Cartilage Tissue Engineering Approaches Need to Assess Fibrocartilage When Hydrogel Constructs Are Mechanically Loaded
Bibliographic record
Abstract
Chondrocytes that are impregnated within hydrogel constructs sense applied mechanical force and can respond by expressing collagens, which are deposited into the extracellular matrix (ECM). The intention of most cartilage tissue engineering is to form hyaline cartilage, but if mechanical stimulation pushes the ratio of collagen type I (Col1) to collagen type II (Col2) in the ECM too high, then fibrocartilage can form instead. With a focus on Col1 and Col2 expression, the first part of this article reviews the latest studies on hyaline cartilage regeneration within hydrogel constructs that are subjected to compression forces (one of the major types of the forces within joints) in vitro . Since the mechanical loading conditions involving compression and other forces in joints are difficult to reproduce in vitro , implantation of hydrogel constructs in vivo is also reviewed, again with a focus on Col1 and Col2 production within the newly formed cartilage. Furthermore, mechanotransduction pathways that may be related to the expression of Col1 and Col2 within chondrocytes are reviewed and examined. Also, two recently-emerged, novel approaches of load-shielding and synchrotron radiation (SR)–based imaging techniques are discussed and highlighted for future applications to the regeneration of hyaline cartilage. Going forward, all cartilage tissue engineering experiments should assess thoroughly whether fibrocartilage or hyaline cartilage is formed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".