Oncology research and clinical decisions based on immunohistochemistry: the importance of the technical aspects that support a complex method

Immunohistochemistry (IHC) is the group of techniques that use antibodies as specific reagents to identify and demonstrate several cell and tissue components that are antigens. This linking allows locating and identifying the in situ presence of various substances by means of color that is associated with the formed antigen-antibody complexes. The practical value of this biotechnology area, widely used in Pathology and Oncology, in diagnostic, prognostic, theranostic and research context, results from the possibility of combining a colour marker with an antibody without causing any damage to specific binding established between antibody and antigen. This provides the microscopic observation of the target locations where the antibody and hence the antigen are present. IHC is presented as a powerful means for identification of several cellular and tissue structures that can be associated with pathologies, and of the consequences, at functional and morphological level, of these same elements action.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Assessment of exposure to the Penicillium glabrum complex in cork industry using complementing methods

Cork oak is the second most dominant forest species in Portugal and makes this country the world leader in cork export. Occupational exposure to Chrysonilia sitophila and the Penicillium glabrum complex in cork industry is common, and the latter fungus is associated with suberosis. However, as conventional methods seem to underestimate its presence in occupational environments, the aim of our study was to see whether information obtained by polymerase chain reaction (PCR), a molecular-based method, can complement conventional findings and give a better insight into occupational exposure of cork industry workers. We assessed fungal contamination with the P. glabrum complex in three cork manufacturing plants in the outskirts of Lisbon using both conventional and molecular methods. Conventional culturing failed to detect the fungus at six sampling sites in which PCR did detect it. This confirms our assumption that the use of complementing methods can provide information for a more accurate assessment of occupational exposure to the P. glabrum complex in cork industry.