{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:30:10Z","timestamp":1779103810383,"version":"3.51.4"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Institute for Digital Molecular Analytics and Science, NTU","award":["EDUNC-33-18-279-V12"],"award-info":[{"award-number":["EDUNC-33-18-279-V12"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-45423-2","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:14:32Z","timestamp":1774984472000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Morphology-guided deep learning for nanoparticle agglomeration diagnostic assays"],"prefix":"10.1038","volume":"16","author":[{"given":"Kartik","family":"Jhawar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Liu","family":"Chu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph B.","family":"DeGrandchamp","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueming","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lalitha Anjali","family":"Peddibhotla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shabina","family":"Banu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farah Najwa Nabila","family":"Mohd Hatta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoli","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"T\u00f6r\u00f6k","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lipo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"45423_CR1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/diagnostics11010009","volume":"11","author":"M Rezaei","year":"2021","unstructured":"Rezaei, M. et al. Point of care diagnostics in the age of covid-19. Diagnostics 11, 9. https:\/\/doi.org\/10.3390\/diagnostics11010009 (2021).","journal-title":"Diagnostics"},{"key":"45423_CR2","doi-asserted-by":"publisher","first-page":"7899","DOI":"10.1021\/acsnano.1c02981","volume":"15","author":"E Valera","year":"2021","unstructured":"Valera, E. et al. COVID-19 point-of-care diagnostics: Present and future. ACS Nano 15, 7899\u20137906. https:\/\/doi.org\/10.1021\/acsnano.1c02981 (2021).","journal-title":"ACS Nano"},{"key":"45423_CR3","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s00216-008-2287-2","volume":"393","author":"GA Posthuma-Trumpie","year":"2009","unstructured":"Posthuma-Trumpie, G. A., Korf, J. & van Amerongen, A. Lateral flow (immuno)assay: Its strengths, weaknesses, opportunities and threats. a literature survey. Anal. Bioanal. Chem. 393, 569\u2013582. https:\/\/doi.org\/10.1007\/s00216-008-2287-2 (2009).","journal-title":"Anal. Bioanal. Chem."},{"issue":"1","key":"45423_CR4","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1186\/s12879-021-06528-3","volume":"21","author":"DA Mistry","year":"2021","unstructured":"Mistry, D. A., Wang, J. Y., Moeser, M.-E., Starkey, T. & Lee, L. Y. W. A systematic review of the sensitivity and specificity of lateral flow devices in the detection of SARS-CoV-2. BMC Infect. Dis. 21, 828 https:\/\/doi.org\/10.1186\/s12879-021-06528-3 (2021).","journal-title":"BMC Infect. Dis."},{"key":"45423_CR5","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/382607a0","volume":"382","author":"CA Mirkin","year":"1996","unstructured":"Mirkin, C. A., Letsinger, R. L., Mucic, R. C. & Storhoff, J. J. A DNA-based method for rationally assembling nanoparticles into macroscopic materials. Nature 382, 607\u2013609. https:\/\/doi.org\/10.1038\/382607a0 (1996).","journal-title":"Nature"},{"key":"45423_CR6","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1038\/382609a0","volume":"382","author":"AP Alivisatos","year":"1996","unstructured":"Alivisatos, A. P. et al. Organization of \u2018nanocrystal molecules\u2019 using dna. Nature 382, 609\u2013611 (1996).","journal-title":"Nature"},{"key":"45423_CR7","doi-asserted-by":"publisher","first-page":"3558","DOI":"10.1021\/jacs.7b01311","volume":"139","author":"S Kim","year":"2017","unstructured":"Kim, S. et al. Optokinetically encoded nanoprobe-based multiplexing strategy for microRNA profiling. J. Am. Chem. Soc. 139, 3558\u20133566 (2017).","journal-title":"J. Am. Chem. Soc."