Injectable hydrogels: An emerging therapeutic strategy for cartilage regeneration
Bibliographic record
Abstract
The impairment of articular cartilage due to traumatic incidents or osteoarthritis has posed significant challenges for healthcare practitioners, researchers, and individuals suffering from these conditions. Due to the absence of an approved treatment strategy for the complete restoration of cartilage defects to their native state, the tissue condition often deteriorates over time, leading to osteoarthritic (OA). However, recent advancements in the field of regenerative medicine have unveiled promising prospects through the utilization of injectable hydrogels. This versatile class of biomaterials, characterized by their ability to emulate the characteristics of native articular cartilage, offers the distinct advantage of minimally invasive administration directly to the site of damage. These hydrogels can also serve as ideal delivery vehicles for a diverse range of bioactive agents, including growth factors, anti-inflammatory drugs, steroids, and cells. The controlled release of such biologically active molecules from hydrogel scaffolds can accelerate cartilage healing, stimulate chondrogenesis, and modulate the inflammatory microenvironment to halt osteoarthritic progression. The present review aims to describe the methods used to design injectable hydrogels, expound upon their applications as delivery vehicles of biologically active molecules, and provide an update on recent advances in leveraging these delivery systems to foster articular cartilage regeneration.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".