Hyaluronic Acid-Based 3D Culture Model for In Vitro Testing of Electrode Biocompatibility
Bibliographic record
Abstract
This work describes the development of a robust and repeatable in vitro 3D culture model of glial scarring, which may be used to evaluate the foreign body response to electrodes and other implants in the central nervous system. The model is based on methacrylated hyaluronic acid, a hydrogel that may be photopolymerized to form an insoluble network. Hydrogel scaffolds were formed at four different macromer concentrations (0.50, 0.75, 1.00, and 1.50% (w/v)). As expected, the elastic modulus of the scaffolds increased with increasing macromer weight fraction. Adult rat brain tested under identical conditions had an elastic modulus range that spanned the elastic modulus of both the 0.50 and 0.75% (w/v) hydrogel samples. Gels formed with higher macromer weight fraction had decreased equilibrium swelling ratio and visibly thicker pore walls relative to gels formed with lower macromer weight fractions. Mixed glial cells (microglia and astrocytes) were then encapsulated in the HA scaffolds. Viability of the mixed cultures was most stable at a cell density of 1 × 10(7) cells/mL. Cell viability at the highest macromer weight fraction tested (1.50% (w/v)) was significantly lower than other tested gels (0.50, 0.75 and 1.00% (w/v)). The inflammatory response of microglia and astrocytes to a microelectrode inserted into the scaffold was assessed over a period of 2 weeks and closely represented that reported in vivo. Microglia responded first to the electrode (increased cell density at the electrode, and activated morphology) followed by astrocytes (appeared to line the electrode in a manner similar to glial scarring). All together, these results demonstrate the potential of the 3D in vitro model system to assess glial scarring in a robust and repeatable manner.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".