Nanomaterials-Based Electrochemical Sensors for In Vitro and In Vivo Analyses of Neurotransmitters
Bibliographic record
Abstract
Neurotransmitters are molecules that transfer chemical signals between neurons to convey messages for any action conducted by the nervous system. All neurotransmitters are medically important; the detection and analysis of these molecules play vital roles in the diagnosis and treatment of diseases. Among analytical strategies, electrochemical techniques have been identified as simple, inexpensive, and less time-consuming processes. Electrochemical analysis is based on the redox behaviors of neurotransmitters, as well as their metabolites. A variety of electrochemical techniques are available for the detection of biomolecules. However, the development of a sensing platform with high sensitivity and selectivity is challenging, and it has been found to be a bottleneck step in the analysis of neurotransmitters. Nanomaterials-based sensor platforms are fascinating for researchers because of their ability to perform the electrochemical analysis of neurotransmitters due to their improved detection efficacy, and they have been widely reported on for their sensitive detection of epinephrine, dopamine, serotonin, glutamate, acetylcholine, nitric oxide, and purines. The advancement of electroanalytical technologies and the innovation of functional nanomaterials have been assisting greatly in in vivo and in vitro analyses of neurotransmitters, especially for point-of-care clinical applications. In this review, firstly, we focus on the most commonly employed electrochemical analysis techniques, in conjunction with their working principles and abilities for the detection of neurotransmitters. Subsequently, we concentrate on the fabrication and development of nanomaterials-based electrochemical sensors and their advantages over other detection techniques. Finally, we address the challenges and the future outlook in the development of electrochemical sensors for the efficient detection of neurotransmitters.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".