Adding tissue variability to digital breast phantoms for mammography and digital breast tomosynthesis simulations

Intra- and inter-patient tissue variability are seldom implemented in digital phantoms for imaging simulations, which can lead to issues when developing and evaluating material differentiation methods. In this work, we evaluated two methods for generating variability in tissue attenuation properties based on measured properties of human tissue. Our goal is to find a sampling method that generates attenuation curves within measured distributions. The first approach parameterizes tissue attenuation curves as a linear combination of aluminum and PMMA. The second approach is based on the Midgley decomposition model, where the attenuation curve is expressed in terms of five coefficients. Attenuation curves were generated by sampling the two- and five-parameter spaces, and they were compared to previous measurements in ex-vivo adipose tissue acquired at 8, 11, 15, 20 and 30 keV. The average differences of the sampled curves relative to the measurements were 1.68% (2-parameter) and 1.31% (5-parameter), and the absolute differences in coefficients of variation were under 2% for both methods. These results indicate that both methods captured the variability present in measured attenuation curves. This study provides preliminary insights into the effectiveness of two methods for adding tissue variability to imaging simulations.</p

Flavonoids diosmetin and hesperetin are potent inhibitors of cytochrome P450 2C9-mediated drug metabolism in vitro.

The aim of this study was to examine in vitro, by means of kinetic analysis and molecular docking simulations, the effects of the flavone diosmetin and its flavanone analog hesperetin on CYP (cytochrome P450) 2C9-mediated drug metabolism. To this purpose, the conversion of diclofenac to 4'-hydroxydiclofenac by human liver microsomes was used as a model assay for assessing the CYP2C9 inhibitory activity of these two flavonoids. Kinetic analyses showed that diosmetin and hesperetin were reversible, dead-end inhibitors of 4'-hydroxydiclofenac formation; their mean K(i) (inhibitor dissociation constant) values were 1.71 \ub1 0.58 and 21.50 \ub1 3.62 \ub5M, respectively. Diosmetin behaved as a competitive inhibitor, since it increased markedly the K(m) (substrate concentration yielding 50% of V(max)) of the reaction without affecting the V(max) (maximum velocity of reaction). Hesperetin modified markedly K(m) and to a lesser extent also modified V(max), thus acting as a mixed competitive-noncompetitive inhibitor. The results of molecular docking simulations were consistent with those of kinetic analysis, since they showed that the putative binding sites of both diosmetin and hesperetin coincided with the CYP2C9 substrate binding site. The demonstration that diosmetin and hesperetin inhibit CYP2C9-mediated diclofenac metabolism at low micromolar concentrations is of potential clinical relevance because CYP2C9 is responsible for the biotransformation of various therapeutically important drugs that have narrow therapeutic indexes

Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients.

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to nucleic acids and highly diverse clinical manifestations. To assess its molecular heterogeneity, we longitudinally profiled the blood transcriptome of 158 pediatric patients. Using mixed models accounting for repeated measurements, demographics, treatment, disease activity (DA), and nephritis class, we confirmed a prevalent IFN signature and identified a plasmablast signature as the most robust biomarker of DA. We detected gradual enrichment of neutrophil transcripts during progression to active nephritis and distinct signatures in response to treatment in different nephritis subclasses. Importantly, personalized immunomonitoring uncovered individual correlates of disease activity that enabled patient stratification into seven groups, supported by patient genotypes. Our study uncovers the molecular heterogeneity of SLE and provides an explanation for the failure of clinical trials. This approach may improve trial design and implementation of tailored therapies in genetically and clinically complex autoimmune diseases. PAPERCLIP. Cell 2016 Apr 21; 165(4):551-65

Predicting failure of hematopoietic stem cell mobilization before it starts: the predicted poor mobilizer (pPM) score

Predicting mobilization failure before it starts may enable patient-tailored strategies. Although consensus criteria for predicted PM (pPM) are available, their predictive performance has never been measured on real data. We retrospectively collected and analyzed 1318 mobilization procedures performed for MM and lymphoma patients in the plerixafor era. In our sample, 180/1318 (13.7%) were PM. The score resulting from published pPM criteria had sufficient performance for predicting PM, as measured by AUC (0.67, 95%CI: 0.63â\u80\u930.72). We developed a new prediction model from multivariate analysis whose score (pPM-score) resulted in better AUC (0.80, 95%CI: 0.76â\u80\u930.84, p < 0001). pPM-score included as risk factors: increasing age, diagnosis of NHL, positive bone marrow biopsy or cytopenias before mobilization, previous mobilization failure, priming strategy with G-CSF alone, or without upfront plerixafor. A simplified version of pPM-score was categorized using a cut-off to maximize positive likelihood ratio (15.7, 95%CI: 9.9â\u80\u9324.8); specificity was 98% (95%CI: 97â\u80\u9398.7%), sensitivity 31.7% (95%CI: 24.9â\u80\u9339%); positive predictive value in our sample was 71.3% (95%CI: 60â\u80\u9380.8%). Simplified pPM-score can â\u80\u9crule inâ\u80\u9d patients at very high risk for PM before starting mobilization, allowing changes in clinical management, such as choice of alternative priming strategies, to avoid highly likely mobilization failure