Enhancing annulus fibrosus tissue formation in porous silk scaffolds
Bibliographic record
Abstract
There is presently no optimal treatment for patients with chronic back pain as a result of degenerative disc disease. Tissue engineering, an annulus fibrosus (AF) construct suitable to repair the damaged AF, is one novel approach to the treatment of this disease. We have previously demonstrated that porous silk scaffolds can support AF cell attachment and extracellular matrix accumulation; however, tissue infiltration and matrix accumulation was not optimal. The purpose of this study was to determine whether the dynamic culture of AF cells seeded into larger average pore size silk scaffolds would improve tissue formation. AF cells were isolated from bovine caudal discs and seeded into porous silk scaffolds and grown in either dynamic or static flow conditions. The cell-seeded scaffolds were grown for up to 4 weeks and evaluated for cell attachment, gene expression, histological appearance, and matrix accumulation. Dynamic culture improved AF tissue formation as the tissue was more cellular and contained significantly more matrix than that formed in static culture. Spatial distribution of tissue was comparable for static and dynamic culture. Varying scaffold pore sizes (200-, 600-, and 1000-microm pore size) demonstrated that an average pore size of 600 microm resulted in the most uniform tissue distribution with the greatest amount of type I collagen. Our study suggests that dynamic flow conditions and scaffold pore size can affect the formation of engineered AF tissue.
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) | 0.000 | 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".