Functional motor neurons differentiating from mouse multipotent spinal cord precursor cells in culture and after transplantation into transected sciatic nerve
Bibliographic record
Abstract
OBJECT: One of the current challenges in neurobiology is to ensure that neural precursor cells differentiate into specific neuron types, so that they can be used for transplantation purposes in patients with neuron loss. The goal of this study was to determine if spinal cord precursor cells could differentiate into motor neurons both in culture and following transplantation into a transected sciatic nerve. METHODS: In cultures with trophic factors, neurons differentiate from embryonic precursor cells and express motor neuronal markers such as choline acetyltransferase (ChAT), Islet-1, and REG2. Reverse transcription-polymerase chain reaction analysis has also demonstrated the expression of Islet-1 in differentiated cultures. A coculture preparation of neurospheres and skeletal myocytes was used to show the formation of neuromuscular connections between precursor cell-derived neurons and myocytes both immunohistochemically and electrophysiologically. Following various survival intervals, precursor cells transplanted distal to a transection of the sciatic nerve differentiated into neurons expressing the motor neuron markers ChAT and the alpha1 1.2 (class C, L-type) voltage-sensitive Ca++ channel subunit. These cells extended axons into the muscle, where they formed cholinergic terminals. CONCLUSIONS: These results demonstrate that motor neurons can differentiate from spinal cord neural precursor cells grown in culture as well as following transplantation into a transected peripheral nerve.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".