Use of Liquid Chromatography with Electrochemical Detection for the Determination of Antioxidants in Less Common Fruits
Bibliographic record
Abstract
Neurodegenerative disorders (NDD) have become the common global health burden over the last several decades. According to World Health Organization (WHO), a staggering 30 million people will be affected by Alzheimer's disease in Europe and the USA by 2050. Effective therapies in this complex field considering the multitude of symptoms associated with NDD indications, have not been found yet. Based on the results of NDD related studies, prevention appears to be the promise alternative. Antioxidative and anti-inflammatory properties are hypothesized for natural phenolics, a group of plant secondary products that may positively impact neurodegenerative diseases. In these studies, phenolic-rich extracts from less common fruit species: Blue honeysuckle (Lonicera edulis, Turcz. ex. Freyn), Saskatoon berry (Amelanchier alnifolia Nutt.), and Chinese hawthorn (Crateagus pinnatifida Bunge) were obtained and analyzed to detect neuroprotective substances content and establish a potential therapeutic value. High performance liquid chromatography with electrochemical detection was optimized and further applied on analysis of the extracts of less common fruit species. It was observed that Chinese hawthorn and Blue honeysuckle extracts are potent source of neuroprotective phenolic antioxidants. In accordance the results, it appears that the fruit or formulated products may have the potential for the prevention of neurodegenerative diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".