Development of <scp>NMR</scp> spectroscopic methods for dynamic detection of acetylcholine synthesis by choline acetyltransferase in hippocampal tissue
Bibliographic record
Abstract
Choline acetyltransferase (ChAT) is the key enzyme for acetylcholine (ACh) synthesis and constitutes a reliable marker for the integrity of cholinergic neurons. Cortical ChAT activity is decreased in the brain of patients suffering from Alzheimer's and Parkinson's diseases. The standard method used to measure the activity of ChAT enzyme relies on a very sensitive radiometric assay, but can only be performed on post-mortem tissue samples. Here, we demonstrate the possibility to monitor ACh synthesis in rat brain homogenates in real time using NMR spectroscopy. First, the experimental conditions of the radiometric assay were carefully adjusted to produce maximum ACh levels. This was important for translating the assay to NMR, which has a low intrinsic sensitivity. We then used (15) N-choline and a pulse sequence designed to filter proton polarization by nitrogen coupling before (1) H-NMR detection. ACh signal was resolved from choline signal and therefore it was possible to monitor ChAT-mediated ACh synthesis selectively over time. We propose that the present approach using a labeled precursor to monitor the enzymatic synthesis of ACh in rat brain homogenates through real-time NMR represents a useful tool to detect neurotransmitter synthesis. This method may be adapted to assess the state of the cholinergic system in the brain in vivo in a non-invasive manner using NMR spectroscopic techniques.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".