Generation of a new bioluminescent model for visualisation of mammary tumour development in transgenic mice
Bibliographic record
Abstract
BACKGROUND: Numerous transgenic models have been generated to study breast cancer. However, despite many advantages, traditional transgenic models for breast cancer are also burdened with difficulties in early detection and longitudinal observation of transgene-induced tumours, which in most cases are randomly located and occur at various time points. Methods such as palpation followed by mechanical measurement of the tumours are of limited value in transgenic models. There is a crucial need for making these previously generated models suitable for modern methods of tumour visualisation and monitoring, e.g. by bioluminescence-based techniques. This approach was successfully used in the current study. RESULTS: A new mouse strain (MMTV-Luc2 mice) expressing Luc2 luciferase primarily in mammary tissue in females, with low-level background expression in internal organs, was generated and bred to homozygosity. After these mice were intercrossed with MMTV-PyVT mice, all double transgenic females developed mammary tumours by the age of 10 weeks, the localisation and progression of which could be effectively monitored using the luminescence-based in vivo imaging. Luminescence-based readout allowed for early visualisation of the locally overgrown mammary tissue and for longitudinal evaluation of local progression of the tumours. When sampled ex vivo at the age of 10 weeks, all tumours derived from MMTV-Luc2PyVT females displayed robust bioluminescent signal. CONCLUSIONS: We have created a novel transgenic strain for visualisation and longitudinal monitoring of mammary tumour development in transgenic mice as an addition and/or a new and more advanced alternative to manual methods. Generation of this mouse strain is vital for making many of the existing mammary tumour transgenic models applicable for in vivo imaging techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".