Past, Present, and Future of Gastrointestinal Microbiota Research in Cats
Bibliographic record
Abstract
The relationship between microbial community and host has profound effects on animal health. A balanced gastrointestinal (GI) microbial population provides nutritional and metabolic benefits to its host, regulates the immune system and various signalling molecules, protects intestines from pathogen invasion and promotes a healthy structure and optimal function of the intestines. With expeditious development of next generation sequencing, molecular techniques have become standard tools for research of microbiota and demonstrated a complex and diverse intestinal ecosystem. Similar to results from other mammals, the vast majority of GI microbiota in cats (over 99%) is composed of the predominant bacterial phyla, Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria. Many nutritional and clinical studies revealed that several different factors and conditions can alter cats' microbiota, including body condition, age, diet and nutrients, inflammatory disease and others. Further research is warranted to determine functional variations of microbiome in disease states and the response to environmental and dietary modulations, to explain the intricate relationship between GI microbiota and genetics and immunity of its host, and also to improve the existing as well as the future molecular techniques. This review focusing on feline GI microbiota, summarises past and present knowledge and looks into the future prospects.
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) | 0.001 | 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 | 0.002 |
| 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".