Tissue‐engineered in vitro modeling of the impact of Schwann cells in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Abstract Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease affecting upper and lower motor neurons (MNs). To investigate whether Schwann cells could be involved in the disease pathogenesis, we developed a tissue‐engineered three‐dimensional (3D) in vitro model that combined MNs cocultured with astrocytes and microglia seeded on top of a collagen sponge populated with epineurium fibroblasts to enable 3D axonal migration. C2C12 myoblasts were seeded underneath the sponge in the presence or absence of Schwann cells. To reproduce an ALS cellular microenvironment, MNs, astrocytes, and microglia were extracted from SOD1G93A mice recapitulating many aspects of the human disease. This 3D ALS in vitro model was compared with a 3D control made of cells isolated from SOD1WT mice. We showed that normal Schwann cells strongly enhanced MN axonal migration in the 3D control model but had no effect in the ALS model. However, ALS‐derived Schwann cells isolated from SOD1G93A mice failed to significantly improve axonal migration in both models. These results suggest that a cell therapy using healthy Schwann cells may not be effective in promoting axonal regeneration in ALS. In addition, this 3D ALS model could be used to study the impact of other cell types on ALS by various combinations of normal and diseased cells.
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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 | 0.001 | 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".