Atrazine induces neurotoxic effects in dopaminergic systems via metabolomic and miRNA alterations
Bibliographic record
Abstract
Atrazine (ATR), a globally utilized herbicide, is known to induce neurotoxic effects on the dopaminergic system at high concentrations. While the roles of metabolomics and non-transcriptomics in energy supplementation, reactive oxygen species (ROS) production, and apoptosis are well-documented, their interactions with ATR have not been thoroughly explored. This study investigated the impact of ATR on the dopaminergic system through integrated metabolomic and non-transcriptomic analyses. We observed significant alterations in miRNA expressions that regulate the AMPK and fatty acid metabolism pathways. These disruptions led to abnormal accumulations of L-carnitine and a notable decrease in L-palmitoylcarnitine (L-PC) in the substantia nigra pars compacta (SNpc), impairing mitochondrial β-oxidation and ATP synthesis. Such metabolic disruptions likely contribute to an inadequate energy supply, hastening neuronal degeneration and autophagy. Additionally, changes in the expression of tyrosine hydroxylase and α-synuclein indicated significant damage of ATR exposure to dopaminergic neurons. These results enhance the understanding of ATR’s neurotoxic mechanisms and highlight the need for further studies and risk assessments of environmental pollutants that affect the dopaminergic system. • ATR led to abnormal accumulations of L-carnitine and a notable decrease in L-PC in SNpc. • The disruptions impaired mitochondrial β-oxidation and ATP synthesis, contributing to an inadequate energy supply for neurons. • Accelerated neuronal degeneration and autophagy were observed as potential consequences of these metabolic disturbances.
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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) | 0.001 | 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 | 0.001 |
| 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".