Tough, Semisynthetic Hydrogels for Adipose Derived Stem Cell Delivery for Chondral Defect Repair
Bibliographic record
Abstract
Cell‐based therapies have great potential to regenerate and repair injured articular cartilage, and a range of synthetic and natural polymer‐based hydrogels have been used in combination with stem cells and growth factors for this purpose. Although the hydrogel scaffolds developed to date possess many favorable characteristics, achieving the required mechanical properties has remained a challenge. A hydrogel system with tunable mechanical properties, composed of a mixture of natural and synthetic polymers, and its use for the encapsulation of adipose derived stem/stromal cells (ASCs) is described. Solutions of methacrylated chondroitin sulfate (MCS) are mixed with solutions of acrylate‐poly(trimethylene carbonate)‐ b ‐poly(ethylene glycol)‐ b ‐poly(trimethylene carbonate)‐acrylate (PEG‐(PTMC‐A) 2 ) in phosphate buffered saline and crosslinked via thermally initiated free radical polymerization. The hydrogel compressive equilibrium moduli and toughness are readily tailored by varying the concentration of the pre‐polymers, as well as the molecular weight of the PEG used to prepare the PEG‐(PTMC‐A) 2 . Two peptide sequences, GVOGEA and GGGGRGDS, are individually conjugated to the MCS to facilitate cell binding. The presence of the peptide ligands yields high ASC viability and long term metabolic activity following encapsulation in hydrogels prepared using the thermal initiator system. Overall, these hydrogels show promise as a minimally invasive ASC delivery strategy for chondral defect repair. image
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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) | 0.000 | 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) | 0.002 | 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".