(Maghemite/poly(D,L-lactide-co-glycolide))/chitosan (core/shell)/shell nanoparticles have been prepared reproducibly by nanoprecipitation solvent evaporation plus coacervation (production performance ≈ 45%, average size ≈ 325 nm). Transmission electron microscopy, energy dispersive X-ray spectroscopy, electrophoretic determinations, and X-ray diffraction patterns demonstrated the satisfactory embedment of iron oxide nanocores within the solid polymer matrix and the formation of an external shell of chitosan in the nanostructure. The adequate magnetic responsiveness of the nanocomposites was characterized in vitro by hysteresis cycle determinations and by visualization of the nanosystem under the influence of a 0.4 T permanent magnet. Safety and biocompatibility of the (core/shell)/shell particles were based on in vitro haemocompatibility studies and cytotoxicity tests against HFF-1 human foreskin fibroblasts and on ex vivo toxicity assessments on tissue samples from Balb/c mice. Transversal relaxivities, determined in vitro at a low magnetic field of 1.44 T, demonstrated their capability as T₂ contrast agents for magnetic resonance imaging, being comparable to that of some iron oxide-based contrast agents. Heating properties were evaluated in a high frequency alternating electromagnetic gradient: a constant maximum temperature of ≈46 °C was generated within ≈50 min, while antitumour hyperthermia tests on T-84 colonic adenocarcinoma cells proved the relevant decrease in cell viability (to ≈ 39%) when treated with the nanosystem under the influence of that electromagnetic field. Finally, in vivo magnetic resonance imaging studies and ex vivo histology determinations of iron deposits postulated the efficacy of chitosan to provide long-circulating capabilities to the nanocomposites, retarding nanoparticle recognition by the mononuclear phagocyte system. To our knowledge, this is the first study describing such a type of biocompatible and long-circulating nanoplatform with promising theranostic applications (biomedical imaging and hyperthermia) against cancer.

A (core/shell)/shell nanostructure (production performance ≈ 50%, mean diameter ≈ 330 nm) was built using maghemite, PLGA, and chitosan. An extensive characterization proved the complete inclusion of the maghemite nuclei into the PLGA matrix (by nanoprecipitation solvent evaporation) and the disposition of the chitosan shell onto the nanocomposite (by coacervation). Short-term stability and the adequate magnetism of the nanocomposites were demonstrated by size and electrokinetic determinations, and by defining the first magnetization curve and the responsiveness of the colloid to a permanent magnet, respectively. Safety of the nanoparticles was postulated when considering the results from blood compatibility studies, and toxicity assays against human colonic CCD-18 fibroblasts and colon carcinoma T-84 cells. Cisplatin incorporation to the PLGA matrix generated appropriate loading values (≈15%), and a dual pH- and heat (hyperthermia)-responsive drug release behaviour (≈4.7-fold faster release at pH 5.0 and 45 °C compared to pH 7.4 and 37 °C). The half maximal inhibitory concentration of the cisplatin-loaded nanoparticles against human lung adenocarcinoma A-549 cells was ≈1.6-fold less than that of the free chemotherapeutic. Such a biocompatible and tri-stimuli responsive (maghemite/PLGA)/chitosan nanostructure may found a promising use for the effective treatment of lung cancer.

In this work we predict, among more than a billion possibilities, the best candidates of halogenated [6]helicenes in order to obtain excellent chiroptical properties in terms of the rotatory strength (R). We have used DFT calculations to randomly create derivatives from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. It is worth noting that the simplest model affords a parametrization that allows to easily predict the value of R for any hexahalogenated [6]helicene. The correlation between calculated and predicted data increases together with the complexity of the model. The results show that some positions and halogens are preferred to increase the R value. In this sense, we have also synthesized the derivatives with the higher predicted R, obtaining excellent correlation among the values obtained experimentally, by DFT-calculations and machine learning predictions.