{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:03:50Z","timestamp":1789646630229,"version":"4.0.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":85,"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\/s41598-026-55976-x","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T07:36:29Z","timestamp":1782286589000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of small-molecule TNF-\u03b1 inhibitor candidates using machine learning-guided screening and multiscale molecular modelling"],"prefix":"10.1038","volume":"16","author":[{"given":"Fan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Licai","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengliang","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongli","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"issue":"6","key":"55976_CR1","doi-asserted-by":"publisher","first-page":"e316","DOI":"10.1016\/S2665-9913(23)00098-X","volume":"5","author":"ML Ferreira","year":"2023","unstructured":"Ferreira, M. L. et al. Global, regional, and national burden of low back pain, 1990\u20132020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol. 5(6), e316\u2013e329 (2023).","journal-title":"Lancet Rheumatol."},{"key":"55976_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fnbeh.2021.760547","volume":"15","author":"X Zhang","year":"2021","unstructured":"Zhang, X. et al. TNF-\u0251 induces methylglyoxal accumulation in lumbar herniated disc of patients with radicular pain. Front. Behav. Neurosci. 15, 760547 (2021).","journal-title":"Front. Behav. Neurosci."},{"key":"55976_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.tice.2024.102422","volume":"89","author":"Q Yao","year":"2024","unstructured":"Yao, Q. et al. The role of TNF-\u03b1 in osteoporosis, bone repair and inflammatory bone diseases: A review. Tissue Cell 89, 102422 (2024).","journal-title":"Tissue Cell"},{"issue":"1","key":"55976_CR4","first-page":"528","volume":"20","author":"Y Zhao","year":"2025","unstructured":"Zhao, Y. et al. Mechanisms and management of self-resolving lumbar disc herniation: bridging molecular pathways to non-surgical clinical success. BMC Musculoskelet. Disord. 20(1), 528 (2025).","journal-title":"BMC Musculoskelet. Disord."},{"issue":"47","key":"55976_CR5","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000040323","volume":"103","author":"J Yang","year":"2024","unstructured":"Yang, J. et al. Evidence from Mendelian randomization analysis combined with meta-analysis for the causal validation of the relationship between 91 inflammatory factors and lumbar disc herniation. Medicine 103(47), e40323 (2024).","journal-title":"Medicine"},{"key":"55976_CR6","volume":"174","author":"H Pan","year":"2023","unstructured":"Pan, H. et al. The mechanisms and functions of TNF-\u03b1 in intervertebral disc degeneration. Cytokine 174, 112119 (2023).","journal-title":"Cytokine"},{"issue":"4","key":"55976_CR7","first-page":"1196","volume":"14","author":"JS Lee","year":"2025","unstructured":"Lee, J. S. et al. Review of recent treatment strategies for lumbar disc herniation (LDH) focusing on nonsurgical and regenerative therapies. Medicina 14(4), 1196 (2025).","journal-title":"Medicina"},{"issue":"11","key":"55976_CR8","first-page":"3952","volume":"14","author":"M Duceac","year":"2025","unstructured":"Duceac, M. et al. A retrospective study of lumbar disk herniation: An analysis of clinical cases and treatment plans. Medicina 14(11), 3952 (2025).","journal-title":"Medicina"},{"key":"55976_CR9","doi-asserted-by":"publisher","first-page":"1376262","DOI":"10.3389\/fphar.2024.1376262","volume":"15","author":"Z Jiang","year":"2024","unstructured":"Jiang, Z. et al. Comparisons of infection events associated with tumor necrosis factor inhibitors in patients with inflammatory arthritis: A systematic review and network meta-analysis. Front. Pharmacol. 