Effect of Polydispersity and Anisotropy in Colloidal and Protein Solutions: an Integral Equation Approach

Application of integral equation theory to complex fluids is reviewed, with particular emphasis to the effects of polydispersity and anisotropy on their structural and thermodynamic properties. Both analytical and numerical solutions of integral equations are discussed within the context of a set of minimal potential models that have been widely used in the literature. While other popular theoretical tools, such as numerical simulations and density functional theory, are superior for quantitative and accurate predictions, we argue that integral equation theory still provides, as in simple fluids, an invaluable technique that is able to capture the main essential features of a complex system, at a much lower computational cost. In addition, it can provide a detailed description of the angular dependence in arbitrary frame, unlike numerical simulations where this information is frequently hampered by insufficient statistics. Applications to colloidal mixtures, globular proteins and patchy colloids are discussed, within a unified framework.Comment: 17 pages, 7 figures, to appear in Interdiscip. Sci. Comput. Life Sci. (2011), special issue dedicated to Prof. Lesser Blu

Accuracy of elastic fusion biopsy in daily practice: results of a multicenter study of 2115 patients

OBJECTIVES: To assess the accuracy of Koelis fusion biopsy for the detection of prostate cancer and clinically significant prostate cancer in the everyday practice. METHODS: We retrospectively enrolled 2115 patients from 15 institutions in four European countries undergoing transrectal Koelis fusion biopsy from 2010 to 2017. A variable number of target (usually 2-4) and random cores (usually 10-14) were carried out, depending on the clinical case and institution habits. The overall and clinically significant prostate cancer detection rates were assessed, evaluating the diagnostic role of additional random biopsies. The cancer detection rate was correlated to multiparametric magnetic resonance imaging features and clinical variables. RESULTS: The mean number of targeted and random cores taken were 3.9 (standard deviation 2.1) and 10.5 (standard deviation 5.0), respectively. The cancer detection rate of Koelis biopsies was 58% for all cancers and 43% for clinically significant prostate cancer. The performance of additional, random cores improved the cancer detection rate of 13% for all cancers (P < 0.001) and 9% for clinically significant prostate cancer (P < 0.001). Prostate cancer was detected in 31%, 66% and 89% of patients with lesions scored as Prostate Imaging Reporting and Data System 3, 4 and 5, respectively. Clinical stage and Prostate Imaging Reporting and Data System score were predictors of prostate cancer detection in multivariate analyses. Prostate-specific antigen was associated with prostate cancer detection only for clinically significant prostate cancer. CONCLUSIONS: Koelis fusion biopsy offers a good cancer detection rate, which is increased in patients with a high Prostate Imaging Reporting and Data System score and clinical stage. The performance of additional, random cores seems unavoidable for correct sampling. In our experience, the Prostate Imaging Reporting and Data System score and clinical stage are predictors of prostate cancer and clinically significant prostate cancer detection; prostate-specific antigen is associated only with clinically significant prostate cancer detection, and a higher number of biopsy cores are not associated with a higher cancer detection rate

Prostate cancer detection rate of transrectal ultrasonography, digital rectal examination, and prostate-specific antigen: results of a five-year study of 6- versus 12-core transperineal prostate biopsy.

The purpose of the present comparative work was the processing and assessment of data collected in a five-year period of urological practice with more than 1.500 transperineal, ultrasound-guided, prostatic biopsies performed. Our aim was to identify advantages and limitations of 6 and 12-core protocols, by extending the evaluation not only to cancer detection rate but also to the other histological findings. A total of 1.151 patients were included in the study. Two subgroups were identified: 836 patients who had undergone a 6-core biopsy from 2001 to 2004, and 315 patients who had undergone a 12-core biopsy from 2005 to 2006. Cancer detection rate was 291/836 (34.8%) in group 1 (6-core biopsy), and 148/315 (47%) in group 2 (12-core biopsy) (P<0.0001). The total number of histological diagnoses other than cancer was 162/836 in group 1 (19.4%) and 103/315 (32.7%) in group 2 (P<0.0001). In prostate biopsy, a higher number of cores seems to definitely improve its diagnostic value by dramatically decreasing the number of negative findings. The 12-core technique is particularly effective in case of prostate-specific antigen (PSA) values ranging between 4.1 and 10 ng/mL combined with a free-to-total PSA ratio below 16%, in case of negative digital rectal examination and when serum prostate-specific antigen levels are lower than 4 ng/mL. On the other hand, in the case of abnormal digital rectal examination, especially when combined with high prostate-specific antigen levels and/or changes detected at transrectal ultrasound, the 6-core technique can be considered a reasonable strategy

Generalization of Porod's law of small-angle scattering to anisotropic samples

Small-angle scattering from anisotropic samples, consisting of homogeneous particles inside a homogeneous medium with a scattering contrast $(\Delta n)^2$, is considered. Along any direction ${\hat {\bm q}}\equiv{\bm q}/|{\bm q}|$ of reciprocal space, at large $q\,({}\equiv |{\bm q}|)$ the Porod plot of the scattering intensity (i.e. $q^4I({\bm q})$ vs. q) shows a plateau whose height depends on ${\hat {\bm q}}$ and reads 4\pi^2 (\Delta n)^2 \sum_{j,l} (1/|\kappa_{{\ab{G},j,l}} (\pm{\hat {\bm q}})|). Here, the sum runs over all the points (labeled by (j,l)) of the surface of the j-th particle of the sample where the normal is either parallel or antiparallel to ${\hat {\bm q}}$, and \kappa_{{\ab{G},j,l}}(\pm{\hat {\bm q}}) is the corresponding Gaussian curvature value