Epidural fat mesenchymal stem cells: Important microenvironmental regulators in health, disease, and regeneration
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) are present in fat tissues throughout the body, yet little is known regarding their biological role within epidural fat. We hypothesize that debridement of epidural fat and/or subsequent loss of MSCs within this tissue, disrupts homeostasis in the vertebral environment resulting in increased inflammation, fibrosis, and decreased neovascularization leading to poorer functional outcomes post-injury/operatively. Clinically, epidural fat is commonly considered a space-filling tissue with limited functionality and therefore typically discarded during surgery. However, the presence of MSCs within epidural fat suggests that itis more biologically active than historically believed and may contribute to the regulation of homeostasis and regeneration in the dural environment. While the current literature supports our hypothesis, it will require additional experimentation to determine if epidural fat is an endogenous driver of repair and regeneration and if so, this tissue should be minimally perturbed from its original location in the spinal canal. Also see the video abstract here https://youtu.be/MIol_IWK1os.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".