Use of induced pluripotent stem cell derived neurons engineered to express BDNF for modulation of stressor related pathology
Bibliographic record
Abstract
Combined cell and gene-based therapeutic strategies offer potential in the treatment of neurodegenerative and psychiatric conditions that have been associated with structural brain disturbances. In the present investigation, we used a novel virus-free re-programming method to generate induced pluripotent stem cells (iPSCs), and then subsequently transformed these cells into neural cells which over-expressed brain derived neurotrophic factor (BDNF). Importantly, the infusion of iPSC derived neural cells (as a cell replacement and gene delivery tool) and BDNF (as a protective factor) influenced neuronal outcomes. Specifically, intracerebroventricular transplantation of iPSC-derived neural progenitors that over-expressed BDNF reversed the impact of immune (lipopolysaccharide) and chronic stressor challenges upon subventricular zone adult neurogenesis, and the iPSC-derived neural progenitor cells alone blunted the stressor-induced corticosterone response. Moreover, our findings indicate that mature dopamine producing neurons can be generated using iPSC procedures and appear to be viable when infused in vivo. Taken together, these data could have important implications for using gene-plus-cell replacement methods to modulate stressor related pathology.
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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) | 0.001 | 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".