Effect of Curcumin-Enriched Silkworms on the Survival Rate of Catfish Infected with Aeromonas hydrophila
Bibliographic record
Abstract
Intensive catfish farming often faces challenges related to outbreaks of Motile Aeromonas Septicemia (MAS).Currently, farmers treat MAS with antibiotics.However, the use of antibiotics poses significant risks as it can lead to antibiotic residues in fish tissue.Therefore, alternative treatments using natural ingredients are needed.The purpose of this study was to evaluate the effects of curcumin-enriched surta worms on the survival rate of catfish infected with Aeromonas hydrophila.The research methods included enriching silkworms with different turmeric powders, namely 0% (K), 0.25% (P1), 0.5% (P2), and 0.75% (P3), feeding silkworms with and without curcumin, diagnosing Aeromonas hydrophila infection, and observing survival rate.Parameters observed included curcumin absorption, silkworm biomass, liver and kidney histology, and survival rate.The results showed that surta worms were able to absorb curcumin through fermentation media, with the highest absorption observed at a 0.75% concentration, yielding a curcumin level of 23.8 mg/kg.Different curcumin content in the cultivation media did not affect silkworm biomass.The results also showed that catfish treated with feed without curcumin had damage to the histology of the liver and kidneys after being infected with Aeromonas hydrophila.Meanwhile, catfish fed with 0.75% turmeric powder were shown to inhibit Aeromonas hydrophila infection; this was evidenced by healthy liver and kidney histology.An independent t-test showed that feeding surta worms with and without curcumin did not affect the survival rate of juvenile catfish infected with Aeromonas hydrophila.
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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) | 0.001 | 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".