A Novel GelMA-OrnMA Electrically Conductive Bioink for Developing Engineered Neural Tissues
Bibliographic record
Abstract
Abstract Electrical conductivity is a crucial requirement of matrices for developing engineered neural tissues. A conductive matrix not only supports cell growth but also provides potential to stimulate the cells. However, electrically conductive matrices often require inclusion of synthetic polymers, nanomaterials and large number of ionic species. While enhancing electrical conductivity, often properties like transparency, mechanical stiffness and biocompatibility are compromised which can render the resulting matrices partially suitable for neural tissue engineering. Further, the byproducts of matrix degradation can have unforeseen influences. Therefore, electrically active matrices are required which provide a suitable combination of electrical conductivity, mechanical properties and biocompatibility. In this work, a novel biomaterial is described which results in optically transparent, electrically conductive and highly biocompatible matrices along with ability to match the native neural tissue stiffness. Using gelatin methacryloyl (GelMA) as base hydrogel, we covalently incorporated zwitterionic functional groups to obtain a composite matrix. The zwitterion moieties were derived from Ornithine by synthesizing ornithine methacryloyl (OrnMA) and blending with GelMA inks. Through systematic characterization we demonstrated the suitability of GelMA-OrnMA hydrogels in providing mechanical stiffness matching the native neural tissues, supporting proliferation of human astrocytes in 3D culture and electrical conductivity in the range required for electrically active cell types like astrocytes. Owing to their electrical conductivity, these matrices also influenced the growth of astrocytes which manifested as significant changes in their organization and morphology. These findings suggest that GelMA-OrnMA has immense potential as a bioink for developing engineered neural tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".