Controlled Non‐Viral Gene Delivery in Cartilage and Bone Repair: Current Strategies and Future Directions
Bibliographic record
Abstract
Abstract Recent advances in the approval and commercialization of gene therapeutics have fostered the return of gene therapy to center stage. But despite new optimism, no Food and Drug Administration approved product exists for the treatment of orthopedic disorders. Non‐viral gene delivery is a promising alternative to recombinant protein administration and viral gene transduction for orthopedic tissue engineering. When applied using appropriately designed systems, it enables temporal control of the overexpression of therapeutic genes, leading to local production of regulatory factors at physiologically relevant levels. Incorporating genetic material into 3D scaffold biomaterials, that is, gene activated scaffolds or hydrogels, presents a particular opportunity to utilize non‐viral gene therapy for in situ transfection of host cells and the regeneration of damaged tissues and organs. But controlled non‐viral gene delivery for musculoskeletal regeneration depends on a multifactorial design in which the choice of gene delivery method, therapeutic gene, and supportive biomaterial play a central role for the success of this strategy. This paper reviews the different modalities of non‐viral gene delivery used for the repair of bone and cartilage, and explores the current challenges and opportunities for the engineering of functional orthopedic tissues using gene activated scaffolds.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".