{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T18:15:17Z","timestamp":1778004917880,"version":"3.51.4"},"reference-count":41,"publisher":"Walter de Gruyter GmbH","issue":"5-6","funder":[{"name":"Princess Nourah bint Abdulrahaman University","award":["PNURSP2025R419"],"award-info":[{"award-number":["PNURSP2025R419"]}]},{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","award":["EEQ\/2023\/000692"],"award-info":[{"award-number":["EEQ\/2023\/000692"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Prostaglandin F2\u03b1 (PGF2\u03b1) is associated with preterm labor and preterm birth. PGF2\u03b1 inhibitors have thus proven to be a promising target in the development of lead compounds to prevent preterm birth. In this work, Quantitative Structural Activity Relationship (QSAR) was implemented on a dataset of 77 compounds of 6-bromo-3-methylquinoline analogues using statistical approach and random selection in the QSARINS software. The Genetic Algorithm-Multiple Linear Regression (GA-MLR) approach was used to predict the best model (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u00a0=\u00a00.8943 and\n                    <jats:italic>Q<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    <jats:sub>LOO<\/jats:sub>\n                    \u00a0=\u00a00.8836). The inclusion of descriptors FNSA-2 and WV.mass resulted in a well\u2010fitted and highly predictable model. Artificial neural network (ANN) analysis was also carried out to validate the model effectiveness. Twenty eight new molecules with better predicted biological activity (pIC50) were designed. The binding energy from the docking study of seven compounds have shown higher binding activity than P10 into prostaglandin F synthase protein (PDB ID: 2F38). The stability of protein\u2013ligand complex was further validated by 100\u202fns molecular dynamics simulation and MM-PBSA binding free energy. DFT and ADME-toxicity analysis also confirmed their drug-likeness properties. Collectively, our findings highlight novel quinoline derivatives as promising lead candidates, warranting further validation through collaborative\n                    <jats:italic>in\u00a0vitro<\/jats:italic>\n                    and\n                    <jats:italic>in\u00a0vivo<\/jats:italic>\n                    studies.\n                  <\/jats:p>","DOI":"10.1515\/znc-2025-0116","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:50:13Z","timestamp":1758235813000},"page":"379-391","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the potency of 6-bromo-3-methylquinoline analogues as prostaglandin F2\u03b1 inhibitors"],"prefix":"10.1515","volume":"81","author":[{"given":"Kaushik","family":"Sarkar","sequence":"first","affiliation":[{"name":"Department of Chemistry , 30189 University of North Bengal , Darjeeling , West Bengal , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandhya","family":"Yadav","sequence":"additional","affiliation":[{"name":"Department of Chemistry , 30189 University of North Bengal , Darjeeling , West Bengal , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ammena Y.","family":"Binsaleh","sequence":"additional","affiliation":[{"name":"Department of Pharmacy Practice , College of Pharmacy , Princess Nourah bint Abdulrahman University , P.O. Box 84428 , Riyadh 11671 , Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nawal","family":"Al-Hoshani","sequence":"additional","affiliation":[{"name":"Department of Biology , College of Science , Princess Nourah bint Abdulrahman University , P.O. Box 84428 , Riyadh 11671 , Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9433-4711","authenticated-orcid":false,"given":"Rajesh Kumar","family":"Das","sequence":"additional","affiliation":[{"name":"Department of Chemistry , 30189 University of North Bengal , Darjeeling , West Bengal , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"2026050518051423947_j_znc-2025-0116_ref_001","doi-asserted-by":"crossref","unstructured":"Slattery, MM, Morrison, J. Preterm delivery. Lancet 2002;360:1489\u201397. https:\/\/doi.org\/10.1016\/s0140-6736-02-11476-0.","DOI":"10.1016\/S0140-6736(02)11476-0"},{"key":"2026050518051423947_j_znc-2025-0116_ref_002","doi-asserted-by":"crossref","unstructured":"Lamont, CD, J\u00f8rgensen, JS, Lamont, RF. The safety of tocolytics used for the inhibition of preterm labour. Expet Opin Drug Saf 2016;15:1163\u201373. https:\/\/doi.org\/10.1080\/14740338.2016.1187128.","DOI":"10.1080\/14740338.2016.1187128"},{"key":"2026050518051423947_j_znc-2025-0116_ref_003","unstructured":"Peebles, D. Intrauterine infection and perinatal brain injury (scientific impact paper no. 3). London: Royal College of Obstetricians Gynaecologists; 2007."},{"key":"2026050518051423947_j_znc-2025-0116_ref_004","doi-asserted-by":"crossref","unstructured":"Kam, KR, Lamont, RF. Developments in the pharmacotherapeutic management of spontaneous preterm labor. Expet Opin Pharmacother 2008;9:1153\u201368. https:\/\/doi.org\/10.1517\/14656566.9.7.1153.","DOI":"10.1517\/14656566.9.7.1153"},{"key":"2026050518051423947_j_znc-2025-0116_ref_005","doi-asserted-by":"crossref","unstructured":"Keelan, J, Blumenstein, M, Helliwell, R, Sato, T, Marvin, K, Mitchell, M. Cytokines, prostaglandins and parturition\u00a0\u2013 A review. Placenta 2003;24:S33\u201346. https:\/\/doi.org\/10.1053\/plac.2002.0948.","DOI":"10.1053\/plac.2002.0948"},{"key":"2026050518051423947_j_znc-2025-0116_ref_006","doi-asserted-by":"crossref","unstructured":"Santos, ACA, Sartori, T, Borelli, P, Fock, RA. Prostaglandin F2\u03b1 in\u00a0vitro can affect basic inflammatory parameters of mesenchymal stem cells and slight modulating some of their immunomodulatory properties. Prostagl Leukot Essent Fat Acids 2020;163:102210. https:\/\/doi.org\/10.1016\/j.plefa.2020.102210.","DOI":"10.1016\/j.plefa.2020.102210"},{"key":"2026050518051423947_j_znc-2025-0116_ref_007","doi-asserted-by":"crossref","unstructured":"Maehara, T, Fujimori, K. Inhibition of prostaglandin F2\u03b1 receptors exaggerates HCl-Induced lung inflammation in mice. Int J Mol Sci 2021;22:12843. https:\/\/doi.org\/10.3390\/ijms222312843.","DOI":"10.3390\/ijms222312843"},{"key":"2026050518051423947_j_znc-2025-0116_ref_008","doi-asserted-by":"crossref","unstructured":"Kim, SH, Riaposova, L, Ahmed, H, Pohl, O, Chollet, A, Gotteland, J-P, et al.. Oxytocin receptor antagonists, atosiban and nolasiban, inhibit prostaglandin F2\u03b1-induced contractions and inflammatory responses in human myometrium. Sci Rep 2019;9:1\u201310. https:\/\/doi.org\/10.1038\/s41598-019-42181-2.","DOI":"10.1038\/s41598-019-42181-2"},{"key":"2026050518051423947_j_znc-2025-0116_ref_009","doi-asserted-by":"crossref","unstructured":"Itoh, K, Ida, Y, Ohguro, H, Hikage, F. Prostaglandin F2\u03b1 agonists induced enhancement in collagen1 expression is involved in the pathogenesis of the deepening of upper eyelid sulcus. Sci Rep 2021;11:1\u201312. https:\/\/doi.org\/10.1038\/s41598-021-88562-4.","DOI":"10.1038\/s41598-021-88562-4"},{"key":"2026050518051423947_j_znc-2025-0116_ref_010","doi-asserted-by":"crossref","unstructured":"Karakaya-Bilen, E, Ribeiro, ES, Bisinotto, RS, G\u00fcmen, A, Santos, JEP. Effect of presynchronization with prostaglandin F2\u03b1 before the 5-d timed AI protocol on ovarian responses and pregnancy in dairy heifers. Theriogenology 2019;132:138\u201343. https:\/\/doi.org\/10.1016\/j.thehttps:\/\/doi.org\/riogenology.2019.03.019.","DOI":"10.1016\/j.theriogenology.2019.03.019"},{"key":"2026050518051423947_j_znc-2025-0116_ref_011","doi-asserted-by":"crossref","unstructured":"Knickmeyer, E, Thomas, J, Locke, J, Bonacker, R, Ciernia, L, Ketchum, J, et al.. Altering duration of the presynchronization period in a long-term progestin-based estrus synchronization protocol for