A five year programme for radioisotope production at the Research Establishment.

This report summarises plans for radioisotope production at Lucas Heights over the period 1966-71 and indicates how these are based on present trends of demand for radioisotopes. The programme is discussed in terms of available staff and facilities; while some small staff increases will be required, the facilities presently being commissioned should be adequate over this period

Mammographic density, lobular involution, and risk of breast cancer

In this review, we propose that age-related changes in mammographic density and breast tissue involution are closely related phenomena, and consider their potential relevance to the aetiology of breast cancer. We propose that the reduction in mammographic density that occurs with increasing age, parity and menopause reflects the involution of breast tissue. We further propose that age-related changes in both mammographic density and breast tissue composition are observable and measurable phenomena that resemble Pike's theoretical construct of ‘breast tissue ageing'. Extensive mammographic density and delayed breast involution are both associated with an increased risk of breast cancer and are consistent with the hypothesis of the Pike model that cumulative exposure of breast tissue to hormones and growth factors that stimulate cell division, as well as the accumulation of genetic damage in breast cells, are major determinants of breast cancer incidence

Biotic and abiotic retention, recycling and remineralization of metals in the ocean

Trace metals shape both the biogeochemical functioning and biological structure of oceanic provinces. Trace metal biogeochemistry has primarily focused on modes of external supply of metals from aeolian, hydrothermal, sedimentary and other sources. However, metals also undergo internal transformations such as abiotic and biotic retention, recycling and remineralization. The role of these internal transformations in metal biogeochemical cycling is now coming into focus. First, the retention of metals by biota in the surface ocean for days, weeks or months depends on taxon-specific metal requirements of phytoplankton, and on their ultimate fate: that is, viral lysis, senescence, grazing and/or export to depth. Rapid recycling of metals in the surface ocean can extend seasonal productivity by maintaining higher levels of metal bioavailability compared to the influence of external metal input alone. As metal-containing organic particles are exported from the surface ocean, different metals exhibit distinct patterns of remineralization with depth. These patterns are mediated by a wide range of physicochemical and microbial processes such as the ability of particles to sorb metals, and are influenced by the mineral and organic characteristics of sinking particles. We conclude that internal metal transformations play an essential role in controlling metal bioavailability, phytoplankton distributions and the subsurface resupply of metals

Mammographic screening and mammographic patterns

Mammography is an effective screening modality for the early detection of breast cancer. The reduction in breast cancer mortality is greater for women aged over 50 at screening than for women aged under 50. Mammography can also contribute to an understanding of the biology of breast cancer. Screening trials provide good evidence for the dedifferentiation of a cancer as it develops over time, and the age dependency of this dedifferentiation explains much of the age difference in the effectiveness of screening. Mammographic density is an important predictor of future breast cancer risk, and has potential as an early endpoint in breast cancer prevention trials. Mammographic density is also an important determinant of mammographic screening sensitivity

Experimental manipulation of radiographic density in mouse mammary gland

INTRODUCTION: Extensive mammographic density in women is associated with increased risk for breast cancer. Mouse models provide a powerful approach to the study of human diseases, but there is currently no model that is suited to the study of mammographic density. METHODS: We performed individual manipulations of the stromal, epithelial and matrix components of the mouse mammary gland and examined the alterations using in vivo and ex vivo radiology, whole mount staining and histology. RESULTS: Areas of density were generated that resembled densities in mammographic images of the human breast, and the nature of the imposed changes was confirmed at the cellular level. Furthermore, two genetic models, one deficient in epithelial structure (Pten conditional tissue specific knockout) and one with hyperplastic epithelium and mammary tumors (MMTV-PyMT), were used to examine radiographic density. CONCLUSION: Our data show the feasibility of altering and imaging mouse mammary gland radiographic density by experimental and genetic means, providing the first step toward modelling the biological processes that are responsible for mammographic density in the mouse

Can the stroma provide the clue to the cellular basis for mammographic density?