},{"key":"45423_CR8","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aau2124","volume":"5","author":"J Seo","year":"2019","unstructured":"Seo, J., Kim, S., Park, H. H., Choi, D. Y. & Nam, J.-M. Nano-bio-computing lipid nanotablet. Sci. Adv. 5, eaau2124 (2019).","journal-title":"Sci. Adv."},{"key":"45423_CR9","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abb3348","volume":"6","author":"S Kim","year":"2020","unstructured":"Kim, S. et al. Nanoparticle-based computing architecture for nanoparticle neural networks. Sci. Adv. 6, eabb3348 (2020).","journal-title":"Sci. Adv."},{"key":"45423_CR10","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1039\/D2QM00186A","volume":"6","author":"WST Tun","year":"2022","unstructured":"Tun, W. S. T. et al. A machine learning colorimetric biosensor based on acetylcholinesterase and silver nanoparticles for the detection of dichlorvos pesticides. Mater. Chem. Front. 6, 1487\u20131498 (2022).","journal-title":"Mater. Chem. Front."},{"key":"45423_CR11","doi-asserted-by":"publisher","first-page":"27675","DOI":"10.1021\/acsomega.0c04255","volume":"5","author":"S Sajed","year":"2020","unstructured":"Sajed, S., Kolahdouz, M., Sadeghi, M. A. & Razavi, S. F. High-performance estimation of lead ion concentration using smartphone-based colorimetric analysis and a machine learning approach. ACS Omega 5, 27675\u201327684 (2020).","journal-title":"ACS Omega"},{"key":"45423_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.snb.2024.135363","volume":"406","author":"X Li","year":"2024","unstructured":"Li, X. et al. Nucleic acid amplification-free biosensor for sensitive and specific cfdna detection based on crispr-cas12a and single nanoparticle dark-field microscopy (dfm) imaging. Sens. Actuators B Chem. 406, 135363 (2024).","journal-title":"Sens. Actuators B Chem."},{"key":"45423_CR13","doi-asserted-by":"publisher","first-page":"9521","DOI":"10.1021\/acs.analchem.1c01539","volume":"93","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Wang, K., Wei, W., Liu, Y. & Liu, S. Multifunctional plasmonic core-satellites nanoprobe for cancer diagnosis and therapy based on a cascade reaction induced by microrna. Anal. Chem. 93, 9521\u20139530 (2021).","journal-title":"Anal. Chem."},{"key":"45423_CR14","doi-asserted-by":"publisher","first-page":"6550","DOI":"10.1021\/acs.analchem.2c05292","volume":"95","author":"D Bennett","year":"2023","unstructured":"Bennett, D. et al. Machine learning color feature analysis of a high throughput nanoparticle conjugate sensing assay. Anal. Chem. 95, 6550\u20136558 (2023).","journal-title":"Anal. Chem."},{"key":"45423_CR15","doi-asserted-by":"publisher","first-page":"10110","DOI":"10.1021\/acs.analchem.3c01904","volume":"95","author":"A Orouji","year":"2023","unstructured":"Orouji, A., Ghasemi, F. & Hormozi-Nezhad, M. R. Machine learning-assisted colorimetric assay based on Au@Ag nanorods for chromium speciation. Anal. Chem. 95, 10110\u201310118 (2023).","journal-title":"Anal. Chem."},{"key":"45423_CR16","doi-asserted-by":"publisher","first-page":"19522","DOI":"10.1021\/acsnano.1c06429","volume":"15","author":"N Wu","year":"2021","unstructured":"Wu, N. et al. Ratiometric 3d dna machine combined with machine learning algorithm for ultrasensitive and high-precision screening of early urinary diseases. ACS Nano 15, 19522\u201319534 (2021).","journal-title":"ACS Nano"},{"key":"45423_CR17","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1021\/acssensors.3c02687","volume":"9","author":"J Yang","year":"2024","unstructured":"Yang, J. et al. Machine learning-assistant colorimetric sensor arrays for intelligent and rapid diagnosis of urinary tract infection. ACS Sens. 9, 1945\u20131956 (2024).","journal-title":"ACS Sens."},{"key":"45423_CR18","doi-asserted-by":"publisher","first-page":"10639","DOI":"10.1021\/acs.analchem.7b02594","volume":"89","author":"B Li","year":"2017","unstructured":"Li, B. et al. Colorimetric sensor array based on gold nanoparticles with diverse surface charges for microorganisms identification. Anal. Chem. 89, 10639\u201310643 (2017).","journal-title":"Anal. Chem."