Facile Fabrication of Injectable Alginate and Poly(3,4‐ethylenedioxythiophene)‐Based Soft Electrodes toward the Goal of Neuro‐Regenerative Applications
Bibliographic record
Abstract
Resident brain neural precursor cells (NPCs) are electrosensitive cells that respond to electric field application by proliferating, differentiating, and undergoing rapid and directed cathodal migration. Harnessing NPC potential is a promising strategy to facilitate neural repair following injury or disease. The use of electric fields to activate NPCs is limited by current electrode designs which are typically made of conductive metals that are stiff and can lead to neuroinflammation following implantation, in part due to the mechanical mismatch between physiological conditions and material. Herein, the design of a novel, injectable biobased soft electrode with properties suitable for electrical stimulation in vivo is explored. The recent interest in using biologically derived polymers which are relatively abundant and afford economic feasibility have been built upon. Sodium alginate is utilized to form soft hydrogels, thereby addressing the issue of mechanical mismatch, and the conductive polymer, poly(3,4-ethylenedioxythiophene) (PEDOT), to generate an innovative new material. It is demonstrated that the optimized alginate PEDOT blend matches the modulus of the brain and is suitable for injection and is not cytotoxic to neural cells. Furthermore, in vivo studies demonstrate minimal activation of inflammatory cells upon implantation in the brain compared to classically used platinum-based electrodes.
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".