Viscoelastic characterization of the human osteosarcoma cancer cell line MG-63 using a fractional-order zener model through automated algorithm design and configuration
Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acq...
Saved in:
| Published in: | Scientific reports Vol. 15; no. 1; pp. 31436 - 25 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
26.08.2025
Nature Publishing Group Nature Portfolio |
| Subjects: | |
| ISSN: | 2045-2322, 2045-2322 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator (
), achieves a performance of
, representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to
, a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned
provided the best result in 19 cases, achieving an average of
, about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that
,
, and
are sensitive to the applied force via a statistical analysis, while
remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics. |
|---|---|
| AbstractList | Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator ( $$\text {MH}_{*}^3$$ ), achieves a performance of $$3.00\times 10^{-3}$$ , representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to $$2.86\times 10^{-3}\pm 2.43\times 10^{-7}$$ , a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned $$\text {MH}_{*}^3$$ provided the best result in 19 cases, achieving an average of $$3.31\times 10^{-3}$$ , about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that $$E_1$$ , $$\tau$$ , and $$\alpha$$ are sensitive to the applied force via a statistical analysis, while $$E_0$$ remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics. Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator ([Formula: see text]), achieves a performance of [Formula: see text], representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to [Formula: see text], a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned [Formula: see text] provided the best result in 19 cases, achieving an average of [Formula: see text], about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that [Formula: see text], τ, and α are sensitive to the applied force via a statistical analysis, while [Formula: see text] remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics. Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator ([Formula: see text]), achieves a performance of [Formula: see text], representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to [Formula: see text], a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned [Formula: see text] provided the best result in 19 cases, achieving an average of [Formula: see text], about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that [Formula: see text], τ, and α are sensitive to the applied force via a statistical analysis, while [Formula: see text] remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics.Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator ([Formula: see text]), achieves a performance of [Formula: see text], representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to [Formula: see text], a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned [Formula: see text] provided the best result in 19 cases, achieving an average of [Formula: see text], about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that [Formula: see text], τ, and α are sensitive to the applied force via a statistical analysis, while [Formula: see text] remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics. Abstract Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator ( $$\text {MH}_{*}^3$$ ), achieves a performance of $$3.00\times 10^{-3}$$ , representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to $$2.86\times 10^{-3}\pm 2.43\times 10^{-7}$$ , a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned $$\text {MH}_{*}^3$$ provided the best result in 19 cases, achieving an average of $$3.31\times 10^{-3}$$ , about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that $$E_1$$ , $$\tau$$ , and $$\alpha$$ are sensitive to the applied force via a statistical analysis, while $$E_0$$ remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics. Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator ( ), achieves a performance of , representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to , a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned provided the best result in 19 cases, achieving an average of , about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that , , and are sensitive to the applied force via a statistical analysis, while remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics. Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator ( ), achieves a performance of , representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to , a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned provided the best result in 19 cases, achieving an average of , about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that , , and are sensitive to the applied force via a statistical analysis, while remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics. |
| ArticleNumber | 31436 |
| Author | Cruz-Duarte, Jorge M. Zárate-Triviño, Diana G. Avina-Cervantes, Juan Gabriel Menchaca, Jorge Luis Duque-Gimenez, Grecia C. Zambrano-Gutierrez, Daniel F. Ortiz-Bayliss, José Carlos Rodriguez-Nieto, Maricela |
| Author_xml | – sequence: 1 givenname: Grecia C. surname: Duque-Gimenez fullname: Duque-Gimenez, Grecia C. organization: Centro de Investigación en Ciencias Físico Matemáticas, Facultad de Ciencias Físico Matemáticas, Universidad Autónoma de Nuevo León – sequence: 2 givenname: Daniel F. surname: Zambrano-Gutierrez fullname: Zambrano-Gutierrez, Daniel F. email: A00836756@tec.mx organization: School of Engineering and Sciences, Tecnologico de Monterrey – sequence: 3 givenname: Maricela surname: Rodriguez-Nieto fullname: Rodriguez-Nieto, Maricela organization: Centro de Investigación en Ciencias Físico Matemáticas, Facultad de Ciencias Físico Matemáticas, Universidad Autónoma de Nuevo León, Secretaría de Ciencia, Humanidades, Tecnología e Innovación – sequence: 4 givenname: Jorge Luis surname: Menchaca fullname: Menchaca, Jorge Luis organization: Centro de Investigación en Ciencias Físico Matemáticas, Facultad de Ciencias Físico Matemáticas, Universidad Autónoma de Nuevo León – sequence: 5 givenname: Jorge M. surname: Cruz-Duarte fullname: Cruz-Duarte, Jorge M. organization: Université de Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRIStAL – sequence: 6 givenname: Diana G. surname: Zárate-Triviño fullname: Zárate-Triviño, Diana G. organization: Laboratorio de Inmunología y Virología, Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León – sequence: 7 givenname: Juan Gabriel surname: Avina-Cervantes fullname: Avina-Cervantes, Juan Gabriel organization: Telematics Research Group, Department of Electronics Engineering, University of Guanajuato – sequence: 8 givenname: José Carlos surname: Ortiz-Bayliss fullname: Ortiz-Bayliss, José Carlos organization: School of Engineering and Sciences, Tecnologico de Monterrey |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40858862$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9ks1u1DAURiNUREvpC7BAltiwCfgvGXuFUAWlUhEbYGvd2DeJR4ld7ASJPhGPiWemlJYFWTh2cnx8df09rY5CDFhVzxl9zahQb7JkjVY15U3NWraRtXhUnXAqm5oLzo_uzY-rs5y3tDwN15LpJ9WxpKpRquUn1a9vPtuIE-TFW2JHSGAXTP4GFh8DiT1ZRiTjOkNZ5AVjhmTjDMRCsJiIxWkikw9IPl3UrSBr9mEgQPqdpxhgqmNyBbzBUMY5OpyKMsV1GAmsS1Et6AhMQ0x-GWfiMPshEAiO2Bh6P6xpX8qz6nEPU8az2_dp9fXD-y_nH-urzxeX5--uaiu1XGrWwQZ7B53sGIDb8F73SjTcSket3OgWHNVWd9I51GAFMtVbDbwTfIPUcXFaXR68LsLWXCc_Q_ppIniz_xDTYCCVXk1oGst419NWo1OScw1Qet42rnHWuiIrrrcH1_XazegshiXB9ED68E_woxniD8O4UIypphhe3RpS_L5iXsxc7qv0HALGNRvBZSuUkFIU9OU_6DauqVzAnhKK8nYjC_Xifkl3tfxJRAH4AbAp5pywv0MYNbvkmUPyTEme2SfP7M4Wh025wGHA9Pfs_-z6DRL74Bo |
| Cites_doi | 10.1038/s41598-023-27547-x 10.1007/s11047-020-09837-9 10.1021/acs.nanolett.2c00736 10.1109/TNB.2022.3165871 10.1007/s002490050213 10.1103/PhysRevLett.87.148102 10.1007/978-3-319-91086-4_14 10.1109/ACCESS.2023.3236836 10.1007/s00170-019-04591-4 10.1002/jemt.24184 10.1007/s11721-023-00227-2 10.1016/j.swevo.2021.100935 10.1007/978-3-319-93025-1_4 10.1038/nnano.2007.388 10.1016/j.micron.2012.01.019 10.1126/science.220.4598.671 10.1088/1361-6463/ac02fa 10.1146/annurev.biophys.050708.133724 10.1016/S0021-9290(99)00175-X 10.1109/mci.2020.2976182 10.1155/2015/931256 10.3322/caac.21492 10.1016/j.aml.2008.06.003 10.1109/4235.985692 10.1002/mma.7037 10.1145/3292500.3330701 10.1115/1.2720924 10.1007/s10867-023-09648-w 10.1038/s41598-017-05383-0 10.1016/j.ejor.2019.10.004 10.1016/j.dsp.2024.104490 10.1116/6.0000544 10.1007/s12046-017-0674-0 10.3390/math8112046 10.1103/physrevresearch.3.043166 10.34133/icomputing.0048 10.1109/4235.585893 10.1038/s41592-018-0015-1 10.1007/s10659-021-09827-7 10.1002/jmr.3022 10.1016/j.jmbbm.2018.11.029 10.1109/tevc.2022.3197298 10.1109/embc40787.2023.10340235 10.3389/fmats.2020.00011 10.1002/9781119454816 10.3390/cells10040887 10.1038/nrmicro1948 10.1111/exd.12535 10.1007/s43032-019-00042-3 10.48550/arXiv.2201.02589 10.1016/j.joca.2005.12.003 10.1016/j.mbm.2024.100082 10.1016/j.amc.2006.08.163 10.1038/nmeth.1218 10.1007/s10409-019-00895-6 10.1016/j.jmbbm.2023.105734 10.1007/978-3-319-96514-7 10.1007/s10462-017-9605-z 10.1002/jemt.23643 10.48550/arXiv.2303.06532 10.1039/d0sm00354a 10.1007/s13402-022-00720-6 10.1007/s10462-020-09893-8 10.1016/j.asoc.2022.108919 10.1088/1478-3975/12/4/046001 10.1016/j.actbio.2004.09.001 10.1111/itor.12001 10.3390/app11125620 10.1016/j.ymssp.2010.09.002 10.1039/c9sm01020c 10.1140/epjp/s13360-020-00843-5 10.1007/s10237-019-01248-9 10.1088/1361-6633/ab1628 10.1109/tnb.2017.2714462 10.1142/9789812386458_0015 10.1007/s10867-016-9423-6 10.3390/ijms241814296 10.1016/j.compchemeng.2015.03.002 10.1016/j.softx.2020.100628 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 2025. The Author(s). The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025 |
| Copyright_xml | – notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-025-16174-3 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Science Journals (ProQuest Database) Biological Science Database ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE CrossRef MEDLINE - Academic Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 25 |
| ExternalDocumentID | oai_doaj_org_article_5c12bf069ed84229aa20465d5dccde0d PMC12381185 40858862 10_1038_s41598_025_16174_3 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 4.4 53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD AASML ABDBF ABUWG ACGFS ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M1P M2P M7P M~E NAO OK1 PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RNT RNTTT RPM SNYQT UKHRP AAYXX AFFHD CITATION CGR CUY CVF ECM EIF NPM 3V. 7XB 88A 8FK K9. M48 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c494t-1ba7efdab4b1aad72f9f8352c4d0c4796ad09c9b4dde9ac3e18fc9a2b327e0d23 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001559641800033&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2045-2322 |
