Computational Oral Absorption Simulation for Low‐Solubility Compounds

Bile micelles play an important role in oral absorption of low‐solubility compounds. Bile micelles can affect solubility, dissolution rate, and permeability. For the pH–solubility profile in bile micelles, the Henderson–Hasselbalch equation should be modified to take bile‐micelle partition into account. For the dissolution rate, in the Nernst–Brunner equation, the effective diffusion coefficient in bile‐micelle media should be used instead of the monomer diffusion coefficient. The diffusion coefficient of bile micelles is 8‐ to 18‐fold smaller than that of monomer molecules. For permeability, the effective diffusion coefficient in the unstirred water layer adjacent to the epithelial membrane, and the free fraction at the epithelial membrane surface should be taken into account. The importance of these aspects is demonstrated here using several in vivo and clinical oral‐absorption data of low‐solubility model compounds. Using the theoretical equations, the food effect on oral absorption is further discussed.     

Circadian rhythms in biologically closed electrical circuits of plants

The circadian clock regulates a wide range of electrophysiological and developmental processes in plants. Here, we discuss the direct influence of a circadian clock on biologically closed electrochemical circuits in vivo. The biologically closed electrochemical circuits in the leaves of C. miniata (Kaffir lily), Aloe vera and Mimosa pudica, which regulate their physiology, were analyzed using the charge stimulation method. Plants are able to memorize daytime and nighttime. Even at continuous light or darkness, plants recognize nighttime or daytime and change the input resistance. The circadian clock can be maintained endogenously and has electrochemical oscillators, which can activate ion channels in biologically closed electrochemical circuits. The activation of voltage gated channels depends on the applied voltage, electrical charge, and the speed of transmission of electrical energy from the electrostimulator to plants.     

Medicinal plants of the Argentine Yungas plants of the Las Yungas biosphere reserve, Northwest of Argentina, used in health care

We have compared the species richness of medicinal plants and the differential patterns of use amongst settlements in the Andean communities of Northwest Argentina which have differing levels of isolation. About 259 ethnoespecies, belonging to 74 plant families, were included, representing between 70 and 80% of the total estimate. The results indicate that Coronopus didymus is the most relevant and important species. The method of use of medicinal plants and the ailments treated by rural doctors compared to those of the layperson is different. Native and exotic plants are used differently according to the body system treated. There are some relationships between internal and external use and body systems and recipes. The greater medicinal species richness found in the less isolated locations is due to external enriching cultural influences.     

Impact of drug discovery on stem cell biology

Despite the rapid technical progress in pharmaceutical industry in the past decade, it is still a great challenge to find new drugs and the situation seems more and more serious. However, the history of pharmaceutical industry clearly indicated that the significance of drug discovery went far beyond providing new drugs. For instance, drugs or candidates could be used as selective probes to reveal novel cellular mechanisms, which is a fundamental tenet of chemical biology. More interestingly, accumulating evidence indicates that drugs and candidates can find important use in stem cell biology. Not only approved drugs but also undeveloped pharmacological agents could serve as efficient agents to regulate stem cell fate. Moreover, the target and activity knowledge accumulated during the drug discovery process will help select the stem cell fate modulators in a rational manner. As the progress in stem cell biology will bring positive influence to drug discovery, it can be expected that the current drug discovery efforts will finally bear great fruits in the future.     

Using phylogeny to investigate the origins of the Cape flora: the importance of taxonomic, gene and genome sampling strategies

Phylogenetic methods hold great promise for the reconstruction of the transition from precursor to modern flora and the identification of underlying factors which drive the process. The phylogenetic methods presently used to address the question of the origin of the Cape flora of South Africa are considered here. The sampling requirements of each of these methods, which include dating of diversifications using calibrated molecular trees, sister pair comparisons, lineage through time plots and biogeographical optimizations, are reviewed. Sampling of genes, genomes and species are considered. Although increased higher-level studies and increased sampling are required for robust interpretation, it is clear that much progress is already made. It is argued that despite the remarkable richness of the flora, the Cape flora is a valuable model system to demonstrate the utility of phylogenetic methods in determining the history of a modern flora.     

Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS)

Artificial immune recognition system (AIRS) classification algorithm, which has an important place among classification algorithms in the field of artificial immune systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including breast cancer, Cleveland heart disease, diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by fuzzy-logic. This system, named as fuzzy-AIRS was used as a classifier in the diagnosis of lymph diseases, which is of great importance in medicine. The classifications of lymph diseases dataset taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. While only AIRS algorithm obtained 83.138% classification accuracy, fuzzy-AIRS classified the lymph diseases dataset with 90.00% accuracy. For lymph diseases dataset, fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.