15, 1376262 (2024).","journal-title":"Front. Pharmacol."},{"key":"55976_CR10","doi-asserted-by":"publisher","first-page":"1169327","DOI":"10.3389\/fphar.2023.1169327","volume":"14","author":"M Li","year":"2023","unstructured":"Li, M. et al. Characteristic analysis of adverse reactions of five anti-TNF\u0251 agents: a descriptive analysis from WHO-VigiAccess. Front. Pharmacol. 14, 1169327 (2023).","journal-title":"Front. Pharmacol."},{"issue":"5","key":"55976_CR11","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1021\/acs.jcim.6b00672","volume":"57","author":"S Chen","year":"2017","unstructured":"Chen, S. et al. Discovery of novel ligands for TNF-\u03b1 and TNF receptor-1 through structure-based virtual screening and biological assay. J. Chem. Inf. Model. 57(5), 1101\u20131111 (2017).","journal-title":"J. Chem. Inf. Model."},{"key":"55976_CR12","doi-asserted-by":"crossref","unstructured":"Macadaeg, K.E. et al. Impact of condoliase on health-related quality of life in participants with radicular leg pain associated with lumbar disk herniation: results from a united states phase 3 clinical trial (LWW, 2025).","DOI":"10.1097\/BRS.0000000000005327"},{"key":"55976_CR13","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.csbj.2020.12.042","volume":"19","author":"LP Tangella","year":"2021","unstructured":"Tangella, L. P. et al. Identification and characterisation of putative drug binding sites in human ATP-binding cassette B5 (ABCB5) transporter. Comput. Struct. Biotechnol. J. 19, 691\u2013704 (2021).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"55976_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.colsurfa.2019.124279","volume":"586","author":"ND AlBab","year":"2020","unstructured":"AlBab, N. D. et al. Inhibition of amyloid fibrillation, enzymatic degradation and cytotoxicity of insulin at carboxyl tailored gold-aryl nanoparticles surface. Colloid. Surf. A. 586, 124279 (2020).","journal-title":"Colloid. Surf. A."},{"issue":"6","key":"55976_CR15","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1111\/cbdd.12006","volume":"80","author":"M Arooj","year":"2012","unstructured":"Arooj, M. et al. Computational studies of novel chymase inhibitors against cardiovascular and allergic diseases: mechanism and inhibition. Chem. Biol. Drug Des. 80(6), 862\u2013875 (2012).","journal-title":"Chem. Biol. Drug Des."},{"issue":"9","key":"55976_CR16","doi-asserted-by":"publisher","first-page":"1714","DOI":"10.3390\/molecules24091714","volume":"24","author":"L Zhou","year":"2019","unstructured":"Zhou, L. et al. Revealing drug-target interactions with computational models and algorithms. Molecules 24(9), 1714 (2019).","journal-title":"Molecules"},{"key":"55976_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2024.157677","volume":"501","author":"K Dou","year":"2024","unstructured":"Dou, K. et al. Switch on amine substrate reactivity towards hexaazaisowurtzitane cage: Insights from a tailored machine learning model. Chem. Eng. J. 501, 157677 (2024).","journal-title":"Chem. Eng. J."},{"issue":"2","key":"55976_CR18","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1179\/2045772313Y.0000000179","volume":"38","author":"H Ni","year":"2015","unstructured":"Ni, H. et al. Curcumin modulates TLR4\/NF-\u03baB inflammatory signaling pathway following traumatic spinal cord injury in rats. J. Spinal Cord. Med. 38(2), 199\u2013206 (2015).","journal-title":"J. Spinal Cord. Med."},{"issue":"3","key":"55976_CR19","first-page":"1374","volume":"11","author":"L Shen","year":"2019","unstructured":"Shen, L. et al. TLR4\/NF-\u03baB axis signaling pathway-dependent up-regulation of miR-625-5p contributes to human intervertebral disc degeneration by targeting COL1A1. Am. J. Transl. Res. 11(3), 1374 (2019).","journal-title":"Am. J. Transl. Res."