Developmental and Lactational Exposure to Dieldrin Alters Mammary Tumorigenesis in Her2/neu Transgenic Mice
Bibliographic record
Abstract
Breast cancer is the most common cancer in Western women and while its precise etiology is unknown, environmental factors are thought to play a role. The organochlorine pesticide dieldrin is a persistent environmental toxicant thought to increase the risk of breast cancer and reduce survival in the human population. The objective of this study was to define the effect of developmental exposure to environmentally relevant concentrations of dieldrin, on mammary tumor development in the offspring. Sexually mature FVB-MMTV/neu female mice were treated with vehicle (corn oil), or dieldrin (0.45, 2.25, and 4.5 microg/g body weight) daily by gavage for 5 days prior to mating and then once weekly throughout gestation and lactation until weaning. Dieldrin concentrations were selected to produce serum levels representative of human background body burdens, occupational exposure, and overt toxicity. Treatment had no effect on litter size, birth weight or the number of pups surviving to weaning. The highest dose of dieldrin significantly increased the total tumor burden and the volume and number of tumors found in the thoracic mammary glands. Increased mRNA and protein expression of the neurotrophin BDNF and its receptor TrkB was increased in tumors from the offspring of dieldrin treated dams. This study indicates that developmental exposure to the environmental contaminant dieldrin causes increased tumor burden in genetically predisposed mice. Dieldrin exposure also altered the expression of BNDF and TrkB, novel modulators of cancer pathogenesis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".