such as LDpred2 when evaluated via the AUC (area under the ROC curve) metric.<\/jats:p>","DOI":"10.3390\/genes13010112","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T01:15:26Z","timestamp":1641518126000},"page":"112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6008-2720","authenticated-orcid":false,"given":"Georg","family":"Hahn","sequence":"first","affiliation":[{"name":"Harvard T.H. Chan School of Public Health, Harvard University, 677 Huntington Ave, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1844-5652","authenticated-orcid":false,"given":"Dmitry","family":"Prokopenko","sequence":"additional","affiliation":[{"name":"Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharon","family":"Lutz","sequence":"additional","affiliation":[{"name":"Harvard T.H. Chan School of Public Health, Harvard University, 677 Huntington Ave, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristina","family":"Mullin","sequence":"additional","affiliation":[{"name":"Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rudolph","family":"Tanzi","sequence":"additional","affiliation":[{"name":"Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4907-1657","authenticated-orcid":false,"given":"Michael","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Medicine, Brigham and Women\u2019s Hospital, Harvard University, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3641-3822","authenticated-orcid":false,"given":"Edwin","family":"Silverman","sequence":"additional","affiliation":[{"name":"Department of Medicine, Brigham and Women\u2019s Hospital, Harvard University, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph","family":"Lange","sequence":"additional","affiliation":[{"name":"Harvard T.H. Chan School of Public Health, Harvard University, 677 Huntington Ave, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"on the behalf of the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s41586-021-03243-6","article-title":"Improving reporting standards for polygenic scores in risk prediction studies","volume":"591","author":"Wand","year":"2021","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1038\/s41588-018-0183-z","article-title":"Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations","volume":"50","author":"Khera","year":"2018","journal-title":"Nat. Genet."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3328","DOI":"10.1038\/s41467-019-11112-0","article-title":"Analysis of polygenic risk score usage and performance in diverse human populations","volume":"10","author":"Duncan","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Knowles, J., and Ashley, E. (2018). Cardiovascular disease: The rise of the genetic risk score. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002546"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421\u2013427.","DOI":"10.1038\/nature13595"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","article-title":"Receiver Operating Characteristic Curve in Diagnostic Test Assessment","volume":"5","author":"Mandrekar","year":"2010","journal-title":"J. Thorac. Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1002\/gepi.22050","article-title":"Polygenic scores via penalized regression on summary statistics","volume":"41","author":"Mak","year":"2017","journal-title":"Genet. Epidemiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.cjca.2016.12.006","article-title":"Genetic Risk Scores for Atrial Fibrillation: Do they Improve Risk Estimation?","volume":"33","author":"Huang","year":"2017","journal-title":"Can. J. Cardiol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hosmer, D., and Lemeshow, S. (2000). Applied Logistic Regression, John Wiley and Sons. [2nd ed.]. Chapter 5.","DOI":"10.1002\/0471722146"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10107-004-0552-5","article-title":"Smooth minimization of non-smooth functions","volume":"103","author":"Nesterov","year":"2005","journal-title":"Math. Program. Ser. A"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hahn, G., Lutz, S.M., Laha, N., and Lange, C. (2020). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv, 1\u201335.","DOI":"10.1101\/2020.09.17.301788"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s11222-021-10010-0","article-title":"A fast and efficient smoothing approach to LASSO regression and an application in statistical genetics: Polygenic risk scores for Chronic obstructive pulmonary disease (COPD)","volume":"31","author":"Hahn","year":"2021","journal-title":"Stat. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/s41588-019-0358-2","article-title":"Genetic meta-analysis of diagnosed Alzheimer\u2019s disease identifies new risk loci and implicates A\u03b2, tau, immunity and lipid processing","volume":"51","author":"Kunkle","year":"2019","journal-title":"Nat. Genet."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/s41588-018-0311-9","article-title":"Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer\u2019s disease risk","volume":"51","author":"Jansen","year":"2019","journal-title":"Nat. Genet."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3109\/15412550903499522","article-title":"Genetic epidemiology of COPD (COPDGene) study design","volume":"7","author":"Regan","year":"2010","journal-title":"COPD"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Priv\u00e9, F., Arbel, J., and Vilhj\u00e1lmsson, B.J. (2019). LDpred2: Better, faster, stronger. Bioinformatics.","DOI":"10.1101\/2020.04.28.066720"},{"key":"ref_17","unstructured":"Ge, T., Chen, C.Y., Ni, Y., Feng, Y.C.A., and Smoller, J.W. (2021, May 08). PRS-CS: A Polygenic Prediction Method That Infers Posterior SNP Effect Sizes under Continuous Shrinkage (CS) Priors Using GWAS Summary Statistics and an External LD Reference Panel. Available online: https:\/\/github.com\/getian107\/PRScs."