The Combined Effects of Topography and Stiffness on Neuronal Differentiation and Maturation Using a Hydrogel Platform
Bibliographic record
Abstract
Biophysical parameters such as substrate topography and stiffness have been shown independently to elicit profound effects on neuronal differentiation and maturation from neural progenitor cells (NPCs) yet have not been investigated in combination. Here, the effects of various micrograting and stiffness combinations on neuronal differentiation and maturation were investigated using a polyacrylamide and N-acryloyl-6-aminocaproic acid copolymer (PAA-ACA) hydrogel with tunable stiffness. Whole laminin was conjugated onto the PAA-ACA surface indirectly or directly to facilitate long-term mouse and human NPC-derived neuron attachment. Three micrograting dimensions (2-10 µm) were patterned onto gels with varying stiffness (6.1-110.5 kPa) to evaluate the effects of topography, stiffness, and their interaction. The results demonstrate that the extracellular matrix (ECM)-modified PAA-ACA gels support mouse and human neuronal cell attachment throughout the differentiation and maturation stages (14 and 28 days, respectively). The interaction between topography and stiffness is shown to significantly increase the proportion of β-tubulin III (TUJ1) positive neurons and microtubule associated protein-2 (MAP2) positive neurite branching and length. Thus, the effects of topography and stiffness cannot be imparted. These results provide a novel platform for neural mechanobiology studies and emphasize the utility of optimizing numerous biophysical cues for improved neuronal yield in vitro.
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".