Adaptable Hydrogels Mediate Cofactor‐Assisted Activation of Biomarker‐Responsive Drug Delivery via Positive Feedback for Enhanced Tissue Regeneration
Bibliographic record
Abstract
The targeted and simultaneous delivery of diverse cargoes with vastly different properties by the same vehicle is highly appealing but challenging. Here, a bioactive nanocomposite hydrogel based on hyaluronic acid and self-assembled pamidronate-magnesium nanoparticles for the localized elution and on-demand simultaneous release of bioactive ions and small molecule drugs is described. The obtained nanocomposite hydrogels exhibit excellent injectability and efficient stress relaxation, thereby allowing easy injection and consequent adaptation of hydrogels to bone defects with irregular shapes. Magnesium ions released from the hydrogels promote osteogenic differentiation of the encapsulated human mesenchymal stem cells (hMSCs) and activation of alkaline phosphatase (ALP). The activated ALP subsequently catalyzes the dephosphorylation (activation) of Dex phosphate, a pro-drug of Dex, and expedites the release of Dex from hydrogels to further promote hMSC osteogenesis. This positive feedback circuit governing the activation and release of Dex significantly enhances bone regeneration at the hydrogel implantation sites. The findings suggest that these injectable nanocomposite hydrogels mediate optimized release of diverse therapeutic cargoes and effectively promote in situ bone regeneration via minimally invasive procedures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".