Anti-Inflammatory and Immunomodulatory Properties of Fermented Plant Foods
Bibliographic record
Abstract
Fermented plant foods are gaining wide interest worldwide as healthy foods due to their unique sensory features and their health-promoting potentials, such as antiobesity, antidiabetic, antihypertensive, and anticarcinogenic activities. Many fermented foods are a rich source of nutrients, phytochemicals, bioactive compounds, and probiotic microbes. The excellent biological activities of these functional foods, such as anti-inflammatory and immunomodulatory functions, are widely attributable to their high antioxidant content and lactic acid-producing bacteria (LAB). LAB contribute to the maintenance of a healthy gut microbiota composition and improvement of local and systemic immunity. Besides, antioxidant compounds are involved in several functional properties of fermented plant products by neutralizing free radicals, regulating antioxidant enzyme activities, reducing oxidative stress, ameliorating inflammatory responses, and enhancing immune system performance. Therefore, these products may protect against chronic inflammatory diseases, which are known as the leading cause of mortality worldwide. Given that a large body of evidence supports the role of fermented plant foods in health promotion and disease prevention, we aim to discuss the potential anti-inflammatory and immunomodulatory properties of selected fermented plant foods, including berries, cabbage, and soybean products, and their effects on gut microbiota.
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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 | 0.000 |
| 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".