Clinical development of liposome-based drugs: formulation, characterization, and therapeutic efficacy

Research on liposome formulations has progressed from that on conventional vesicles to new generation liposomes, such as cationic liposomes, temperature sensitive liposomes, and virosomes, by modulating the formulation techniques and lipid composition. Many research papers focus on the correlation of blood circulation time and drug accumulation in target tissues with physicochemical properties of liposomal formulations, including particle size, membrane lamellarity, surface charge, permeability, encapsulation volume, shelf time, and release rate. This review is mainly to compare the therapeutic effect of current clinically approved liposome-based drugs with free drugs, and to also determine the clinical effect via liposomal variations in lipid composition. Furthermore, the major preclinical and clinical data related to the principal liposomal formulations are also summarized

MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm

The prediction of ligand binding sites is an essential part of the drug discovery process. Knowing the location of binding sites greatly facilitates the search for hits, the lead optimization process, the design of site-directed mutagenesis experiments and the hunt for structural features that influence the selectivity of binding in order to minimize the drug's adverse effects. However, docking is still the rate-limiting step for such predictions; consequently, much more efficient algorithms are required. In this article, the design of the MEDock web server is described. The goal of this sever is to provide an efficient utility for predicting ligand binding sites. The MEDock web server incorporates a global search strategy that exploits the maximum entropy property of the Gaussian probability distribution in the context of information theory. As a result of the global search strategy, the optimization algorithm incorporated in MEDock is significantly superior when dealing with very rugged energy landscapes, which usually have insurmountable barriers. This article describes four different benchmark cases that span a diverse set of different types of ligand binding interactions. These benchmarks were compared with the use of the Lamarckian genetic algorithm (LGA), which is the major workhorse of the well-known AutoDock program. These results demonstrate that MEDock consistently converged to the correct binding modes with significantly smaller numbers of energy evaluations than the LGA required. When judged by a threshold of the number of energy evaluations consumed in the docking simulation, MEDock also greatly elevates the rate of accurate predictions for all benchmark cases. MEDock is available at and

Asian Students’ Cultural Orientation and Computer Self-Efficacy Significantly Related to Online Inquiry-Based Learning Outcomes on the Go-Lab Platform

Learning and teaching Mendelian genetics are central topics in school science. This study explored factors associated with the learning outcomes of Taiwanese junior high school students in an online inquiry learning environment. Research within face-to-face classroom settings had revealed that Asian students are more likely to be tutor-oriented and collectivistic learners. However, results of how these orientations affect learning in online environments are needed. In this analysis, seventh-grade students from Taiwan (N = 290) completed a genetics lesson using an Inquiry Learning Space (ILS) on the Go-Lab platform. Students were randomly assigned conditions in which support was provided either by general text or by an expert person in the form of a cartoon figure. In addition, students completed questionnaires assessing their cultural orientations, as well as their computer self-efficacy. Results revealed that the presence of a virtual expert did not influence students’ learning outcomes. However, the extent to which students identified as collectivistic and their level of computer self-efficacy were positively associated with the learning outcomes. Students’ computer self-efficacy was positively related to their behavioral intentions as well. These results illustrate the importance of Asian students’ disciplined personality and computer self-efficacy for online inquiry-based learning.</p

Arrhythmia and other modifiable risk factors in incident dementia and MCI among elderly individuals with low educational levels in Taiwan

IntroductionThere is increasing evidence that arrhythmia is a risk factor for dementia; however, it appears that arrhythmia affects the cognitive function of individuals differentially across age groups, races, and educational levels. Demographic differences including educational level have also been found to moderate the effects of modifiable risk factors for cognitive decline.MethodsThis study recruited 1,361 individuals including a group of cognitively unimpaired (CU) individuals, a group of patients with mild cognitive impairment (MCI), and a group of patients with dementia with low education levels. The participants were evaluated in terms of modifiable risk factors for dementia, including arrhythmia and neuropsychiatric symptoms.ResultsCox proportional hazard regression models revealed that among older MCI patients (&gt;75 years), those with arrhythmia faced an elevated risk of dementia. Among younger MCI patients, those taking anti-hypertensive drugs faced a relatively low risk of dementia. Among younger MCI patients, male sex and higher educational level were associated with an elevated risk of dementia. Among CU individuals, those with coronary heart disease and taking anti-lipid compounds faced an elevated risk of MCI and those with symptoms of depression faced an elevated risk of dementia.DiscussionThe risk and protective factors mentioned above could potentially be used as markers in predicting the onset of dementia in clinical settings, especially for individuals with low educational levels

Cross-reactivities and cross-neutralization of different envelope glycoproteins E2 antibodies against different genotypes of classical swine fever virus

Classical swine fever (CSF) is a highly contagious swine disease caused by the classical swine fever virus (CSFV), wreaking havoc on global swine production. The virus is divided into three genotypes, each comprising 4–7 sub-genotypes. The major envelope glycoprotein E2 of CSFV plays an essential role in cell attachment, eliciting immune responses, and vaccine development. In this study, to study the cross-reaction and cross-neutralizing activities of antibodies against different genotypes (G) of E2 glycoproteins, ectodomains of G1.1, G2.1, G2.1d, and G3.4 CSFV E2 glycoproteins from a mammalian cell expression system were generated. The cross-reactivities of a panel of immunofluorescence assay-characterized serum derived from pigs with/without a commercial live attenuated G1.1 vaccination against different genotypes of E2 glycoproteins were detected by ELISA. Our result showed that serum against the LPCV cross-reacted with all genotypes of E2 glycoproteins. To evaluate cross-neutralizing activities, hyperimmune serum from different CSFV E2 glycoprotein-immunized mice was also generated. The result showed that mice anti-E2 hyperimmune serum exhibited better neutralizing abilities against homologous CSFV than heterogeneous viruses. In conclusion, the results provide information on the cross-reactivity of antibodies against different genogroups of CSFV E2 glycoproteins and suggest the importance of developing multi-covalent subunit vaccines for the complete protection of CSF

Protemot: prediction of protein binding sites with automatically extracted geometrical templates

Geometrical analysis of protein tertiary substructures has been an effective approach employed to predict protein binding sites. This article presents the Protemot web server that carries out prediction of protein binding sites based on the structural templates automatically extracted from the crystal structures of protein–ligand complexes in the PDB (Protein Data Bank). The automatic extraction mechanism is essential for creating and maintaining a comprehensive template library that timely accommodates to the new release of PDB as the number of entries continues to grow rapidly. The design of Protemot is also distinctive by the mechanism employed to expedite the analysis process that matches the tertiary substructures on the contour of the query protein with the templates in the library. This expediting mechanism is essential for providing reasonable response time to the user as the number of entries in the template library continues to grow rapidly due to rapid growth of the number of entries in PDB. This article also reports the experiments conducted to evaluate the prediction power delivered by the Protemot web server. Experimental results show that Protemot can deliver a superior prediction power than a web server based on a manually curated template library with insufficient quantity of entries. Availability: