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Advances in Cell and Tissue Engineering: Biomechanical Modeling and Mechanobiology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 594

Special Issue Editors


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Guest Editor
Nanoscience and Nanoengineering, South Dakota School of Mines and Technology, 501 East Saint Joseph Street, Rapid City, SD 57703, USA
Interests: biomechanical engineering; biomechanics; biomedical modeling; biomaterials; biomedical imaging; cell culture; immunoblotting; cell imaging; cell signaling; immunofluorescence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA 18015, USA
2. Department and Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
Interests: biomechanics; dynamic loading; modelling of cell motility; gait analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding pathology and treating diseases require detailed observations and analyses of research targets at multiple scales, from nano to macro. Most diseases arise due to imbalances in cell and tissue homeostasis, which are affected by cell activity and differentiation. Cells can sense, respond, and adapt to mechanical stimuli, often differentiating between static and dynamic stimuli. Traditional mechanical processes applied to monolayers of cells, intact tissues, or engineered tissue structures can stimulate cell differentiation and promote metabolic changes. Cell differentiation and reproduction can promote tissue repair and regeneration, thereby impacting the treatment of tissues, organs, and systems within the body. Mechanobiological studies can help explain macroscopic pathogenesis from the microscopic level through cellular activities. These studies are further enhanced when coupled with advanced numerical analysis techniques, including but not limited to finite element analysis, computational fluid dynamics, multiphysics modeling, multi-scale modeling, agent-based modeling, network analyses, and artificial intelligence (AI)-based methodologies. The integration of these analytical tools with biomechanical and mechanobiological data can deepen our understanding and enable predictions of complex mechanobiological processes, which may lead to biomechanical or pharmacomechanical solutions to treat a wide variety of diseases.

This Special Issue aims to explore the potential of numerical and mechanical analyses in the biomedical field to accelerate the understanding of pathogenesis, accelerate drug development, and accelerate the development of novel disease treatments. We welcome numerical studies, experimental studies, and comprehensive reviews.

Dr. Scott Wood
Prof. Dr. Arkady Voloshin
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mechanobiological therapy
  • mechanical analysis in biomedicine
  • cell culture
  • biomechanics
  • cell differentiation
  • mechanotransduction
  • numerical techniques in biomedicine

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Published Papers (2 papers)

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Research

12 pages, 5706 KiB  
Communication
How to Use the Osteoclast Identifier Software
by Guofan Lv, Hans-Peter Wiesmann and Benjamin Kruppke
Appl. Sci. 2025, 15(8), 4208; https://doi.org/10.3390/app15084208 - 11 Apr 2025
Viewed by 170
Abstract
The OC_Identifier software is programmed at the Max Bergmann Center for Biomaterials to make a low-threshold cell culture analysis available. This is a user manual for the OC_Identifier software. This software is used to classify and detect four different cell types based on [...] Read more.
The OC_Identifier software is programmed at the Max Bergmann Center for Biomaterials to make a low-threshold cell culture analysis available. This is a user manual for the OC_Identifier software. This software is used to classify and detect four different cell types based on the developmental stages of osteoclast maturation. The software uses AI models for this purpose, but these can be selected and changed without programming knowledge for flexible adaptation to new AI models and training data. This also makes it easy to compare different AI models, such as those based on different training data or training cycles, etc. In addition, the software calculates the percentage of each cell type among the total number of detected cells and displays detailed test results, including the position and confidence value of the detected cells. With this software and the instructions provided, we hope to enable a broad community to perform the AI-based image analysis of osteoclasts and their development from monocytes, and we hope for future expansion into co- and triple-culture models, for example. This should enable biomaterial characterisation based on a better morphological cell evaluation and replace time-consuming and costly biochemical and, if necessary, PCR analyses with AI image analysis. Full article
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19 pages, 5435 KiB  
Article
Artificial Intelligence for Image-Based Identification of Osteoclasts and Assessment of Their Maturation—Using the OC_Identifier
by Guofan Lv, Christiane Heinemann, Hans-Peter Wiesmann and Benjamin Kruppke
Appl. Sci. 2025, 15(8), 4159; https://doi.org/10.3390/app15084159 - 10 Apr 2025
Cited by 1 | Viewed by 191
Abstract
A form of AI was developed and trained to classify four different cell types, with a particular focus on identifying, counting, and determining the maturity of osteoclasts. Osteoclasts, formed by the fusion of monocytes, show clear morphological differences in their maturation, from small [...] Read more.
A form of AI was developed and trained to classify four different cell types, with a particular focus on identifying, counting, and determining the maturity of osteoclasts. Osteoclasts, formed by the fusion of monocytes, show clear morphological differences in their maturation, from small mononuclear cells to large multinuclear cells. The developed AI used YOLOv5m models to analyze these cell types based on microscopic images. The AI showed a certain degree of correlation with biochemical analyses (TRAP 5b, CAII). Despite this success, several challenges were identified. The homogeneity of the training data, limited by standardized cell culture conditions, limited the coverage of all osteoclast properties. Furthermore, the AI did not take into account the number of cell nuclei or the specific amount of DNA in the cells, which impaired the precision of the analysis of multinucleated osteoclasts. In the future, the introduction of weighting factors for cell nuclei could optimize the agreement of AI results with biochemical analyses. In summary, the developed AI technology offers a promising tool for cell identification and analysis, especially in osteoclast research. With further developments, this technology could significantly increase the efficiency and accuracy of cell analysis and promote practical applications in research and diagnostics. Full article
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