Bibliographic record
Abstract
Abstract Dopamine is a key catecholamine neurotransmitter and it has critical roles in the function of the human central nervous system. Abnormal release of dopamine is related to neurological diseases and depression. Therefore, it is necessary to monitor dopamine levels in vivo and in real time to understand its physiological roles. In this review, we discuss dopamine detection focusing on the molecular recognition methods including enzymes, antibodies, and aptamers, as well as new advances based on nanomaterials and molecularly imprinted polymers (MIPs). A large fraction of these sensors rely on electrochemical detection to fulfill the requirement of fast, in situ, and in vivo detection with a high spatial and temporal resolution. These methods need to overcome interferences from molecules with a similar redox potential. In addition, fluorescent and colorimetric sensors based on aptamers are also quite popular, and care needs to be taken to validate specific dopamine binding. Combining aptamers or MIPs with electrochemistry promises to achieve rapid detection and increased selectivity. In this article, we pay more attention to the molecular recognition mechanism and critically review the sensor designs. In the end, some future directions are discussed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".