Dietary Component-Induced Inflammation and Its Amelioration by Prebiotics, Probiotics, and Synbiotics
Bibliographic record
Abstract
A balanced diet with many dietary components maintains immune homeostasis directly by interacting with innate and adaptive immune components or indirectly through gut microbiota and their metabolites. Dietary components may inhibit pro-inflammatory mediators and promote anti-inflammatory functions or vice versa. Western diets with imbalanced dietary components skew the immune balance toward pro-inflammation and induce intestinal inflammation, consequently leading to many intestinal and systemic inflammatory diseases like ulcerative colitis, Crohn's disease, irritable bowel syndrome, cardiovascular problems, obesity, and diabetes. The dietary component-induced inflammation is usually chronic in nature and frequently caused or accompanied by alterations in gut microbiota. Therefore, microbiome-targeted therapies such as probiotics, prebiotics and synbiotics hold great potentials to amend immune dysregulation and gut dysbiosis, preventing and treating intestinal and systemic inflammatory diseases. Probiotics, prebiotics and synbioitcs are progressively being added to foods and beverages, with claims of health benefits. However, the underlining mechanisms of these interventions for preventing and treating dietary component-induced inflammation are still not very clear. In addition, possibly ineffective or negative consequences of some probiotics, prebiotics and synbiotics call for stringent testing and regulation. Here, we will first briefly review inflammation, in terms of its types and the relationship between different dietary components and immune responses. Then, we focus on current knowledge about the direct and indirect effects of probiotics, prebiotics and synbiotics on intestinal and systemic inflammation. Understanding how probiotics, prebiotics and synbiotics modulate the immune system and gut microbiota will improve our strategies for preventing and treating dietary component-induced intestinal inflammation and inflammatory 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".