timed artificial insemination of beef heifers. Theriogenology 2019;136:66\u201371. https:\/\/doi.org\/10.1016\/j.theriogenology.2019.06.027.","DOI":"10.1016\/j.theriogenology.2019.06.027"},{"key":"2026050518051423947_j_znc-2025-0116_ref_012","doi-asserted-by":"crossref","unstructured":"Crankshaw, D, Gaspar, V. Effects of prostanoids on the rat\u2019s myometrium in\u00a0vitro during pregnancy. Biol Reprod 1992;46:392\u2013400. https:\/\/doi.org\/10.1095\/biolreprod46.3.392.","DOI":"10.1095\/biolreprod46.3.392"},{"key":"2026050518051423947_j_znc-2025-0116_ref_013","doi-asserted-by":"crossref","unstructured":"Phillippe, M, Saunders, T, Basa, A. Intracellular mechanisms underlying prostaglandin F2\u03b1-stimulated phasic myometrial contractions. Am J Physiol Endocrinol Metabol 1997;273:E665\u201373. https:\/\/doi.org\/10.1152\/ajpendo.1997.273.4.e665.","DOI":"10.1152\/ajpendo.1997.273.4.E665"},{"key":"2026050518051423947_j_znc-2025-0116_ref_014","doi-asserted-by":"crossref","unstructured":"Norton, ME, Merrill, J, Cooper, B, Kuller, JA, Clyman, RI. Neonatal complications after the administration of indomethacin for preterm labor. N Engl J Med 1993;329:1602\u20137. https:\/\/doi.org\/10.1056\/nejm199311253292202.","DOI":"10.1056\/NEJM199311253292202"},{"key":"2026050518051423947_j_znc-2025-0116_ref_015","doi-asserted-by":"crossref","unstructured":"Pohl, O, Marchand, L, Gotteland, JP, Coates, S, T\u00e4ubel, J, Lorch, U. Pharmacokinetics, safety and tolerability of OBE022, a selective prostaglandin F2\u03b1 receptor antagonist tocolytic: a first\u2010in\u2010human trial in healthy postmenopausal women. Br J Clin Pharmacol 2018;84:1839\u201355. https:\/\/doi.org\/10.1111\/bcp.13622.","DOI":"10.1111\/bcp.13622"},{"key":"2026050518051423947_j_znc-2025-0116_ref_016","doi-asserted-by":"crossref","unstructured":"Bourguet, CB, Lubell, WD. Synthesis and alkylation of aza-gly-pro building blocks of peptidomimetic libraries for developing prostaglandin F2\u03b1 receptor modulators as therapeutics to inhibit preterm labor. Peptide Libraries 2015:81\u201391. https:\/\/doi.org\/10.1007\/978-1-4939-2020-4-6.","DOI":"10.1007\/978-1-4939-2020-4_6"},{"key":"2026050518051423947_j_znc-2025-0116_ref_017","doi-asserted-by":"crossref","unstructured":"Hammers, AL, Sanchez-Ramos, L, Kaunitz, AM. Antenatal exposure to indomethacin increases the risk of severe intraventricular hemorrhage, necrotizing enterocolitis, and periventricular leukomalacia: a systematic review with metaanalysis. Am J Obstet Gynecol 2015;212:505. e1\u2013. e13. https:\/\/doi.org\/10.1016\/j.ajog.2014.10.1091.","DOI":"10.1016\/j.ajog.2014.10.1091"},{"key":"2026050518051423947_j_znc-2025-0116_ref_018","doi-asserted-by":"crossref","unstructured":"Hanwell, MD, Curtis, DE, Lonie, DC, Vandermeersch, T, Zurek, E, Hutchison, GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminf 2012;4:1\u201317. https:\/\/doi.org\/10.1186\/1758-2946-4-17.","DOI":"10.1186\/1758-2946-4-17"},{"key":"2026050518051423947_j_znc-2025-0116_ref_019","doi-asserted-by":"crossref","unstructured":"Yap, CWJJ. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 2011;32:1466\u201374. https:\/\/doi.org\/10.1002\/jcc.21707.","DOI":"10.1002\/jcc.21707"},{"key":"2026050518051423947_j_znc-2025-0116_ref_020","doi-asserted-by":"crossref","unstructured":"Gramatica, P, Chirico, N, Papa, E, Cassani, S, Kovarich, S. QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem 2013;34:2121\u201332. https:\/\/doi.org\/10.1002\/jcc.23361.","DOI":"10.1002\/jcc.23361"},{"key":"2026050518051423947_j_znc-2025-0116_ref_021","doi-asserted-by":"crossref","unstructured":"Saxena, A, Prathipati, P. Comparison of mlr, pls and ga-mlr in qsar analysis. SAR QSAR Environ Res 2003;14:433\u201345. https:\/\/doi.org\/10.1080\/10629360310001624015.","DOI":"10.1080\/10629360310001624015"},{"key":"2026050518051423947_j_znc-2025-0116_ref_022","doi-asserted-by":"crossref","unstructured":"Rajabi, M, Shafiei, F. QSAR models for predicting aquatic toxicity of esters using genetic algorithm-multiple linear regression methods. Comb Chem High Throughput Screen 2019;22:317\u201325. https:\/\/doi.org\/10.2174\/1386207322666190618150856.","DOI":"10.2174\/1386207322666190618150856"},{"key":"2026050518051423947_j_znc-2025-0116_ref_023","doi-asserted-by":"crossref","unstructured":"Golbraikh, A, Shen, M, Xiao, Z, Xiao, Y-D, Lee, K-H, Tropsha, A. Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 2003;17:241\u201353.","DOI":"10.1023\/A:1025386326946"},{"key":"2026050518051423947_j_znc-2025-0116_ref_024","doi-asserted-by":"crossref","unstructured":"Gramatica, P. Principles of QSAR models validation: internal and external. QSAR Comb Sci 2007;26:694\u2013701. https:\/\/doi.org\/10.1002\/qsar.200610151.","DOI":"10.1002\/qsar.200610151"},{"key":"2026050518051423947_j_znc-2025-0116_ref_025","doi-asserted-by":"crossref","unstructured":"Chirico, N, Gramatica, P. Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 2011;51:2320\u201335. https:\/\/doi.org\/10.1021\/ci200211n.","DOI":"10.1021\/ci200211n"},{"key":"2026050518051423947_j_znc-2025-0116_ref_026","doi-asserted-by":"crossref","unstructured":"Morris, GM, Huey, R, Lindstrom, W, Sanner, MF, Belew, RK, Goodsell, DS, et al.. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. 2009;30:2785-91, https:\/\/doi.org\/10.1002\/jcc.21256.","DOI":"10.1002\/jcc.21256"},{"key":"2026050518051423947_j_znc-2025-0116_ref_027","doi-asserted-by":"crossref","unstructured":"Lee, S, Tran, A, Allsopp, M, Lim, JB, H\u00e9nin, J, Klauda, JB. CHARMM36 united atom chain model for lipids and surfactants. J Phys Chem B 2014;118:547\u201356. https:\/\/doi.org\/10.1021\/jp410344g.","DOI":"10.1021\/jp410344g"},{"key":"2026050518051423947_j_znc-2025-0116_ref_028","doi-asserted-by":"crossref","unstructured":"Vanommeslaeghe, K, Hatcher, E, Acharya, C, Kundu, S, Zhong, S, Shim, J, et al.. CHARMM general force field: a force field for drug\u2010like molecules compatible with the CHARMM all\u2010atom additive biological force fields. J Comput Chem 2010;31:671\u201390. https:\/\/doi.org\/10.1002\/jcc.21367.","DOI":"10.1002\/jcc.21367"},{"key":"2026050518051423947_j_znc-2025-0116_ref_029","doi-asserted-by":"crossref","unstructured":"Vanommeslaeghe, K, MacKerell, J. ADJJoci, modeling. Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 2012;52:3144\u201354. https:\/\/doi.org\/10.1021\/ci300363c.","DOI":"10.1021\/ci300363c"},{"key":"2026050518051423947_j_znc-2025-0116_ref_030","doi-asserted-by":"crossref","unstructured":"Kumari, R, Kumar, R. Consortium OSDD, Lynn AJJoci, modeling. g_mmpbsa. A GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 2014;54:1951\u201362. https:\/\/doi.org\/10.1021\/ci500020m.","DOI":"10.1021\/ci500020m"},{"key":"2026050518051423947_j_znc-2025-0116_ref_031","doi-asserted-by":"crossref","unstructured":"Pires, DE, Blundell, TL, Ascher, DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 2015;58:4066\u201372. https:\/\/doi.org\/10.1021\/acs.jmedchem.5b00104.","DOI":"10.1021\/acs.jmedchem.5b00104"},{"key":"2026050518051423947_j_znc-2025-0116_ref_032","unstructured":"Frisch, M, Trucks, G, Schlegel, H, Scuseria, G, Robb, M, Cheeseman, J, et al.. Gaussian 16. Wallingford, CT: Gaussian, Inc.; 2016."