<scp>RNase1</scp> can modulate gut microbiota and metabolome after <scp> <i>Aeromonas hydrophila</i> </scp> infection in blunt snout bream
Bibliographic record
Abstract
Pancreatic ribonuclease (RNase1) of Megalobrama amblycephala exhibits both antimicrobial and digestive activity. The gut microbiome improve the digestion and metabolic capacity and enhance the functioning of the immune system of the host against pathogenic bacteria. In this study, we aimed to assess the protective effect of RNase1 on Aeromonas hydrophila-induced inflammation and intestinal microbial metabolism. Megalobrama amblycephala were randomly divided into three groups: control (injected PBS), infection (A. hydrophila-injected), and treatment group (RNase1 pretreatment 24 h before the A. hydrophila injection). The morphological symptoms were significantly alleviated by RNase1. RNase1 reshaped the perturbed gut microbiota by upregulating Proteobacteria and Vibrio richness and downregulating Firmicutes, Chlamydiae, Bacillus, and Gemmobacter richness. The lysophosphatidylcholine, (±) 17 HETE, D- (+) -cellobiose, and PC (20:5) in the treatment group were restored by RNase 1 protein treatment to the level of the control group. In the treatment group, phospholipid metabolism, fatty acid metabolism, glucose metabolism and lipid metabolism were different from the control and infection groups. The proinflammatory factors concentration in intestinal samples significantly increased after A. hydrophila infection. Our results revealed that RNase1 plays an important role in resistance to pathogen invasion, reducing inflammation, and improving intestinal function, thus inhibiting the occurrence of disease.
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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) | 0.000 | 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 | 0.001 |
| 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".