Modulation of oligodendrocyte differentiation and maturation by combined biochemical and mechanical cues
Bibliographic record
Abstract
Extracellular matrix (ECM) proteins play a key role during oligodendrogenesis. While fibronectin (FN) is involved in the maintenance and proliferation of oligodendrocyte progenitor cells (OPCs), merosin (MN) promotes differentiation into oligodendrocytes (OLs). Mechanical properties of the ECM also seem to affect OL differentiation, hence this study aimed to clarify the impact of combined biophysical and biochemical elements during oligodendrocyte differentiation and maturation using synthetic elastic polymeric ECM-like substrates. CG-4 cells presented OPC- or OL-like morphology in response to brain-compliant substrates functionalised with FN or MN, respectively. The expression of the differentiation and maturation markers myelin basic protein--MBP--and proteolipid protein--PLP--(respectively) by primary rat oligodendrocytes was enhanced in presence of MN, but only on brain-compliant conditions, considering the distribution (MBP) or amount (PLP) of the protein. It was also observed that maturation of OLs was attained earlier (by assessing PLP expression) by cells differentiated on MN-functionalised brain-compliant substrates than on standard culture conditions. Moreover, the combination of MN and substrate compliance enhanced the maturation and morphological complexity of OLs. Considering the distinct degrees of stiffness tested ranging within those of the central nervous system, our results indicate that 6.5 kPa is the most suitable rigidity for oligodendrocyte differentiation.
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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".