Combining dopamine and glucose sensings on paper devices for the metabolic study of neurosecretion
Bibliographic record
Abstract
Glucose, the main source of energy of the human body, and dopamine, a major neurotransmitter, are two analytes widely investigated in the study of the brain. In many pathologies, a dysfunction in their metabolic pathways can be observed, leading to neurological disorders. Better understanding the interplays between secretion and cellular metabolism is critical to better address these diseases. In this study, we study the simultaneous detection of glucose consumption and dopamine secretion using a paper-based electrode (PBE). An electrode made of carbon nanotube-coated paper was functionalized with platinum nanoparticles and glucose oxidase to gain sensitivity towards glucose. Maximal current density ( J m a x ) and Michaelis–Menten constant ( K m ) were respectively 12 . 4 ± 2 . 0 μ A.mm −2 and 7.6 ± 1.5 mM for the glucose calibration. The results suggest that dopamine secretion and glucose consumption can be measured in a neuron cell model using the developed paper-based sensor. After stimulating the cells, glucose and dopamine concentration decreased by 1.1 mM and increased by 7 . 1 μ M, respectively. In addition, to confirm the sensor’s detection of dopamine secretion, the impact of L-DOPA, a dopamine precursor, was tested. Dopamine secretion increased two-fold after incubation with L-DOPA, while glucose consumption remained unchanged. This opens new opportunities for quantitative, rapid multianalyte sensing of the chemical inputs and outputs of cellular mechanisms with an easy-to-use and affordable device. • A paper sensor for the co-detection of dopamine and glucose was built. • Changes in glucose and dopamine levels during secretion were measured. • The impact of L-DOPA, a dopamine precursor, was also considered. • This study puts forward paper devices for chemical analyses of metabolism in cells.
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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 | 0.000 |
| 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) | 0.000 | 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".