},{"key":"2026050518051423947_j_znc-2025-0116_ref_033","doi-asserted-by":"crossref","unstructured":"Andersson, MP, Uvdal, P. New scale factors for harmonic vibrational frequencies using the B3LYP density functional method with\u00a0the\u00a0triple-\u03b6\u00a0basis\u00a0set 6-311+ G (d, p). J Phys Chem 2005;109:2937\u201341. https:\/\/doi.org\/10.1021\/jp045733a.","DOI":"10.1021\/jp045733a"},{"key":"2026050518051423947_j_znc-2025-0116_ref_034","doi-asserted-by":"crossref","unstructured":"Radha, S, Muthu, KK. Identification of key structural features of phosphate and thiophosphate tricyclic coumarin analogs as STS inhibitors. Med Drug Discov 2021;12:100108. https:\/\/doi.org\/10.1016\/j.medidd.2021.100108.","DOI":"10.1016\/j.medidd.2021.100108"},{"key":"2026050518051423947_j_znc-2025-0116_ref_035","doi-asserted-by":"crossref","unstructured":"Ponzoni, I, Sebasti\u00e1n-P\u00e9rez, V, Mart\u00ednez, MJ, Roca, C, De la Cruz P\u00e9rez, C, Cravero, F, et al.. QSAR classification models for predicting the activity of inhibitors of beta-secretase (BACE1) associated with Alzheimer\u2019s disease. Sci Rep 2019;9:1\u201313.","DOI":"10.1038\/s41598-019-45522-3"},{"key":"2026050518051423947_j_znc-2025-0116_ref_036","doi-asserted-by":"crossref","unstructured":"Estrada, E, D\u00edaz, GA, Delgado, E. Predicting infinite dilution activity coefficients of organic compounds in water by quantum-connectivity descriptors. J Comput Aided Mol Des 2006;20:539\u201348. https:\/\/doi.org\/10.1007\/s10822-006-9079-6.","DOI":"10.1007\/s10822-006-9079-6"},{"key":"2026050518051423947_j_znc-2025-0116_ref_037","doi-asserted-by":"crossref","unstructured":"Subramani, AK, Sivaperuman, A, Natarajan, R, Bhandare, RR, Shaik, AB. QSAR and molecular docking studies of pyrimidine-coumarin-triazole conjugates as prospective anti-breast cancer agents. Molecules 2022;27:1845. https:\/\/doi.org\/10.3390\/molecules27061845.","DOI":"10.3390\/molecules27061845"},{"key":"2026050518051423947_j_znc-2025-0116_ref_038","doi-asserted-by":"crossref","unstructured":"Barzegar, A, Zamani-Gharehchamani, E, Kadkhodaie-Ilkhchi, A. ANN QSAR workflow for predicting the inhibition of HIV-1 reverse transcriptase by pyridinone non-nucleoside derivatives. Future Med Chem 2017;9:1175\u201391. https:\/\/doi.org\/10.4155\/fmc-2017-0040.","DOI":"10.4155\/fmc-2017-0040"},{"key":"2026050518051423947_j_znc-2025-0116_ref_039","doi-asserted-by":"crossref","unstructured":"Bhowmik, D, Sharma, RD, Prakash, A, Kumar, D. Identification of nafamostat and VR23 as COVID-19 drug candidates by targeting 3CLpro and PLpro. J Mol Struct 2021;1233:130094. https:\/\/doi.org\/10.1016\/j.molstruc.2021.130094.","DOI":"10.1016\/j.molstruc.2021.130094"},{"key":"2026050518051423947_j_znc-2025-0116_ref_040","doi-asserted-by":"crossref","unstructured":"Daoui, O, Mazoir, N, Bakhouch, M, Salah, M, Benharref, A, Gonzalez-Coloma, A, et al.. 3D-QSAR, ADME-Tox, and molecular docking of semisynthetic triterpene derivatives as antibacterial and insecticide agents. Struct Chem 2022;33:1063\u201384. https:\/\/doi.org\/10.1007\/s11224-022-01912-4.","DOI":"10.1007\/s11224-022-01912-4"},{"key":"2026050518051423947_j_znc-2025-0116_ref_041","doi-asserted-by":"crossref","unstructured":"Mumit, MA, Pal, TK, Alam, MA, Islam, MA-A-A-A, Paul, S, Sheikh, MC. DFT studies on vibrational and electronic spectra, HOMO\u2013LUMO, MEP, HOMA, NBO and molecular docking analysis of benzyl-3-N-(2, 4, 5-trimethoxyphenylmethylene) hydrazinecarbodithioate. J Mol Struct 2020;1220:128715. https:\/\/doi.org\/10.1016\/j.molstruc.2020.128715.","DOI":"10.1016\/j.molstruc.2020.128715"}],"container-title":["Zeitschrift f\u00fcr Naturforschung C"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/znc-2025-0116\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/znc-2025-0116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T18:05:45Z","timestamp":1778004345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/znc-2025-0116\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"references-count":41,"journal-issue":{"issue":"5-6","published-online":{"date-parts":[[2025,9,17]]},"published-print":{"date-parts":[[2026,5,26]]}},"alternative-id":["10.1515\/znc-2025-0116"],"URL":"https:\/\/doi.org\/10.1515\/znc-2025-0116","relation":{},"ISSN":["0939-5075","1865-7125"],"issn-type":[{"value":"0939-5075","type":"print"},{"value":"1865-7125","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,17]]}}}