Mammographic density is recognised as a useful phenotypic biomarker of breast cancer risk. Deeper understanding is needed of the cellular basis, but evidence is limited because of difficulty in designing studies to validate hypotheses. The ductal epithelial components do not adequately explain the physical and dynamic features observed. The stroma is thought to interact with ductal structures in cancer initiation. Stromal tissues might account for the mammographic features, and this interplay can be hypothesised to relate risk to density. In a paper in this issue of Breast Cancer Research, Alowami has shown a relationship between density and stromal proteins, which might provide useful insight into mammographic density

High-risk mammographic parenchymal patterns and anthropometric measures: a case–control study

Mammographic parenchymal patterns are related to breast cancer risk and are also affected by anthropometric measure. We carried out a case–control study comprising 200 cases with high-risk (P2 and DY) mammographic parenchymal pattern and 200 controls with low-risk (N1 and P1) patterns in order to investigate the effect of body size and shape and breast size on mammographic patterns. Women in the highest quartile of body mass index (BMI) distribution were significantly less likely to have a high-risk pattern (odds ratio (OR) = 0.21, 95% confidence interval (CI) 0.08–0.52, P-value for trend = 0.001) compared to those in the lowest quartile. Relative to women with a waist to hip ratio (WHR) of less than 0.75, the OR of having a high-risk pattern in women with a WHR greater than 0.80 was 0.30 (95% CI 0.14–0.63). Breast size as measured by cup size was significantly and negatively related to high-risk pattern. Our study indicates that both BMI and WHR are negatively associated with high-risk patterns. However, both phenomena are associated with increased risk of breast cancer in post-menopausal women. This negative confounding of two positive risk factors means that the effect of parenchymal patterns on risk will tend to be underestimated when not adjusted for BMI and WHR and vice versa. Thus we may have underestimated the importance of BMI and mammographic parenchymal patterns in the past. Further studies are needed to determine a measure of parenchymal density that is independent of anthropometric measures and breast size. © 1999 Cancer Research Campaig

Percentage density, Wolfe's and Tabár's mammographic patterns: agreement and association with risk factors for breast cancer

INTRODUCTION: The purpose of this report was to classify mammograms according to four methods and to examine their agreement and their relationship to selected risk factors for breast cancer. METHOD: Mammograms and epidemiological data were collected from 987 women, aged 55 to 71 years, attending the Norwegian Breast Cancer Screening Program. Two readers each classified the mammograms according to a quantitative method (Cumulus or Madena software) and one reader according to two qualitative methods (Wolfe and Tabár patterns). Mammograms classified in the reader-specific upper quartile of percentage density, Wolfe's P2 and DY patterns, or Tabár's IV and V patterns, were categorized as high-risk density patterns and the remaining mammograms as low-risk density patterns. We calculated intra-reader and inter-reader agreement and estimated prevalence odds ratios of having high-risk mammographic density patterns according to selected risk factors for breast cancer. RESULTS: The Pearson correlation coefficient was 0.86 for the two quantitative density measurements. There was moderate agreement between the Wolfe and Tabár classifications (Kappa = 0.51; 95% confidence interval 0.46 to 0.56). Age at screening, number of children and body mass index (BMI) showed a statistically significant inverse relationship with high-risk density patterns for all four methods (all P < 0.05). After adjustment for percentage density, the Wolfe classification was not associated with any of the risk factors for breast cancer, whereas the association with number of children and BMI remained statistically significant for the Tabár classification. Adjustment for Wolfe or Tabár patterns did not alter the associations between these risk factors and percentage mammographic density. CONCLUSION: The four assessments methods seem to capture the same overall associations with risk factors for breast cancer. Our results indicate that the quantitative methods convey additional information over the qualitative methods

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study

© 2018, The Author(s). Abstract: To determine the validity of the Australian clinical prediction tool Criteria for Screening and Triaging to Appropriate aLternative care (CRISTAL) based on objective clinical criteria to accurately identify risk of death within 3 months of admission among older patients. Methods: Prospective study of ≥ 65 year-olds presenting at emergency departments in five Australian (Aus) and four Danish (DK) hospitals. Logistic regression analysis was used to model factors for death prediction; Sensitivity, specificity, area under the ROC curve and calibration with bootstrapping techniques were used to describe predictive accuracy. Results: 2493 patients, with median age 78–80 years (DK–Aus). The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% CI 7.7–8.6 vs. 5.8 95% CI 5.6–5.9) and Danish mean 7.1 (95% CI 6.6–7.5 vs. 5.5 95% CI 5.4–5.6). The model with Fried Frailty score was optimal for the Australian cohort but prediction with the Clinical Frailty Scale (CFS) was also good (AUROC 0.825 and 0.81, respectively). Values for the Danish cohort were AUROC 0.764 with Fried and 0.794 using CFS. The most significant independent predictors of short-term death in both cohorts were advanced malignancy, frailty, male gender and advanced age. CriSTAL’s accuracy was only modest for in-hospital death prediction in either setting. Conclusions: The modified CriSTAL tool (with CFS instead of Fried’s frailty instrument) has good discriminant power to improve prognostic certainty of short-term mortality for ED physicians in both health systems. This shows promise in enhancing clinician’s confidence in initiating earlier end-of-life discussions