Controlled Osteogenic Differentiation of Human Mesenchymal Stem Cells Using Dexamethasone-Loaded Light-Responsive Microgels
Bibliographic record
Abstract
Human mesenchymal stem cells (hMSCs), which have the ability to differentiate into osteoblasts, show promise for bone tissue engineering and bone defect treatment. While there are a number of approaches currently available to accomplish this, e.g., utilizing biodegradable materials loaded with the synthetic glucocorticoid osteogenic inducer dexamethasone (DEX), there are still many disadvantages with the current technologies. Here, we generated light-responsive microgels that we showed are capable of loading and releasing DEX in a light-triggered fashion, with the released DEX being able to induce hMSC differentiation into osteoblasts. Specifically, light-responsive poly(N-isopropylacrylamide-co-nitrobenzyl methacrylate) (pNIPAm-co-NBMA) microgels were synthesized via free radical precipitation polymerization and their size, morphology, and chemical composition were characterized. We then went on to show that the microgels could be loaded with DEX (via what we think are hydrophobic interactions) and released upon exposure to UV light. We went on to show that the DEX released from the microgels was still capable of inducing osteogenic differentiation of hMSCs using an alamarBlue assay and normalized alkaline phosphatase (ALP) activity assay. We also investigated how hMSC differentiation was impacted by intermittent DEX released from UV-exposed microgels. Finally, we confirmed that the microgels themselves were not cytotoxic to hMSCs. Taken together, the DEX-loaded light-responsive microgels reported here may find a use for niche clinical applications, e.g., bone tissue repair.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".