Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration
Angiogenesis is one of the first stages in fracture healing and bone repair. Therefore, numerous studies evaluating the effect of Mg as a promising degradable, metallic biomaterial on the proliferation and function of endothelial cells have been performed. However, these studies lack methodological...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025002399 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849433429955837952 |
|---|---|
| author | Heike Helmholz Redon Resuli Marius Tacke Jalil Nourisa Sven Tomforde Roland Aydin Regine Willumeit-Römer Berit Zeller-Plumhoff |
| author_facet | Heike Helmholz Redon Resuli Marius Tacke Jalil Nourisa Sven Tomforde Roland Aydin Regine Willumeit-Römer Berit Zeller-Plumhoff |
| author_sort | Heike Helmholz |
| collection | DOAJ |
| description | Angiogenesis is one of the first stages in fracture healing and bone repair. Therefore, numerous studies evaluating the effect of Mg as a promising degradable, metallic biomaterial on the proliferation and function of endothelial cells have been performed. However, these studies lack methodological homogeneity and therefore differ in fundamental conclusions. Here, Mg-concentration-, donor- and cell age- dependent relations to primary human umbilical cord vein endothelial cells (HUVEC) proliferation and migration were investigated systematically. The generated data were utilized to develop regression models in order to assess and predict the cell response on Mg exposition in a concentration range of 2–20 mM Mg in cell culture medium extract. A concentration of > 2 mM already induced a detrimental effect in the sensitive primary HUVECs. Molecular data quantifying angiogenesis markers supported this finding. An increased migration capacity has been observed at a concentration of 10 mM Mg. We compared linear regression, random forests, support vector machines, neural networks and large language models for the prediction of HUVEC proliferation for a number of scenarios. Using these machine learning methods, we were able to predict the proliferation of HUVECs for missing Mg concentrations and for missing passages with mean absolute errors below 10 % and as low as 8.5 %, respectively. Due to strong differences between the cell behaviour of different donors, information for missing donors can be predicted with mean absolute errors of 15.7 % only. Support vector machines with linear kernel performed best on the tested data, but large language models also showed promising results. |
| format | Article |
| id | doaj-art-662c05a12bad4bb7b998ddf51ffe6cfe |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-662c05a12bad4bb7b998ddf51ffe6cfe2025-08-20T03:27:02ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01272711271810.1016/j.csbj.2025.06.023Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migrationHeike Helmholz0Redon Resuli1Marius Tacke2Jalil Nourisa3Sven Tomforde4Roland Aydin5Regine Willumeit-Römer6Berit Zeller-Plumhoff7Institute of Metallic Biomaterials, Helmholz-Zentrum Hereon, Geesthacht, GermanyInstitute of Metallic Biomaterials, Helmholz-Zentrum Hereon, Geesthacht, GermanyInstitute of Material Systems Modeling, Helmholz-Zentrum Hereon, Geesthacht, GermanyInstitute of Material Systems Modeling, Helmholz-Zentrum Hereon, Geesthacht, GermanyDepartment of Computer Science, Faculty of Engineering, Kiel University, Kiel, GermanyInstitute of Material Systems Modeling, Helmholz-Zentrum Hereon, Geesthacht, Germany; Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, GermanyInstitute of Metallic Biomaterials, Helmholz-Zentrum Hereon, Geesthacht, Germany; Kiel, Nano, Surface, and Interface Science - KiNSIS, Kiel University, GermanyInstitute of Metallic Biomaterials, Helmholz-Zentrum Hereon, Geesthacht, Germany; Kiel, Nano, Surface, and Interface Science - KiNSIS, Kiel University, Germany; Data-driven Analysis and Design of Materials, Faculty of Mechanical Engineering and Marine Technologies, University of Rostock, Germany; Department Life, Light & Matter, Interdisciplinary Faculty, University of Rostock, Germany; Corresponding author at: Institute of Metallic Biomaterials, Helmholz-Zentrum Hereon, Geesthacht, Germany.Angiogenesis is one of the first stages in fracture healing and bone repair. Therefore, numerous studies evaluating the effect of Mg as a promising degradable, metallic biomaterial on the proliferation and function of endothelial cells have been performed. However, these studies lack methodological homogeneity and therefore differ in fundamental conclusions. Here, Mg-concentration-, donor- and cell age- dependent relations to primary human umbilical cord vein endothelial cells (HUVEC) proliferation and migration were investigated systematically. The generated data were utilized to develop regression models in order to assess and predict the cell response on Mg exposition in a concentration range of 2–20 mM Mg in cell culture medium extract. A concentration of > 2 mM already induced a detrimental effect in the sensitive primary HUVECs. Molecular data quantifying angiogenesis markers supported this finding. An increased migration capacity has been observed at a concentration of 10 mM Mg. We compared linear regression, random forests, support vector machines, neural networks and large language models for the prediction of HUVEC proliferation for a number of scenarios. Using these machine learning methods, we were able to predict the proliferation of HUVECs for missing Mg concentrations and for missing passages with mean absolute errors below 10 % and as low as 8.5 %, respectively. Due to strong differences between the cell behaviour of different donors, information for missing donors can be predicted with mean absolute errors of 15.7 % only. Support vector machines with linear kernel performed best on the tested data, but large language models also showed promising results.http://www.sciencedirect.com/science/article/pii/S2001037025002399Regression modelsHUVECCell proliferationLarge Language Model |
| spellingShingle | Heike Helmholz Redon Resuli Marius Tacke Jalil Nourisa Sven Tomforde Roland Aydin Regine Willumeit-Römer Berit Zeller-Plumhoff Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration Computational and Structural Biotechnology Journal Regression models HUVEC Cell proliferation Large Language Model |
| title | Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration |
| title_full | Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration |
| title_fullStr | Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration |
| title_full_unstemmed | Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration |
| title_short | Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration |
| title_sort | regression models for the prediction of the influence of magnesium ions on primary endothelial cell huvec proliferation and migration |
| topic | Regression models HUVEC Cell proliferation Large Language Model |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025002399 |
| work_keys_str_mv | AT heikehelmholz regressionmodelsforthepredictionoftheinfluenceofmagnesiumionsonprimaryendothelialcellhuvecproliferationandmigration AT redonresuli regressionmodelsforthepredictionoftheinfluenceofmagnesiumionsonprimaryendothelialcellhuvecproliferationandmigration AT mariustacke regressionmodelsforthepredictionoftheinfluenceofmagnesiumionsonprimaryendothelialcellhuvecproliferationandmigration AT jalilnourisa regressionmodelsforthepredictionoftheinfluenceofmagnesiumionsonprimaryendothelialcellhuvecproliferationandmigration AT sventomforde regressionmodelsforthepredictionoftheinfluenceofmagnesiumionsonprimaryendothelialcellhuvecproliferationandmigration AT rolandaydin regressionmodelsforthepredictionoftheinfluenceofmagnesiumionsonprimaryendothelialcellhuvecproliferationandmigration AT reginewillumeitromer regressionmodelsforthepredictionoftheinfluenceofmagnesiumionsonprimaryendothelialcellhuvecproliferationandmigration AT beritzellerplumhoff regressionmodelsforthepredictionoftheinfluenceofmagnesiumionsonprimaryendothelialcellhuvecproliferationandmigration |