Additional file 2: of Reliability of breath by breath spirometry and relative flow-time indices for pulmonary function testing in horses

Raw data. (XLS 414 kb

Additional file 1: Figure S1. of Reliability of breath by breath spirometry and relative flow-time indices for pulmonary function testing in horses

Representative volume (green) and flow (pink) traces obtained during spontaneous respiration showing uniform volume and flow traces which would satisfy inclusion criteria for analysis. Note biphasic expiratory and inspiratory respiration. Table S1. Respiratory parameters measured for each of three selected breaths. Data from each breath was evaluated to ensure inclusion criteria were met (≤10% difference in inspiratory and expiratory volumes, ≤10% difference in time to peak inspiratory and expiratory flow (Tpif, Tpef) for the three breaths). Table S2. Definition of relative flow-time variables calculated for each of three selected breaths. Figure S2. Mean difference and 95% confidence intervals between days for absolute measures (respiratory frequency, Rf; tidal volume, Vt; peak inspiratory and expiratory flows, PIF and PEF; time to PEF, Tpef) determined during pulmonary function testing over three consecutive days. Figure S3. Mean difference and 95% confidence intervals between days for relative expiratory flow indices (ratio of time to PEF to expiratory time; Tpef/Te; ratio of time to PEF to total breath duration; Tpef/Tt) determined during pulmonary function testing over three consecutive days. Figure S4. Mean difference and 95% confidence intervals between days for relative flow-time indices determined during pulmonary function testing over. (PDF 525 kb

Additional file 1: of A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies

Univariate modelling results from training dataset. This table shows the univariate modelling results of all candidate texture features considered using training dataset. (DOCX 76 kb

Additional file 1: of Genetic variants of prospectively demonstrated phenocopies in BRCA1/2 kindreds

The concentration in a 10 ml PCR was 1xThermopol Reaction Buffer with 2 mM MgS04, 0.3 μM “reverse” primers, 0.15 μM “forward” primer, 0.1 μM, 6-Carboxyfluorescein-GC clamp primer, 600 μM dNTP, 100 μg Bovine Serum Albumine (Sigma-Aldrich, Oslo, Norway) and 0.75 U Taq DNA polymerase. Plates were sealed with two strips of electrical tape (Clas Ohlson, Oslo, Norway). The temperature cycling was repeated 35 times; 94 °C for 30 s, annealing temperature held for 30 s and extension at 72 °C for 60 s (Eppendorf Mastercycler ep gradient S (Eppendorf, Hamburg, Germany)). Table S1. primers used to amplify PCR product to be analysed by cycling temperature capillary electrophoresis. (DOCX 16 kb

Additional file 2: Figure S1. of Intermittent energy restriction induces changes in breast gene expression and systemic metabolism

Changes in gene expression due to CER are also changed in approximately half of the IER participants, but are not clearly correlated with lymphocytes. The heatmap colours show log2 fold change in gene expression of the baseline samples relative to the post-diet samples (green = downregulated, red = upregulated, black = no change). The arrows show subjects predicted to have an energy restriction (orange = CER, blue = IER) or unchanged profiles (grey). Similar results were obtained when considering all probe sets on the array. (XLSX 268 kb

Additional file 1: Table S1. of Intermittent energy restriction induces changes in breast gene expression and systemic metabolism

Dietary intake at baseline and after four to five weeks of IER, during restricted and unrestricted days of IER (n = 23). Table S2. a LCMS+ changes in identified serum metabolites after four to five weeks of IER, on restricted and unrestricted days of IER. b LCMS– changes in identified serum metabolites after four to five weeks of IER on restricted and unrestricted days of IER. c GCMS changes in identified serum metabolites after four to five weeks of IER on restricted and unrestricted days of IER. d GC-MS changes in identified urine metabolites after four to five weeks of IER on restricted and unrestricted days of IER. Table S3 Comparison of baseline characteristics, physical and metabolic changes between the molecular responders and non-responders identified by breast tissue gene expression following IER. Table S4 Percentage change in body weight, BMI, adiposity, lipid and hormone levels with four to five weeks of IER in comparison with four to five weeks of 60 % continuous energy restriction. (PPTX 233 kb

Additional file 1: Table S1. of A comparison of five methods of measuring mammographic density: a case-control study

Risk of developing breast cancer using continuous measures of different density methods (OR per SD). Table S2. P values based on likelihood ratio comparing different models for density methods using the subset of those with data on all methods. (DOCX 26 kb

Additional file 1: Appendix 1. of Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort

Density residual: description of methods. Appendix 2: Table S1. Univariate and multivariate performance of breast density and the Tyrer-Cuzick and Gail risk models, subgroup analysis by time of cancer diagnosis. (PDF 396 kb

A genome-wide association study reveals variants in ARL15 that influence adiponectin levels.

The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD). We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels (n = 8,531) and sought validation of the lead single nucleotide polymorphisms (SNPs) in 5 additional cohorts (n = 6,202). Five SNPs were genome-wide significant in their relationship with adiponectin (P< or =5x10(-8)). We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P< or =0.011 to declare statistical significance for these disease associations. SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels (P-combined = 9.2x10(-19) for lead SNP, rs266717, n = 14,733). A novel variant in the ARL15 (ADP-ribosylation factor-like 15) gene was associated with lower circulating levels of adiponectin (rs4311394-G, P-combined = 2.9x10(-8), n = 14,733). This same risk allele at ARL15 was also associated with a higher risk of CHD (odds ratio [OR] = 1.12, P = 8.5x10(-6), n = 22,421) more nominally, an increased risk of T2D (OR = 1.11, P = 3.2x10(-3), n = 10,128), and several metabolic traits. Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle. These findings identify a novel protein, ARL15, which influences circulating adiponectin levels and may impact upon CHD risk. The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD). We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels (n = 8,531) and sought validation of the lead single nucleotide polymorphisms (SNPs) in 5 additional cohorts (n = 6,202). Five SNPs were genome-wide significant in their relationship with adiponectin (P≤5×10−8). We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P≤0.011 to declare statistical significance for these disease associations. SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels (P-combined = 9.2×10−19 for lead SNP, rs266717, n = 14,733). A novel variant in the ARL15 (ADP-ribosylation factor-like 15) gene was associated with lower circulating levels of adiponectin (rs4311394-G, P-combined = 2.9×10−8, n = 14,733). This same risk allele at ARL15 was also associated with a higher risk of CHD (odds ratio [OR] = 1.12, P = 8.5×10−6, n = 22,421) more nominally, an increased risk of T2D (OR = 1.11, P = 3.2×10−3, n = 10,128), and several metabolic traits. Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle. These findings identify a novel protein, ARL15, which influences circulating adiponectin levels and may impact upon CHD risk

Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer

INTRODUCTION: Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice. METHODS: More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer 'stem' cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account. RESULTS: The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working. CONCLUSIONS: With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years