Nanomaterials Applications in Cartilage Tissue Engineering
Bibliographic record
Abstract
Articular cartilage is the smooth layer of soft tissue that covers our bones and allows for the painless movement of our joints. Because of joint pathologies such as arthritis, cartilage can degrade over time in some individuals, causing them to live with considerable pain and reduced mobility. The high prevalence of arthritis and the absence of a cure for osteoarthritis, its most common form, have fueled sustained efforts to develop tissue engineering and regenerative medicine strategies aimed at regenerating cartilage. Despite a number of clinical advances that elicit cartilage repair, true regeneration remains elusive. Recent years have seen an increased use of nanoscale materials in the development of therapies for joint pathologies. Nanomaterials are comparable in scale to the principal building blocks of cartilage extracellular matrix, namely collagen and proteoglycan aggregates. Similarly, nanoparticles are sufficiently small to allow diffusion through the pores of the dense cartilage extracellular matrix and cell targeting. In this chapter, the organization of cartilage’s main building blocks will be reviewed from the nano- to macroscale, and sub-micron particles that participate in cell-cell communication will be highlighted. Efforts to design scaffolds incorporating cell-instructive nanoscale features and to tailor the mechanical properties, or even engineer spatial organization, in scaffolds for cartilage repair using nanomaterials will also be discussed. Finally, key design criteria in nanoparticle synthesis to enable targeted therapeutic delivery will be examined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".