The Role of Fibers and Bioactive Compounds in Gut Microbiota Composition and Health
Bibliographic record
Abstract
A balance between tolerating beneficial microbes and implementing proinflammatory responses toward harmful microbes that invade the body. If the balance is shifted in favor of the harmful microbes, or a dysbiosis of the microbial composition occurs, this can lead to an array of inflammatory-related illnesses. This chapter discusses the mechanisms by which diseases manifest as a result of dysbiosis. It explores the environmental factors that maintain gut homeostasis and diseases that may result from dysbiosis. The chapter also explores dietary compounds that promote a healthy gut microbiota composition, epidemiological studies looking at demographics that may influence the composition, and how fibers and bioactive compounds can protect against diseases resulting from dysbiosis in the gut microbiota. It also examines how important gut microbiota homeostasis is to our health by its involvement in different chronic diseases across different demographics. The chapter shows how the gut-brain axis is involved in illnesses such as anxiety, depression, autism, and dementia.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".