Insights into toxicity of polychlorinated naphthalenes to multiple human endocrine receptors: Mechanism and health risk analysis
Bibliographic record
Abstract
This study explored the combined disruption mechanism of polychlorinated naphthalenes (PCNs) on the three key receptors (estrogen receptor, thyroid receptor, and adrenoceptor) of the human endocrine system. The intensity of PCN endocrine disruption on these receptors was first determined using a molecular docking method. A comprehensive index of PCN endocrine disruption to human was quantified by analytic hierarchy process and fuzzy analysis. The mode of action between PCNs and the receptors was further identified to screen the molecular characteristics influencing PCN endocrine disruption through molecular docking and fractional factorial design. Quantitative structure–activity relationship (QSAR) models were established to investigate the toxic mechanism due to PCN endocrine disruption. The results showed that the lowest occupied orbital energy (ELUMO) was the most important factor contributing to the toxicity of PCNs on the endocrine receptors, followed by the orbital energy difference (ΔE) and positive Millikan charge (q+). Furthermore, the strategies were formulated through adjusting the nutritious diet to reduce health risk for the workers in PCN contaminated sites and the effectiveness and feasibility were assessed by molecular dynamic simulation. The simulation results indicated that the human health risk caused by PCN endocrine disruption could be effectively decreased by nutritional supplementation. The binding ability between PCNs and endocrine receptors significantly declined (up to −16.45%) with the supplementation of vitamins (A, B2, B12, C, and E) and carotene. This study provided the new insights to reveal the toxic mechanism of PCNs on human endocrine systems and the recommendations on nutritional supplements for health risk reduction. The methodology and findings could serve as valuable references for screening of potential endocrine disruptors and developing appropriate strategies for PCN or other persistent organic pollution control and health risk management.
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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 | 0.001 |
| 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".