},{"key":"45423_CR19","doi-asserted-by":"publisher","first-page":"5164","DOI":"10.1021\/acs.analchem.4c06395","volume":"97","author":"C Song","year":"2025","unstructured":"Song, C. et al. Deep learning-assisted ultrasensitive detection of gold nanoparticles using light microscopy images captured by a cellphone camera. Anal. Chem. 97, 5164\u20135170 (2025).","journal-title":"Anal. Chem."},{"key":"45423_CR20","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1021\/acs.analchem.3c03968","volume":"96","author":"X-Y Wang","year":"2024","unstructured":"Wang, X.-Y. et al. Holistic prediction of AuNP aggregation in diverse aqueous suspensions based on machine vision and dark-field scattering imaging. Anal. Chem. 96, 1506\u20131514 (2024).","journal-title":"Anal. Chem."},{"key":"45423_CR21","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1021\/acsnano.2c10159","volume":"17","author":"N Shiaelis","year":"2023","unstructured":"Shiaelis, N. et al. Virus detection and identification in minutes using single-particle imaging and deep learning. ACS Nano 17, 697\u2013710 (2023).","journal-title":"ACS Nano"},{"key":"45423_CR22","doi-asserted-by":"publisher","first-page":"7127","DOI":"10.1007\/s00216-023-04978-z","volume":"415","author":"S Liu","year":"2023","unstructured":"Liu, S. et al. A deep learning-enabled smartphone platform for rapid and sensitive colorimetric detection of dimethoate pesticide. Anal. Bioanal. Chem. 415, 7127\u20137138 (2023).","journal-title":"Anal. Bioanal. Chem."},{"key":"45423_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.nantod.2023.101968","volume":"52","author":"J Kang","year":"2023","unstructured":"Kang, J. et al. Deepgt: Deep learning-based quantification of nanosized bioparticles in bright-field micrographs of Gires-Tournois biosensor. Nano Today 52, 101968 (2023).","journal-title":"Nano Today"},{"key":"45423_CR24","doi-asserted-by":"publisher","first-page":"363","DOI":"10.3390\/bios14080363","volume":"14","author":"K-X Jin","year":"2024","unstructured":"Jin, K.-X., Shen, J., Wang, Y.-J., Yang, Y. & Cao, S.-H. Enhanced nanoparticle recognition via deep learning-accelerated plasmonic sensing. Biosensors 14, 363 (2024).","journal-title":"Biosensors"},{"key":"45423_CR25","doi-asserted-by":"publisher","unstructured":"McAffee, D.\u00a0B., Hu, Q., Arnob, A., Wu, H.-J. & Groves, J.\u00a0T. Machine-learning strategy for high error tolerance in image-based digital molecular assays. bioRxiv 2025\u201308, https:\/\/doi.org\/10.1101\/2025.08.14.670347 (2025). Preprint.","DOI":"10.1101\/2025.08.14.670347"},{"key":"45423_CR26","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.mam.2005.12.001","volume":"27","author":"F Watzinger","year":"2006","unstructured":"Watzinger, F., Ebner, K. & Lion, T. Detection and monitoring of virus infections by real-time PCR. Mol. Aspects Med. 27, 254\u2013298 (2006).","journal-title":"Mol. Aspects Med."},{"key":"45423_CR27","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1093\/clinchem\/hvaa029","volume":"66","author":"DKW Chu","year":"2020","unstructured":"Chu, D. K. W. et al. Molecular Diagnosis of a Novel Coronavirus (2019-nCoV) Causing an Outbreak of Pneumonia. Clin. Chem. 66, 549\u2013555 (2020).","journal-title":"Clin. Chem."},{"key":"45423_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/nano11030780","author":"P Bevilacqua","year":"2021","unstructured":"Bevilacqua, P. et al. Antifouling strategies of nanoparticles for diagnostic and therapeutic application: A systematic review of the literature. Nanomaterials https:\/\/doi.org\/10.3390\/nano11030780 (2021).","journal-title":"Nanomaterials"},{"key":"45423_CR29","doi-asserted-by":"publisher","first-page":"8313","DOI":"10.1021\/ac0613582","volume":"78","author":"SJ Hurst","year":"2006","unstructured":"Hurst, S. J., Lytton-Jean, A. K. R. & Mirkin, C. A. Maximizing DNA loading on a range of gold nanoparticle sizes. Anal. Chem. 78, 8313\u20138318 (2006).","journal-title":"Anal. Chem."