<scp>PDGF</scp>‐Releasing Hydrogels for Enhanced Proliferation of Human Nucleus Pulposus Cells
Bibliographic record
Abstract
ABSTRACT Hydrogels offer a promising solution for sustained and controlled drug delivery and cell‐tissue biocompatibility. In the intervertebral disc (IVD), delivering growth factors faces challenges due to the antagonistic inflammatory environment and continuous mechanical stress, which can degrade biological agents and may reduce their local activity. To address this, we investigated the prolonged release of platelet‐derived growth factor isoforms BB (PDGF‐BB) and AB (PDGF‐AB) by using N‐desulfated heparin methacrylamide (Hep‐N) crosslinked within matrix‐metalloproteinase sensitive poly(ethylene glycol) (PEG) hydrogels. Using electrostatic interactions between the heparin derivative and PDGF, we optimized a sustained release dose of PDGF‐BB from the hydrogel in the presence of collagenase to mimic the in vivo environment. We then assessed the effects of PDGF released from PEG‐hydrogel on human nucleus pulposus (NP) Cells. The MTT assay confirmed that 100 and 200 ng doses significantly increased cell viability by 2.52‐fold and 2.46‐fold on Day 3, respectively. RT‐qPCR analysis revealed that PDGF‐AB and PDGF‐BB upregulated the expression of proliferation marker Ki‐67 (MKI67) on both Day 3 and Day 5. Additionally, collagen type II alpha 1 chain (COL2A1) was significantly upregulated in the PDGF‐AB group on Day 5, indicating potential anabolic effects. These findings could pave the way for long‐term in vivo studies on sustainable PDGF treatment for IVD degeneration.
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) | 0.000 | 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) | 0.001 | 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".