Lead does not affect transcription of intestinal zinc-binding proteins in growing rats.
Bibliographic record
Abstract
Environmental lead exposure remains a serious concern for the growth and development of children. Micronutrient status may affect the absorption and tissue accumulation of lead, but the mechanisms of gastrointestinal uptake and transport remain unknown. Thus, our objective was to investigate the effects of lead on the mRNA levels of intestinal zinc transporter 4 (ZIP4), metallothionein (MT), cysteine-rich intestinal protein (CRIP), and divalent metal transporter 1 (DMT1) in growing rats fed marginal, adequate, and supplemental zinc diets. Weanling Sprague Dawley rats were assigned to marginal zinc (MZ; 8 mg Zn/kg diet), zinc-adequate control (CT; 30 mg Zn/kg), zinc-adequate diet-restricted (DR; 30 mg Zn/kg), or supplemental zinc (SZ; 300 mg Zn/kg) groups, with and without lead acetate-containing drinking water (200 mg Pb/l) for 3 weeks. Duodenum was analyzed for ZIP4, MT, CRIP, and DMT1 mRNA levels by real-time reverse transcription-polymerase chain reaction and MT immunolocalization. Tissues were analyzed for zinc, lead, and iron by inductively coupled plasma spectrometry. MZ rats had higher duodenal ZIP4 mRNA levels, lower MT mRNA levels, lower MT immunostaining intensity, and lower zinc concentrations than DR, CT, and SZ. Duodenal DMT1 mRNA levels were lower in DR and SZ compared with MZ. Tissue lead concentrations responded to dietary zinc with SZ < CT < DR
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".