Enhancing photodynamic and radionuclide therapy by small interfering RNA (siRNA)-RAD51 transfection via self-emulsifying delivery systems (SNEDDS).

BACKGROUND AIMS: Gene-silencing by small interfering RNA (siRNA) is an attractive therapy to regulate cancer death, tumor recurrence or metastasis. Because siRNAs are easily degraded, it is necessary to develop transport and delivery systems to achieve efficient tumor targeting. Self-nanoemulsifying systems (SNEDDS) have been successfully used for pDNA transport and delivery, so they may be useful for siRNA. The aim of this work is to introduce siRNA-RAD51 into a SNEDDS prepared with Phospholipon-90G, Labrafil-M1944-CS and Cremophor-RH40 and evaluate its efficacy in preventing homologous recombination of DNA double-strand breaks caused by photodynamic therapy (PDT) and ionizing radiation (IR). METHODS: The siRNA-RAD51 was loaded into SNEDDS using chitosan. Transfection capacity was estimated by comparison with Lipofectamine-2000. RESULTS: SNEDDS(siRNA-RAD51) induced gene silencing effect on the therapies evaluated by cell viability and clonogenic assays using T47D breast cancer cells. CONCLUSIONS: SNEDDS(siRNA-RAD51) shown to be an effective siRNA-delivery system to decrease cellular resistance in PDT or IR.

Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph.

Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases.