Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms

Breast cancer exhibits a high mortality rate and it is the most invasive cancer in women. An analysis from histopathological images could predict this disease. In this way, computational image processing might support this task. In this work a proposal which employes deep learning convolutional neural networks is presented. Then, an ensemble of networks is considered in order to obtain an enhanced recognition performance of the system by the consensus of the networks of the ensemble. Finally, a genetic algorithm is also considered to choose the networks that belong to the ensemble. The proposal has been tested by carrying out several experiments with a set of benchmark images.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Chemical generation of checkpoint inhibitory T cell engagers for the treatment of cancer

Bispecific T cell engagers (BiTEs), a subset of bispecific antibodies (bsAbs), can promote a targeted cancer cell’s death by bringing it close to a cytotoxic T cell. Checkpoint inhibitory T cell engagers (CiTEs) comprise a BiTE core with an added immunomodulatory protein, which serves to reverse cancer-cell immune-dampening strategies, improving efficacy. So far, protein engineering has been the main approach to generate bsAbs and CiTEs, but improved chemical methods for their generation have recently been developed. Homogeneous fragment-based bsAbs constructed from fragment antigen-binding regions (Fabs) can be generated using click chemistry. Here we describe a chemical method to generate biotin-functionalized three-protein conjugates, which include two CiTE molecules, one containing an anti-PD-1 Fab and the other containing an immunomodulatory enzyme, Salmonella typhimurium sialidase. The CiTEs’ efficacy was shown to be superior to that of the simpler BiTE scaffold, with the sialidase-containing CiTE inducing substantially enhanced T cell-mediated cytotoxicity in vitro. The chemical method described here, more generally, enables the generation of multi-protein constructs with further biological applications. [Figure not available: see fulltext.

Employing defined bioconjugates to generate chemically functionalised gold nanoparticles for in vitro diagnostic applications

Novel methods for introducing chemical and biological functionality to the surface of gold nanoparticles serve to increase the utility of this class of nanomaterials across a range of applications. To date, methods for functionalising gold surfaces have relied upon uncontrollable non-specific adsorption, bespoke chemical linkers, or non-generalisable protein–protein interactions. Herein we report a versatile method for introducing functionality to gold nanoparticles by exploiting the strong interaction between chemically functionalised bovine serum albumin (f-BSA) and citrate-capped gold nanoparticles (AuNPs). We establish the generalisability of the method by introducing a variety of functionalities to gold nanoparticles using cheap, commercially available chemical linkers. The utility of this approach is further demonstrated through the conjugation of the monoclonal antibody Ontruzant to f-BSA–AuNPs using inverse electron-demand Diels–Alder (iEDDA) click chemistry, a hitherto unexplored chemistry for AuNP–IgG conjugation. Finally, we show that the AuNP–Ontruzant particles generated via f-BSA–AuNPs have a greater affinity for their target in a lateral flow format when compared to conventional physisorption, highlighting the potential of this technology for producing sensitive diagnostic tests