Immobilization of cadmium and lead by <i>Lactobacillus rhamnosus</i> GR-1 mitigates apical-to-basolateral heavy metal translocation in a Caco-2 model of the intestinal epithelium
Bibliographic record
Abstract
Heavy metals are highly toxic elements that contaminate the global food supply and affect human and wildlife health. Purification technologies are often too expensive or not practically applicable for large-scale implementation, especially in impoverished nations where heavy metal contamination is widespread. Lactobacillus rhamnosus GR-1 (LGR-1) was shown in previous work to reduce heavy metal bioaccumulation in a Tanzanian cohort of women and children through indeterminant mechanisms. Here, it was hypothesized that LGR-1 could sequester the heavy metals lead (Pb) and cadmium (Cd), thereby reducing their absorption across intestinal epithelium. LGR-1 and other lactobacilli significantly reduced the amount of Pb and Cd in solution at all concentrations tested (0.5 mg/L – 50 mg/L) and exhibited sustained binding profiles over a 48-hour period. Relative binding efficiency of LGR-1 decreased as Pb concentration increased, with an absolute minimum binding threshold apparent at concentrations of 2 mg/L and above. Electron microscopy revealed that Pb formed irregular cell-surface clusters on LGR-1, while Cd appeared to form intracellular polymeric clusters. Additionally, LGR-1 was able to significantly reduce apical-to-basolateral translocation of Pb and Cd in a Caco-2 model of the intestinal epithelium. These findings demonstrate the absorbent properties of LGR-1 can immobilize Pb and Cd, effectively reducing their translocation across the intestinal epithelium in vitro. Oral administration of heavy metal-binding Lactobacillus spp. (many of which are known human symbionts and strains of established probiotics) may offer a simple and effective means to reduce the amount of heavy metals absorbed from foods in contaminated regions of 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) | 0.000 | 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".