| IngestDate | Tue Oct 14 19:08:03 EDT 2025 Tue Nov 04 02:05:40 EST 2025 Sat Nov 01 14:08:14 EDT 2025 Sat Nov 01 15:10:43 EDT 2025 Thu Sep 04 05:00:41 EDT 2025 Sat Nov 29 07:36:19 EST 2025 Wed Aug 27 01:34:30 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Viscoelasticity Atomic force microscopy Metaheuristic Fractional-order zener model Automated algorithm design Cells Hyper-heuristic |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c494t-1ba7efdab4b1aad72f9f8352c4d0c4796ad09c9b4dde9ac3e18fc9a2b327e0d23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/3243802674?pq-origsite=%requestingapplication% |
| PMID | 40858862 |
| PQID | 3243802674 |
| PQPubID | 2041939 |
| PageCount | 25 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_5c12bf069ed84229aa20465d5dccde0d pubmedcentral_primary_oai_pubmedcentral_nih_gov_12381185 proquest_miscellaneous_3246383443 proquest_journals_3243802674 pubmed_primary_40858862 crossref_primary_10_1038_s41598_025_16174_3 springer_journals_10_1038_s41598_025_16174_3 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-08-26 |
| PublicationDateYYYYMMDD | 2025-08-26 |
| PublicationDate_xml | – month: 08 year: 2025 text: 2025-08-26 day: 26 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2025 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | D Guan (16174_CR79) 2021; 3 16174_CR46 R Qu (16174_CR67) 2020; 15 F Renaud (16174_CR25) 2011; 25 C-Y Lin (16174_CR27) 2020; 7 16174_CR41 P-H Wu (16174_CR20) 2018; 15 MD Ortigueira (16174_CR50) 2024; 149 SJ Han (16174_CR7) 2022; 45 16174_CR47 KC Neuman (16174_CR55) 2008; 5 S Kirkpatrick (16174_CR63) 1983; 220 W Zhang (16174_CR60) 2021; 145 H Schillers (16174_CR81) 2017; 7 M Li (16174_CR31) 2016; 42 SS Rao (16174_CR62) 2019 G Jumarie (16174_CR49) 2009; 22 JM Cruz-Duarte (16174_CR45) 2020; 8 C Li (16174_CR48) 2007; 187 F Bray (16174_CR1) 2018; 68 16174_CR76 16174_CR33 16174_CR34 EK Burke (16174_CR69) 2019 16174_CR78 16174_CR35 SE Cross (16174_CR14) 2007; 2 M Clerc (16174_CR75) 2002; 6 E Darling (16174_CR30) 2006; 14 16174_CR37 S Suresh (16174_CR9) 2005; 1 D Wirtz (16174_CR57) 2009; 38 DC Lin (16174_CR72) 2007; 129 RM Christensen (16174_CR51) 2013 N Liu (16174_CR5) 2023; 22 YM Efremov (16174_CR22) 2020; 16 P Alfaro-Fernández (16174_CR42) 2020; 282 DF Zambrano-Gutierrez (16174_CR65) 2023; 11 YF Dufrêne (16174_CR54) 2008; 6 A Serra-Aguila (16174_CR26) 2019; 35 JM Cruz-Duarte (16174_CR68) 2021; 11 JM Cruz-Duarte (16174_CR44) 2021; 66 16174_CR24 A Tzanetos (16174_CR40) 2021; 54 K Pogoda (16174_CR80) 2022; 22 Y Xie (16174_CR53) 2019; 91 16174_CR64 JM Cruz-Duarte (16174_CR74) 2020; 12 M Lekka (16174_CR21) 1999; 28 M Rodríguez-Nieto (16174_CR3) 2020; 19 N Pillay (16174_CR70) 2018 M Li (16174_CR23) 2017; 16 A Altayyeb (16174_CR11) 2020; 27 G Pesce (16174_CR56) 2020; 135 L Ovalle-Flores (16174_CR52) 2023; 140 A Weber (16174_CR82) 2022; 85 JM Cruz-Duarte (16174_CR61) 2021; 44 M Sarna (16174_CR10) 2014; 23 16174_CR71 MM Bras (16174_CR6) 2024; 50 CT Mierke (16174_CR16) 2019; 82 K Sörensen (16174_CR66) 2015; 22 Y Li (16174_CR8) 2019; 105 16174_CR12 16174_CR13 M Lekka (16174_CR15) 2012; 43 NF Alkayem (16174_CR36) 2022; 123 16174_CR19 A Bonfanti (16174_CR32) 2020; 16 Y Zhang (16174_CR77) 2015; 2015 Z Liu (16174_CR18) 2024; 2 16174_CR17 RM Hochmuth (16174_CR58) 2000; 33 B Carmichael (16174_CR59) 2015; 12 W Yi (16174_CR43) 2022; 27 B Fabry (16174_CR29) 2001; 87 O Gaidai (16174_CR2) 2023; 13 G Runel (16174_CR4) 2021; 10 F De Sousa (16174_CR28) 2021; 54 E Darling (16174_CR73) 2006; 14 J de Armas (16174_CR39) 2022; 21 DH Wolpert (16174_CR38) 1997; 1 |
