Reparación de la lesión medular mediante trasplante de células aldainoglia e inhibición de la actividad RhoGTPasa.
Bibliographic record
Abstract
A possible therapy for repairing a spinal cord injury (SCI) is the effective modulation of cellular and molecular elements involved in the process of glial scarring. The aldainoglia cells are neural precursor cells with a high capacity to differentiate into neurons and promote growth, ensheathment and axonal myelination of resident neurons. These important features of the aldainoglia can be combined with the specific inhibition of RhoGTPase activity in astroglia and microglia that produces a reduction in glial proliferation, retraction of astroglial cells and production of myelin by oligodendrocytes. We have been working in experimental models of CNS injury, such as spinal cord contusion in rats and striatal lacunar infarction in mice; and we observed that administration of glycolipid inhibitor for RhoGTPase or aldainoglia cells, respectively, produced a significant increase in the functional recovery in treated animals. A therapy that combines both treatments with neuro-regenerative properties is quite desired in the treatment of SCI because a functional potentiation of neurons and oligodendrocytes, would result in a better recovery of locomotor rhythm. Here we propose that the treatment of spinal cord injuries with aldainoglia obtained from neurospheres, plus the local administration of an inhibitor of RhoGTPases have an additive effect that could be facilitate recovery after SCI.
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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) | 0.002 | 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".