Conditional GDNF Expression in Induced Pluripotent Stem Cell Neural Progenitor Cells using Cell State-specific Promoters for Treatment of Spinal Cord Injuries
Bibliographic record
Abstract
Neural progenitor cell (NPC) transplantation for tissue repair and regeneration following spinal cord injury (SCI) has demonstrated promising outcomes providing direct integration into the host tissue and trophic support. The potential to enhance the effectiveness of NPCs using trophic factors, including glial cell-derived neurotrophic factor (GDNF), has drawn significant interest. GDNF has broad effects post-SCI, including endogenous and transplanted cell survival, remyelination, and as a cell fate determinant. However, continuous delivery of trophic factors prohibits adjustment or cessation in response to uncertain adverse side effects. Herein, we bioengineered multiple vectors to allow for the conditional expression of GDNF. Engineered human induced pluripotent stem cell (iPSC)-NPCs successfully secreted vastly elevated levels of GDNF in vitro, with concentrations decreasing as cells terminally differentiated and were validated for promoter stability, differentiation capacity, and gene expression. These results provide an approach to enhance the regenerative effects of hiPSC-NPCs while limiting adverse effects.
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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".