Biosensors and sensors for dopamine detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it