| References_xml | – volume: 13 start-page: 303 year: 2023 ident: 16174_CR2 publication-title: Sci. Rep. doi: 10.1038/s41598-023-27547-x – volume: 21 start-page: 265 year: 2022 ident: 16174_CR39 publication-title: Nat. Comput. doi: 10.1007/s11047-020-09837-9 – volume: 22 start-page: 4725 year: 2022 ident: 16174_CR80 publication-title: Nano Lett. doi: 10.1021/acs.nanolett.2c00736 – volume: 22 start-page: 113 year: 2023 ident: 16174_CR5 publication-title: IEEE Trans. Nanobiosci. doi: 10.1109/TNB.2022.3165871 – volume: 28 start-page: 312 year: 1999 ident: 16174_CR21 publication-title: Eur. Biophys. J. doi: 10.1007/s002490050213 – volume: 87 year: 2001 ident: 16174_CR29 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.87.148102 – start-page: 453 volume-title: Handbook of Metaheuristics, International Series in Operations Research & Management Science year: 2019 ident: 16174_CR69 doi: 10.1007/978-3-319-91086-4_14 – volume: 11 start-page: 7262 year: 2023 ident: 16174_CR65 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3236836 – volume: 105 start-page: 4967 year: 2019 ident: 16174_CR8 publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-019-04591-4 – volume: 85 start-page: 3284 year: 2022 ident: 16174_CR82 publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.24184 – ident: 16174_CR41 doi: 10.1007/s11721-023-00227-2 – volume: 66 year: 2021 ident: 16174_CR44 publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2021.100935 – ident: 16174_CR64 doi: 10.1007/978-3-319-93025-1_4 – volume: 2 start-page: 780 year: 2007 ident: 16174_CR14 publication-title: Nat. Nanotechnol. doi: 10.1038/nnano.2007.388 – volume: 43 start-page: 1259 year: 2012 ident: 16174_CR15 publication-title: Micron doi: 10.1016/j.micron.2012.01.019 – volume: 220 start-page: 671 year: 1983 ident: 16174_CR63 publication-title: Science doi: 10.1126/science.220.4598.671 – volume: 54 year: 2021 ident: 16174_CR28 publication-title: J. Phys. D Appl. Phys. doi: 10.1088/1361-6463/ac02fa – volume: 38 start-page: 301 year: 2009 ident: 16174_CR57 publication-title: Annu. Rev. Biophys. doi: 10.1146/annurev.biophys.050708.133724 – volume: 33 start-page: 15 year: 2000 ident: 16174_CR58 publication-title: J. Biomech. doi: 10.1016/S0021-9290(99)00175-X – volume: 15 start-page: 14 year: 2020 ident: 16174_CR67 publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/mci.2020.2976182 – volume: 2015 year: 2015 ident: 16174_CR77 publication-title: Math. Probl. Eng. doi: 10.1155/2015/931256 – volume: 68 start-page: 394 year: 2018 ident: 16174_CR1 publication-title: CA A Cancer J. Clin. doi: 10.3322/caac.21492 – volume: 22 start-page: 378 year: 2009 ident: 16174_CR49 publication-title: Appl. Math. Lett. doi: 10.1016/j.aml.2008.06.003 – volume: 6 start-page: 58 year: 2002 ident: 16174_CR75 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.985692 – volume: 44 start-page: 4366 year: 2021 ident: 16174_CR61 publication-title: Math. Methods Appl. Sci. doi: 10.1002/mma.7037 – ident: 16174_CR46 doi: 10.1145/3292500.3330701 – volume: 129 start-page: 430 year: 2007 ident: 16174_CR72 publication-title: J. Biomech. Eng. doi: 10.1115/1.2720924 – volume: 50 start-page: 55 year: 2024 ident: 16174_CR6 publication-title: J. Biol. Phys. doi: 10.1007/s10867-023-09648-w – volume: 7 start-page: 5117 year: 2017 ident: 16174_CR81 publication-title: Sci. Rep. doi: 10.1038/s41598-017-05383-0 – volume: 282 start-page: 835 year: 