Tunable Engineered Extracellular Matrix Materials: Polyelectrolyte Multilayers Promote Improved Neural Cell Growth and Survival
Bibliographic record
Abstract
Poly-d-lysine (PDL) and poly-l-lysine are standard surfaces for culturing neural cells; however, both are relatively unstable, costly, and the coated surface typically must be prepared immediately before use. Here, polyelectrolyte multilayers (PEMs) are employed as highly stable, relatively inexpensive, alternative substrates to support primary neural cell culture. Initial findings identify specific silk-based PEMs that significantly outperform the capacity of PDL to promote neuronal survival and process extension. Based on these results, a library of PEM variants, including commercial and bio-sourced polyelectrolytes, is generated and three silk-based PEMs that substantially outperform PDL as a substrate for primary neurons in cell culture are identified. Further, testing these PEM variants as substrates for primary oligodendrocyte progenitors demonstrates that one silk-based PEM functions significantly better than PDL. These findings reveal specificity of cellular responses, indicating that PEMs may be tuned to optimally support different neural cell types.
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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) | 0.001 | 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".