},{"key":"45423_CR30","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1093\/infdis\/jiaa507","volume":"222","author":"EI Patterson","year":"2020","unstructured":"Patterson, E. I. et al. Methods of inactivation of SARS-CoV-2 for downstream biological assays. J. Infect. Dis. 222, 1462\u20131467 (2020).","journal-title":"J. Infect. Dis."},{"key":"45423_CR31","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"TM Cover","year":"1967","unstructured":"Cover, T. M. & Hart, P. E. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21\u201327 (1967).","journal-title":"IEEE Trans. Inf. Theory"},{"key":"45423_CR32","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher, R. A. The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179\u2013188 (1936).","journal-title":"Ann. Eugen."},{"key":"45423_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2 edn. (Springer, 2009).","edition":"2 edn"},{"key":"45423_CR34","doi-asserted-by":"crossref","unstructured":"Cortes, C. & Vapnik, V. Support-vector networks. In Advances in Neural Information Processing Systems (NeurIPS) (1995).","DOI":"10.1007\/BF00994018"},{"key":"45423_CR35","volume-title":"Contrast limited adaptive histogram equalization, 474\u2013485","author":"K Zuiderveld","year":"1994","unstructured":"Zuiderveld, K. Contrast limited adaptive histogram equalization, 474\u2013485 (Academic Press Professional Inc, 1994)."},{"key":"45423_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-45524-2","volume-title":"Handbook of biological confocal microscopy","author":"J Pawley","year":"2006","unstructured":"Pawley, J. Handbook of biological confocal microscopy Vol. vol. 236 (Springer Science & Business Media, 2006)."},{"key":"45423_CR37","doi-asserted-by":"publisher","first-page":"2276","DOI":"10.1109\/TPAMI.2010.55","volume":"32","author":"DB Goldman","year":"2010","unstructured":"Goldman, D. B. Vignette and exposure calibration and compensation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 2276\u20132288. https:\/\/doi.org\/10.1109\/TPAMI.2010.55 (2010).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"45423_CR38","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006).","journal-title":"J. Mach. Learn. Res."},{"key":"45423_CR39","doi-asserted-by":"crossref","unstructured":"Cao, J. et al. In Shapeconv: Shape-aware convolutional layer for indoor rgb-d semantic segmentation, 7088\u20137097 (Proceedings of the IEEE\/CVF international conference on computer vision, 2021).","DOI":"10.1109\/ICCV48922.2021.00700"},{"key":"45423_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2020.103015","volume":"75","author":"FB Tek","year":"2021","unstructured":"Tek, F. B., Cam, I. & Karl\u0131, D. Adaptive convolution kernel for artificial neural networks. J. Vis. Commun. Image Represent. 75, 103015 (2021).","journal-title":"J. Vis. Commun. Image Represent."},{"key":"45423_CR41","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X. & Yang, J. In Selective kernel networks, 510\u2013519 Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, (2019).","DOI":"10.1109\/CVPR.2019.00060"},{"key":"45423_CR42","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 834\u2013848 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"45423_CR43","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E. & Darrell, T. In Fully convolutional networks for semantic segmentation, 3431\u20133440 Proceedings of the IEEE conference on computer vision and pattern recognition, (2015).","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"45423_CR44","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, 234\u2013241 (Springer, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"45423_CR45","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J. et al. Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43, 3349\u20133364 (2020).