No abstract

Different positive pharmacological effects have been attributed to the natural product resveratrol (RSV), including antioxidant, antiaging, and cancer chemopreventive properties. However, its low bioavailability and rapid metabolic degradation has led to the suspicion that many of the biological activities of this compound observed in vitro may not be attainable in humans. To improve its bioavailability and pharmacokinetic profile, attempts have been made to encapsulate RSV into lipid-based nanocarrier systems. Here, the dioctadecyldimethylammonium bromide (DODAB):monoolein (MO) liposomal system (1:2) loaded with RSV revealed appropriate characteristics for drug release purposes: reduced size for cellular uptake (157 ± 23 nm), stability up to 80 days, positive surface charge (ζ ≈ +40 mV), and a controlled biphasic release of RSV from the lipid nanocarriers over a period of almost 50 h at pH 5.0 and 7.4. Moreover, the encapsulation efficiency of the nanocarrier ranged from 70% to 92% and its RSV loading capacity from 9% to 14%, when [RSV] was between 100 and 200 μM. The partition coefficient (K p) of RSV between lipid and aqueous phase was log K p = 3.37 ± 0.10, suggesting moderate to high lipophilicity of this natural compound and reinforcing the lipid nanocarriers’ suitability for RSV incorporation. The thermodynamic parameters of RSV partitioning in the lipid nanocarriers at 37 °C (ΔH = 43.76 ± 5.68 kJ mol–1; ΔS = 0.20 ± 0.005 kJ mol–1; and ΔG = −18.46 ± 3.48 kJ mol–1) reflected the spontaneity of the process and the establishment of hydrophobic interactions. The cellular uptake mechanism of the RSV-loaded nanocarriers labeled with the lipophilic fluorescent probe 1,6-diphenyl-1,3,5-hexatriene (DPH) was studied in the eukaryotic model system Saccharomyces cerevisiae. Thirty minutes after incubation, yeast cells readily internalized nanocarriers and the spots of blue fluorescence of DPH clustered around the central vacuole in lipid droplets colocalized with the green fluorescence of the lipophilic endocytosis probe FM1-43. Subsequent studies with the endocytosis defective yeast deletion mutant (end3Δ) and with the endocytosis inhibitor methyl-β-cyclodextrin supported the involvement of an endocytic pathway. This novel nanotechnology approach opens good perspectives for medical applications.

Dynamic tariffs are expected to be implemented as commercial offers of electricity retailers in smart grids, conveying price signals aimed at shaping usage patterns, with potential benefits to enhance grid efficiency and reduce end-users' costs. Retailers and consumers have divergent goals. While the objective of the retailer is to maximize profits, the purpose of consumers is to minimize their electricity bill. The interaction between the retailer and consumers can be modeled by means of bi-level (BL) programming: the retailer sets the prices to be charged to consumers and these react by scheduling flexible appliances according to those prices and their comfort requirements. In this work, two hybrid BL optimization approaches are proposed to solve this problem, considering one leader (retailer) and multiple followers (consumers). Two population-based approaches were developed, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm, to deal with the upper level problem, both encompassing an exact mixed-integer linear programming solver to address the lower level optimization problem. Different scenarios were generated, comprising one leader (retailer imposing different price schemes) and three followers (with different consumer profiles). Typical residential appliances were considered, with different operation cycles. Also, diverse tariff structures set by the retailer were analyzed. The performance of the two algorithms was compared. Results revealed a consistent superiority of PSO over GA.

The vast majority of methods available for sequence comparison rely on a first sequence alignment step, which requires a number of assumptions on evolutionary history and is sometimes very difficult or impossible to perform due to the abundance of gaps (insertions/deletions). In such cases, an alternative alignment-free method would prove valuable. Our method starts by a computation of a generalized suffix tree of all sequences, which is completed in linear time. Using this tree, the frequency of all possible words with a preset length L—L-words—in each sequence is rapidly calculated. Based on the L-words frequency profile of each sequence, a pairwise standard Euclidean distance is then computed producing a symmetric genetic distance matrix, which can be used to generate a neighbor joining dendrogram or a multidimensional scaling graph. We present an improvement to word counting alignment-free approaches for sequence comparison, by determining a single optimal word length and combining suffix tree structures to the word counting tasks. Our approach is, thus, a fast and simple application that proved to be efficient and powerful when applied to mitochondrial genomes. The algorithm was implemented in Python language and is freely available on the web.

Background and Purpose: Xerostomia is one of the most frequent long term side-effects experienced by head-and-neck cancer patients undergoing radiation therapy, reducing drastically the quality-of-life of patients. In the present study, a prediction model for xerostomia after radiotherapy is proposed. Material and Methods: Model construction was based on a dataset of 138 patients with headand-neck cancer treated at the Portuguese Institute of Oncology of Coimbra (IPOCFG) with Intensity Modulated Radiation Therapy, using different data mining predictors. The models considered dosimetric information and patient specific features known prior to treatment to estimate which patients will experience xerostomia (G0 vs G1/G2 according to RTOG/EORTC). The quality of the classifiers was assessed by applying cross-validation procedures and was validated by different datasets. ROC/AUC, precision and recall were the measures used to evaluate the models' performance. Results: Age, gender, severity of xerostomia prior to radiation therapy and planned mean (physical) dose in both parotids revealed to be relevant predictors of xerostomia. The best model was the one based on random forests. The method produced an AUC equal to 0.73, a precision of 72% and a recall of 83% considering the threshold 0.5. Conclusions: The ability to discriminate patients according to their features helps to achieve personalized radiation therapy treatments. Random forests revealed to be a good classification method for predicting the binary response "risk for xerostomia induced by radiation therapy at 12 months", showing a high discriminative ability.