{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T21:43:17Z","timestamp":1791582197905,"version":"4.3.4"},"reference-count":65,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Exposure to radiation has been associated with increased risk of delivering small-for-gestational-age (SGA) newborns. There are no tools to predict SGA newborns in pregnant women exposed to radiation before pregnancy. Here, we aimed to develop an array of machine learning (ML) models to predict SGA newborns in women exposed to radiation before pregnancy. Patients\u2019 data was obtained from the National Free Preconception Health Examination Project from 2010 to 2012. The data were randomly divided into a training dataset (n = 364) and a testing dataset (n = 91). Eight various ML models were compared for solving the binary classification of SGA prediction, followed by a post hoc explainability based on the SHAP model to identify and interpret the most important features that contribute to the prediction outcome. A total of 455 newborns were included, with the occurrence of 60 SGA births (13.2%). Overall, the model obtained by extreme gradient boosting (XGBoost) achieved the highest area under the receiver-operating-characteristic curve (AUC) in the testing set (0.844, 95% confidence interval (CI): 0.713\u20130.974). All models showed satisfied AUCs, except for the logistic regression model (AUC: 0.561, 95% CI: 0.355\u20130.768). After feature selection by recursive feature elimination (RFE), 15 features were included in the final prediction model using the XGBoost algorithm, with an AUC of 0.821 (95% CI: 0.650\u20130.993). ML algorithms can generate robust models to predict SGA newborns in pregnant women exposed to radiation before pregnancy, which may thus be used as a prediction tool for SGA newborns in high-risk pregnant women.<\/jats:p>","DOI":"10.3390\/jpm12040550","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T21:34:29Z","timestamp":1648762469000},"page":"550","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Development and Evaluation of a Machine Learning Prediction Model for Small-for-Gestational-Age Births in Women Exposed to Radiation before Pregnancy"],"prefix":"10.3390","volume":"12","author":[{"given":"Xi","family":"Bai","sequence":"first","affiliation":[{"name":"Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibo","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyun","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2413-0646","authenticated-orcid":false,"given":"Hui","family":"Pan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S855","DOI":"10.1016\/j.ajog.2017.12.004","article-title":"Evidence-based national guidelines for the management of suspected fetal growth restriction: Comparison, consensus, and controversy","volume":"218","author":"McCowan","year":"2018","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1002\/uog.1806","article-title":"Does antenatal identification of small-for-gestational age fetuses significantly improve their outcome?","volume":"25","author":"Lindqvist","year":"2005","journal-title":"Ultrasound. Obstet. Gynecol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1111\/j.0001-6349.2004.00602.x","article-title":"Restricted fetal growth in sudden intrauterine unexplained death","volume":"83","author":"Gardosi","year":"2004","journal-title":"Acta Obstet. Et. Gynecol. Scand."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"f108","DOI":"10.1136\/bmj.f108","article-title":"Maternal and fetal risk factors for stillbirth: Population based study","volume":"346","author":"Gardosi","year":"2013","journal-title":"BMJ"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/1476-069X-5-3","article-title":"The association between low level exposures to ambient air pollution and term low birth weight: A retrospective cohort study","volume":"5","author":"Dugandzic","year":"2006","journal-title":"Environ. Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/1476-069X-10-32","article-title":"Individual exposures to drinking water trihalomethanes, low birth weight and small for gestational age risk: A prospective Kaunas cohort study","volume":"10","author":"Grazuleviciene","year":"2011","journal-title":"Environ. Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/1476-069X-9-44","article-title":"Ambient air pollution exposure and full-term birth weight in California","volume":"9","author":"Jesdale","year":"2010","journal-title":"Environ. Health"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1269\/jrr.32.327","article-title":"Mortality among the offspring (F1) of atomic bomb survivors, 1946\u20131985","volume":"32","author":"Yoshimoto","year":"1991","journal-title":"J. Radiat. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1093\/jrr\/rrw120","article-title":"Low-dose or low-dose-rate ionizing radiation-induced bioeffects in animal models","volume":"58","author":"Tang","year":"2017","journal-title":"J. Radiat. