Biomarkers for pancreatic cancer:Recent achievements in proteomics and genomics through classical and multivariate statistical methods

Pancreatic cancer(PC) is one of the most aggressive and lethal neoplastic diseases. A valid alternative to the usual invasive diagnostic tools would certainly be the determination of biomarkers in peripheral fluids to provide less invasive tools for early diagnosis. Nowadays, biomarkers are generally investigated mainly in peripheral blood and tissues through high-throughput omics techniques comparing control vs pathological samples. The results can be evaluated by two main strategies:(1) classical methods in which the identification of significant biomarkers is accomplished by monovariate statistical tests where each biomarker is considered as independent from the others; and(2) multivariate methods, taking into consideration the correlations existing among the biomarkers themselves. This last approach is very powerful since it allows the identification of pools of biomarkers with diagnostic and prognostic performances which are superior to single markers in terms of sensitivity, specificity and robustness. Multivariate techniques are usually applied with variable selection procedures to provide a restricted set of biomarkers with the best predictive ability; however, standard selection methods are usually aimed at the identification of the smallest set of variables with the best predictive ability and exhaustivity is usually neglected. The exhaustive search for biomarkers is instead an important alternative to standard variable selection since it can provide information about the etiology of the pathology by producing a comprehensive set of markers. In this review, the most recent applications of the omics techniques(proteomics, genomics and metabolomics) to the identification of exploratory biomarkers for PC will be presented with particular regard to the statistical methods adopted for their identification. The basic theory related to classical and multivariate methods for identification of biomarkers is presented and then, the most recent applications in this field are discussed.

Dye-sensitized Er^(3+)-doped CaF_(2)nanoparticles for enhanced near-infrared emission at 1.5μm

Lanthanide(Ln)-doped nanoparticles have shown potential for applications in various fields.However,the weak and narrow absorption bands of the Ln ions(Ln^(3+)),hamper efficient optical pumping and severely limit the emission intensity.Dye sensitization is a promising way to boost the near-infrared(NIR)emission of Er^(3+),hence promoting possible application in optical amplification at 1.5μm,a region that is much sought after for telecommunication technology.Herein,we introduce the fluorescein isothiocyanate(FITC)organic dye with large absorption cross section as energy donor of small-sized(∼3.6 nm)Er^(3+)-doped CaF_(2)nanoparticles.FITC molecules on the surface of CaF_(2)work as antennas to efficiently absorb light,and provide the indirect sensitization of Er^(3+)boosting its emission.In this paper,we employ photoluminescence and transient absorption spectroscopy,as well as density functional theory calculations,to provide an in-depth investigation of the FITC→Er^(3+)energy transfer process.We show that an energy transfer efficiency of over 89%is achieved in CaF_(2):Er^(3+)@FITC nanoparticles resulting in a 28 times enhancement of the Er^(3+)NIR emission with respect to bare CaF_(2):Er^(3+).Through the multidisciplinary approach used in our work,we are able to show that the reason for such high sensitization efficiency stems from the suitable size and geometry of the FITC dye with a localized transition dipole moment at a short distance from the surface of the nanoparticle.