Interactions between blood lead (Pb) concentration, oxidative stress, cellular immune response and reproductive status in livestock from a mining area
Bibliographic record
Abstract
Chronic exposure to lead (Pb) in livestock grazing in abandoned mining areas affects animal welfare and productivity, as well as represents a significant food safety risk. Here, we evaluate the physiological effects of Pb exposure in goats maintained under extensive farming conditions in a non-remediated mining area. We monitored blood, fecal, and milk Pb levels in two groups of goats, pregnant (n = 17) and lactating (n = 24), kept in different enclosures with high soil Pb concentrations (geometric means of 270 and 143 μg/g, respectively) in Sierra Madrona mining district (Spain). We also studied the influence of Pb exposure on the ability to mount a cellular immune response, and on oxidative stress and biochemical biomarkers measured in blood. Blood Pb concentration was higher in pregnant than in lactating goats, but this difference was not observed in fecal Pb concentration. Pb levels in feces and milk concentrations were correlated with those measured in blood, with 11% of milk samples showing Pb concentrations above the maximum level (ML) for Pb in raw milk established by the EU (0.02 μg/g wet weight). Animals with increased blood Pb levels showed reduced concentrations of retinol in plasma, but these Pb levels did not affect the cellular immune response. The stimulation of the cellular immune response in lactating goats was associated with an increase in blood Pb and calcium levels. The reproductive status and age of goats significantly affected several oxidative stress, antioxidants and plasma biochemistry variables. Goats grazing on soils contaminated by past Pb mining activities may be susceptible to detrimental health effects mediated by retinol deficiency. In view of the detected transfer of Pb through milk, special attention should be paid to the food safety of derived products (i.e. cheese).
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".