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"45423_CR46","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.neucom.2022.07.069","volume":"506","author":"P Zhang","year":"2022","unstructured":"Zhang, P., Zhu, J., Zhang, C., Rong, Z. & Wu, Y. Semi-global shape-aware attention network for image segmentation and retrieval. Neurocomputing 506, 369\u2013379 (2022).","journal-title":"Neurocomputing"},{"key":"45423_CR47","doi-asserted-by":"crossref","unstructured":"Kuo, W., Angelova, A., Malik, J. & Lin, T.-Y. In Shapemask: Learning to segment novel objects by refining shape priors, 9207\u20139216 (Proceedings of the IEEE\/CVF international conference on computer vision, 2019).","DOI":"10.1109\/ICCV.2019.00930"},{"key":"45423_CR48","unstructured":"Liu, S. et al. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"45423_CR49","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V. & Fidler, S. In Gated-scnn: Gated shape cnns for semantic segmentation, 5229\u20135238 Proceedings of the IEEE\/CVF international conference on computer vision, (2019).","DOI":"10.1109\/ICCV.2019.00533"},{"key":"45423_CR50","doi-asserted-by":"crossref","unstructured":"Liu, X. et al. In Delving into shape-aware zero-shot semantic segmentation, 2999\u20133009 Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2023).","DOI":"10.1109\/CVPR52729.2023.00293"},{"key":"45423_CR51","doi-asserted-by":"crossref","unstructured":"Feng, A., You, C., Wang, S. & Tassiulas, L. In Kergnns: Interpretable graph neural networks with graph kernels, vol.\u00a036, 6614\u20136622 Proceedings of the AAAI conference on artificial intelligence, (2022).","DOI":"10.1609\/aaai.v36i6.20615"},{"key":"45423_CR52","unstructured":"Zhang, L. et al. Dual graph convolutional network for semantic segmentation. arXiv preprint arXiv:1909.06121 (2019)."},{"key":"45423_CR53","doi-asserted-by":"crossref","unstructured":"Xu, J., Dai, E., Luo, D., Zhang, X. & Wang, S. In Shape-aware graph spectral learning, 2692\u20132701 Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, (2024).","DOI":"10.1145\/3627673.3679604"},{"key":"45423_CR54","doi-asserted-by":"publisher","first-page":"6017","DOI":"10.1109\/TIP.2022.3205215","volume":"31","author":"A Bera","year":"2022","unstructured":"Bera, A., Wharton, Z., Liu, Y., Bessis, N. & Behera, A. Sr-gnn: Spatial relation-aware graph neural network for fine-grained image categorization. IEEE Trans. Image Process. 31, 6017\u20136031 (2022).","journal-title":"IEEE Trans. Image Process."},{"key":"45423_CR55","doi-asserted-by":"crossref","unstructured":"Zhou, Y. et al. In Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images, 0\u20130 (Proceedings of the IEEE\/CVF international conference on computer vision workshops, 2019).","DOI":"10.1109\/ICCVW.2019.00050"},{"key":"45423_CR56","unstructured":"Morphognn: Morphological embedding for single neuron with graph neural networks. bioRxiv (2022)."},{"key":"45423_CR57","unstructured":"Shen, Y., Zhou, B., Xiong, X., Gao, R. & Wang, Y.\u00a0G. In How graph neural networks enhance convolutional neural networks towards mining the topological structures from histology, vol.\u00a08 (ICML Workshop on Computational Biology, 2022)."},{"key":"45423_CR58","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M. & Monfardini, G. The graph neural network model. IEEE Trans. Neural Netw. 20, 61\u201380. https:\/\/doi.org\/10.1109\/TNN.2008.2005605 (2009).","journal-title":"IEEE Trans. Neural Netw."},{"key":"45423_CR59","doi-asserted-by":"crossref","unstructured":"Jeon, Y. & Kim, J. In Active convolution: Learning the shape of convolution for image classification, 4201\u20134209 Proceedings of the IEEE conference on computer vision and pattern recognition, (2017).","DOI":"10.1109\/CVPR.2017.200"},{"key":"45423_CR60","doi-asserted-by":"crossref","unstructured":"Dai, J. et al. In Deformable convolutional networks, 764\u2013773 Proceedings of the IEEE international conference on computer vision, (2017).","DOI":"10.1109\/ICCV.2017.89"},{"key":"45423_CR61","unstructured":"Moddy2024. Resnet-9. https:\/\/github.com\/Moddy2024\/ResNet-9 (2024). Accessed: 2025-06-19."},{"key":"45423_CR62","unstructured":"Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV (2021)."},{"key":"45423_CR63","unstructured":"Convnext v2: Co-designing and scaling convnets with masked autoencoders. In CVPR (2023)."