Role of Chlorogenic Acids in Controlling Oxidative and Inflammatory Stress Conditions
Bibliographic record
Abstract
Chlorogenic acids (CGAs) are esters formed between caffeic and quinic acids, and represent an abundant group of plant polyphenols present in the human diet. CGAs have different subgroups that include caffeoylquinic, p-coumaroylquinic, and feruloyquinic acids. Results of epidemiological studies suggest that the consumption of beverages such as coffee, tea, wine, different herbal infusions, and also some fruit juices is linked to reduced risks of developing different chronic diseases. These beverages contain CGAs present in different concentrations and isomeric mixtures. The underlying mechanism(s) for specific health benefits attributed to CGAs involves mitigating oxidative stress, and hence the related adverse effects associated with an unbalanced intracellular redox state. There is also evidence to show that CGAs exhibit anti-inflammatory activities by modulating a number of important metabolic pathways. This review will focus on three specific aspects of the relevance of CGAs in coffee beverages; namely: (1) the relative composition of different CGA isomers present in coffee beverages; (2) analysis of in vitro and in vivo evidence that CGAs and individual isomers can mitigate oxidative and inflammatory stresses; and (3) description of the molecular mechanisms that have a key role in the cell signaling activity that underlines important functions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".