SECTION OF BIOMEDICAL SCIENCES: Abstracts of the Annual Meeting 23–24 June 2010
Bibliographic record
Abstract
Mammary microcalcifications are one of the most reliable mammographic features of non-palpable breast cancer.They are often the sole indicator of the disease but despite this the molecular mechanisms involved in their formation remain unclear.There are two types of calcifications associated with breast disease.Calcium oxalate is associated with benign lesions of the breast, whereas hydroxyapatite is associated with benign and malignant tumors.The aim of this study was to assess and characterise the mineralization potential of various mammary cell lines in vitro and in vivo.Additional studies are also underway to investigate the molecular mechanisms involved in this process.Human and mouse mammary cell lines (MCF10a, Hs578T, Hs578Ts(i) 8 , 4T1, 4T1.2) were grown in an osteogenic cocktail (ascorbic acid, beta-glycerophosphate, ±dexamethasone) in either monolayer or 3D collagen GAG scaffolds for up to 4 weeks.Alizarin red S and von Kossa staining, colorimetric calcium assay and Raman spectroscopy were used to assess and characterise mineralization.Real-time RTPCR was used to examine the expression of osteopontin, alkaline phosphatase, bone sialoprotein and collagen type I.In addition, 4T1 and 4T1.2 cells were implanted into the mammary fat pad of BALB/c mice and tumors produced were fixed and embedded in paraffin wax to investigate in vivo mineralization potential of these cell lines.Statistical analysis was carried out using a 2-way ANOVA and Bonferroni post-tests.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.499 | 0.293 |
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".