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection Via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. B Meter."},{"key":"ref_19","unstructured":"Hahn, G., Lutz, S.M., Laha, N., and Lange, C. (2021, May 08). smoothedLasso: Smoothed LASSO Regression via Nesterov Smoothing. Available online: https:\/\/cran.r-project.org\/src\/contrib\/Archive\/smoothedLasso\/smoothedLasso_1.4.tar.gz."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1038\/s41596-020-0353-1","article-title":"Tutorial: A guide to performing polygenic risk score analyses","volume":"15","author":"Choi","year":"2020","journal-title":"Nat. Protoc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1038\/nature08185","article-title":"Common polygenic variation contributes to risk of schizophrenia and bipolar disorder","volume":"460","author":"Purcell","year":"2009","journal-title":"Nature"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1111\/jcpp.12295","article-title":"Research Review: Polygenic methods and their application to psychiatric traits","volume":"55","author":"Wray","year":"2014","journal-title":"J. Child Psychol. Psychiatry"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.ajhg.2015.09.001","article-title":"Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores","volume":"97","author":"Yang","year":"2015","journal-title":"Am. J. Hum. Genet."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s10519-015-9770-2","article-title":"Local true discovery rate weighted polygenic scores using GWAS summary data","volume":"46","author":"Mak","year":"2016","journal-title":"Behav. Genet."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1038\/s41588-018-0193-x","article-title":"Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits","volume":"50","author":"Zhang","year":"2018","journal-title":"Nat. Genet."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5086","DOI":"10.1038\/s41467-019-12653-0","article-title":"Improved polygenic prediction by Bayesian multiple regression on summary statistics","volume":"10","author":"Zeng","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1038\/s41467-019-09718-5","article-title":"Polygenic prediction via Bayesian regression and continuous shrinkage priors","volume":"10","author":"Ge","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_28","first-page":"1","article-title":"Improved genetic prediction of complex traits from individual-level data or summary statistics","volume":"12","author":"Zhang","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization Paths for Generalized Linear Models via Coordinate Descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_30","unstructured":"Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Tay, K., Simon, N., and Qian, J. (2021, May 08). glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. Available online: https:\/\/cran.r-project.org\/package=glmnet."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1137\/080716542","article-title":"A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems","volume":"2","author":"Beck","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"giz082","DOI":"10.1093\/gigascience\/giz082","article-title":"PRSice-2: Polygenic Risk Score software for biobank-scale data","volume":"8","author":"Choi","year":"2019","journal-title":"Gigascience"},{"key":"ref_33","unstructured":"Zhang, W., Tang, J., and Wang, N. (2016, January 15\u201318). Using the Machine Learning Approach to Predict Patient Survival from High-Dimensional Survival Data. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China."},{"key":"ref_34","first-page":"5","article-title":"Machine Learning techniques and Polygenic Risk Score application to prediction genetic diseases","volume":"9","author":"Mamaniab","year":"2020","journal-title":"Adv. Distrib. Comput. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1038\/s10038-020-00832-7","article-title":"Deep neural network improves the estimation of polygenic risk scores for breast cancer","volume":"66","author":"Zhang","year":"2021","journal-title":"J. Hum. Genet."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, S., Ji, X., Cho, M., Joo, J., and Moore, J. (2021). DL-PRS: A novel deep learning approach to polygenic risk scores. BMC Bioinform.","DOI":"10.21203\/rs.3.rs-423764\/v1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Peng, J., Li, J., Han, R., Wang, Y., Han, L., Peng, J., Wang, T., Hao, J., Shang, X., and Wei, Z. (2021). A Deep Learning-based Genome-wide Polygenic Risk Score for Common Diseases Identifies Individuals with Risk. medRxiv.","DOI":"10.1101\/2021.11.17.21265352"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1002\/gepi.22279","article-title":"Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status","volume":"44","author":"Gola","year":"2020","journal-title":"Genet. Epidemiol."},{"key":"ref_39","unstructured":"R Core Team (2014). R: A Language and Environment for Statistical Computing, R Foundation for Stat Comp."},{"key":"ref_40","unstructured":"Mak, T., Porsch, R., Choi, S., Zhou, X., and Sham, P. (2021, May 08). Lassosum: A Method for Computing LASSO\/Elastic Net Estimates of a Linear Regression Problem Given Summary Statistics from GWAS and Genome-Wide Meta-Analyses. Available online: https:\/\/github.com\/tshmak\/lassosum."