},{"key":"55976_CR20","doi-asserted-by":"crossref","unstructured":"Lipinski, C.A.J.D.d.t.T., Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov. Today: Technol. 1(4), 337\u2013341 (2004).","DOI":"10.1016\/j.ddtec.2004.11.007"},{"key":"55976_CR21","unstructured":"Team, R. & Landrum, G.J.R. Available at, RDKit: open-source cheminformatics. (2024)."},{"issue":"6","key":"55976_CR22","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"JL Durant","year":"2002","unstructured":"Durant, J. L. et al. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42(6), 1273\u20131280 (2002).","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"55976_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"55976_CR24","doi-asserted-by":"crossref","unstructured":"Rokach, L., Maimon, O.J.I.T.o.S. Man, & P.C. Cybernetics, Top-down induction of decision trees classifiers-a survey. In IEEE Transactions on Systems, Man, and Cybernetics, Part C 35(4), 476\u2013487 (2005).","DOI":"10.1109\/TSMCC.2004.843247"},{"key":"55976_CR25","doi-asserted-by":"crossref","unstructured":"Watson, P.J.J.o.c.i. and modeling, Naive bayes classification using 2D pharmacophore feature triplet vectors 48(1), 166\u2013178 (2008).","DOI":"10.1021\/ci7003253"},{"key":"55976_CR26","first-page":"782","volume":"7","author":"M Thafar","year":"2019","unstructured":"Thafar, M. et al. Comparison study of computational prediction tools for drug-target binding affinities. Front. Genet. 7, 782 (2019).","journal-title":"Front. Genet."},{"key":"55976_CR27","unstructured":"Maestro, S.J.S., LLC, New York, NY, Maestro. 2020, 682 (2020)."},{"issue":"7","key":"55976_CR28","doi-asserted-by":"publisher","first-page":"4291","DOI":"10.1021\/acs.jctc.1c00302","volume":"17","author":"C Lu","year":"2021","unstructured":"Lu, C. et al. OPLS4: improving force field accuracy on challenging regimes of chemical space. J. Chem. Theory Comput. 17(7), 4291\u20134300 (2021).","journal-title":"J. Chem. Theory Comput."},{"key":"55976_CR29","doi-asserted-by":"crossref","unstructured":"Chen, I.-J., N.J.J.o.c.i. Foloppe, and modeling, Drug-like bioactive structures and conformational coverage with the LigPrep\/ConfGen suite: comparison to programs MOE and catalyst. 50(5), 822\u2013839 (2010).","DOI":"10.1021\/ci100026x"},{"issue":"11","key":"55976_CR30","doi-asserted-by":"publisher","first-page":"7106","DOI":"10.1021\/acs.jctc.1c00810","volume":"17","author":"Y Yang","year":"2021","unstructured":"Yang, Y. et al. Efficient exploration of chemical space with docking and deep learning. J. Chem. Theory Comput. 17(11), 7106\u20137119 (2021).","journal-title":"J. Chem. Theory Comput."},{"issue":"W1","key":"55976_CR31","doi-asserted-by":"publisher","first-page":"W443","DOI":"10.1093\/nar\/gkv315","volume":"43","author":"S Salentin","year":"2015","unstructured":"Salentin, S. et al. PLIP: fully automated protein\u2013ligand interaction profiler. Nucl. Acid. Res. 43(W1), W443\u2013W447 (2015).","journal-title":"Nucl. Acid. Res."},{"key":"55976_CR32","doi-asserted-by":"crossref","unstructured":"Ioakimidis, L. et al. Benchmarking the reliability of QikProp. Correlation between experimental and predicted values. J. Comput.-Aid. Mol. Design 27(4), 445\u2013456 (2008).","DOI":"10.1002\/qsar.200730051"},{"key":"55976_CR33","doi-asserted-by":"crossref","unstructured":"Phillips, J.C. et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys. 153(4) (2020).","DOI":"10.1063\/5.0014475"},{"issue":"8","key":"55976_CR34","doi-asserted-by":"publisher","first-page":"3696","DOI":"10.1021\/acs.jctc.5b00255","volume":"11","author":"JA Maier","year":"2015","unstructured":"Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11(8), 3696\u20133713 (2015).","journal-title":"J. Chem. Theory Comput."