Structured Bilaminar Coculture Outperforms Stem Cells and Disc Cells in a Simulated Degenerate Disc Environment
Bibliographic record
Abstract
STUDY DESIGN: This study explores the use of bilaminar coculture pellets of mesenchymal stem cells (MSCs) and nucleus pulposus cells (NPCs) as a cell-based therapy for intervertebral disc regeneration. The pellets were tested under conditions that mimic the degenerative disc. OBJECTIVE: Our goal was to optimize our cell-based therapy in vitro under conditions representative of the eventual diseased tissue. SUMMARY OF BACKGROUND DATA: Harnessing the potential of stem cells is an important strategy for regenerative medicine. Our approach directed the behavior of stem cells by mimicking embryonic processes underlying cartilage and intervertebral disc development. Prior experiments have shown that bilaminar coculture can help differentiate MSC and substantially improve new matrix deposition. METHODS: We have designed a novel spherical bilaminar cell pellet (BCP) where MSCs are enclosed in a shell of NPC. There were 3 groups: MSC, NPC, and BCP. The pellets were tested under 3 different culture conditions: 1) in a bioreactor that provides pressure and hypoxia (mimicking normal disc conditions): 2) with inflammatory cytokines (IL-1b and TNF-a); and 3) a bioreactor with inflammation (mimicking painful disc conditions). RESULTS: When cultured in the bioreactor, the NPC pellets produced significantly more glycosaminoglycans (GAGs) per cell than the other groups: 70% to 80% more than the BCP and the MSC alone. When cultured in an inflammatory environment, the MSC and BCP groups produced 30% to 34% more GAGs per cell than NPC (P < 0.05). When the pellets were cultured in a bioreactor with inflammation, the BCP made 25% more GAGs per cell than the MSC and 57% more than the NPC (P < 0.05). CONCLUSION: This study shows that BCPs outperform controls in a simulated degenerated disc environment. Adapting inductive mechanisms from development to trigger differentiation and restore diseased tissue has many advantages. As opposed to strategies that require growth factor supplements or genetic manipulations, our method is self-sustaining, targeted, and minimally invasive injection.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".