<i>In vitro</i> study of axonal migration and myelination of motor neurons in a three‐dimensional tissue‐engineered model
Bibliographic record
Abstract
Primary motor neurons are difficult to study in conventional culture systems because of their short-term survival without trophic support from glia. In addition, axonal migration on a two-dimensional Petri dish does not reflect the three-dimensional (3D) environment in vivo. A unique in vitro 3D model of motor nerve regeneration was developed to study motor neuron axonal migration and myelination. Mouse spinal cord motor neurons were seeded on a collagen sponge populated with Schwann cells and fibroblasts. This fibroblast-populated sponge was intended to mimic the connective tissue through which motor axons have to elongate in vivo. Addition of conventional neurotrophic supplements was not required for motor neuron survival but was necessary to promote deep neurite outgrowth, as assessed by immunostaining of neurofilament M. A vigorous neurite elongation was detected inside the sponge after only 14 days of neuron culture, reaching more than 850 microm. The model also allowed the maturation of motor fibers as one-third of them were positive for neurofilament H. Neurites growing in the sponge were subject to myelination when Schwann cells were present, as shown by myelin basic protein immunostaining and electron microscopy. We demonstrated in this model the spontaneous formation of numerous thick myelin sheaths surrounding motor fibers after long-term culture (28 days). Thus, this model might be a valuable tool to study the effect of various cells and/or attractive or repulsive molecules on motor neurite outgrowth in vitro and also for the study of myelination and pathogenesis of motor neuron diseases.
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) | 0.001 | 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) | 0.001 | 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".