Synthetic nanoscale electrostatic particles as growth factor carriers for cartilage repair
Bibliographic record
Abstract
The efficient transport of biological therapeutic materials to target tissues within the body is critical to their efficacy. In cartilage tissue, the lack of blood vessels prevents the entry of systemically administered drugs at therapeutic levels. Within the articulating joint complex, the dense and highly charged extracellular matrix (ECM) hinders the transport of locally administered therapeutic molecules. Consequently, cartilage injury is difficult to treat and frequently results in debilitating osteoarthritis. Here we show a generalizable approach in which the electrostatic assembly of synthetic polypeptides and a protein, insulin-like growth factor-1 (IGF-1), can be used as an early interventional therapy to treat injury to the cartilage. We demonstrated that poly(glutamic acid) and poly(arginine) associated with the IGF-1 via electrostatic interactions, forming a net charged nanoscale polyelectrolyte complex (nanoplex). We observed that the nanoplex diffused into cartilage plugs in vitro and stimulated ECM production. In vivo, we monitored the transport, retention and therapeutic efficacy of the nanoplex in an established rat model of cartilage injury. A single therapeutic dose, when administered within 48 hours of the injury, conferred protection against cartilage degradation and controlled interleukin-1 (IL-1) mediated inflammation. IGF-1 contained in the nanoplex was detected in the joint space for up to 4 weeks following administration and retained bioactivity. The results indicate the potential of this approach as an early intervention therapy following joint injury to delay or even entirely prevent the onset of osteoarthritis.
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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) | 0.000 | 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 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".