Compact 256-channel multi-well microelectrode array system for <i>in vitro</i> neuropharmacology test
Bibliographic record
Abstract
Microelectrode arrays (MEAs) have been extensively used to measure extracellular spike activity from cultured neurons using multiple electrodes embedded in a planar glass substrate. This system has been implemented to investigate drug effects by detecting pharmacological perturbation reflected in spontaneous network activity. By configuring multiple wells in an MEA, a high-throughput electrophysiological assay has become available, speeding up drug tests. Despite its merits in acquiring massive amounts of electrophysiological data, the high cost and the bulky size of commercial multi-well MEA systems and most importantly its lack of customizability prevent potential users from fully implementing the system in drug experiments. In this work, we have developed a microelectrode array based drug testing platform by incorporating a custom-made compact 256-channel multi-well MEA in a standard microscope slide and commercial application-specific integrated circuit (ASIC) chip based recording system. We arranged 256 electrodes in 16 wells to maximize data collection from a single chip. The multi-well MEA in this work has a more compact design with reduced chip size compared to previously reported multi-well MEAs. Four synaptic modulators (NMDA, AMPA, bicuculline (BIC) and ATP) were applied to a multi-well MEA and neural spike activity was analyzed to study their neurophysiological effects on cultured neurons. Analyzing various neuropharmacological compounds has become much more accessible by utilizing commercially available digital amplifier chips and customizing a user-preferred analog-front-end interface design with additional benefits in reduced platform size and cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".