{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T08:10:48Z","timestamp":1779351048274,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":49,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-47053-0","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:35:32Z","timestamp":1775144132000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modelling of hybrid deep ensemble learning based skin lesion detection using FCNN denoising and inception-dilated ResNetV2"],"prefix":"10.1038","volume":"16","author":[{"given":"Jaya Prakash","family":"Sunkavalli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis","family":"Pustokhin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E.","family":"Laxmi Lydia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. Prameela","family":"Rani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srijana","family":"Acharya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhanu","family":"Shrestha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheol","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"47053_CR1","doi-asserted-by":"crossref","unstructured":"Gouda, W., Sama, N. U., Al-Waakid, G., Humayun, M. & Jhanjhi, N. Z. June. Detection of skin cancer based on skin lesion images using deep learning. In Healthcare.10 (7), 1183. MDPI. (2022).","DOI":"10.3390\/healthcare10071183"},{"issue":"2","key":"47053_CR2","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s10462-020-09865-y","volume":"54","author":"A Adegun","year":"2021","unstructured":"Adegun, A. & Viriri, S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif. Intell. Rev. 54 (2), 811\u2013841 (2021).","journal-title":"Artif. Intell. Rev."},{"key":"47053_CR3","doi-asserted-by":"crossref","unstructured":"Vesal, S., Ravikumar, N. & Maier, A. November. SkinNet: A deep learning framework for skin lesion segmentation. In 2018 IEEE nuclear science symposium and medical imaging conference proceedings (NSS\/MIC) (pp. 1\u20133). IEEE. (2018).","DOI":"10.1109\/NSSMIC.2018.8824732"},{"issue":"10","key":"47053_CR4","doi-asserted-by":"publisher","first-page":"2912","DOI":"10.1109\/JBHI.2020.2973614","volume":"24","author":"L Song","year":"2020","unstructured":"Song, L., Lin, J., Wang, Z. J. & Wang, H. An end-to-end multi-task deep learning framework for skin lesion analysis. IEEE J. biomedical health Inf. 24 (10), 2912\u20132921 (2020).","journal-title":"IEEE J. biomedical health Inf."},{"key":"47053_CR5","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.1109\/ACCESS.2019.2960504","volume":"8","author":"M Goyal","year":"2019","unstructured":"Goyal, M., Oakley, A., Bansal, P., Dancey, D. & Yap, M. H. Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. Ieee Access. 8, 4171\u20134181 (2019).","journal-title":"Ieee Access."},{"issue":"8","key":"47053_CR6","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.3390\/diagnostics11081390","volume":"11","author":"MA Kassem","year":"2021","unstructured":"Kassem, M. A., Hosny, K. M., Dama\u0161evi\u010dius, R. & Eltoukhy, M. M. Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review. Diagnostics 11 (8), 1390 (2021).","journal-title":"Diagnostics"},{"key":"47053_CR7","doi-asserted-by":"publisher","first-page":"208264","DOI":"10.1109\/ACCESS.2020.3037258","volume":"8","author":"LF Li","year":"2020","unstructured":"Li, L. F. et al. Deep learning in skin disease image recognition: A review. Ieee Access. 8, 208264\u2013208280 (2020).","journal-title":"Ieee Access."},{"issue":"11","key":"47053_CR8","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1111\/exd.13777","volume":"27","author":"J Yap","year":"2018","unstructured":"Yap, J., Yolland, W. & Tschandl, P. Multimodal skin lesion classification using deep learning. Exp. Dermatol. 27 (11), 1261\u20131267 (2018).","journal-title":"Exp. Dermatol."},{"issue":"5","key":"47053_CR9","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1109\/TMI.2021.3136682","volume":"41","author":"P Yao","year":"2021","unstructured":"Yao, P. et al. Single model deep learning on imbalanced small datasets for skin lesion classification. IEEE Trans. Med. Imaging. 41 (5), 1242\u20131254 (2021).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"47053_CR10","doi-asserted-by":"crossref","unstructured":"Nancy, G. & Bhuvaneswari, E. A deep learning-driven multi-layer digital twin framework with miot for precision oncology in cancer diagnosis. Journal Intell. Syst. & Internet Things, 17(1). (2025).","DOI":"10.54216\/JISIoT.170102"},{"key":"47053_CR11","doi-asserted-by":"publisher","first-page":"111325","DOI":"10.1016\/j.patcog.2024.111325","volume":"161","author":"L Tan","year":"2025","unstructured":"Tan, L., Wu, H., Zhu, J., Liang, Y. & Xia, J. Clinical-inspired skin lesions recognition based on deep hair removal with multi-level feature fusion. Pattern Recogn. 