A fishy gut feeling – current knowledge on gut microbiota in teleosts
Bibliographic record
Abstract
The importance of the gastrointestinal microbiota (GM) in health and disease is widely recognized. Although less is known in fish than in mammals, advances in molecular techniques, such as 16S rRNA sequencing, have facilitated characterization of fish GM, comprising resident autochthonous and transient allochthonous bacteria. The microbial diversity and composition are strongly influenced by diet. High-protein diets, including alternative ingredients like plant and insect proteins, modify GM, impacting beneficial bacteria e.g. Cetobacterium . Lipids affect microbial metabolism and short-chain fatty acid (SCFA) production, while excessive carbohydrates can disrupt GM balance, causing enteritis. Dietary additives, including probiotics, prebiotics, and antibiotics, effectively modulate GM. Probiotics enhance immunity and growth, prebiotics support beneficial bacteria, and antibiotics, though effective against pathogens, disrupt microbial diversity and may promote antibiotic resistance. Environmental factors, such as temperature, salinity, and pollution, significantly influence GM. Elevated temperatures and salinity shifts alter microbial composition, and pollutants introduce toxins that compromise intestinal function and microbial diversity. Stress and pathogen infections further destabilize GM, often favoring pathogenic bacteria. GM communicates with the host via metabolites such as SCFAs, bile acids, and neurotransmitters, regulating appetite, energy metabolism, immunity, and neural functions. Additionally, GM influences the immune system by interacting with epithelial cells and stimulating immune responses. Despite recent advances, further research is needed to elucidate species-specific mechanisms underlying GM-host interactions, the ecological implications of GM diversity, and its applications in aquaculture to optimize fish health and performance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".