Lutein: Separation, Antioxidant Activity, and Potential Health Benefits
Bibliographic record
Abstract
Lutein is an oxygen-containing carotenoid found in many food plants. Although it is not a vitamin A precursor, many health beneficial effects have been associated with high dietary intake of this phytochemical. Lutein, along with its isomer zeaxanthin, may be the most important xanthophylls for human, as they have been found to play important roles in protecting human from many chronic diseases. In particular, lutein has been found in recent years to protect against age-related macular degeneration (AMD), a leading cause of irreversible vision loss in the elderly population. Lutein is also found to enhance immune function, to prevent cancer, coronary heart disease, and to protect skin from damages caused by ultraviolet light. Our recent study also showed a protective role of lutein against mutagens. Lutein is a strong antioxidant, which may help explain its above physiological functions. However, currently accumulated knowledge on lutein requires further studies on several aspects including the mechanisms of its various bioactivities in the biological system, and how it is absorbed and transported to the site of action. All these require good separation, quantification and detection methods for low concentrations of lutein. Samples containing trace amount of lutein in biological tissues resulting from animal and human clinical trials are particularly challenging. High performance liquid chromatography (HPLC) couple with photodiode array and mass spectrometric detector (LC-DAD-MS) will continue to play an important role in quantification and identification of lutein. Preparative high-speed counter-current chromatography may be a good tool for high quality lutein standard for the various bioassays and trials. This chapter is therefore to briefly review the chemistry and biochemistry of lutein, with an emphasis on its occurrence, distribution, separation and bioactivities, and discuss about its possible adverse effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".