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112","DOI":"10.2307\/3578576","article-title":"Evidence of radiation-induced reduction of height and body weight from repeated measurements of adults exposed in childhood to the atomic bombs","volume":"140","author":"Otake","year":"1994","journal-title":"Radiat. Res."},{"key":"ref_11","first-page":"146","article-title":"Radiation procedures performed on U.S. women during pregnancy: Findings from two 1980 surveys","volume":"99","author":"Hamilton","year":"1984","journal-title":"Public Health Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1097\/00001648-199805000-00010","article-title":"Adverse reproductive outcomes among women exposed to low levels of ionizing radiation from diagnostic radiography for adolescent idiopathic scoliosis","volume":"9","author":"Goldberg","year":"1998","journal-title":"Epidemiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1097\/AOG.0b013e3181f87c4b","article-title":"Reproductive outcomes for survivors of childhood cancer","volume":"116","author":"Hudson","year":"2010","journal-title":"Obstet. Gynecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1001\/jama.291.16.1987","article-title":"Antepartum dental radiography and infant low birth weight","volume":"291","author":"Hujoel","year":"2004","journal-title":"JAMA"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1038\/bmt.2013.146","article-title":"Application of machine learning algorithms for clinical predictive modeling: A data-mining approach in SCT","volume":"49","author":"Shouval","year":"2014","journal-title":"Bone Marrow Transplant."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s00223-020-00734-y","article-title":"Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men","volume":"107","author":"Wu","year":"2020","journal-title":"Calcif. Tissue Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine Learning in Medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kuhle, S., Maguire, B., Zhang, H., Hamilton, D., Allen, A.C., Joseph, K.S., and Allen, V.M. (2018). Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: A retrospective cohort study. BMC Pregnancy Childbirth, 18.","DOI":"10.1186\/s12884-018-1971-2"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1002\/uog.22129","article-title":"Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics and medical history","volume":"56","author":"Papastefanou","year":"2020","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1002\/pd.5903","article-title":"Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor","volume":"41","author":"Saw","year":"2021","journal-title":"Prenat. Diagn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ajog.2009.08.026","article-title":"Paternal factors and low birthweight, preterm, and small for gestational age births: A systematic review","volume":"202","author":"Shah","year":"2010","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1136\/jech-2015-206894","article-title":"Paternal education and adverse birth outcomes in Canada","volume":"71","author":"Shapiro","year":"2017","journal-title":"J. Epidemiol. Community Health"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/1476-069X-11-40","article-title":"Using new satellite based exposure methods to study the association between pregnancy PM\u2082.\u2085 exposure, premature birth and birth weight in Massachusetts","volume":"11","author":"Kloog","year":"2012","journal-title":"Environ. Health"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e011227","DOI":"10.1136\/bmjopen-2016-011227","article-title":"Investigating the association between prepregnancy body mass index and adverse pregnancy outcomes: A large cohort study of 536\u2005098 Chinese pregnant women in rural China","volume":"6","author":"Pan","year":"2016","journal-title":"BMJ Open"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e174872","DOI":"10.1001\/jamapediatrics.2017.4872","article-title":"Association of Long-term Exposure to Airborne Particulate Matter of 1 \u03bcm or Less With Preterm Birth in China","volume":"172","author":"Wang","year":"2018","journal-title":"JAMA Pediatr."},{"key":"ref_26","first-page":"162","article-title":"Design of the National Free Preconception Health Examination Project in China","volume":"95","author":"Zhang","year":"2015","journal-title":"Natl. Med. J. China"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13260","DOI":"10.1021\/acs.est.8b02917","article-title":"An Ensemble Machine-Learning Model To Predict Historical PM(2.5) Concentrations in China from Satellite Data","volume":"52","author":"Xiao","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"ref_28","first-page":"97","article-title":"Chinese neonatal birth weight curve for different gestational age","volume":"53","author":"Zhu","year":"2015","journal-title":"Zhonghua Er Ke Za Zhi"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2028962","DOI":"10.1080\/2162402X.2022.2028962","article-title":"A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer","volume":"11","author":"Gong","year":"2022","journal-title":"Oncoimmunology"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lim, L.J., Lim, A.J.W., Ooi, B.N.S., Tan, J.W.L., Koh, E.T., Chong, S.S., Khor, C.C., Tucker-Kellogg, L., Lee, C.G., and