AbstractDimethoxycurcumin (DMC) is a lipophilic analog of curcumin found in Curcuma longa Linn., which is known to possess significant activity against various cancer cell lines. The purpose of this study was to develop suitable liposomal formulations in order to overcome DMC’s poor water solubility and to study the aggregation kinetic profile using the fractal analysis. DMC was incorporated into liposomal formulations composed of DPPC, DPPC:DPPG:chol (9:1:1 molar ratio) and DPPC:DODAP:chol (9:1:1 molar ratio) liposomes. Light scattering techniques were used to elucidate the physicochemical parameters of the liposomal formulations with and without DMC. The structural characteristics of the incorporated molecule were found to be crucial and promote the aggregation mechanism depending also on the liposomes’ composition. The results of our study contribute to the overall scientific efforts to prepare efficient carriers for DMC and could be a useful tool in order to study more efficiently the kinetics of the aggregation process of the liposomal carriers. 相似文献
Identifying local adaptation is crucial in conservation biology to define ecotypes and establish management guidelines. Local adaptation is often inferred from the detection of loci showing a high differentiation between populations, the so‐called FST outliers. Methods of detection of loci under selection are reputed to be robust in most spatial population models. However, using simulations we showed that FST outlier tests provided a high rate of false‐positives (up to 60%) in fractal environments such as river networks. Surprisingly, the number of sampled demes was correlated with parameters of population genetic structure, such as the variance of FSTs, and hence strongly influenced the rate of outliers. This unappreciated property of river networks therefore needs to be accounted for in genetic studies on adaptation and conservation of river organisms. 相似文献
Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.
Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.
Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data. 相似文献