Hydroxyapatite–Silicon Scaffold Promotes Osteogenic Differentiation of CGF Primary Cells s

Simple Summary: The aim of this study was to identify new and innovative strategies to improve the tissue-regeneration process. Concentrated growth factor (CGF) is an autologous biomaterial rich in growth factors and multipotent stem cells. The purpose of our study was to evaluate the osteogenic differentiation of CGF primary cells in the presence of a hydroxyapatite–silicon scaffold, which represents a very interesting material in the field of bone reconstructive surgery. Our findings showed that the hydroxyapatite–silicon scaffold provided support to primary CGF cells by enhancing osteogenic differentiation. These data suggest interesting perspectives in the use of CGF together with scaffolds in the field of regenerative medicine. Abstract: The application of scaffolding materials together with stem cell technologies plays a key role in tissue regeneration. Therefore, in this study, CGF (concentrated growth factor), which represents an autologous and biocompatible blood-derived product rich in growth factors and multipotent stem cells, was used together with a hydroxyapatite and silicon (HA-Si) scaffold, which represents a very interesting material in the field of bone reconstructive surgery. The aim of this work was to evaluate the potential osteogenic differentiation of CGF primary cells induced by HASi scaffolds. The cellular viability of CGF primary cells cultured on HA-Si scaffolds and their structural characterization were performed by MTT assay and SEM analysis, respectively. Moreover, the matrix mineralization of CGF primary cells on the HA-Si scaffold was evaluated through Alizarin red staining. The expression of osteogenic differentiation markers was investigated through mRNA quantification by real-time PCR. We found that the HA-Si scaffold was not cytotoxic for CGF primary cells, allowing their growth and proliferation. Furthermore, the HASi scaffold was able to induce increased levels of osteogenic markers, decreased levels of stemness markers in these cells, and the formation of a mineralized matrix. In conclusion, our results suggest that HA-Si scaffolds can be used as a biomaterial support for CGF application in the field of tissue regeneration

Anticancer effects of novel resveratrol analogues on human ovarian cancer cells

Resveratrol, a naturally occurring phytoalexin, has long been known to play an important regulatory role in key functions in cell physiology. This multifunctional role of resveratrol is explained by its ability to interact with several targets of various cell pathways. In the recent past, synthetic chemical modifications have been made in an attempt to enhance the biological effects of resveratrol, including its anti-cancer properties. In this study, we investigated the molecular mechanisms of action of novel trans-restricted analogues of resveratrol in which the C-C double bond of the natural derivative has been replaced by diaryl-substituted imidazole analogues. In ovarian cancer models, the results of in vitro screening revealed that the resveratrol analogues exhibited enhanced anti-proliferative properties compared with resveratrol. We found that the resveratrol analogues also significantly inhibited Akt and MAPK signalling and reduced the migration of IL-6 and EGF-treated cells. Finally, in ascite-derived cancer cells, we demonstrated that the resveratrol analogues reduced the expression of epithelial mesenchymal transition (EMT) markers. Collectively, these findings indicate the enhanced anti-cancer properties of the resveratrol analogues

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration

Combined fit to the spectrum and composition data measured by the Pierre Auger Observatory including magnetic horizon effects

The measurements by the Pierre Auger Observatory of the energy spectrum and mass composition of cosmic rays can be interpreted assuming the presence of two extragalactic source populations, one dominating the flux at energies above a few EeV and the other below. To fit the data ignoring magnetic field effects, the high-energy population needs to accelerate a mixture of nuclei with very hard spectra, at odds with the approximate E$^{-2}$ shape expected from diffusive shock acceleration. The presence of turbulent extragalactic magnetic fields in the region between the closest sources and the Earth can significantly modify the observed CR spectrum with respect to that emitted by the sources, reducing the flux of low-rigidity particles that reach the Earth. We here take into account this magnetic horizon effect in the combined fit of the spectrum and shower depth distributions, exploring the possibility that a spectrum for the high-energy population sources with a shape closer to E$^{-2}$ be able to explain the observations