Advances in applications of molecular dynamics simulation techniques to the research of self-assembled nano-drug delivery systems / 药学学报

Molecular dynamics simulation technology relies on Newtonian mechanics to simulate the motion of molecular system of the real system by computer simulation. It has been used in the research of self-assembly processes illustration and macroscopic performance prediction of self-assembly nano-drug delivery systems (NDDS) in recent years, which contributes to the facilitation and accurate design of preparations. In this review, the definitions, catalogues, and the modules of molecular dynamics simulation techniques are introduced, and the current status of their applications are summarized in the acquisition and analysis of microscale information, such as particle size, morphology, the formation of microdomains, and molecule distribution of the self-assembly NDDS and the prediction of their macroscale performances, including stability, drug loading capacity, drug release kinetics and transmembrane properties. Moreover, the existing applications of the molecular dynamic simulation technology in the formulation prediction of self-assembled NDDS were also summarized. It is expected that the new strategies will promote the prediction of NDDS formulation and lay a theoretical foundation for an appropriate approach in NDDS studies and a reference for the wider application of molecular dynamics simulation technology in pharmaceutics.

Aplicaciones de la modelación jerárquica a la predicción del rendimiento académico en la carrera de Medicina / Applications of the hierarchical modeling to academic performance prediction in Medicine career

Este trabajo se realizó con el propósito de poner de manifiesto las ventajas del enfoque jerárquico en la identificación de predictores contextuales que influyen en el rendimiento académico individual y que pueden modificar el efecto de los predictores individuales. A partir de los datos obtenidos de tres instituciones del país: el Instituto de Ciencias básicas y preclínicas (ICBP) Victoria de Girón, la Facultad de Ciencias médicas Julio Trigo y la Facultad de Ciencias médicas Ernesto Guevara de Pinar del Río, se pudo demostrar la existencia de variables contextuales que modifican la influencia de las variables individuales sobre el rendimiento. Por último, se reunieron evidencias suficientes que fundamentan la necesidad de incorporar el enfoque jerárquico al pronóstico del rendimiento y al estudio de los factores no individuales que inciden sobre él

The aim of present paper is to make clear the advantages of the hierarchical approach in identification of contextual predictor influencing on the individual academic performance and that may to modifiy the effect of individual predictors. From the data obtained from three national institutions: Victoria de Girón Basic and Preclinical Sciences Institute (BPSI) Julio Trigo Medical Sciences Faculty, and the Ernesto Guevera Medical Sciences Faculty of Pinar del Río province, it was possible to demonstrate the existence of contextual variables modifying the influence of individual variables on performance. Finally, we collected enough evidences on the base of the need to add the hierarchical approach to performance prognosis and to study of non-individual factors impacting on it

Árboles de regresión y otras opciones metodológicas aplicadas a la predicción del rendimiento académico / Regression trees and other methodological options applied to the prediction of academic performance

El presente trabajo se realizó con el fin de construir un algoritmo para detectar estudiantes con alto riesgo de fracaso académico e identificar los mejores predictores del rendimiento. Se caracterizaron los estudiantes que ingresaron en el primer año en el ICBP "Victoria de Girón" durante el curso 2001-2002 de acuerdo con su índice académico del preuniversitario, índice escalafonario, exámenes de ingreso, prueba de inteligencia y un indicador de su motivación profesional. Se emplearon árboles de clasificación para identificar los predictores relevantes y sus puntos de corte óptimos. Se utilizó un modelo de regresión ordinal para evaluar la importancia relativa de los predictores y proponer el algoritmo de predicción. A partir del índice escalafonario, exclusivamente, se obtuvo un procedimiento de clasificación, que permitió identificar a los estudiantes de mayor riesgo de fracaso académico. Los puntos de corte fueron 87 y 91 puntos, que definen una tricotomía para el pronóstico del rendimiento

This paper is aimed at constructing an algorithm to detect students at high risk for academic failure and at identifying the best preformance predictors. The students that were admitted in the first year at Victoria de Girón Institute of Preclinical Basic Sciences during the course 2001-2002 were characterized according to their preuniversity academic index, roster index, admission test, intelligence test and an indicator of their professional motivation. Classification trees were used to identify the relevant predictors and their optimal cut-off points. A model of ordinal regression was used to evaluate the relative importance of the predictors and to propose the prediction algorithm.Starting only from the roster index, it was obtained a classification procedure that allowed to identify students at the highest risk for academic failure. The cut-offs were 87 and 91 points, which define a trichotomy for the performance prognosis.

La modelación jerárquica y los efectos de grupo en la predicción del rendimiento académico / The hierarchical modelling and the group effects in the prediction of academic performance

El presente trabajo se realizó con el propósito de mostrar que el grupo es un modificador de la relación entre el rendimiento académico y sus predictores y con ello, fundamentar la necesidad de recurrir a la modelación jerárquica para la predicción del rendimiento. Se aplicaron modelos con coeficientes aleatorios, especialmente apropiados para la circunstancia frecuente de casos agrupados, en la que los supuestos usuales de los modelos lineales ordinarios dejan de ser válidos y los modelos clásicos, inaplicables. Se constató que algunos de los predictores tradicionales tenían relevancia condicionada al grupo, aunque no parecían tener relevancia marginal. Se demostró así que el grupo es un modulador de la relación entre el rendimiento académico y algunos de sus predictores. La consecuencia de mayor trascedencia fue que la asignación de un estudiante a un grupo podía influir considerablemente en su rendimiento académico, independientemente de sus condiciones iniciales

The present paper was aimed at demonstrating that the group acts as a modifier of the relation between the academic performance and its predictors, and at founding the need of resorting to hierarchical modelling to predict this performance. Models with randomized coefficients, specially appropriate for the frequent circumstance of grouped cases, where the suppossed ordinary lineal models are not valid anymore and the classical models are unapplicable, were applied. It was proved that some of the traditonal predictors were relevant according to the group, though they did not seem to have marginal relevancy. This way, it was demonstrated that the group was a modullator of the relation between academic performance and some of its predictors. The most significant consequence was that the assignation of a student to a group may influence considerably on his academic performance, independently of its initial conditions.

Predictive power of resident selection criteria for clinical performance in general surgery residency.

To evaluate the ability of the resident selection criteria to predict clinical performance of general surgery residents, the application files and resident evaluations of 35 general surgery residents who were in the residency program of the Department of Surgery, Faculty of Medicine Siriraj Hospital during the 2000- 2001 and 2001- 2002 academic years were reviewed. A correlation study was done using scores from three selection criteria (medical school grades, letters of recommendation, and interview) predictors and clinical performance ratings as outcomes. The interview scores were the best predictor for overall performance of residents in the first and second years. The GPA scores were the best predictor for overall third–year performance. Each selection criterion contributed unique predictive ability for resident performance. The combination of interview scores, scores from letters of recommendation, GPA scores, and ages at admission could predict 60.5% of the total variance in the overall first-year performance scores (R=0.778, p=0.012). The combination of interview scores and score from letters of recommendation could predict 31.4% of the total variance in the overall performance score in the second year (R=0.56, p=0.049). None of the multiple regression models demonstrated statistically significant prediction for the third-year overall performance.