Reshaping fecal gut microbiota composition by growing with <i>Polygonum cuspidatum</i>, <i>Houttuynia cordata</i>, and <i>Ipomoea aquatica</i>
Bibliographic record
Abstract
We carried out sequencing of samples cultivated in floating beds with different Chinese medicinal herbs (Control, Houttuynia cordata Thunb., Polygonum cuspidatum, and a combination of H. cordata with Ipomoea aquatica Forssk.; named groups A, B, C, D, respectively) to analyze changes in the composition of gut microbiota of tilapia feces. Fusobacteria (ranging from 49.0% to 73.3%), Firmicutes (12.3%–37.8%), and Proteobacteria (5.1%–23.0%) were found to be the most dominant phyla present in all samples. The operational taxonomic units and the Ace and Chao1 indices of groups A and D were significantly higher than those of group C. Polygonum cuspidatum decreased the species richness and diversity of microbial communities in tilapia intestinal feces. The phylum WCHB1-60, order Enterobacteriales, and genus Plesiomonas significantly decreased (in group A); the species Plesiomonas shigelloides significantly decreased (in groups B and C); and the genus Leucobacter significantly increased (in group D) when compared with the control. The relative abundance of the class Verrucomicrobiae (groups B vs C) significantly decreased. In the presence of I. aquatica, the phylum Bacteroidetes significantly decreased, while the genera Leucobacter and Pelotomaculum significantly increased. The ratio of Bacteroidetes to Firmicutes was significantly higher in groups B and C relative to the controls, while it decreased significantly in group D. The algae (i.e., Anabaena and Microcystis) and beneficial pathogenic bacteria decreased in groups C and D, respectively. In addition, Enterovibrio decreased in all treatment groups. The present data demonstrate that floating bed cultivation with Chinese medicinal herbs significantly alters the gut microbiota of tilapia, which may enhance its immune activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".