A microfluidic method for dopamine uptake measurements in dopaminergic neurons
Bibliographic record
Abstract
Dopamine (DA) is a classical neurotransmitter and dysfunction in its synaptic handling underlies many neurological disorders, including addiction, depression, and neurodegeneration. A key to understanding DA dysfunction is the accurate measurement of dopamine uptake by dopaminergic neurons. Current methods that allow for the analysis of dopamine uptake rely on standard multiwell-plate based ELISA, or on carbon-fibre microelectrodes used in in vivo recording techniques. The former suffers from challenges associated with automation and analyte degradation, while the latter has low throughput and is not ideal for laboratory screening. In response to these challenges, we introduce a digital microfluidic platform to evaluate dopamine homeostasis in in vitro neuron culture. The method features voltammetric dopamine sensors with limit of detection of 30 nM integrated with cell culture sites for multi-day neuron culture and differentiation. We demonstrate the utility of the new technique for DA uptake assays featuring in-line culture and analysis, with a determination of uptake of approximately ∼32 fmol in 10 min per virtual microwell (each containing ∼200 differentiated SH-SY5Y cells). We propose that future generations of this technique will be useful for drug discovery for neurodegenerative disease as well as for a wide range of applications that would benefit from integrated cell culture and electroanalysis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".