161, 111325 (2025).","journal-title":"Pattern Recogn."},{"issue":"3","key":"47053_CR12","doi-asserted-by":"publisher","first-page":"10978","DOI":"10.48084\/etasr.5594","volume":"13","author":"K Rajeshkumar","year":"2023","unstructured":"Rajeshkumar, K., Ananth, C. & Mohananthini, N. Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model. Eng. Technol. Appl. Sci. Res. 13 (3), 10978\u201310983 (2023).","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"47053_CR13","doi-asserted-by":"crossref","unstructured":"Hamrani, A. & Godavarty, A. Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging. arXiv preprint https:\/\/arxiv.org\/abs\/2503.18170 (2025)","DOI":"10.3390\/bioengineering12101036"},{"issue":"4","key":"47053_CR14","doi-asserted-by":"publisher","first-page":"326","DOI":"10.3390\/bioengineering12040326","volume":"12","author":"A Khan","year":"2025","unstructured":"Khan, A., Sajid, M. Z., Khan, N. A., Mahgoub, A. Y. & Abbas, Q. CAD-Skin: A Hybrid Convolutional Neural Network\u2013Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer. Bioengineering 12 (4), 326 (2025).","journal-title":"Bioengineering"},{"key":"47053_CR15","doi-asserted-by":"crossref","unstructured":"Kumar, A., Selvaprakash, T., Ponarasu, B. & Venkateswaran, K. December. Harnessing CRNN for Robust and Efficient Skin Cancer Detection in Medical Imaging. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1\u20137). IEEE. (2024).","DOI":"10.1109\/ICERCS63125.2024.10894780"},{"key":"47053_CR16","doi-asserted-by":"publisher","first-page":"106292","DOI":"10.1016\/j.bspc.2024.106292","volume":"95","author":"RR Babu","year":"2024","unstructured":"Babu, R. R. & Philip, F. M. Optimised deep learning for skin lesion segmentation and skin cancer detection. Biomed. Signal Process. Control. 95, 106292 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"47053_CR17","first-page":"100865","volume":"29","author":"MVS Ramprasad","year":"2023","unstructured":"Ramprasad, M. V. S., Nagesh, S. S. V., Sahith, V. & Lankalapalli, R. K. Hierarchical agglomerative clustering-based skin lesion detection with region based neural networks classification. Measurement: Sens. 29, 100865 (2023).","journal-title":"Measurement: Sens."},{"key":"47053_CR18","doi-asserted-by":"crossref","unstructured":"Ogudo, K. A., Surendran, R. & Khalaf, O. I. Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model. Comput. Syst. Sci. Eng., 44(1). (2023).","DOI":"10.32604\/csse.2023.024154"},{"key":"47053_CR19","doi-asserted-by":"publisher","first-page":"113623","DOI":"10.1109\/ACCESS.2023.3313977","volume":"11","author":"T Mahmood","year":"2023","unstructured":"Mahmood, T., Rehman, A., Saba, T., Nadeem, L. & Bahaj, S. A. O. Recent advancements and future prospects in active deep learning for medical image segmentation and classification. IEEE Access. 11, 113623\u2013113652 (2023).","journal-title":"IEEE Access."},{"key":"47053_CR20","doi-asserted-by":"publisher","first-page":"101077","DOI":"10.1016\/j.currproblcancer.2024.101077","volume":"49","author":"KM Selvaraj","year":"2024","unstructured":"Selvaraj, K. M., Gnanagurusubbiah, S., Roy, R. R. R. & Balu, S. Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics. Curr. Probl. Cancer. 49, 101077 (2024).","journal-title":"Curr. Probl. Cancer"},{"issue":"1","key":"47053_CR21","doi-asserted-by":"publisher","first-page":"7425","DOI":"10.1038\/s41598-025-92293-1","volume":"15","author":"JD Dorathi Jayaseeli","year":"2025","unstructured":"Dorathi Jayaseeli, J. D. et al. An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models. Sci. Rep. 15 (1), 7425 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"47053_CR22","doi-asserted-by":"publisher","first-page":"9336","DOI":"10.1038\/s41598-024-57393-4","volume":"14","author":"R Ali","year":"2024","unstructured":"Ali, R., Manikandan, A., Lei, R. & Xu, J. A novel SpaSA based hyper-parameter optimised FCEDN with adaptive CNN classification for skin cancer detection. Sci. Rep. 14 (1), 9336 (2024).","journal-title":"Sci. Rep."},{"issue":"5","key":"47053_CR23","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s42452-025-06596-z","volume":"7","author":"MHI Bijoy","year":"2025","unstructured":"Bijoy, M. H. I. et al. SkinIncept: an ensemble transfer learning-based approach for multiclass skin disease classification using InceptionV3 and InceptionResNetV2. Discover Appl. Sci. 7 (5), 433 (2025).","journal-title":"Discover Appl. Sci."