ABSTRACT
Introducción: El presente artículo resume la guía de práctica clínica (GPC) para la prevención, diagnóstico y manejo de infecciones asociadas al acceso de hemodiálisis en el Seguro Social del Perú (EsSalud). Objetivo: Proveer recomendaciones clínicas basadas en evidencia para la prevención, diagnóstico y tratamiento de infecciones asociadas al acceso de hemodiálisis en EsSalud. Material y Métodos: Se conformó un grupo elaborador de la guía (GEG) que incluyó especialistas y metodólogos. El GEG formuló 7 preguntas clínicas. Se realizó búsquedas sistemáticas de revisiones sistemáticas y estudios primarios en PubMed y CENTRAL de Cochrane entre noviembre del 2019 y marzo del 2020. Se seleccionó la evidencia para responder a las preguntas clínicas planteadas. La certeza de la evidencia fue evaluada usando la metodología Grading of Recommendations Assessment, Development, and Evaluation (GRADE). El GEG usó la metodología GRADE para revisar la evidencia y formular recomendaciones, los puntos de buena práctica clínica y el flujograma de tratamiento. Finalmente, la GPC fue aprobada con Resolución N° 116-IETSI-ESSALUD-2020. Resultados: La presente GPC abordó 7 preguntas clínicas, divididas en tres temas: tamizaje, diagnóstico, estadiaje y tratamiento. En base a estas preguntas se formularon 12 recomendaciones (11 fuertes y 1 condicional), 33 puntos de buena práctica clínica y 2 flujogramas de manejo. Conclusión: El presente artículo resume la metodología y las conclusiones basadas en evidencias de la GPC para la prevención, diagnóstico y tratamiento de infecciones asociadas al acceso de hemodiálisis en EsSalud.
Background: This article summarizes the clinical practice guidelines (CPG) for the prevention, diagnosis and management of infections associated with hemodialysis access of the Peruvian Health Social Security (EsSalud). To provide clinical Objective: recommendations based on evidence for the prevention, diagnosis and management of infections associated with hemodialysis access in EsSalud. Aguideline development Material and Methods:group (GDG) was established, including medical specialists and methodologists. The GDG formulated 7 clinical questions to be answered in this CPG. Systematic searches of systematic reviews and primary studies (when pertinent) were conducted in PubMed, and CENTRAL(Cochrane) from November 2019 to March 2020. The evidence was selected to answer each of the clinical questions. The accuracy of the evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation(GRADE) methodology. In periodic work meetings, the GEG used the GRADE methodology to review the evidence and formulate the recommendations, the points of good clinical practice and the treatment flow chart. Finally, the CPG was approved with Resolution No. 116-IETSI-ESSALUD-2020. The present Results:CPG addressed 7 clinical questions of four topics: prevention, diagnosisandtreatment.Basedonthesequestions,12 recommendations (11strongrecommendationsand1weak recommendation), 33 points of good clinical practice, and 2 flowcharts were formulated. This paper summarizes Conclusion:the methodology and evidence-based conclusions from the CPG for the prevention, diagnosis and management of infections associated with hemodialysis access of EsSalud
ABSTRACT
The blood-brain barrier permeation has been investigated by using a topological substructural molecular design approach (TOPS-MODE). A linear regression model was developed to predict the in vivo blood-brain partitioning coefficient on a data set of 119 compounds, treated as the logarithm of the blood-brain concentration ratio. The final model explained the 70% of the variance and it was validated through the use of an external validation set (33 compounds of the 119, MAE = 0.33), a leave-one-out crossvalidation (q(2) = 0.65, S(press) = 0.43), fivefold full crossvalidation (removing 28 compounds in each cycle, MAE = 33, RMSE = 0.43) and the prediction of +/- values for an external test set (85.7% of good prediction). This methodology evidenced that the hydrophobicity increase the blood-brain barrier permeation, while the polar surface and its interaction with the atomic mass of compounds decrease it; suggesting the capacity of the TOPS-MODE descriptors to estimate brain penetration potential of new drug candidates. Finally, by the present approach, positive and negative substructural contributions to the brain permeation were identified, and their possibilities in the lead generation and optimization processes were evaluated.
Subject(s)
Blood-Brain Barrier/metabolism , Models, Biological , Capillary Permeability/physiology , Computer Simulation , Linear Models , Predictive Value of TestsABSTRACT
A simple stochastic approach, designed to model the movement of electrons throughout chemical bonds, is introduced. This model makes use of a Markov matrix to codify useful structural information in QSAR. The self-return probabilities of this matrix throughout time ((SR)pi(k)) are then used as molecular descriptors. Firstly, a calculation of (SR)pi(k) is made for a large series of anticancer and non-anticancer chemicals. Then, k-Means Cluster Analysis allows us to split the data series into clusters and ensure a representative design of training and predicting series. Next, we develop a classification function through Linear Discriminant Analysis (LDA). This QSAR discriminates between anticancer compounds and non-active compounds with a correct global classification of 90.5% in the training series. The model also correctly classified 86.07% of the compounds in the predicting series. This classification function is then used to perform a virtual screening of a combinatorial library of coumarins. In this connection, the biological assay of some furocoumarins, selected by virtual screening using the present model, gives good results. In particular, a tetracyclic derivative of 5-methoxypsoralen (5-MOP) has an IC50 against HL-60 tumoral line around 6 to 10 times lower than those for 8-MOP and 5-MOP (reference drugs), respectively. Finally, application of Iso-contribution Zone Analysis (IZA) provides structural interpretation of the biological activity predicted with this QSAR.
