Investigation of the effects of Bifidobacterium bifidum as a probiotic on liver function enzymes due to exposure to E.coli O157H7 in Koi fish (Cyprinus rubrofuscus)
Bibliographic record
Abstract
Under intensive aquaculture conditions, fish are exposed to many pathogens, due to that strengthening their immune system is an alternative significant way to reduce mortality rate and disease-related complications. Probiotics are valuable oral nutrition supplements for this purpose. In the present investigation, there were used Koi fish (n=120) with the characteristics of 10±3 Cm in length and an average of 20±1 g in weight classified in 4 groups under 2 replication. The classification of groups was as follow: T1; 24-day treated with the probiotic diet of Bifidobacterium bifidum (1.5×108 CFU/ mL), T2; 24-day treated with the probiotic diet of Bifidobacterium bifidum and 72 hours of exposure to E.coli O157H7 pathogen (1.5×108 CFU/ mL), Sham group; 24-day probiotic-free commercial feed treatment and 72 hours of exposure to E.coli O157H7, and control group (C); which had fed neither the probiotics nor any pathogens. In the present scientific investigation, the feeding processes were done twice a day at 10:00 a.m. and 2:00 p.m. where the samples were stored at 12:12 cycle of dark and light. ALT, AST and ALP serum levels were examined to determine the symptoms of disease caused by the pathogen. These liver function tests (LFT) were examined on days 24 and 27. As a conclusion, it is found that probiotic complements feeding cause to decrease the LFT level compared with the control group on days of 24 and 27 (p<0.05). Also, in the probiotic-fed group, after exposure to the pathogen, the level of serum LFT increased compared with the control group (p<0.05), the probiotic utilization as a supplement reveals the better efficiency of the liver during ageing, as well (p<0.05).
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".