2020 ident: 16174_CR42 publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2019.10.004 – volume: 149 year: 2024 ident: 16174_CR50 publication-title: Digital Signal Process. doi: 10.1016/j.dsp.2024.104490 – ident: 16174_CR24 doi: 10.1116/6.0000544 – ident: 16174_CR76 doi: 10.1007/s12046-017-0674-0 – volume: 8 start-page: 2046 year: 2020 ident: 16174_CR45 publication-title: Mathematics doi: 10.3390/math8112046 – volume: 3 year: 2021 ident: 16174_CR79 publication-title: Phys. Rev. Res. doi: 10.1103/physrevresearch.3.043166 – ident: 16174_CR37 doi: 10.34133/icomputing.0048 – volume: 1 start-page: 67 year: 1997 ident: 16174_CR38 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.585893 – volume: 15 start-page: 491 year: 2018 ident: 16174_CR20 publication-title: Nat. Methods doi: 10.1038/s41592-018-0015-1 – volume: 145 start-page: 117 year: 2021 ident: 16174_CR60 publication-title: J. Elast. doi: 10.1007/s10659-021-09827-7 – ident: 16174_CR13 doi: 10.1002/jmr.3022 – volume: 91 start-page: 54 year: 2019 ident: 16174_CR53 publication-title: J. Mech. Behav. Biomed. Mater. doi: 10.1016/j.jmbbm.2018.11.029 – volume: 27 start-page: 1072 year: 2022 ident: 16174_CR43 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/tevc.2022.3197298 – ident: 16174_CR17 doi: 10.1109/embc40787.2023.10340235 – volume: 7 start-page: 11 year: 2020 ident: 16174_CR27 publication-title: Front. Mater. doi: 10.3389/fmats.2020.00011 – volume-title: Engineering Optimization: Theory and Practice year: 2019 ident: 16174_CR62 doi: 10.1002/9781119454816 – volume-title: Theory of Viscoelasticity year: 2013 ident: 16174_CR51 – volume: 10 start-page: 887 year: 2021 ident: 16174_CR4 publication-title: Cells doi: 10.3390/cells10040887 – ident: 16174_CR47 – volume: 6 start-page: 674 year: 2008 ident: 16174_CR54 publication-title: Nat. Rev. Microbiol. doi: 10.1038/nrmicro1948 – volume: 23 start-page: 813 year: 2014 ident: 16174_CR10 publication-title: Exp. Dermatol. doi: 10.1111/exd.12535 – volume: 27 start-page: 364 year: 2020 ident: 16174_CR11 publication-title: Reprod. Sci. doi: 10.1007/s43032-019-00042-3 – ident: 16174_CR33 doi: 10.48550/arXiv.2201.02589 – volume: 14 start-page: 571 year: 2006 ident: 16174_CR73 publication-title: Osteoarthritis Cartilage doi: 10.1016/j.joca.2005.12.003 – volume: 2 year: 2024 ident: 16174_CR18 publication-title: Mechanobiol. Med. doi: 10.1016/j.mbm.2024.100082 – volume: 187 start-page: 777 year: 2007 ident: 16174_CR48 publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2006.08.163 – volume: 5 start-page: 491 year: 2008 ident: 16174_CR55 publication-title: Nat. Methods doi: 10.1038/nmeth.1218 – volume: 35 start-page: 1191 year: 2019 ident: 16174_CR26 publication-title: Acta. Mech. Sin. doi: 10.1007/s10409-019-00895-6 – volume: 140 year: 2023 ident: 16174_CR52 publication-title: J. Mech. Behav. Biomed. Mater. doi: 10.1016/j.jmbbm.2023.105734 – volume-title: Hyper-Heuristics: Theory and Applications year: 2018 ident: 16174_CR70 doi: 10.1007/978-3-319-96514-7 – ident: 16174_CR35 doi: 10.1007/s10462-017-9605-z – ident: 16174_CR78 doi: 10.1002/jemt.23643 – ident: 16174_CR71 doi: 10.48550/arXiv.2303.06532 – volume: 16 start-page: 6002 year: 2020 ident: 16174_CR32 publication-title: Soft Matter doi: 10.1039/d0sm00354a – volume: 45 start-page: 1119 year: 2022 ident: 16174_CR7 publication-title: Cell. Oncol. doi: 10.1007/s13402-022-00720-6 – volume: 14 start-page: 571 year: 2006 ident: 16174_CR30 publication-title: Osteoarthritis Cartilage doi: 10.1016/j.joca.2005.12.003 – volume: 54 start-page: 1841 year: 2021 ident: 16174_CR40 publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-020-09893-8 – volume: 123 year: 2022 ident: 16174_CR36 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2022.108919 – volume: 12 year: 2015 ident: 16174_CR59 publication-title: Phys. Biol. doi: 10.1088/1478-3975/12/4/046001 – volume: 1 start-page: 15 year: 2005 ident: 16174_CR9 publication-title: Acta Biomater. doi: 10.1016/j.actbio.2004.09.001 – volume: 22 start-page: 3 year: 2015 ident: 16174_CR66 publication-title: Int. Trans. Oper. Res. doi: 10.1111/itor.12001 – volume: 11 start-page: 5620 year: 2021 ident: 16174_CR68 publication-title: Appl. Sci. doi: 10.3390/app11125620 – volume: 25 start-page: 991 year: 2011 ident: 16174_CR25 publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2010.09.002 – volume: 16 start-page: 64 year: 2020 ident: 16174_CR22 publication-title: Soft Matter doi: 10.1039/c9sm01020c – volume: 135 start-page: 949 year: 2020 ident: 16174_CR56 publication-title: Eur. Phys. J. Plus doi: 10.1140/epjp/s13360-020-00843-5 – volume: 19 start-page: 801 year: 2020 ident: 16174_CR3 publication-title: Biomech. Model. Mechanobiol. doi: 10.1007/s10237-019-01248-9 – volume: 82 year: 2019 ident: 16174_CR16 publication-title: Rep. Prog. Phys. doi: 10.1088/1361-6633/ab1628 – volume: 16 start-page: 523 year: 2017 ident: 16174_CR23 publication-title: IEEE Trans. Nanobiosci. doi: 10.1109/tnb.2017.2714462 – ident: 16174_CR19 doi: 10.1142/9789812386458_0015 – volume: 42 start-page: 551 year: 2016 ident: 16174_CR31 publication-title: J. Biol. Phys. doi: 10.1007/s10867-016-9423-6 – ident: 16174_CR12 doi: 10.3390/ijms241814296 – ident: 16174_CR34 doi: 10.1016/j.compchemeng.2015.03.002 – volume: 12 year: 2020 ident: 16174_CR74 publication-title: SoftwareX doi: 10.1016/j.softx.2020.100628 |
| SSID | ssj0000529419 |
| Score | 2.458188 |
| Snippet | Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers.... Abstract Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 31436 |
| SubjectTerms | 639/166/985 639/705/117 Algorithms Atomic force microscopy Automation Biomechanical Phenomena Biomechanics Bone cancer Calculus Cell Line, Tumor Cells Coefficient of variation Design Elasticity Fractional-order zener model Heuristic Humanities and Social Sciences Humans Hyper-heuristic Laplace transforms Mechanical properties Metaheuristic Microscopy, Atomic Force multidisciplinary Optimization Osteosarcoma Osteosarcoma - pathology Parameter estimation Physiology Science Science (multidisciplinary) Statistical analysis Viscoelasticity Viscosity |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELVQBRIXxDeBggaJG1hNbCexj4AoXKg4AOotmtjxdqU2qZLdSuUX8TMZ29mly4e4cF1H0chvPH6zmXnD2AsvK5t743nrneCUf3luKvRcWdfVymJVo47DJuqjI318bD5dGfUVasKSPHDauIPSFqL1eWU6p5UQBlFQSle60ll6Xe5C9CXWcyWZSqrewqjCzF0yudQHE91UoZtMlDxQesXlzk0UBfv_xDJ_L5b85YtpvIgOb7NbM4OE18nyO-xa199lN9JMyct77PvX5WSHjkgxrYPd6jGndksYPBDlgziaD0KDxzCRqw9nCDbgP0L4Jx8C94SP73klIRTGLwDBj6kFAk95lOuEb0GwGuIkHZin_QCuV_QqIrGAp4thXK5OzsDFGhHA3gHl3n65WCenu8--HL77_PYDn8cxcKuMWvGixbrzDlvVFoiuFgRx4G9WudyqmiB2ubGmVRQxDVrZFdpbg6KVoiaghHzA9vqh7x6FeipdBKEvX1uvHMUEoVFYLCmdaiuLmLGXG2ia86S60cSv5VI3CciGgGwikI3M2JuA3vbJoJgdfyA_amY_av7lRxnb32DfzMd4aohtSh1mdKmMPd8u0wEMWGDfDev4DMUwqRTZ8TC5ytaSIB-nKWfMmN5xoh1Td1f65UkU-S4ClyIylbFXG3_7adff9-Lx_9iLJ-ymCAclpyBa7bO91bjunrLr9mK1nMZn8aT9ALQSMnU priority: 102 providerName: Directory of Open Access Journals |
| Title | Viscoelastic characterization of the human osteosarcoma cancer cell line MG-63 using a fractional-order zener model through automated algorithm design and configuration |
