Bibliographic record
Abstract
The nervous system is the target of various diseases or injuries that could lead to major handicaps or be life threatening. Innovative strategies using tissue‐engineered nerve conduits or in vitro models could help to understand and repair these disorders. To bridge the gap between nerve stumps after a peripheral nerve injury, we developed a tissue‐engineered neural tube to promote nerve repair. This living nerve conduit made of fibroblasts was prepared to serve as a nerve guide to enhance recovery of sensory and motor functions of the transected nerve. The nervous system is also the target of life threatening neurodegenerative diseases such as the amyotrophic lateral sclerosis. We developed a tissue‐engineered model mimicking the spinal cord in vitro to better understand the pathogenesis of these disorders. Motor neurons were cultured on the top of a fibroblast‐populated sponge mimicking the connective tissue through which motor axons elongate in vivo. This model promoted axonal migration and the spontaneous formation of numerous thick myelin sheaths wrapping around motor fibers in presence of Schwann cells. The 3D engineering of the peripheral and the central nervous systems by tissue engineering could help to better understand the pathogenesis of neurodegenerative diseases, to find curative treatments and to repair peripheral nerve damage or even spinal cord injuries.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".