Should the <i>in vitro</i> colorimetric assays in antioxidant and lipid oxidation evaluation be abandoned? A critical review focusing on bioactive molecule screening assays in <i>in vitro</i> and <i>in vivo</i> models
Bibliographic record
Abstract
Increasing evidence has proven the potent antioxidative effectiveness of bioactives in natural products for preventing/suppressing chronic diseases. In this connection, the development of efficient methods that are suitable to screen bioactives in vitro and in vivo tests has taken place. Thus, a variety of assays have been used in the extraction of bioactives, testing of their antioxidant potential in both in vitro and in vivo model, and evaluating lipid oxidation are comprehensively discussed here. This review mainly focuses on the principle and the use of individual assays in both in vitro and in vivo models. Given that induvial assays have pros and cons due to the discrepancy in the reaction environment and applied biological system, application to the various assays in order to complement the drawbacks of each assay is highly recommended to obtain the reasonable information from experimental trials.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".