Wrap it up: myelination of transplanted neurons for repair
Bibliographic record
Abstract
Degeneration or damage of neuronal circuits in the central nervous system can lead to an irreversible loss of neurons and function in the affected brain region. Neuronal transplantation is a promising therapeutic approach consisting of introducing healthy cells into the damaged or diseased regions to restore lost circuits. To achieve successful neuronal transplantation, proper integration of the graft in the host circuitry is necessary. This includes the restoration of connectivity as well as the recapitulation of the physiological characteristics of the lost endogenous neurons. An often-overlooked aspect to assess the integration of transplanted neurons is the acquisition of cell-extrinsic features, such as myelination. This review explores the interaction between transplanted cells and endogenous oligodendroglia, the evidence of myelination in different neuronal transplantation models, and the checkpoints that can influence graft myelination in the injured or diseased brain. Additionally, it discusses how appropriate myelin ensheathment could help overcome some challenges faced in the field of neuronal replacement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".