Isolation and in vitro selection of actinomycetes strains as potential probiotics for aquaculture
Bibliographic record
Abstract
AIM: This study was designed to describe a series of in vitro tests that may aid the discovery of probiotic strains from actinomycetes. MATERIALS AND METHODS: Actinomycetes were isolated from marine sediments using four different isolation media, followed by antimicrobial activity and toxicity assessment by the agar diffusion method and the hemolysis of human blood cells, respectively. Extracellular enzymatic production was monitored by the hydrolysis of proteins, lipids and carbohydrates. Tolerance to different pH values and salt concentrations was also determined, followed by hydrophobicity analysis and genetic identification of the most promising strains. RESULTS: Five out of 31 isolated strains showed antimicrobial activity against three Vibrio species. Three non-hemolytic strains (N7, RL8 and V4) among these active isolates yielded positive results in hydrophobicity tests and exhibited good growth at salt concentrations ranging from 0% to 10%, except strain RL8, which required a salt concentration >0.6%. Although these strains did not grow at pH<3, they showed different enzymatic activities. Phylogenetic analysis revealed that strains N7 and V4 have more than 99% identity with several Streptomyces species, whereas the closest matches to strain RL8 are Streptomyces panacagri and Streptomyces flocculus, with 98% and 98.2% similarity, respectively. CONCLUSION: Three actinomycetes strains showing probiotic-like properties were discovered using several in vitro tests that can be easily implemented in different institutions around the world.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".