Effect of diet on the gut mycobiome and potential implications in inflammatory bowel disease
Bibliographic record
Abstract
The gut microbiome is a complex, unique entity implicated in the prevention, pathogenesis, and progression of common gastrointestinal diseases. While largely dominated by bacterial populations, advanced sequencing techniques have identified co-inhabiting fungal communities, collectively referred to as the mycobiome. Early studies identified that gut inflammation is associated with altered microbial composition, known as gut dysbiosis. Altered microbial profiles are implicated in various pathological diseases, such as inflammatory bowel disease (IBD), though their role as a cause or consequence of systemic inflammation remains the subject of ongoing research. Diet plays a crucial role in the prevention and management of various diseases and is considered to be an essential regulator of systemic inflammation. This review compiles current literature on the impact of dietary modulation on the mycobiome, showing that dietary changes can alter the fungal architecture of the gut. Further research is required to understand the impact of diet on gut fungi, including the metabolic pathways and enzymes involved in fungal fermentation. Additionally, investigating whether dietary modulation of the gut mycobiome could be utilized as a therapy in IBD is essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".