A co-culture nanofibre scaffold model of neural cell degeneration in relevance to Parkinson’s disease
Bibliographic record
Abstract
Current therapeutic strategies for Parkinson's disease (PD) aim to delay progression or replace damaged neurons by restoring the original neuronal structures. The poor regenerative capacity of neural tissue highlights the need for the development of cellular environments to model the pathogenesis of PD. In the current work, we have characterised the growth, survival and response to PD mimetics of human SH-SY5Y neuroblastoma and U-87MG glioblastoma cell lines cultured on polyacrylonitrile (PAN) and Jeffamine® doped polyacrylonitrile (PJ) nano-scaffolds. Differentiation induced by a range of agents was evaluated by immunoassays of neural protein biomarkers. PAN and PJ nanofibre scaffolds provided suitable three-dimensional (3D) environment to support the growth, differentiation and network formation of dopaminergic neuron- and astrocyte-like cell populations, respectively. The scaffolds selectively supported the survival and differentiation of both cell populations with prolonged neuronal survival when exposed to PD mimetics in the presence of astrocytes in a co-culture model. Such 3D nanoscaffold-based assays could aid our understanding of the molecular basis of PD mimetic-induced Parkinsonism and the discovery of neuroprotective agents.
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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 | 0.001 | 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".