Silkworm (Bombyx mori) gut bacteria respond positively to exposure of magnetic Fe3O4@urease-doped nanoconjugates: potential reconstruction for nutritional efficiency
Bibliographic record
Abstract
The gut microbiota of the silkworm ( Bombyx mori ) plays a vital role in metabolic processes, including nutrient digestion and immune regulation. However, the specific bacterial communities that enhance feed utilization and improve amino acid composition in silk and pupae remain unclear. In this study, we explored the mechanisms through which gut bacteria influence nutritional efficiency and the biosynthesis of high-quality silk and pupae by exposing fifth-instar silkworms to magnetic Fe₃O₄@urease-doped nanoconjugates. Our findings revealed that Fe 3 O 4 @urease nanoconjugates had no detrimental effects on the silkworm gut. Dominant genera in the MF group included Enterococcus , Staphylococcus , Pantoea , Klebsiella and Glutamicibacter ( p < 0.05), while Linear Discriminant Analysis (LDA) and Effect Size (LEfSe) identified Dialister , Rhizobium and Parabacteroides as biomarkers. Functional prediction revealed bacteria in the Fe 3 O 4 @urease nanoconjugates treatment group were involved in metabolic activities, including amino acid synthesis, protein degradation and immune development. HPLC-LC-MS analysis revealed significantly higher levels of the amino acids Histidine (1077.06%) and Arginine (1020.45%) in the Fe 3 O 4 @urease nanoconjugates treatment group. In summary, magnetic Fe₃O₄@urease-doped nanoconjugates effectively modulated B. mori gut microbiota, enhancing digestion and nutrient assimilation, and providing a scientific basis for the rational implementation of bacteria control for enhancing amino acid contents in silk and pupae.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".