Background The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility single nucleotide polymorphisms (SNPs) is not known. Methods Using 750 cases and 405 controls from the population-based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent risk-associated SNPs. We used logistic regression to estimate the odds ratio per adjusted standard deviation for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement. Results The odds ratios for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding odds ratios were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score. Conclusions By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%. Impact Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

Summary Bacterial antibiotic susceptibility was rapidly estimated for Escherichia coli and Staphylococcus spp. by flow cytometry. This was achieved by measuring the uptake of a negatively charged membrane potential sensitive dye bis‐(1,3‐dibutylbarbituric acid) trimethine oxonol and observing changes in low‐angle light scatter (excitation light scattered by up to 15°). Estimations of ampicillin, gentamicin and ciprofloxacin susceptibilities were possible within 2–5 h from a plate culture, depending on the species and antibiotic used. This includes the time necessary to establish steady‐state growth in liquid culture.

Various dyes were assessed for their ability to discriminate between viable and non-viable bacteria. Two methods of killing were employed: by heat treatment or by gramicidin treatment. Staining was carried out in two ways; by staining directly in the medium or by washing cells prior to staining in buffer. Carbocyanine and rhodamine 123 dyes only exhibited small changes in fluorescence between viable and non-viable populations of bacteria. Both oxonol dye (bis 1,3-dibutylbarbituric acid trimethine oxonol) and calcafluor white proved much more useful.

For African American or Hispanic women, the extent to which clinical breast cancer risk prediction models are improved by including information on susceptibility single nucleotide polymorphisms (SNPs) is unknown, even though these women comprise increasing proportions of the US population and represent a large proportion of the world’s population. We studied 7539 African American and 3363 Hispanic women from the Women’s Health Initiative. The age-adjusted 5-year risks from the BCRAT and IBIS risk prediction models were measured and combined with a risk score based on >70 independent susceptibility SNPs. Logistic regression, adjusting for age group, was used to estimate risk associations with log-transformed age-adjusted 5-year risks. Discrimination was measured by the odds ratio (OR) per standard deviation (SD) and the area under the receiver operator curve (AUC). When considered alone, the ORs for African American women were 1.28 for BCRAT, and 1.04 for IBIS. When combined with the SNP risk score (OR 1.23), the corresponding ORs were 1.39 and 1.22. For Hispanic women the corresponding ORs were 1.25 for BCRAT, and 1.15 for IBIS. When combined with the SNP risk score (OR 1.39), the corresponding ORs were 1.48 and 1.42. There was no evidence that any of the combined models were not well calibrated. Including information on known breast cancer susceptibility loci provides approximately 10 and 19 % improvement in risk prediction using BCRAT for African Americans and Hispanics, respectively. The corresponding figures for IBIS are approximately 18 and 26 %, respectively.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-015-3641-7) contains supplementary material, which is available to authorized users.

Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors–not age and gender–that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.