Cristalización de proteínas en el diseño de fármacos en los últimos 50 años

We live in an era where we expect to be able to visit our doctor and obtain a pill to cure any ailment from which we suffer. Yet, this is still not the case. Many of the current cures are still derived from natural sources although new drugs are increasingly the result of intelligent design. In this process, X-ray protein crystallography now plays a major and effective role in the discovery of new treatments. The developments that have made this possible have evolved during the past fifty years. The methods for crystallizing macromolecules and determining their structures by X-ray crystallography have been automated and the speed for X-ray data acquisition is several orders of magnitude faster. Fifty years ago it took several years to solve a single structure. Now, several protein–ligand complexes can be determined in single day. High-throughput crystallography is considered to be a great asset to the drug discovery process, providing a fast way to tailor drug candidates to their targets by analysing their binding mode in detail. Crystallization remains the main challenge.Vivimos en una época en la que esperamos ir al médico y obtener una pastilla para curar cualquier dolencia que padezcamos; por desgracia, esta expectativa no es real. Aunque muchos de los remedios en uso provienen de fuentes naturales, la mayoría de los nuevos medicamentos son el resultado de la investigación científica. En el proceso de diseño y descubrimiento de fármacos, la cristalografía de proteínas juega un papel central. Los conocimientos que han hecho esto posible han venido evolucionando desde hace cincuenta años aproximadamente. Los métodos de cristalización de macromoléculas y la determinación de sus estructuras a través de la cristalografía de rayos X han sido automatizados y miniaturizados y la velocidad de la adquisición de datos de difracción ha aumentado en varios órdenes de magnitud. Si hace cincuenta años la resolución de una sola estructura podría llevar varios años, actualmente se pueden determinar las estructuras de varios complejos proteína-ligando en un solo día. La cristalografía de alto rendimiento hoy día es un gran recurso en el proceso del descubrimiento de fármacos pues proporciona una manera rápida y precisa de adaptar los fármacos candidatos a las dianas mediante el análisis de su modo de unión. La cristalización sigue siendo el principal desafío

Credit achievement ability during distance learning era: the case of Statistics in Medicine course

AIM In this study, the effects of the DL on academic career were investigated. BACKGROUND Distance Learning (DL) became mandatory in Italy from March 2020, due to COVID19 emergency. DESIGN The performances of students in Medical Statistics course of the Nursing degree in three campus of the University of Turin (Aosta, Beinasco and Cuneo) in the Academic Years 2019-2020 and 2020-2021 were considered. METHODS The study is based on 308 students, 48% of whom both attended the lessons and took the exams in DL. The effect of DL on student’s performance was evaluated using Logistic regression models and the results are showed in terms of odds ratios adjusted for gender, age and campus. RESULTS The results show that DL did not bring particular limitations to the students, highlighting on the contrary evident benefits in terms of organization and management of lessons and exams. Moreover, the level of students’ satisfaction at the end of the course increased in DL. CONCLUSION DL seems to do not affect the student’s ability on achieve credits, at least in mathematical subjects. More investigations are needed considering all courses’ types

A simple PSA-based computational approach predicts the timing of cancer relapse in prostatectomized patients

Abstract Recurrences of prostate cancer affect approximately one quarter of patients who have undergone radical prostatectomy. Reliable factors to predict time to relapse in specific individuals are lacking. Here, we present a mathematical model that evaluates a biologically sensible parameter (α) that can be estimated by the available follow-up data, in particular by the PSA series. This parameter is robust and highly predictive for the time to relapse, also after administration of adjuvant androgen deprivation therapies. We present a practical computational method based on the collection of only four postsurgical PSA values. This study offers a simple tool to predict prostate cancer relapse. Cancer Res; 76(17); 4941–7. ©2016 AACR.</jats:p

A new numerical method for processing longitudinal data: clinical applications

Background: Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control and weather forecasting. Given some longitudinal data, i.e. scattered measurements, the aim consists in approximating the parameters involved in the dynamics of the considered process. For this problem, a large variety of well-known methods have already been developed. Results: Here, we propose an alternative approach to be used as effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data. In particular, our mixed model, that uses Radial Basis Functions (RBFs) combined with Stochastic Optimization Algorithms (SOMs), is here presented and tested on clinical data. Further, we also carry out comparisons with other methods that are widely used in this framework. Conclusions: The main advantages of the proposed method are the flexibility with respect to the datasets, meaning that it is effective also for truly irregularly distributed data, and its ability to extract reliable information on the evolution of the dynamics