ReviewBioImpacts : BI2025
Molecular dynamics simulation in tissue engineering.
Review in BioImpacts : BI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Development of Novel Inhibitors for Alzheimer's Disease fromACS omega · 2026Article
- Moroccan virgin olive oil triterpenes: HPLC-DAD-ELSD profiling and computational investigation of anti-diabetic potential.BioImpacts : BI · 2026Article
- Unraveling the role of MAG, PTEN, and NOTCH1 in axonal regeneration: a network analysis and molecular dynamics study of siRNA/drugs/nanocarriers interactions.Journal of translational medicine · 2025Article
- Smart biomaterials: as active immune modulators to shape pro-regenerative microenvironments.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: In tissue engineering, the interaction among three primary elements, namely cells, material scaffolds, and stimuli, plays a pivotal role in determining the fate of cells and the formation of new tissue. Understanding the characteristics of these components and their interplay through various methodologies can significantly enhance the efficiency of the designed tissue engineering system. In silico methods, such as molecular dynamics (MD) simulation, use mathematical calculations to investigate molecular properties and can overcome the limitations of laboratory methods in delivering adequate molecular-level information. Methods: The studies that used molecular dynamics simulation, either alone or in combination with other techniques, have been reviewed in this paper. Results: The review explores the use of molecular dynamics simulations in studying substrate formation mechanism and its optimization. It highlights MD simulations' role in predicting biomolecule binding strength, understanding substrate properties' impact on biological activity, and factors influencing cell attachment and proliferation. Despite limited studies, MD simulations are considered a reliable tool for identifying ideal substrates for cell proliferation. The review also touches on MD simulations' contribution to cell differentiation studies, emphasizing their role in designing engineered extracellular matrix for desired cell fates. Conclusion: Molecular dynamics simulation as a non-laboratory tool has many capabilities in providing basic and practical information about the behavior of the molecular components of the cell as well as the interaction of the cell and its components with the surrounding environment. Using this information along with other information obtained from laboratory tools can ultimately lead to the advancement of tissue engineering through the development of more appropriate and efficient methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.