In vitro biocompatibility evaluation of functional electrically stimulating microelectrodes on primary glia
Bibliographic record
Abstract
Neural interfacing devices interact with the central nervous system to alleviate functional deficits arising from disease or injury. This often entails the use of invasive microelectrode implants that elicit inflammatory responses from glial cells and leads to loss of device function. Previous work focused on improving implant biocompatibility by modifying electrode composition; here, we investigated the direct effects of electrical stimulation on glial cells at the electrode interface. A high-throughput in vitro system that assesses primary glial cell response to biphasic stimulation waveforms at 0 mA, 0.15 mA, and 1.5 mA was developed and optimized. Primary mixed glial cell cultures were generated from heterozygous CX3CR-1+/EGFP mice, electrically stimulated for 4 h/day over 3 days using 75 μm platinum-iridium microelectrodes, and biomarker immunofluorescence was measured. Electrodes were then imaged on a scanning electron microscope to assess sustained electrode damage. Fluorescence and electron microscopy analyses suggest varying degrees of localized responses for each biomarker assayed (Hoescht, EGFP, GFAP, and IL-1β), a result that expands on comparable in vivo models. This system allows for the comparison of a breadth of electrical stimulation parameters, and opens another avenue through which neural interfacing device developers can improve biocompatibility and longevity of electrodes in tissue.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".