},{"key":"ref_41","unstructured":"Priv\u00e9, F., Blum, M., and Aschard, H. (2021, May 08). bigsnpr: Analysis of Massive SNP Arrays. Available online: https:\/\/cran.r-project.org\/package=bigsnpr."},{"key":"ref_42","unstructured":"Hahn, G., Lutz, S.M., Laha, N., and Lange, C. (2021, May 08). smoothedLasso: Smoothed LASSO Regression via Nesterov Smoothing. Available online: https:\/\/cran.r-project.org\/package=smoothedLasso."},{"key":"ref_43","unstructured":"Falbel, D., Allaire, J., Chollet, F., Studio, R., Tang, Y., Bijl, W.V.D., Studer, M., and Keydana, S. (2021, May 08). keras: R Interface to \u2018Keras\u2019. Available online: https:\/\/cran.r-project.org\/package=keras."},{"key":"ref_44","unstructured":"Falbel, D., Allaire, J., Studio, R., Tang, Y., Eddelbuettel, D., Golding, N., and Kalinowski, T. (2021, May 08). Tensorflow: R Interface to \u2018TensorFlow\u2019. Available online: https:\/\/cran.r-project.org\/package=tensorflow."},{"key":"ref_45","unstructured":"Zeng, J., Yang, J., Zhang, F., Zheng, Z., Lloyd-Jones, L., and Goddard, M. (2021, May 08). GCTB: A Tool for Genome-Wide Complex Trait Bayesian Analysis. Available online: https:\/\/cnsgenomics.com\/software\/gctb\/#Overview."},{"key":"ref_46","unstructured":"Speed, D. (2021, May 08). MegaPRS. Available online: http:\/\/dougspeed.com\/prediction\/."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1038\/ng.3643","article-title":"A reference panel of 64,976 haplotypes for genotype imputation","volume":"48","author":"McCarthy","year":"2016","journal-title":"Nat. Genet."},{"key":"ref_48","unstructured":"Partners (2021, May 08). Partners Healthcare Biobank. Available online: https:\/\/biobank.partners.org."},{"key":"ref_49","unstructured":"World Health Organization (2021). International Statistical Classification of Diseases and Related Health Problems (ICD), World Health Organization. Available online: https:\/\/www.who.int\/standards\/classifications\/classification-of-diseases."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/0895-4356(94)90129-5","article-title":"Validation of a combined comorbidity index","volume":"47","author":"Charlson","year":"1994","journal-title":"J. Clin. Epidemiol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Karlson, E.W., Boutin, N.T., Hoffnagle, A.G., and Allen, N.L. (2016). Building the Partners HealthCare Biobank at Partners Personalized Medicine: Informed Consent, Return of Research Results, Recruitment Lessons and Operational Considerations. J. Pers. Med., 6.","DOI":"10.3390\/jpm6010002"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1093\/bioinformatics\/btq559","article-title":"Robust relationship inference in genome-wide association studies","volume":"26","author":"Manichaikul","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_53","unstructured":"Chen, W.M. (2021, May 08). KING: Kinship-Based INference for Gwas. Available online: https:\/\/kingrelatedness.com\/."},{"key":"ref_54","unstructured":"Purcell, S., and Chang, C. (2021, May 08). PLINK2 (v2.00, 31 Aug 2020). Available online: www.cog-genomics.org\/plink\/2.0\/."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4799","DOI":"10.1038\/s41467-020-18534-1","article-title":"Risk prediction of late-onset Alzheimer\u2019s disease implies an oligogenic architecture","volume":"11","author":"Zhang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ware, E.B., Faul, J.D., Mitchell, C.M., and Bakulski, K.M. (2020). Considering the APOE locus in Alzheimer\u2019s disease polygenic scores in the Health and Retirement Study: A longitudinal panel study. BMC Med. Genom., 13.","DOI":"10.1186\/s12920-020-00815-9"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit."},{"key":"ref_58","unstructured":"NHLBI TOPMed (2021, October 13). Genetic Epidemiology of COPD (COPDGene) Funded by the National Heart, Lung, and Blood Institute (NHLBI) in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, Available online: https:\/\/www.ncbi.nlm.nih.gov\/projects\/gap\/cgi-bin\/study.cgi?study_id=phs000951.v5.p5."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lutz, S.M., Cho, M.H., Young, K., Hersh, C.P., Castaldi, P.J., McDonald, M.L., Regan, E., Mattheisen, M., DeMeo, D.L., and Parker, M. (2015). A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry. BMC Genet., 16.","DOI":"10.1186\/s12863-015-0299-4"},{"key":"ref_60","unstructured":"Bolli, A., Domenico, P.D., and Bott\u00e0, G. (2021, May 08). Software as a Service for the Genomic Prediction of Complex Diseases, Available online: http:\/\/xxx.lanl.gov\/abs\/10.1101\/763722."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1038\/s41436-018-0418-5","article-title":"The illusion of polygenic disease risk prediction","volume":"21","author":"Wald","year":"2019","journal-title":"Genet. Med."},{"key":"ref_62","unstructured":"NIAGADS (2021, May 08). NG00075\u2014IGAP Rare Variant Summary Statistics\u2014Kunkle et al. (2019). Available online: https:\/\/www.niagads.org\/datasets\/ng00075."},{"key":"ref_63","unstructured":"CTG Lab (2021, May 08). Summary Statistics for Alzheimer\u2019s Dementia from Iris Jansen et al., 2019. Available online: https:\/\/ctg.cncr.nl\/software\/summary_statistics."}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/13\/1\/112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:00:32Z","timestamp":1760364032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/13\/1\/112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,6]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["genes13010112"],"URL":"https:\/\/doi.org\/10.3390\/genes13010112","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,6]]}}}