The Role of Butyric Acid and Microorganisms in Chronic Inflammatory Diseases and Microbiome-Based Therapeutics
Bibliographic record
Abstract
Zhilin Liu, Yonghong Jiang, Qiuyue Fan, Sai Li, Yanni Wang Department of Paediatrics, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, People’s Republic of ChinaCorrespondence: Yonghong Jiang, Email jyh203225@126.comAbstract: Butyric acid, produced by gut microbiota fermentation, has gained significant attention over the past decade. It shows strong therapeutic potential in both experimental and clinical treatments for inflammatory diseases across multiple systems. However, factors such as the host’s environment, genetics, and microbial lineage transmission influence gut microecology and butyric acid metabolism, resulting in variable and sometimes opposing, therapeutic effects. Consequently, precise personalized medicine is essential for diseases related to microbes and butyric acid. This review first introduces the fundamentals of butyric acid, focusing on its immune mechanisms and its effects on early-life microbiota. It then summarizes how microbes and butyric acid contribute to the treatment of systemic inflammatory diseases (eg, autoimmune diseases (AIDs), asthma, metabolic syndrome) and discusses the concept of Microbial Precision Therapy (MPT). Understanding butyric acid provides deeper insight into managing inflammatory diseases and supports precise medication and personalized therapy. This approach may offer more effective and safer strategies for multi-system inflammatory disorders.Keywords: butyric acid, inflammatory diseases, microbial precision therapy, intestinal flora
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".