Effects of insulin and analogues on carcinogen-induced mammary tumours in high-fat-fed rats
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Bibliographic record
Abstract
It is not fully clarified whether insulin glargine, an analogue with a high affinity for insulin-like growth factor-1 receptor (IGF-1R), increases the risk for cancers that abundantly express IGF-1R such as breast cancer or some types of breast cancer. To gain insight into this issue, female Sprague–Dawley rats fed a high-fat diet were given the carcinogen N-methyl-N-nitrosourea and randomly assigned to vehicle (control), NPH (unmodified human insulin), glargine or detemir ( n = 30 per treatment). Insulins were given subcutaneously (15 U/kg/day) 5 days a week. Mammary tumours were counted twice weekly, and after 6 weeks of treatment, extracted for analysis. None of the insulin-treated groups had increased mammary tumour incidence at any time compared with control. At 6 weeks, tumour multiplicity was increased with NPH or glargine ( P < 0.05) and tended to be increased with detemir ( P = 0.2); however, there was no difference among insulins (number of tumours per rat: control = 0.8 ± 0.1, NPH = 1.8 ± 0.3, glargine = 1.5 ± 0.4, detemir = 1.4 ± 0.4; number of tumours per tumour-bearing rat: control = 1.3 ± 0.1, NPH = 2.2 ± 0.4, glargine = 2.7 ± 0.5, detemir = 2.3 ± 0.5). IGF-1R expression in tumours was lower than that in Michigan Cancer Foundation-7 (MCF-7) cells, a cell line that shows greater proliferation with glargine than unmodified insulin. In rats, glargine was rapidly metabolised to M1 that does not have greater affinity for IGF-1R. In conclusion, in this model of oestrogen-dependent breast cancer in insulin-resistant rats, insulin and insulin analogues increased tumour multiplicity with no difference between insulin types.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 it