Compounded topographical and physicochemical cueing by micro-engineered chitosan substrates on rat dorsal root ganglion neurons and human mesenchymal stem cells
Bibliographic record
Abstract
Given the intertwined physicochemical effects exerted in vivo by both natural and synthetic (e.g., biomaterial) interfaces on adhering cells, the evaluation of structure-function relationships governing cellular response to micro-engineered surfaces for applications in neuronal tissue engineering requires the use of in vitro testing platforms which consist of a clinically translatable material with tunable physiochemical properties. In this work, we micro-engineered chitosan substrates with arrays of parallel channels with variable width (20 and 60 μm). A citric acid (CA)-based crosslinking approach was used to provide an additional level of synergistic cueing on adhering cells by regulating the chitosan substrate's stiffness. Morphological and physicochemical characterization was conducted to unveil the structure-function relationships which govern the activity of rat dorsal root ganglion neurons (DRGs) and human mesenchymal stem cells (hMSCs), ultimately singling out the key role of microtopography, roughness and substrate's stiffness. While substrate's stiffness predominantly affected hMSC spreading, the modulation of the channels' design affected the neuronal architecture's complexity and guided the morphological transition of hMSCs. Finally, the combined analysis of tubulin expression and cell morphology allowed us to cast new light on the predominant role of the microtopography over substrate's stiffness in the process of hMSCs neurogenic differentiation.
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 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".