Probiotic bacteria as potential detoxification tools: assessing their heavy metal binding isotherms
Bibliographic record
Abstract
Dietary exposure to heavy metals may have detrimental effects on human and animal health, even at low concentrations. Specific probiotic bacteria may have properties that enable them to bind toxins from food and water. We assessed the interaction of probiotic bacteria with cadmium and lead in vitro as an initial screening step to identify strains for heavy metal decontamination in food and intestinal models. Binding isotherms for cadmium and lead were characterized for Lactobacillus rhamnosus LC-705, Propionibacterium freudenreichii subsp. shermanii JS and a mix of them used by the food industry. Differences among the strains and their combinations in binding performance at a range of concentrations between 0.1 and 100 mg.L-1 were evaluated with the Langmuir model for biosorption. The effects of pH, contact time, and viability on the binding capacities were also investigated. All strains and their combinations were found to bind cadmium and lead efficiently at low concentration ranges commonly observed in foods. However, the two strains and their combinations differed significantly in their maximum binding capacities and affinities represented by the Langmuir constants Qmax and b, respectively. The binding seemed to occur instantaneously and in a pH-dependent manner, which can be perfectly described by a segmented linear-plateau model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".