},{"key":"45423_CR64","unstructured":"Anonymous. Clustervig: Efficient vision graph networks via token clustering. arXiv preprint arXiv:2509.25570 (2025)."},{"key":"45423_CR65","unstructured":"Anonymous. Attentionvig: Vision graph models with attention-based token interaction. arXiv preprint arXiv:2501.10640 (2025)."},{"key":"45423_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M. & Sun, J. In Shufflenet: An extremely efficient convolutional neural network for mobile devices, 6848\u20136856 (Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018).","DOI":"10.1109\/CVPR.2018.00716"},{"key":"45423_CR67","unstructured":"Howard, A.\u00a0G. et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"key":"45423_CR68","doi-asserted-by":"crossref","unstructured":"Han, K. et al. In Ghostnet: More features from cheap operations, 1580\u20131589 (Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020).","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"45423_CR69","unstructured":"Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (ICML), 6105\u20136114 (Proceedings of Machine Learning Research (PMLR), 2019)."},{"key":"45423_CR70","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1038\/nbt1100","volume":"23","author":"C S\u00f6nnichsen","year":"2005","unstructured":"S\u00f6nnichsen, C., Reinhard, B. M., Liphardt, J. & Alivisatos, A. P. A molecular ruler based on plasmon coupling of single gold and silver nanoparticles. Nat. Biotechnol. 23, 741\u2013745 (2005).","journal-title":"Nat. Biotechnol."},{"key":"45423_CR71","doi-asserted-by":"publisher","first-page":"9600","DOI":"10.1021\/ja103240g","volume":"132","author":"JIL Chen","year":"2010","unstructured":"Chen, J. I. L., Chen, Y. & Ginger, D. S. Plasmonic nanoparticle dimers for optical sensing of dna in complex media. J. Am. Chem. Soc. 132, 9600\u20139601 (2010).","journal-title":"J. Am. Chem. Soc."},{"key":"45423_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.snb.2024.135645","volume":"410","author":"A Pennacchio","year":"2024","unstructured":"Pennacchio, A. et al. A bacterial biosensor based on gold nanoparticles functionalized by a hydrophobin-chimera and combined with machine learning for user-friendly detection. Sens. Actuators B Chem. 410, 135645 (2024).","journal-title":"Sens. Actuators B Chem."},{"key":"45423_CR73","doi-asserted-by":"publisher","first-page":"e461","DOI":"10.1016\/S2666-5247(21)00143-9","volume":"2","author":"S Pickering","year":"2021","unstructured":"Pickering, S. et al. Comparative performance of SARS-CoV-2 lateral flow antigen tests and association with detection of infectious virus in clinical specimens: A single-centre laboratory evaluation study. Lancet Microbe 2, e461\u2013e471 (2021).","journal-title":"Lancet Microbe"},{"key":"45423_CR74","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-97489-9","volume":"11","author":"AI Cubas-Atienzar","year":"2021","unstructured":"Cubas-Atienzar, A. I. et al. Limit of detection in different matrices of 19 commercially available rapid antigen tests for the detection of SARS-CoV-2. Sci. Rep. 11, 18313. https:\/\/doi.org\/10.1038\/s41598-021-97489-9 (2021).","journal-title":"Sci. Rep."},{"key":"45423_CR75","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/j.bios.2017.08.025","volume":"99","author":"D Sun","year":"2018","unstructured":"Sun, D. & Hu, T. Y. A low cost mobile phone dark-field microscope for nanoparticle-based quantitative studies. Biosens. Bioelectron. 99, 513\u2013518 (2018).","journal-title":"Biosens. Bioelectron."},{"key":"45423_CR76","unstructured":"de Le\u00f3n Derby, M.\u00a0D. et al. Ntdscope: A multi-contrast portable microscope for disease diagnosis. medRxiv 2025.03.17.25324136 (2025)."},{"key":"45423_CR77","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-63993-z","volume":"16","author":"M Loretan","year":"2025","unstructured":"Loretan, M. et al. Direct single-molecule detection and super-resolution imaging with a low-cost portable smartphone-based microscope. Nat. Commun. 