Decreased Erythrocyte CCS Content is a Biomarker of Copper Overload in Rats
Bibliographic record
Abstract
Copper (Cu) is an essential trace metal that is toxic in excess. It is therefore important to be able to accurately assess Cu deficiency or overload. Cu chaperone for Cu/Zn superoxide dismutase (CCS) protein expression is elevated in tissues of Cu-deficient animals. Increased CCS content in erythrocytes is particularly sensitive to decreased Cu status. Given the lack of a non-invasive, sensitive and specific biomarker for the assessment of Cu excess, we investigated whether CCS expression in erythrocytes reflects Cu overload. Rats were fed diets containing normal or high levels of Cu for 13 weeks. Diets contained 6.3 +/- 0.6 (Cu-N), 985 +/- 14 (Cu-1000) or 1944 +/- 19 (Cu-2000) mg Cu/kg diet. Rats showed a variable response to the high Cu diets. Some rats showed severe Cu toxicity, while other rats showed no visible signs of toxicity and grew normally. Also, some rats had high levels of Cu in liver, whereas others had liver Cu concentrations within the normal range. Erythrocyte CCS protein expression was 30% lower in Cu-2000 rats compared to Cu-N rats (P < 0.05). Notably, only rats that accumulated high levels of Cu in liver had lower erythrocyte CCS (47% reduction, P < 0.05) compared to rats fed normal levels of Cu. Together, these data indicate that decreased erythrocyte CCS content is associated with Cu overload in rats and should be evaluated further as a potential biomarker for assessing Cu excess in humans.
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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) | 0.001 | 0.000 |
| 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 | 0.000 | 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".