Extraction and isolation of acetylcholinesterase inhibitors from <i>Citrus limon</i> peel using an in vitro method
Bibliographic record
Abstract
A simple and efficient ultrafiltration-liquid chromatography-mass spectrometry-based method was developed for the rapid screening and identification of ligands from Citrus limon peel, which are suitable acetylcholinesterase inhibitors. Subsequently, the anti-Alzheimer's activity of these compounds was assessed using a PC12 cell model. Six major compounds, viz. neoeriocitrin, isonaringin, naringin, hesperidin, neohesperidin, and limonin, were identified as potent acetylcholinesterase inhibitors. A continuous and efficient online method, which involved the use of a microwave-assisted extraction device, solvent concentration tank, and centrifugal partition chromatography column, was developed for the scale-up of these compounds, and the obtained compounds presented high purity. Next, their bioactivity was evaluated using a PC12 cell model. This novel approach, which was based on ultrafiltration-liquid chromatography-mass spectrometry, microwave-assisted extraction online coupled with solvent concentration tank, and centrifugal partition chromatography along with in vitro evaluation, could represent a powerful tool for the screening and extraction of acetylcholinesterase inhibitors from complex matrices, and could be a useful platform for the large-scale production of bioactive and nutraceutical ingredients.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".