DLK1-expressing neural progenitor cells promote tissue repair and functional recovery after cervical spinal cord injury
Bibliographic record
Abstract
Spinal cord injury (SCI) elicits a hostile microenvironment characterized by inflammation, gliosis, and disrupted signaling pathways that collectively impede neural repair. Neural progenitor cells (NPCs) represent a promising regenerative approach, yet their survival and differentiation are often compromised in this setting. Here, we investigated whether engineering NPCs to overexpress the Notch pathway modulator Delta-like non-canonical Notch ligand 1 (DLK1) could overcome these limitations and improve functional outcomes after cervical SCI in rats. NPCs were engineered to express DLK1 under a Pax6 promoter-driven expression system, ensuring elevated DLK1 levels during the progenitor state. Following transplantation of DLK1-overexpressing NPCs or control NPCs, we assessed graft survival, lineage differentiation, behavioral performance, and electrophysiological integration over 12 weeks. DLK1-expressing NPCs exhibited significantly greater retention in the injured spinal cord and showed enhanced neuronal differentiation alongside reduced astrocytic commitment compared to controls. Behavioral tests-including forelimb grip strength and CatWalk gait assessments-demonstrated that DLK1-modified NPCs conferred robust improvements in forelimb motor coordination and overall locomotion. Concordantly, electrophysiological recordings revealed increased motor-evoked potential amplitudes and area-under-the-curve values in animals receiving DLK1-transduced NPC grafts, indicative of strengthened synaptic integration within the host motor circuitry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".