| URI | https://link.springer.com/article/10.1038/s41598-025-16174-3 https://www.ncbi.nlm.nih.gov/pubmed/40858862 https://www.proquest.com/docview/3243802674 https://www.proquest.com/docview/3246383443 https://pubmed.ncbi.nlm.nih.gov/PMC12381185 https://doaj.org/article/5c12bf069ed84229aa20465d5dccde0d |
| Volume | 15 |
| WOSCitedRecordID | wos001559641800033&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Journals (ProQuest Database) customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwELbYFiQuvB-BZWUkbmBtYruxfUIs2gUOrSoEqJwix467lXaTpWmR4BfxM5lx0q7K68LFhzqqJplvxmN75htCngWRuzSYwMrgOYP9V2Amt4FJ5yslnc2V1bHZhJpM9Gxmpv2BW9unVW58YnTUvnF4Rn4IC7_Q2C5Jvrz4wrBrFN6u9i009sgQmcrkgAyPjifT99tTFrzHkpnpq2VSoQ9bWLGwqoyPGIb2komdFSkS9_8p2vw9afKXm9O4IJ3c_N9XuUVu9KEofdVh5za5UtV3yLWuOeW3u-THp0Xrmgqia5inbkvs3NVt0iZQiB1p7PFHsVKkacFmmnNLHQJpSfFKgGIQS8dvWC4oZtjPqaVh2dVS2DMWeT_pd2S-prElD-3bBlG7XsFfQTRM7dkcZF-dnlMfk02orT2FTXxYzNcdeu-RjyfHH16_ZX1fB-akkSuWlVZVwdtSlpm1XnHACgaCTvrUSQVY8alxppTgeo11osp0cMbyUnBVpZ6L-2RQN3X1EBOzdIaMYUG5ID04F64td3YE-7Iyd9Ym5PlGt8VFR99RxGt3oYsOCQUgoYhIKERCjlD92yeRejv-0CznRW_JxchlvAxpbiqvJefGWp7KfORH3gG-U5-Q_Y3Wi94ftMWlyhPydDsNloy6sHXVrOMz4AyFlCDHgw5rW0mQh07D5jMhegeFO6LuztSL08gWnmFQBlFZQl5sAHsp19-_xaN_v8Zjcp2jDaXgZ_N9Mlgt19UTctV9XS3a5QHZUzMVR33Qm-RBPO2AccynOCoYh9N34-nnnzgHR20 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaqAoJLedNAASPBCawmjjd2DgjxKq3arjgUtLfg-LFdqU3KZhdUfhEnfiMzTrLV8rr1wDWOoonzzTcz8TwIeezTzMQ-96z0ljOIvzzLM-2ZMNZJYXQmtQrDJuRwqEaj_P0K-dHXwmBaZc-JgahtbfAf-SYY_lThuCTx4uQzw6lReLraj9BoYbHrTr9CyNY833kD3_cJ51tvD15vs26qADMiFzOWlFo6b3UpykRrKzlIim6IETY2QoKkNs5NXgpQ_Fyb1CXKm1zzMuXSxRYbHQDlXwAel5hCJkdy8U8HT81Ekne1OXGqNhuwj1jDxgcMAwnB0iX7F8YE_Mm3_T1F85dz2mD-tq7-bxt3jax1jjZ92WrGdbLiqhvkUjt68_Qm-f5x0pjaQewA69Qs2la3Vam09hQ8YxomGFKsg6kbeIP6WFODajKleOBB0UWn--9YllKsHxhTTf20rRTRRyx0NaXfsK83DQOHaDcUier5DB4Fvj7VR2PYq9nhMbUhlYbqylJTV34ynre6eYt8OJdtuk1Wq7py65h2phLsh-al8cICdXKludEDiDrLzGgdkac9loqTtjlJEZIKUlW0yCsAeUVAXpFG5BXCbXEnNhYPF-rpuOh4qhiYhJc-znJnleA815rHIhvYgTWgvbGNyEaPsqJju6Y4g1hEHi2WgafwW-jK1fNwD1B9KgTIcafF9kIS7LKnILSOiFpC_ZKoyyvV5DD0Qk_Q5QSfMyLPegU5k-vve3H336_xkFzePtjfK_Z2hrv3yBWO-huDRck2yOpsOnf3yUXzZTZppg8CAVDy6bwV5ydNCKDD |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaqFhCX8oZAASPBCaxNHOfhA0JAWahKV3sA1Fvq-LFdqU3KZhdUfhG_gV_HjJNstbxuPXCNo2jizDePeGY-Qh67ONWhk46VznAG-ZdjMlWOCW1sJrRKM5V7solsNMr39-V4jfzoe2GwrLK3id5Qm1rjP_IBOP44R7okMXBdWcR4e_ji5DNDBik8ae3pNFoV2bWnXyF9a57vbMO3fsL58M2H1-9YxzDAtJBizqJSZdYZVYoyUspkHKTGkEQLE2qRgdQmlFqWAoyAVDq2Ue60VLyMeWZDg0MPwPxvZCJJEF17fLz8v4MnaCKSXZ9OGOeDBnwl9rPxhGFSIVi84gs9ZcCf4tzfyzV_ObP1rnB45X_exKtkswvA6csWMdfImq2uk4stJefpDfL907TRtYWcAtapXo6zbrtVae0oRMzUMxtS7I-pG3iD-lhRjfCZUTwIoRi60723LI0p9hVMqKJu1naQqCPmp53Sbzjvm3oiItqRJVG1mMOjIAeg6mgCezU_PKbGl9hQVRmq68pNJ4sWszfJx3PZpltkvaorewfL0fII56S5TDthwKTyXHGtEshGy1QrFZCnvV4VJ-3QksIXG8R50WphAVpYeC0s4oC8QtVb3okDx_2FejYpOvtVJDripQtTaU0uOJdK8VCkiUmMBlSHJiBbvcYVnRVsijN1C8ij5TLYL_wWqrL1wt8DLiAWAuS43er5UhKcvpdDyh2QfAUBK6KurlTTQz8jPcJQFGLRgDzrwXIm19_34u6_X-MhuQR4Kd7vjHbvkcscoRyCo0m3yPp8trD3yQX9ZT5tZg-8LaDk4Lxx8xOJwamQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Viscoelastic+characterization+of+the+human+osteosarcoma+cancer+cell+line+MG-63+using+a+fractional-order+zener+model+through+automated+algorithm+design+and+configuration&rft.jtitle=Scientific+reports&rft.au=Duque-Gimenez%2C+Grecia+C.&rft.au=Zambrano-Gutierrez%2C+Daniel+F.&rft.au=Rodriguez-Nieto%2C+Maricela&rft.au=Menchaca%2C+Jorge+Luis&rft.date=2025-08-26&rft.pub=Nature+Publishing+Group+UK&rft.eissn=2045-2322&rft.volume=15&rft_id=info:doi/10.1038%2Fs41598-025-16174-3&rft_id=info%3Apmid%2F40858862&rft.externalDocID=PMC12381185 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |