{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,15]],"date-time":"2026-11-15T12:09:42Z","timestamp":1794744582936,"version":"4.9.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-34282-y","type":"journal-article","created":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T05:32:14Z","timestamp":1767418334000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Physics informed machine learning for predictive toxicology and optimization of curcumin nanocarriers"],"prefix":"10.1038","volume":"16","author":[{"given":"Abbas","family":"Rahdar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonia","family":"Fathi-karkan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"34282_CR1","doi-asserted-by":"publisher","first-page":"820806","DOI":"10.3389\/fphar.2022.820806","volume":"13","author":"S Fuloria","year":"2022","unstructured":"Fuloria, S. et al. A comprehensive review on the therapeutic potential of Curcuma longa Linn. in relation to its major active constituent curcumin. Front. Pharmacol. 13, 820806 (2022).","journal-title":"Front. Pharmacol."},{"key":"34282_CR2","doi-asserted-by":"publisher","first-page":"1430284","DOI":"10.3389\/fphar.2024.1430284","volume":"15","author":"P Zhang","year":"2024","unstructured":"Zhang, P., Liu, H., Yu, Y., Peng, S. & Zhu, S. Role of Curcuma longae Rhizoma in medical applications: Research challenges and opportunities. Front. Pharmacol. 15, 1430284 (2024).","journal-title":"Front. Pharmacol."},{"key":"34282_CR3","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.660712","volume":"11","author":"H Wang","year":"2021","unstructured":"Wang, H. et al. Curcumin regulates cancer progression: Focus on ncRNAs and molecular signaling pathways. Front. Oncol. 11, 660712 (2021).","journal-title":"Front. Oncol."},{"issue":"8","key":"34282_CR4","doi-asserted-by":"publisher","first-page":"694","DOI":"10.3109\/1061186X.2016.1157883","volume":"24","author":"W Liu","year":"2016","unstructured":"Liu, W. et al. Oral bioavailability of curcumin: problems and advancements. J. Drug Target. 24(8), 694\u2013702 (2016).","journal-title":"J. Drug Target."},{"key":"34282_CR5","doi-asserted-by":"publisher","first-page":"1603018","DOI":"10.3389\/fimmu.2025.1603018","volume":"16","author":"MT El-Saadony","year":"2025","unstructured":"El-Saadony, M. T. et al. Curcumin, an active component of turmeric: Biological activities, nutritional aspects, immunological, bioavailability, and human health benefits-a comprehensive review. Front. Immunol. 16, 1603018 (2025).","journal-title":"Front. Immunol."},{"issue":"14","key":"34282_CR6","doi-asserted-by":"publisher","first-page":"2580","DOI":"10.3390\/cancers16142580","volume":"16","author":"A Amaroli","year":"2024","unstructured":"Amaroli, A. et al. The bright side of curcumin: A narrative review of its therapeutic potential in cancer management. Cancers 16(14), 2580 (2024).","journal-title":"Cancers"},{"issue":"6","key":"34282_CR7","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.3390\/biomedicines13061464","volume":"13","author":"R Boyuklieva","year":"2025","unstructured":"Boyuklieva, R., Zahariev, N., Simeonov, P., Penkov, D. & Katsarov, P. Next-generation drug delivery for neurotherapeutics: The promise of stimuli-triggered nanocarriers. Biomedicines. 13(6), 1464 (2025).","journal-title":"Biomedicines."},{"key":"34282_CR8","doi-asserted-by":"crossref","unstructured":"Mohamed RRGA, Ali SM, Ahmed IS, Rawas-Qalaji M, Hussain Z. Next-generation nanocarriers for colorectal cancer: passive, active, and stimuli-responsive strategies for precision therapy. Biomaterials Science. 2025.","DOI":"10.1039\/D5BM01176K"},{"issue":"6","key":"34282_CR9","doi-asserted-by":"publisher","first-page":"1801002","DOI":"10.1002\/adhm.201801002","volume":"8","author":"N Wang","year":"2019","unstructured":"Wang, N., Cheng, X., Li, N., Wang, H. & Chen, H. Nanocarriers and their loading strategies. Adv. Healthcare Mater. 8(6), 1801002 (2019).","journal-title":"Adv. Healthcare Mater."},{"key":"34282_CR10","doi-asserted-by":"crossref","unstructured":"Fr\u00f6hlich E. The role of surface charge in cellular uptake and cytotoxicity of medical nanoparticles. Int. J. Nanomed. 2012:5577\u201391.","DOI":"10.2147\/IJN.S36111"},{"issue":"9\u201310","key":"34282_CR11","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1002\/smll.201201390","volume":"9","author":"X Duan","year":"2013","unstructured":"Duan, X. & Li, Y. Physicochemical characteristics of nanoparticles affect circulation, biodistribution, cellular internalization, and trafficking. Small 9(9\u201310), 1521\u20131532 (2013).","journal-title":"Small"},{"issue":"14","key":"34282_CR12","doi-asserted-by":"publisher","first-page":"4218","DOI":"10.1039\/C6CS00636A","volume":"46","author":"S Behzadi","year":"2017","unstructured":"Behzadi, S. et al. Cellular uptake of nanoparticles: Journey inside the cell. Chem. Soc. Rev. 46(14), 4218\u20134244 (2017).","journal-title":"Chem. Soc. Rev."},{"key":"34282_CR13","unstructured":"Glorioso V. Formulation, Characterization, and Machine Learning Prediction of Poly (lactic-co-glycolic) acid Nanoparticles for Oncological Pregnant Women Treatment: Politecnico di Torino; 2025."},{"key":"34282_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijbiomac.2024.135123","volume":"279","author":"N Chaurawal","year":"2024","unstructured":"Chaurawal, N. et al. Development of fucoidan\/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery. Int. J. Biol. Macromol. 279, 135123 (2024).","journal-title":"Int. J. Biol. Macromol."},{"issue":"8","key":"34282_CR15","doi-asserted-by":"publisher","first-page":"983","DOI":"10.3390\/molecules21080983","volume":"21","author":"I Babajide Mustapha","year":"2016","unstructured":"Babajide Mustapha, I. & Saeed, F. Bioactive molecule prediction using extreme gradient boosting. Molecules 21(8), 983 (2016).","journal-title":"Molecules"},{"issue":"1","key":"34282_CR16","doi-asserted-by":"publisher","first-page":"8840","DOI":"10.1038\/s41598-025-92725-y","volume":"15","author":"K Almansour","year":"2025","unstructured":"Almansour, K. & Alqahtani, A. S. Utilization of machine learning approach for production of optimized PLGA nanoparticles for drug delivery applications. Sci. Rep. 15(1), 8840 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"34282_CR17","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1038\/s41598-024-84450-9","volume":"15","author":"WA Mahdi","year":"2025","unstructured":"Mahdi, W. A., Alhowyan, A. & Obaidullah, A. J. Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models. Sci. Rep. 15(1), 1017 (2025).","journal-title":"Sci. Rep."},{"issue":"8","key":"34282_CR18","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1021\/acs.chemrestox.2c00375","volume":"36","author":"K Sinha","year":"2023","unstructured":"Sinha, K., Ghosh, N. & Sil, P. C. A review on the recent applications of deep learning in predictive drug toxicological studies. Chem. Res. Toxicol. 36(8), 1174\u20131205 (2023).","journal-title":"Chem. Res. Toxicol."},{"issue":"7","key":"34282_CR19","doi-asserted-by":"publisher","first-page":"525","DOI":"10.3390\/toxics13070525","volume":"13","author":"R Zhang","year":"2025","unstructured":"Zhang, R., Wen, H., Lin, Z., Li, B. & Zhou, X. Artificial intelligence-driven drug toxicity prediction: Advances, challenges, and future directions. Toxics. 13(7), 525 (2025).","journal-title":"Toxics."},{"issue":"1","key":"34282_CR20","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s44379-025-00016-0","volume":"1","author":"C Meng","year":"2025","unstructured":"Meng, C., Griesemer, S., Cao, D., Seo, S. & Liu, Y. When physics meets machine learning: A survey of physics-informed machine learning. Mach. Learn. Comput. Sci. Eng. 1(1), 20 (2025).","journal-title":"Mach. Learn. Comput. Sci. Eng."