Cell adhesion molecule immobilized gold surfaces for enhanced neuron‐electrode interfaces
Bibliographic record
Abstract
To provide a long-term solution for increasing the biocompatibility of neuroprosthetics, approaches to reduce the side effects of invasive neuro-implantable devices are still in need of improvement. Physical, chemical, and bioactive design aspects of the biomaterials are proven to be important for providing proper cell-to-cell, cell-to-material interactions. Particularly, modification of implant surfaces with bioactive cues, especially cell adhesion molecules (CAMs) that capitalize on native neural adhesion mechanisms, are promising candidates in favor of providing efficient interfaces. Within this concept, this study utilized specific CAMs, namely N-Cadherin (Neural cadherin, N-Cad) and neural cell adhesion molecule (NCAM), to enhance neuron-electrode contact by mimicking the cell-to-ECM interactions for improving the survival of cells and promoting neurite outgrowth. For this purpose, representative gold electrode surfaces were modified with N-Cadherin, NCAM, and the mixture (1:1) of these molecules. Modifications were characterized, and the effect of surface modification on both differentiated and undifferentiated neuroblastoma SH-SY5Y cell lines were compared. The findings demonstrated the successful modification of these molecules which subsequently exhibited biocompatible properties as evidenced by the cell viability results. In cell culture experiments, the CAMs displayed promising results in promoting neurite outgrowth compared to conventional poly-l-lysine coated surfaces, especially NCAM and N-Cad/NCAM modified surfaces clearly showed significant improvement. Overall, this optimized approach is expected to provide an insight into the action mechanisms of cells against the local environment and advance processes for the fabrication of alternative neural interfaces.
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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) | 0.001 | 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".