Leong, K.P. (2022). Machine Learning using Genetic and Clinical Data Identifies a Signature that Robustly Predicts Methotrexate Response in Rheumatoid Arthritis. Rheumatology.","DOI":"10.1093\/rheumatology\/keac032"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lu, C., Song, J., Li, H., Yu, W., Hao, Y., Xu, K., and Xu, P. (2022). Predicting Venous Thrombosis in Osteoarthritis Using a Machine Learning Algorithm: A Population-Based Cohort Study. J. Pers. Med., 12.","DOI":"10.3390\/jpm12010114"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s13195-021-00879-4","article-title":"Data analysis with Shapley values for automatic subject selection in Alzheimer\u2019s disease data sets using interpretable machine learning","volume":"13","author":"Bloch","year":"2021","journal-title":"Alzheimer\u2019s. Res. Ther."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"191","DOI":"10.2307\/2347628","article-title":"Ridge estimators in logistic regression","volume":"41","year":"1992","journal-title":"Appl. Stat."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s40031-014-0176-y","article-title":"Weighted hybrid decision tree model for random forest classifier","volume":"97","author":"Kulkarni","year":"2016","journal-title":"J. Inst. Eng. Ser. B."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TNNLS.2020.3009776","article-title":"GBDT-MO: Gradient-Boosted Decision Trees for Multiple Outputs","volume":"32","author":"Zhang","year":"2021","journal-title":"IEEE. Trans. Neural Netw. Learn. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"110363","DOI":"10.1016\/j.envres.2020.110363","article-title":"Quantitative structure-property relationships for the calculation of the soil adsorption coefficient using machine learning algorithms with calculated chemical properties from open-source software","volume":"196","author":"Kobayashi","year":"2021","journal-title":"Environ. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","article-title":"CatBoost for big data: An interdisciplinary review","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9952","DOI":"10.1038\/s41598-020-67024-3","article-title":"Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_40","first-page":"41","article-title":"Applications of Support Vector Machine (SVM) Learning in Cancer Genomics","volume":"15","author":"Huang","year":"2018","journal-title":"Cancer Genom. Proteom."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.neuroscience.2016.06.025","article-title":"A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging","volume":"331","author":"Long","year":"2016","journal-title":"Neuroscience"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"586202","DOI":"10.3389\/fpsyg.2020.586202","article-title":"Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach","volume":"11","author":"Prout","year":"2020","journal-title":"Front. Psychol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1158\/1055-9965.EPI-09-0287","article-title":"Pregnancy outcomes among adult survivors of childhood cancer in the British Childhood Cancer Survivor Study","volume":"18","author":"Reulen","year":"2009","journal-title":"Cancer Epidemiol. Prev. Biomark."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1067\/mob.2002.126643","article-title":"Pregnancy outcome of female survivors of childhood cancer: A report from the Childhood Cancer Survivor Study","volume":"187","author":"Green","year":"2002","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1093\/jnci\/djj394","article-title":"Female survivors of childhood cancer: Preterm birth and low birth weight among their children","volume":"98","author":"Signorello","year":"2006","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1186\/s12940-020-00630-w","article-title":"Spatiotemporal association of low birth weight with Cs-137 deposition at the prefecture level in Japan after the Fukushima nuclear power plant accidents: An analytical-ecologic epidemiological study","volume":"19","author":"Scherb","year":"2020","journal-title":"Environ. Health"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1214\/18-AOS1696","article-title":"Feature elimination in kernel machines in moderately high dimensions","volume":"47","author":"Dasgupta","year":"2019","journal-title":"Ann. Stat."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"103800","DOI":"10.1016\/j.ebiom.2021.103800","article-title":"Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients","volume":"75","author":"Lim","year":"2022","journal-title":"EBioMedicine"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104614","DOI":"10.1016\/j.ijmedinf.2021.104614","article-title":"Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients","volume":"156","author":"Ntakolia","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ntakolia, C., Kokkotis, C., Moustakidis, S., and Tsaopoulos, D. (2021). Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics, 11.","DOI":"10.3390\/diagnostics11020285"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1016\/j.jcin.2019.06.013","article-title":"Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement","volume":"12","author":"Kim","year":"2019","journal-title":"JACC Cardiovasc. Interv."},{"key":"ref_52","first-page":"993","article-title":"Effect of parental anthropometric parameters on neonatal birth weight and birth length","volume":"31","author":"Stoini","year":"2007","journal-title":"Coll. Antropol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Myklestad, K., Vatten, L.J., Magnussen, E.B., Salvesen, K., and Romundstad, P.R. (2013). Do parental heights influence pregnancy length?: A population-based prospective study, HUNT 2. BMC Pregnancy Childbirth, 13.","DOI":"10.1186\/1471-2393-13-33"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10995-017-2407-8","article-title":"Fathers Count: The Impact of Paternal Risk Factors on Birth Outcomes","volume":"22","author":"Meng","year":"2018","journal-title":"Matern. Child. Health J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1017\/S2040174416000635","article-title":"Indicators of fetal growth and adult liver enzymes: The Bogalusa Heart Study and the Cardiovascular Risk in Young Finns Study","volume":"8","author":"Harville","year":"2017","journal-title":"J. Dev. Orig. Health Dis."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3889\/oamjms.2017.013","article-title":"Platelet Count in First Trimester of Pregnancy as a Predictor of Perinatal Outcome","volume":"5","author":"Larroca","year":"2017","journal-title":"Maced. J. Med. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1097\/OLQ.0000000000000592","article-title":"Adverse Birth Outcomes and Maternal Neisseria gonorrhoeae Infection: A Population-Based Cohort Study in Washington State","volume":"44","author":"Heumann","year":"2017","journal-title":"Sex. Transm. Dis."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1097\/OLQ.0b013e3181f2e85f","article-title":"Sexually transmitted infections and adverse pregnancy outcomes among women attending inner city public sexually transmitted diseases clinics","volume":"38","author":"Johnson","year":"2011","journal-title":"Sex. Transm. Dis."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e010984","DOI":"10.1136\/bmjopen-2015-010984","article-title":"Small-for-gestational age and its association with maternal blood glucose, body mass index and stature: A perinatal cohort study among Chinese women","volume":"6","author":"Leng","year":"2016","journal-title":"BMJ Open"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"339.e1","DOI":"10.1016\/j.ajog.2009.07.002","article-title":"A systematic review of outcomes of maternal weight gain according to the Institute of Medicine recommendations: Birthweight, fetal growth, and postpartum weight retention","volume":"201","author":"Viswanathan","year":"2009","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_61","first-page":"53","article-title":"Pregnancy weight gain and postpartum loss: Avoiding obesity while optimizing the growth and development of the fetus","volume":"56","author":"Lederman","year":"2001","journal-title":"J. Am. Med. Women\u2019s Assoc."},{"key":"ref_62","first-page":"533","article-title":"Radioprotective Effect of Arbutin in Megavoltage Therapeutic X-irradiated Mice using Liver Enzymes Assessment","volume":"9","author":"Nadi","year":"2019","journal-title":"J. Biomed. Phys. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1080\/09553002.2017.1332438","article-title":"A review of radiation countermeasures focusing on injury-specific medicinals and regulatory approval status: Part I. Radiation sub-syndromes, animal models and FDA-approved countermeasures","volume":"93","author":"Singh","year":"2017","journal-title":"Int. J. Radiat. Biol."},{"key":"ref_64","first-page":"661","article-title":"The occupational and procreation health of immigrant female workers in electron factory","volume":"29","author":"Fan","year":"2011","journal-title":"Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"14519","DOI":"10.3390\/ijerph121114519","article-title":"Association of Exposure to Radio-Frequency Electromagnetic Field Radiation (RF-EMFR) Generated by Mobile Phone Base Stations with Glycated Hemoglobin (HbA1c) and Risk of Type 2 Diabetes Mellitus","volume":"12","author":"Meo","year":"2015","journal-title":"Int. J. Environ. Res. Public Health"}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/12\/4\/550\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:47:54Z","timestamp":1760136474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/12\/4\/550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,31]]},"references-count":65,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["jpm12040550"],"URL":"https:\/\/doi.org\/10.3390\/jpm12040550","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,31]]}}}