Human Rett-derived neuronal progenitor cells in 3D graphene scaffold as an <i>in vitro</i> platform to study the effect of electrical stimulation on neuronal differentiation
Bibliographic record
Abstract
Studies of electrical stimulation therapies for the treatment of neurological disorders, such as deep brain stimulation, have almost exclusively been performed using animal-models. However, because animal-models can only approximate human brain disorders, these studies should be supplemented with an in vitro human cell-culture based model to substantiate the results of animal-based studies and further investigate therapeutic benefit in humans. This study presents a novel approach to analyze the effect of electrical stimulation on the neurogenesis of patient-induced pluripotent stem cell (iPSC) derived neural progenitor cell (NPC) lines, in vitro using a 3D graphene scaffold system. The iPSC-derived hNPCs used to demonstrate the system were collected from patients with Rett syndrome, a debilitating neurodevelopmental disorder. The graphene scaffold readily supported both the wild-type and Rett NPCs. Electrical stimulation parameters were optimized to accommodate both wild-type and Rett cells. Increased cell maturation and improvements in cell morphology of the Rett cells was observed after electrical stimulation. The results of the pilot study of electrical stimulation to enhance Rett NPCs neurogenesis were promising and support further investigation of the therapy. Overall, this system provides a valuable tool to study electrical stimulation as a potential therapy for neurological disorders using patient-specific cells.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".