Probiotic lactobacilli: a potential prophylactic treatment for reducing pesticide absorption in humans and wildlife
Bibliographic record
Abstract
Numerous pesticides are used in agriculture, gardening, and wildlife-control. Despite their intended toxicity to pests, these compounds can also cause harm to wildlife and humans due to their ability to potentially bioaccumulate, leach into soils, and persist in the environment. Humans and animals are commonly exposed to these compounds through agricultural practices and consumption of contaminated foods and water. Pesticides can cause a range of adverse effects in humans ranging from minor irritation, to endocrine or nervous system disruption, cancer, or even death. A convenient and cost-effective method to reduce unavoidable pesticide absorption in humans and wildlife could be the use of probiotic lactobacilli. Lactobacillus is a genus of Gram-positive gut commensal bacteria used in the production of functional foods, such as yoghurt, cheese, sauerkraut and pickles, as well as silage for animal feed. Preliminary in vitro experiments suggested that lactobacilli are able to degrade some pesticides. Probiotic Lactobacillus rhamnosus GR-1-supplemented yoghurt reduced the bioaccumulation of mercury and arsenic in pregnant women and children. A similar study is warranted to test if this approach can reduce pesticide absorption in vivo, given that the lactobacilli can also attenuate reactive oxygen production, enhance gastrointestinal barrier function, reduce inflammation, and modulate host xenobiotic metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".