Characterization and development of novel polymeric coatings for enhanced long-term neural cell support
Bibliographic record
Abstract
In this Thesis, contributions were made to improve the understanding of the characteristics required for a polymer coating to support long-term neural cell cultures more effectively. Polylysine has long served as one of the primary polymer coatings for adherent cell cultures; however, its susceptibility to proteolysis limits its effectiveness for maintaining stable, healthy cultures over extended periods. Recently, hyperbranched polyglycerol amine (dPGA) has emerged as a promising alternative, particularly for neural cell cultures, due to its enhanced stability against protease degradation. Although dPGA offers improved support for cell growth, the specific physical properties contributing to its success remain unclear and warrant further investigation. Chapter 2 presents the physical and chemical characterization of dPGA immobilized on a substrate to help explain its effectiveness. It was found that dPGA exhibits a high density of positive charges, significant surface roughness, and intrinsic mobility — all factors that favour neural cell adhesion and differentiation. To address limitations associated with single-layer polymer coatings, Chapter 3 explores the incorporation of dPGA into polyelectrolyte multilayers using poly(acrylic acid) (PAA) as the counter polyanion. It was demonstrated that a 1.5-bilayer coating produced results comparable to a single layer of dPGA, while additional bilayers negatively affected neural cell survival. This outcome may be attributed to increased material softness, decrease of surface roughness, and decreasing intrinsic mobility of dPGA in the layers presented to the cells due to the influencing characteristics of PAA. In summary, these Chapters together identify key characteristics necessary in a polymer coating to enhance neural cell culture, contributing to a more targeted approach for the development of next-generation polymeric coatings
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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".