Antibacterial properties of extracts of Solidago canadensis and their potential use as dietary supplements in red swamp crayfish (Procambarus clarkii)
Bibliographic record
Abstract
Canadian goldenrod (Solidago canadensis) is one of the most destructive invasive weeds in Southeastern China. To evaluate its potential application as dietary supplement in red swamp crayfish (Procambarus clarkii), the antibacterial properties of aqueous and ethanol extracts of this plant against three major pathogenic bacteria in crayfish aquaculture were examined. Inhibition zone tests and determination of minimum inhibitory concentration revealed that the extracts had lower antibacterial activity than extracts from two traditional medicinal plants that possess antibacterial properties, garlic (Allium sativum) and cortex phellodendri (Phellodendron chinense). However, they did exhibit greater antibacterial effects than extracts from another widely used medicinal plant, Sophora flavescens, and an aquatic weed, Alternanthera philoxeroides. Aqueous extracts of Canadian goldenrod gave greater inhibition than the ethanol extracts. Crayfish fed a diet with 2% these aqueous extracts exhibited significantly higher enzyme activity of alkaline phosphatase, catalase and phenoloxidase (P < 0.05). Based on the results of this study, we conclude that aqueous extracts of Canadian goldenrod are highly promising for the development of new dietary supplement for use in crayfish aquaculture.
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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 | 0.001 | 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".