In Vitro Engineered ECM‐incorporated Hydrogels for Osteochondral Tissue Repair: A Cell‐Free Approach
Bibliographic record
Abstract
Prevalence of osteoarthritis has been increasing in aging populations, which has necessitated the use of advanced biomedical treatments. These involve grafts or delivering drug molecules entrapped in scaffolds. However, such treatments often show suboptimal therapeutic effects due to poor half-life and off-target effects of drug molecules. As a countermeasure, a 3D printable robust hydrogel-based tissue-repair platform is developed containing decellularized extracellular matrix (dECM) from differentiated mammalian cells as the therapeutic cargo. Here, pre-osteoblastic and pre-chondrogenic murine cells are differentiated in vitro, decellularized, and incorporated into methacrylated gelatin (GelMA) solutions to form osteogenic (GelO) and chondrogenic (GelC) hydrogels, respectively. Integrating the bioactive dECM from differentiated cell sources allows GelO and GelC to induce differentiation in human adipose-derived stem cells (hASCs) toward osteogenic and chondrogenic lineages. Further, GelO and GelC can be covalently adhered using a carbodiimide coupling reaction, forming a multi-layered hydrogel with potential application as a bioactive osteochondral plug. The designed multi-layered hydrogel can also induce differentiation of hASCs in vitro. In conclusion, the bioactive dECM carrying 3D printed robust hydrogel offers a promising new drug and cell-free therapeutic strategy for bone and cartilage repair and future osteoarthritis management.
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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 | 0.000 | 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) | 0.001 | 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".