},{"key":"47053_CR24","doi-asserted-by":"publisher","first-page":"101495","DOI":"10.1016\/j.imu.2024.101495","volume":"47","author":"DK Saha","year":"2024","unstructured":"Saha, D. K., Joy, A. M., Majumder, A. & YoTransViT A transformer and CNN method for predicting and classifying skin diseases using segmentation techniques. Inf. Med. Unlocked. 47, 101495 (2024).","journal-title":"Inf. Med. Unlocked"},{"issue":"1","key":"47053_CR25","doi-asserted-by":"publisher","first-page":"15680","DOI":"10.1038\/s41598-025-00025-2","volume":"15","author":"A Shaik","year":"2025","unstructured":"Shaik, A. et al. An attention based hybrid approach using CNN and BiLSTM for improved skin lesion classification. Sci. Rep. 15 (1), 15680 (2025).","journal-title":"Sci. Rep."},{"issue":"12","key":"47053_CR26","doi-asserted-by":"publisher","first-page":"5233","DOI":"10.3390\/app14125233","volume":"14","author":"R Madinakhon","year":"2024","unstructured":"Madinakhon, R., Mukhtorov, D. & Cho, Y. I. Integrating Principal Component Analysis and Multi-Input Convolutional Neural Networks for Advanced Skin Lesion Cancer Classification. Appl. Sci. 14 (12), 5233 (2024).","journal-title":"Appl. Sci."},{"key":"47053_CR27","doi-asserted-by":"publisher","first-page":"205520762513518","DOI":"10.1177\/20552076251351858","volume":"11","author":"K Manzoor","year":"2025","unstructured":"Manzoor, K., Gilal, N. U., Agus, M. & Schneider, J. Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network. Digit. Health. 11, 20552076251351858 (2025).","journal-title":"Digit. Health"},{"key":"47053_CR28","doi-asserted-by":"crossref","unstructured":"Narendra, M., Harshini, T. S. & Anbarasi, L. J. Advancing Skin Disease Diagnosis: A Multimodal Approach Utilising Telegram Api Token Chatbot for Text and Image Analysis in Skin Disease Classification. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3516884"},{"issue":"1","key":"47053_CR29","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1007\/s44174-025-00434-x","volume":"4","author":"S Saha","year":"2026","unstructured":"Saha, S. et al. SkinFLNet: a federated learning approach for skin cancer detection utilizing skin dermoscopy images. Biomedical Mater. Devices. 4 (1), 921\u2013935 (2026).","journal-title":"Biomedical Mater. Devices"},{"issue":"1","key":"47053_CR30","doi-asserted-by":"publisher","first-page":"31234","DOI":"10.1038\/s41598-025-15655-9","volume":"15","author":"AS Al-Waisy","year":"2025","unstructured":"Al-Waisy, A. S. et al. A deep learning framework for automated early diagnosis and classification of skin cancer lesions in dermoscopy images. Sci. Rep. 15 (1), 31234 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"47053_CR31","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/biomedicines14010097","volume":"14","author":"U Santaniello","year":"2026","unstructured":"Santaniello, U. et al. Emerging Artificial Intelligence Models for Estimating Breslow Thickness from Dermoscopic Images. Biomedicines 14 (1), 97 (2026).","journal-title":"Biomedicines"},{"key":"47053_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al. A convnet for the 2020s. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 11976\u201311986). (2022).","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"47053_CR33","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE\/CVF international conference on computer vision, 10012\u201310022 (2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"1","key":"47053_CR34","doi-asserted-by":"publisher","first-page":"7252","DOI":"10.1038\/s41598-025-91446-6","volume":"15","author":"K Nawaz","year":"2025","unstructured":"Nawaz, K. et al. Skin cancer detection using dermoscopic images with convolutional neural network. Sci. Rep. 15 (1), 7252 (2025).","journal-title":"Sci. Rep."},{"key":"47053_CR35","doi-asserted-by":"crossref","unstructured":"Singh, S. et al. Real-time skin disease diagnosis using image classification techniques. In Artificial Intelligence and Sustainable Innovation (673\u2013679). CRC. (2026).","DOI":"10.1201\/9781003654049-96"},{"issue":"1","key":"47053_CR36","doi-asserted-by":"publisher","first-page":"37823","DOI":"10.1038\/s41598-025-21783-z","volume":"15","author":"HA Owida","year":"2025","unstructured":"Owida, H. A. et al. A deep learning-based dual-branch framework for automated skin lesion segmentation and classification via dermoscopic Images. Sci. Rep. 15 (1), 37823 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"47053_CR37","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s12559-025-10538-7","volume":"18","author":"J Singh","year":"2026","unstructured":"Singh, J., Gill, J. & Kumar, Y. Automated Detection and Diagnosis of Bacterial Skin Infections using Deep Learning with Segmentation Techniques. Cogn. Comput. 