Using transcranial direct current stimulation to regenerate macroglia in stroke-induced mouse models
Bibliographic record
Abstract
Stroke is a neurological condition resulting from blood vessel blockage in the brain, leading to neural tissue death. Astrocytes and oligodendrocytes are important cells involved in repairing neuronal injuries and maintaining myelin sheaths, respectively. Oxygen and nutrient deprivation during stroke cause the death of these cells. Previous studies have suggested that electrical stimulation of neurons can promote the generation of glial cells. However, limited research has been conducted on the use of transcranial direct current stimulation (tDCS) to promote glial cell generation in patients specifically with stroke. In this study, we aim to investigate the potential of tDCS as a future treatment for stroke. 100 female rats will undergo induced ischemic stroke at eight weeks of age. Stroke presence will be confirmed using the Garcia scale, which assesses sensory and motor ability in rats. The rats will then be randomly assigned to either a control group or an intervention group. Both groups will undergo Golgi impregnation to measure the initial prevalence of astrocytes and oligodendrocytes. The intervention group will receive consecutive tDCS sessions for five days, while the control group will not receive any intervention. At the end of the study, Golgi impregnation will be performed again to observe changes in macroglia levels, and the Garcia scale will be used to detect changes in mobility or sensation. We anticipate that tDCS treatment in stroke-induced rats will increase levels of astrocytes and oligodendrocytes, leading to improved sensory and motor abilities. Understanding the effects of tDCS on macroglia in rats post-stroke could pave the way for developments aimed at enhancing long-term mobility and sensation in stroke patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".