Multi-Species Probiotics as Sustainable Strategy to Alleviate Polyamide Microplastic-Induced Stress in Nile Tilapia
Bibliographic record
Abstract
Microplastic particles exhibit multiple toxic effects, disrupting physiological processes in fish, such as Nile tilapia (Oreochromis niloticus), a widely cultured species. Probiotics could help counter polyamide microplastic toxicity while promoting fish health and sustainable aquaculture. A 6-week experiment was conducted on Nile tilapia included four treatments: (1) without polyamide microplastics and/or probiotics (control) and (2) with polyamide microplastics (PA-MP), (3) probiotics (Pr.), or (4) polyamide microplastics and probiotics (PA-MP + Pr.). The outcomes demonstrate that exposure to polyamide microplastics caused poorer growth performance and survivability along with reduced hemoglobin, and upregulated glucose levels, which were restored by probiotics application. The prevalence of erythrocytic abnormalities increased in the polyamide microplastic group but probiotics supplementation reduced the anomalies. Fish exposed to polyamide microplastics exhibited a lower frequency of goblet cells than other groups. Moreover, expression of antioxidant genes (SOD and CAT) and immune genes (IL-1β, IFN-γ, and TNF-α) was higher during polyamide microplastic exposure, which was downregulated in the polyamide microplastics along with probiotics group. These findings suggest that multi-species probiotics relieve microplastic-induced stress and hindrance of growth in Nile tilapia, which will help sustainable aquaculture practices safeguard fish health and maintain aquaculture productivity by alleviating adverse impacts of microplastic pollution in waterbodies.
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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 | 0.000 |
| 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".