18 (1), 5 (2026).","journal-title":"Cogn. Comput."},{"issue":"1","key":"47053_CR38","doi-asserted-by":"publisher","first-page":"24533","DOI":"10.1038\/s41598-025-09938-4","volume":"15","author":"MAA Mahmud","year":"2025","unstructured":"Mahmud, M. A. A. et al. Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images. Sci. Rep. 15 (1), 24533 (2025).","journal-title":"Sci. Rep."},{"key":"47053_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2019.01.010","volume":"108","author":"A Abbasi","year":"2019","unstructured":"Abbasi, A., Monadjemi, A., Fang, L., Rabbani, H. & Zhang, Y. Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks. Comput. Biol. Med. 108, 1\u20138 (2019).","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"47053_CR40","doi-asserted-by":"publisher","first-page":"6490","DOI":"10.1038\/s41598-025-91322-3","volume":"15","author":"A Khalili Fakhrabadi","year":"2025","unstructured":"Khalili Fakhrabadi, A., Shahbazzadeh, M. J., Jalali, N. & Eslami, M. A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity. Sci. Rep. 15 (1), 6490 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"47053_CR41","doi-asserted-by":"publisher","first-page":"10841","DOI":"10.1038\/s41598-025-94654-2","volume":"15","author":"L Chen","year":"2025","unstructured":"Chen, L., Lin, X., Ma, L. & Wang, C. A BiLSTM model enhanced with multi-objective arithmetic optimisation for COVID-19 diagnosis from CT images. Sci. Rep. 15 (1), 10841 (2025).","journal-title":"Sci. Rep."},{"key":"47053_CR42","doi-asserted-by":"crossref","unstructured":"Ishaque, N. B. M. & Florence, S. M. Development of a Deep Learning Predictive Model for Estimating Higher Heating Value in Municipal Solid Management. Cleaner Eng. Technology, 100966. (2025).","DOI":"10.1016\/j.clet.2025.100966"},{"key":"47053_CR43","doi-asserted-by":"crossref","unstructured":"Chaudhari, D. D. R. et al. A Hybrid PKI and Spiking Neural Network Approach for Enhancing Security and Energy Efficiency in IoMT-Based Healthcare 5.0. SLAS technology, p.100284. (2025).","DOI":"10.1016\/j.slast.2025.100284"},{"key":"47053_CR44","unstructured":"https:\/\/www.kaggle.com\/datasets\/nodoubttome\/skin-cancer9-classesisic [Accessed on 23 April 2025]."},{"issue":"2","key":"47053_CR45","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.3390\/app13021210","volume":"13","author":"SB Mukadam","year":"2023","unstructured":"Mukadam, S. B. & Patil, H. Y. Skin cancer classification framework using enhanced super resolution generative adversarial network and custom convolutional neural network. Appl. Sci. 13 (2), 1210 (2023).","journal-title":"Appl. Sci."},{"issue":"11","key":"47053_CR46","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.3390\/diagnostics13111911","volume":"13","author":"M Naqvi","year":"2023","unstructured":"Naqvi, M., Gilani, S. Q., Syed, T., Marques, O. & Kim, H. C. Skin cancer detection using deep learning\u2014a review. Diagnostics 13 (11), 1911 (2023).","journal-title":"Diagnostics"},{"issue":"7","key":"47053_CR47","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.3390\/cancers15072179","volume":"15","author":"M Tahir","year":"2023","unstructured":"Tahir, M. et al. DSCC_Net: multi-classification deep learning models for diagnosing of skin cancer using dermoscopic images. Cancers 15 (7), 2179 (2023).","journal-title":"Cancers"},{"issue":"3","key":"47053_CR48","doi-asserted-by":"publisher","first-page":"374","DOI":"10.3390\/diagnostics15030374","volume":"15","author":"S Vuran","year":"2025","unstructured":"Vuran, S., Ucan, M., Akin, M. & Kaya, M. Multi-classification of skin lesion images including Mpox disease using transformer-based deep learning architectures. Diagnostics 15 (3), 374 (2025).","journal-title":"Diagnostics"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47053-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47053-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47053-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T07:59:21Z","timestamp":1779350361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47053-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["47053"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-47053-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"3 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"15824"}}