An Improved Method for Production of Recombinant Human Glutamic Acid Decarboxylase 65 for Use in Phytopharmaceutical Assessment
Bibliographic record
Abstract
The pharmacological activity of neuroactive phytochemicals on recombinant human glutamic acid decarboxylase 65 (hGAD65) was investigated. GAD catalyzes the conversion of glutamic acid to γ-aminobutyric acid (GABA), which acts as an important inhibitory neurotransmitter in the central nervous system (CNS). We describe an improved method in which recombinant hGAD65 was expressed at high levels using a maltose binding protein (MBP) fusion system. The expression and purification process was superior to the commonly used glutathioneS.-transferase (GST) fusion partner. The in vitro. system developed here detected both enzyme inhibition and stimulation, under varying pyridoxal-5′-phosphate (PLP) concentrations. The known GAD inhibitor, 3-mercaptopropionic acid, was tested as a positive control and had an IC50 = 12.3 μ M. Phytochemicals were tested (10 μ g/mL) for their effects on in vitro. hGAD65 activity. Minor inhibition was seen with the ethanol extract of Panax quinquefolius. L. (ginsenosides) (23%), betulinic acid (27%), and valerenic acid (20%). An increase in hGAD65 activity by approximately 20% was observed with bilobalide and asiaticoside. As a result, these small changes in GAD activity may have physiologic implications. The possibility that phytochemicals influence GABAergic neurotransmission in vivo. and the mechanisms by which it may occur is 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".