Fenretinide Promotes Functional Recovery and Tissue Protection after Spinal Cord Contusion Injury in Mice
Bibliographic record
Abstract
The inflammatory response is thought to contribute to secondary damage after spinal cord injury (SCI). Polyunsaturated fatty acids (PUFAs) play an important role in the onset and resolution of inflammation. Arachidonic acid (AA), an omega-6 PUFA, contributes to the initiation of inflammatory responses, whereas docosahexaenoic acid (DHA), an omega-3 PUFA, has antiinflammatory effects. Therefore, decreasing AA and increasing DHA levels after SCI might be expected to attenuate inflammation after SCI and promote tissue protection and functional recovery. We show here that daily oral administration of fenretinide after spinal cord contusion injury led to a significant decrease in AA and an increase in DHA levels in plasma and injured spinal cord tissue. This was accompanied by a significant reduction in tissue damage and improvement in locomotor recovery. Fenretinide also reduced the expression of proinflammatory genes and the levels of oxidative stress markers after SCI. In addition, in vitro studies demonstrated that fenretinide reduced TNF-alpha (tumor necrosis factor-alpha) expression by reactive microglia. These results demonstrate that fenretinide treatment after SCI can reduce inflammation and tissue damage in the spinal cord and improve locomotor recovery. These beneficial effects may be mediated via the ability of fenretinide to modulate PUFA homeostasis. Since fenretinide is currently in clinical trials for the treatment of cancers, this drug might be a good candidate for the treatment of acute SCI in humans.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".