},{"issue":"9","key":"55976_CR35","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1002\/jcc.20035","volume":"25","author":"J Wang","year":"2004","unstructured":"Wang, J. et al. Development and testing of a general amber force field. J. Comput. Chem. 25(9), 1157\u20131174 (2004).","journal-title":"J. Comput. Chem."},{"key":"55976_CR36","doi-asserted-by":"crossref","unstructured":"Jakalian, A., Jack, D.B. & C.I.J.J.o.c.c. Bayly. Fast, efficient generation of high\u2010quality atomic charges. AM1\u2010BCC model: II. Parameterization and validation. J. Comput. Chem. 23(16), 1623\u20131641 (2002).","DOI":"10.1002\/jcc.10128"},{"issue":"2","key":"55976_CR37","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1063\/1.445869","volume":"79","author":"WL Jorgensen","year":"1983","unstructured":"Jorgensen, W. L. et al. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79(2), 926\u2013935 (1983).","journal-title":"J. Chem. Phys."},{"key":"55976_CR38","doi-asserted-by":"crossref","unstructured":"Joung, I.S. & T.E.J.T.j.o.p.c.B. Cheatham III, Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J. Phys. Chem. B 112(30), 9020\u20139041 (2008).","DOI":"10.1021\/jp8001614"},{"issue":"19","key":"55976_CR39","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1063\/1.470117","volume":"103","author":"U Essmann","year":"1995","unstructured":"Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103(19), 8577\u20138593 (1995).","journal-title":"J. Chem. Phys."},{"key":"55976_CR40","doi-asserted-by":"crossref","unstructured":"Ryckaert, J.-P., Ciccotti, G. & H.J.J.J.o.c.p. Berendsen, Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23(3), 327\u2013341 (1977).","DOI":"10.1016\/0021-9991(77)90098-5"},{"key":"55976_CR41","doi-asserted-by":"crossref","unstructured":"Miller III, B.R. et al. MMPBSA. py: an efficient program for end-state free energy calculations. J. Chem. Theory Comput. 8(9), 3314\u20133321 (2012).","DOI":"10.1021\/ct300418h"},{"key":"55976_CR42","doi-asserted-by":"crossref","unstructured":"Luo, R., David, L. & M.K.J.J.o.c.c. Gilson, Accelerated Poisson\u2013Boltzmann calculations for static and dynamic systems. J. Comput. Chem. 23(13), 1244\u20131253 (2002).","DOI":"10.1002\/jcc.10120"},{"key":"55976_CR43","doi-asserted-by":"crossref","unstructured":"Onufriev, A. et al. Exploring protein native states and large\u2010scale conformational changes with a modified generalized born model. Protein.: Struct. Funct. Bioinform. 55(2), 383\u2013394 (2004).","DOI":"10.1002\/prot.20033"},{"key":"55976_CR44","doi-asserted-by":"crossref","unstructured":"Weiser, J., Shenkin, P.S. & W.C.J.J.o.C.C. Still Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J. Comput. Chem. 20(2), 217\u2013230 (1999).","DOI":"10.1002\/(SICI)1096-987X(19990130)20:2<217::AID-JCC4>3.0.CO;2-A"},{"key":"55976_CR45","doi-asserted-by":"crossref","unstructured":"Massova, I. & P.A.J.J.o.t.A.C.S. Kollman, Computational alanine scanning to probe protein\u2212 protein interactions: a novel approach to evaluate binding free energies. J. Am. Chem. Soc. 121(36), 8133\u20138143 (1999).","DOI":"10.1021\/ja990935j"},{"key":"55976_CR46","unstructured":"Frisch, M.e. et al. Gaussian 16. Gaussian, inc. (Wallingford, CT, 2016)."},{"key":"55976_CR47","unstructured":"Parr, R.G. Density functional theory of atoms and molecules. in horizons of quantum chemistry. In Proc. 3rd International Congress of Quantum Chemistry Held at Kyoto (Japan, Springer, 1989)."