Exploring Bone Cell Research Using Bone-on-a-Chip Models and Microfluidics: A Literature Review
Bibliographic record
Abstract
Introduction: Organ-on-a-chip models are becoming popular due to its success in modeling human tissues and organs, to mimic human physiology and understand how diseases or drugs affect organs. Traditional 2-dimensional in vitro models are limited in recreating complicated bone structure and examining cell-cell interactions. Alternatively, bone-on-a-chip models establish biomimetic conditions to accurately recapitulate the complexity of the bone. However, bone-on-a-chip models as 3D culture systems do not accurately replicate the bone microenvironment. Rather, microfluidic devices allow for fluid control on a microscale or nanoscale level and the incorporation of fluid shear stress normally experienced by bone cells. The goal of this review paper is to summarize advancements to bone-on-a-chip models. Methods: Relevant articles were selected through a computerized search using GEOBASE and PubMED. Search terms included ‘microfluidic devices AND bones’, ‘organ-on-a-chip models’, ‘bone-on-a-chip models’, ‘PDMS AND bone regeneration’, ‘PolyHIPE AND bone regeneration’ and ‘bone scaffolds’. Results: Microfluidic chips are fabricated using soft lithography and poly-di-methyl siloxane (PDMS) which is a biocompatible, synthetic polymer that is used as a cell culture substrate but is too stiff to facilitate bone regeneration. Hydroxyapatite (HA), lined with PDMS, is commonly used, but the substrate degrades at a much slower rate. Moreover, β-tricalcium-phosphate (β-TCP) as a bone scaffold is both porous and degrades faster hence existing studies have used it to generate a dense extracellular matrix. Discussion: The studies examined in this paper highlight contributions made to scaffolds and microfluidics using bone-on-a-chip models. Notably, scaffolds must be osteoconductive to allow bone cells to adhere, proliferate and form an extracellular matrix on its surface and pore. While PDMS is both osteoconductive and biocompatible, its rigidity poses a concern. Both β-TCP and HA have capabilities for cell-mediated resorption and are more favourable substrates. Additionally, by incorporating microfluidics with bone-on-a-chip models, cells experience greater fluid shear stress similar to that of loading within the bone. Conclusion: In sum, advancements to bone-on-a-chip platforms are ongoing and the many published studies discussed in this paper aim to optimize both the design and materials used to create long lasting impacts on the rapidly growing field of cell and tissue engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".