Direct and cell signaling-based, geometry-induced neuronal differentiation of neural stem cells
Bibliographic record
Abstract
Neural Stem Cells (NSCs) are multipotent precursors inhabiting the subventricular and hippocampal subgranular regions of the adult mammalian brain, able to self-renew and differentiate into neurons, astrocytes, and oligodendrocytes, the three primary neural cell types of the adult brain. NSC fate is influenced by the physical and chemical microenvironment experienced by the cell, both in vitro and in vivo. Towards characterizing the influence of topographical, geometric cues on NSC fate, we fabricated highly aligned, single- and double-layer polystyrene nanofiber meshes. Seeding of NSCs on laminin-coated fibers induces polarized NSC morphology and cellular elongation in the directions of fiber alignment, with cells extending membranous processes over hundreds of microns along the fiber surfaces. Additionally, these aligned fiber substrates promote neuronal lineage specification of NSCs with an efficiency of 82.3 ± 11.1% within days of seeding. Moreover, not only do cells on fibers yield neurons, but also neighboring cells in close proximity to those differentiating on aligned fibers, with an efficiency of 72.8 ± 9.7%. This neighboring, cell-induced differentiation occurs without cell-cell contact over millimetres away from the fibers, suggesting a paracrine signaling effect not previously reported for NSCs undergoing neurogenesis. In contrast, NSCs farther away from these fiber substrates nearly uniformly yield glia.
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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".