In-silico study of reducing human health risk of POP residues' direct (from tea) or indirect exposure (from tea garden soil): Improved rhizosphere microbial degradation, toxicity control, and mechanism analysis
Bibliographic record
Abstract
The accumulation of potentially harmful substances in tea garden soils and tea leaves, especially persistent organic pollutants (POPs), is a special concern for tea consumers worldwide. However, their potential health and ecological risks in tea gardens have rarely been investigated. This study proposed measures to improve the degradation ability of POPs by the tea rhizosphere and to reduce the human health risks caused by POPs after tea consumption. In this study, the binding energy values of six types of POPs and the degraded protein were used to reflect the degradation ability and calculated using molecular dynamic simulations. The main root secretions (i.e., catechin, glucose, arginine, and oxalic acid) were selected and applied with a combination of tea fertilizer and trace element combination (i.e., urea, straw, and copper element), leading to an improved degradation ability (49.59 %) of POPs. To investigate the mechanisms of the factors that affect the degradation ability, molecular docking, tensor singular value decomposition methods, multivariate correlation analysis and 2D-QSAR model were used. The results showed that the solvation energy and solvent accessible surface area are the main forces, and the molecular weight, boiling point, and topological radius of the POPs were the key molecular features affecting their degradation ability. Based on the three key characteristics, a diet avoidance scheme (i.e., avoiding lysine, maslinic acid, ethanol, perfluorocaproic acid, and cholesterol with tea), which can reduce the binding ability of POP residues to aromatic hydrocarbon receptors by 506.13 %. This work will provide theoretical strategies to improve the quality and safety of tea production and reduce the potential risks of harmful substance residues in tea garden soils and tea leaves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".