16, 8937. https:\/\/doi.org\/10.1038\/s41467-025-63993-z (2025).","journal-title":"Nat. Commun."},{"key":"45423_CR78","doi-asserted-by":"publisher","first-page":"13773","DOI":"10.1021\/acs.langmuir.5b03534","volume":"31","author":"JH Heo","year":"2015","unstructured":"Heo, J. H. et al. Ultrastable-stealth large gold nanoparticles with DNA directed biological functionality. Langmuir 31, 13773\u201313782 (2015).","journal-title":"Langmuir"},{"key":"45423_CR79","doi-asserted-by":"publisher","first-page":"13629","DOI":"10.1021\/la203632c","volume":"27","author":"S Xu","year":"2011","unstructured":"Xu, S., Yuan, H., Xu, A., Wang, J. & Wu, L. Rapid synthesis of stable and functional conjugates of DNA\/gold nanoparticles mediated by Tween 80. Langmuir 27, 13629\u201313634 (2011).","journal-title":"Langmuir"},{"key":"45423_CR80","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1002\/jcc.21596","volume":"32","author":"JN Zadeh","year":"2011","unstructured":"Zadeh, J. N. et al. NUPACK: Analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170\u2013173 (2011).","journal-title":"J. Comput. Chem."},{"key":"45423_CR81","doi-asserted-by":"crossref","unstructured":"Fornace, M.\u00a0E. et al. NUPACK: Analysis and Design of Nucleic Acid Structures, Devices, and Systems. ChemRxiv Version 1 (2022). Preprint.","DOI":"10.26434\/chemrxiv-2022-xv98l"},{"key":"45423_CR82","doi-asserted-by":"publisher","first-page":"13461","DOI":"10.1021\/acs.langmuir.9b02652","volume":"35","author":"R Wu","year":"2019","unstructured":"Wu, R., Jiang, L.-P., Zhu, J.-J. & Liu, J. Effects of small molecules on DNA adsorption by gold nanoparticles and a case study of Tris(2-carboxyethyl)phosphine (TCEP). Langmuir 35, 13461\u201313468 (2019).","journal-title":"Langmuir"},{"key":"45423_CR83","doi-asserted-by":"publisher","first-page":"4499","DOI":"10.1021\/jacs.8b01510","volume":"140","author":"B Liu","year":"2018","unstructured":"Liu, B., Wu, P., Huang, Z., Ma, L. & Liu, J. Bromide as a robust backfiller on gold for precise control of DNA conformation and high stability of spherical nucleic acids. J. Am. Chem. Soc. 140, 4499\u20134502 (2018).","journal-title":"J. Am. Chem. Soc."},{"key":"45423_CR84","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/0003-2697(86)90413-6","volume":"152","author":"RM Amasino","year":"1986","unstructured":"Amasino, R. M. Acceleration of nucleic acid hybridization rate by polyethylene glycol. Anal. Biochem. 152, 304\u2013307 (1986).","journal-title":"Anal. Biochem."},{"key":"45423_CR85","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1002\/ppsc.201500238","volume":"33","author":"T Donnelly","year":"2016","unstructured":"Donnelly, T., Faulds, K. & Graham, D. Investigation of silver nanoparticle assembly following hybridization with different lengths of DNA. Part. Part. Syst. Charact. 33, 404\u2013411 (2016).","journal-title":"Part. Part. Syst. Charact."},{"key":"45423_CR86","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1016\/j.jmoldx.2020.04.211","volume":"22","author":"AT Bender","year":"2020","unstructured":"Bender, A. T., Sullivan, B. P., Lillis, L. & Posner, J. D. Enzymatic and chemical-based methods to inactivate endogenous blood ribonucleases for nucleic acid diagnostics. J. Mol. Diagn. 22, 1030\u20131040 (2020).","journal-title":"J. Mol. Diagn."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-45423-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-45423-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-45423-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:03:27Z","timestamp":1779102207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-45423-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":86,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["45423"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-45423-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]},"assertion":[{"value":"7 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 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"}}],"article-number":"15248"}}