},{"issue":"1","key":"34282_CR21","doi-asserted-by":"publisher","first-page":"42190","DOI":"10.1038\/s41598-025-26145-3","volume":"15","author":"S Fathi-Karkan","year":"2025","unstructured":"Fathi-Karkan, S., Rahdar, A. & Shirzad, M. Integrating machine-learning and nanotechnology to quantify pH-modulated oxaliplatin release. Sci. Rep. 15(1), 42190 (2025).","journal-title":"Sci. Rep."},{"key":"34282_CR22","unstructured":"Rehman D. Physics-informed Deep Learning and Differentiable Mechanistic Models for Multicomponent Transport Phenomena: Massachusetts Institute of Technology; 2024."},{"key":"34282_CR23","unstructured":"Jacobsen C. Enhancing Physical Modeling with Interpretable Physics-Aware Machine Learning 2024."},{"issue":"3","key":"34282_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12145-025-01796-y","volume":"18","author":"K Merabet","year":"2025","unstructured":"Merabet, K. et al. Predicting water quality variables using gradient boosting machine: global versus local explainability using SHapley Additive Explanations (SHAP). Earth Sci. Inf. 18(3), 1\u201334 (2025).","journal-title":"Earth Sci. Inf."},{"issue":"8","key":"34282_CR25","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.3390\/jpm12081314","volume":"12","author":"J Chuah","year":"2022","unstructured":"Chuah, J., Kruger, U., Wang, G., Yan, P. & Hahn, J. Framework for testing robustness of machine learning-based classifiers. J. Person. Med. 12(8), 1314 (2022).","journal-title":"J. Person. Med."},{"key":"34282_CR26","doi-asserted-by":"crossref","unstructured":"Parashar AK, Jamliya A, Nasrat S, Soni R. XGBoost for heart disease prediction achieving high accuracy with robust machine learning techniques. Int. J. Innov. Sci. Eng. Manag. 2025:185\u201391.","DOI":"10.69968\/ijisem.2025v4i3185-191"},{"issue":"1","key":"34282_CR27","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s11831-024-10146-y","volume":"32","author":"MM Azad","year":"2025","unstructured":"Azad, M. M., Cheon, Y., Raouf, I., Khalid, S. & Kim, H. S. Intelligent computational methods for damage detection of laminated composite structures for mobility applications: A comprehensive review. Arch. Comput. Methods Eng. 32(1), 441\u2013469 (2025).","journal-title":"Arch. Comput. Methods Eng."},{"issue":"9392","key":"34282_CR28","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1016\/S0140-6736(03)14568-0","volume":"362","author":"A Das","year":"2003","unstructured":"Das, A. et al. Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: Internal and external validation of a predictive model. Lancet 362(9392), 1261\u20131266 (2003).","journal-title":"Lancet"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34282-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34282-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34282-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:00:25Z","timestamp":1769767225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34282-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,2]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["34282"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-34282-y","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,2]]},"assertion":[{"value":"9 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare no competing financial or personal interests. AI-assisted tools were employed exclusively to improve language clarity and grammatical accuracy. The study design, data analysis, interpretation, and all intellectual content are the original work of the authors, who take full responsibility for the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study did not involve human participants, animal experiments, or the use of any personally identifiable data. All analyses were performed on previously published; publicly accessible datasets extracted from peer-reviewed scientific literature. Therefore, formal ethical approval was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All authors agreed with the content and all gave consent to submit the manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"4172"}}