Modified Huangqi Shengmai Yin enhances ruminal microbiome and metabolites activity in dairy cows with subclinical mastitis
Bibliographic record
Abstract
Introduction Bovine mastitis, especially subclinical mastitis (SCM), with minimal clinical signs, is detrimental due to its resistance to treatment, recurrence, and substantial economic impact on global dairy industry. The modified form of Huangqi Shengmai Yin (HSY), classical traditional herbal medicine renowned for its effects in antimicrobial and circulatory-enhancing and thus beneficial for subclinical mastitis, has been developed for treatment attempt of SCM, yet its therapeutic effect and mechanism remains unclear. This study aims to investigate the therapeutic effects of mHSY on SCM in cows, and elucidate its potential therapeutic mechanism. Methods In this study, mHSY was given orally to cows with SCM. After a 3-day treatment regimen, the therapeutic effects were evaluated. 16S diversity sequencing and metabolomics were used to elucidate the therapeutic mechanism of HSY. Results The SCM was significantly alleviated after the 3-day treatment with HSY. In cows infected with SCM, there were significant alterations in rumen fluid microbiota, particularly proportions of Enterobacter, Desulfovibrio, and Flavonifractor, implying a pivotal role for these bacteria in SCM. Furthermore, the therapeutic potential of HSY is linked to improving the proportion of beneficial bacteria (e.g., Succinivibrionaceae_UCG-001) and re-establishing a balanced ruminal bacterial profile. Modulation of fatty acid and amino acid metabolism, as evidenced by changes in metabolite profiles, is a critical aspect of SCM and can be markedly ameliorated with mHSY administration. Conclusion mHSY shows significant inhibitory effects on SCM, which may be attributed to regulating ruminal microbiota and metabolic pathways in vivo.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".