Characterization of Calcium‐ and Strontium‐Polyphosphate Particles Toward Drug Delivery into Articular Cartilage
Bibliographic record
Abstract
Drug delivery into articular cartilage poses many challenges due in part to its lack of vasculature. While intra-articular injections are effective for the local administration of drugs, small molecules are rapidly cleared from the synovial fluid. As such, there is a need to develop effective drug delivery strategies to improve the residence times of bioactive molecules in the joint and elicit a sustained therapeutic effect. In this study, calcium- and strontium-polyphosphate particles are synthesized and characterized as potential drug carriers into articular cartilage. Physicochemical characterization reveals that the particles exhibit a spherical morphology, have a negative zeta potential, and are nanoscale in size. Biological characterization in chondrocytes confirms cellular uptake of the particles and demonstrates both size and concentration-dependent cytotoxicity at high concentrations. Furthermore, treatment of chondrocytes with these particles results in a reduction in cell proliferation and metabolic activity, confirming biological effects. Finally, incubation with cartilage tissue explants suggests successful uptake, despite the particles exhibiting a negative surface charge. Therefore, from the results of this study, these polyphosphate-based particles have potential as a drug carrier into articular cartilage and warrant further development.
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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".