Effects of long-term cadmium exposure on urinary metabolite profiles in mice
Bibliographic record
Abstract
Cadmium (Cd) is a common environmental pollutant with known toxic effects on the kidney. Urinary metabolomics is a promising approach to study mechanism by which Cd-induced nephrotoxicity. The aim of this study was to elucidate the mechanism of Cd toxicity and to develop specific biomarkers by identifying urinary metabolic changes after a long-term of Cd exposure and with the critical concentration of Cd in the kidney. Urine samples were collected from wild-type 129/Sv mice after 67 weeks of 300 ppm Cd exposure and analyzed by ultra performance liquid chromatography connected with quadrupole time of flight mass spectrometer (UPLC-QTOF-MS) based metabolomics approach. A total of 40 most differentiated metabolites (9 down-regulated and 31 up-regulated) between the control and Cd-exposed group were identified. The majority of the regulated metabolites are amino acids (glutamine, L-aspartic acid, phenylalanine, tryptophan, and D-proline) indicating that amino acid metabolism pathways are affected by long-term exposure of Cd. However, there are also some nucleotides (guanosine, guanosine monophosphate, cyclic AMP, uridine), amino acid derivatives (homoserine, N-acetyl-L-aspartate, N-acetylglutamine, acetyl-phenylalanine, carboxymethyllysine), and peptides. Results of pathway analysis showed that the arginine and proline metabolism, purine metabolism, alanine, aspartate and glutamate metabolism, and aminoacyl-tRNA biosynthesis were affected compared to the control. This study demonstrates that metabolomics is useful to elucidate the metabolic responses and biological effects induced by Cd-exposure.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".