Detection of amino acid neurotransmitters by surface enhanced Raman scattering and hollow core photonic crystal fiber
Bibliographic record
Abstract
The present work explores the feasibility of using surface enhanced Raman scattering (SERS) for detecting the neurotransmitters such as glutamate (GLU) and gamma-amino butyric acid (GABA). These amino acid neurotransmitters that respectively mediate fast excitatory and inhibitory neurotransmission in the brain, are important for neuroendocrine control, and upsets in their synthesis are also linked to epilepsy. Our SERS-based detection scheme enabled the detection of low amounts of GLU (10-7 M) and GABA (10-4 M). It may complement existing techniques for characterizing such kinds of neurotransmitters that include high-performance liquid chromatography (HPLC) or mass spectrography (MS). This is mainly because SERS has other advantages such as ease of sample preparation, molecular specificity and sensitivity, thus making it potentially applicable to characterization of experimental brain extracts or clinical diagnostic samples of cerebrospinal fluid and saliva. Using hollow core photonic crystal fiber (HC-PCF) further enhanced the Raman signal relative to that in a standard cuvette providing sensitive detection of GLU and GABA in micro-litre volume of aqueous solutions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".