},{"key":"55976_CR48","doi-asserted-by":"crossref","unstructured":"Parr, R.G. & R.G.J.J.o.t.A.c.s. Pearson, Absolute hardness: companion parameter to absolute electronegativity. J. Am. Chem. Soc. 105(26), 7512\u20137516 (1983).","DOI":"10.1021\/ja00364a005"},{"issue":"1","key":"55976_CR49","volume":"5","author":"H Sharma","year":"2024","unstructured":"Sharma, H. et al. AI-powered solutions for casualty assessment in drug safety and patient care. PLoS ONE 5(1), e02506882313855 (2024).","journal-title":"PLoS ONE"},{"key":"55976_CR50","first-page":"975","volume":"22","author":"T Yu","year":"2023","unstructured":"Yu, T. et al. Machine learning approaches to study the structure-activity relationships of LpxC inhibitors. J. Cheminform. 22, 975 (2023).","journal-title":"J. Cheminform."},{"issue":"9","key":"55976_CR51","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0288053","volume":"18","author":"FB Ashraf","year":"2023","unstructured":"Ashraf, F. B. et al. Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches. PLoS ONE 18(9), e0288053 (2023).","journal-title":"PLoS ONE"},{"issue":"7","key":"55976_CR52","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.3390\/molecules25071622","volume":"25","author":"DK Lokwani","year":"2020","unstructured":"Lokwani, D. K. et al. Structure-based site of metabolism (SOM) prediction of ligand for CYP3A4 enzyme: comparison of Glide XP and Induced Fit Docking (IFD). Molecules 25(7), 1622 (2020).","journal-title":"Molecules"},{"key":"55976_CR53","doi-asserted-by":"crossref","unstructured":"Didigwu, O.K. & C.O. Nnadi. Drug-likeness, pharmacokinetics, and toxicity prediction of phytotoxic terpenoids. In Proceedings. (MDPI, 2024).","DOI":"10.3390\/proceedings2024102047"},{"issue":"8","key":"55976_CR54","first-page":"1919","volume":"66","author":"AM da Fonseca","year":"2024","unstructured":"da Fonseca, A. M. et al. Screening of potential inhibitors targeting the main protease structure of SARS-CoV-2 via molecular docking, and approach with molecular dynamics, RMSD, RMSF, H-bond, SASA and MMGBSA. J. Braz. Chem. Soc. 66(8), 1919\u20131933 (2024).","journal-title":"J. Braz. Chem. Soc."},{"issue":"26","key":"55976_CR55","doi-asserted-by":"publisher","first-page":"16307","DOI":"10.1021\/acsomega.0c02183","volume":"5","author":"D-D Li","year":"2020","unstructured":"Li, D.-D. et al. Molecular dynamics analysis of binding sites of epidermal growth factor receptor kinase inhibitors. ACS Omega 5(26), 16307\u201316314 (2020).","journal-title":"ACS Omega"},{"key":"55976_CR56","doi-asserted-by":"crossref","unstructured":"Kabsch, W. & C.J.B.O.R.o.B. Sander. Dictionary of protein secondary structure: pattern recognition of hydrogen\u2010bonded and geometrical features. Biopolym.: Orig. Res. Biomol. 22(12), 2577\u20132637 (1983).","DOI":"10.1002\/bip.360221211"},{"key":"55976_CR57","doi-asserted-by":"crossref","unstructured":"MIu, L., Bogatyreva, N. & O.J.M.b. Galzitskaia. Radius of gyration is indicator of compactness of protein structure. Mol. Biol. 42(4), 701\u2013706 (2008).","DOI":"10.1134\/S0026893308040195"},{"key":"55976_CR58","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-981-97-4022-2_8","volume-title":"Geographic Data Analysis Using R","author":"X He","year":"2024","unstructured":"He, X. Principal component analysis (PCA). In Geographic Data Analysis Using R 155\u2013165 (Springer, 2024)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55976-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55976-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55976-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:02:24Z","timestamp":1789642944000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55976-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,24]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["55976"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-55976-x","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,24]]},"assertion":[{"value":"2 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"28966"}}