Substrate-Dependent Galvanotaxis of Directly Reprogrammed Human Neural Precursor Cells
Bibliographic record
Abstract
Background: Neural precursor cells (NPCs) hold great promise for neural repair. Endogenous NPCs, found in the subventricular zone of the adult brain, proliferate and migrate toward lesion sites; however, it is not sufficient for neural repair. NPCs are electrosensitive cells that undergo directed migration in an electric field (EF). Here, we examined the EF-induced migration of a clinically relevant human NPC population. Materials & Methods: We examined the effects of different substrates and microenvironments on human NPC galvanotaxis. Results: Human NPCs increased their migration speed in the presence of an EF, and the direction of migration (anodal vs. cathodal) varied between substrates. The secretome and extracellular pH were not significant factors in EF-induced migration; however, our results are consistent with substrate stiffness playing a role in the direction of cell migration. Conclusion: These findings provide insight into the importance of the microenvironment on modulating human NPC migration and highlight substrate-dependent considerations for neurorepair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".