Selenized milk casein in the diet of BALB/c nude mice reduces growth of intramammary MCF-7 tumors
Bibliographic record
Abstract
BACKGROUND: Dietary selenium has the potential to reduce growth of mammary tumors. Increasing the Se content of cows' milk proteins is a potentially effective means to increase Se intake in humans. We investigate the effects of selenized milk protein on human mammary tumor progression in immunodeficient BALB/c nude mice. METHODS: Four isonitrogenous diets with selenium levels of 0.16, 0.51, 0.85 and 1.15 ppm were formulated by mixing low- and high-selenium milk casein isolates with a rodent premix. MCF-7 cells were inoculated into the mammary fat pad of female BALB/c nude mice implanted with slow-release 17 β-estradiol pellets. Mice with palpable tumors were randomly assigned to one of the four diets for 10 weeks, during which time weekly tumor caliper measurements were conducted. Individual growth curves were fit with the Gompertz equation. Apoptotic cells and Bcl-2, Bax, and Cyclin D1 protein levels in tumors were determined. RESULTS: There was a linear decrease in mean tumor volume at 70 days with increasing Se intake (P < 0.05), where final tumor volume decreased 35% between 0.16 and 1.15 ppm Se. There was a linear decrease in mean predicted tumor volume at 56, 63 and 70 days, and the number of tumors with a final volume above 500 mm3, with increasing Se intake (P < 0.05). This tumor volume effect was associated with a decrease in the proportion of tumors with a maximum growth rate above 0.03 day-1. The predicted maximum volume of tumors (Vmax) and the number of tumors with a large Vmax, were not affected by Se-casein. Final tumor mass, Bcl-2, Bax, and Cyclin D1 protein levels in tumors were not significantly affected by Se-casein. There was a significantly higher number of apoptotic cells in high-Se tumors as compared to low-Se tumors. CONCLUSIONS: Taken together, these results suggest that turnover of cells in the tumor, but not its nutrient supply, were affected by dairy Se. We have shown that 1.1 ppm dietary Se from selenized casein can effectively reduce tumor progression in an MCF-7 xenograft breast cancer model. These results show promise for selenized milk protein as an effective supplement during chemotherapy.
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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 | 0.000 |
| 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".