Els Serveis de suport a l'aprenentatge i la docència de la Biblioteca de l'Escola Superior d'Agricultura de Barcelona en l'Espai Europeu d'Educació Superior

Projecte sobre Biblioteques Universitàries (PBU) presentat en la convocatòria d'oposicions a l'escala facultativa d'arxius, biblioteques i museus de la UPC de novembre de 2004

V Simposio Científico de Alumnos de la Facultad de Ciencias del Mar y Ambientales

El Simposio Científico de Alumnos de la Facultad de Ciencias del Mar y Ambientales (SACMA), se ha consolidado como una actividad de referencia en la divulgación de la investigación realizad por los estudiantes de la Facultad de ciencias del Mar y Ambientales de la Universidad de Cádiz, celebrando su quinta edición. En este libro de resúmenes se recogen las comunicaciones tanto orales como tipo pósteres presentadas por los alumnos de Grado y Posgrado de la Universidad de Cádiz

Introducing ATR-FTIR Spectroscopy through Analysis of Acetaminophen Drugs: Practical Lessons for Interdisciplinary and Progressive Learning for Undergraduate Students.

Infrared (IR) spectroscopy is a vibrational spectroscopic technique useful in chemical, pharmaceutical, and forensic sciences. It is essential to identify chemicals for reasons spanning from scientific research and academic practices to quality control in companies. However, in some university degrees, graduate students do not get the proficiency to optimize the experimental parameters to obtain the best IR spectra; to correlate the IR spectral bands with the molecular vibrations (chemical elucidation); to have some criteria for any substance identification (especially relevant in quality control to recognize counterfeit); and to apply chemometrics for comparing, visualizing, and classifying the IR spectra. This work presents an experimental laboratory practice for an introductory teaching of the IR instrumental conditions in the identification of substances based on visual spectra comparison and statistical analysis and matching. Then, the selected IR conditions are applied to different commercial drugs, in the solid state or insolution, mostly composed of acetaminophen. Finally, the students apply chemometrics analysis to the IR data. This practice was designed for the training in a chemistry subject for undergraduate students of the chemistry, pharmacy, or forensics degrees, among others related to science, medical, food, or technological sciences.Instituto Universitario de Investigación en Ciencias Policiales (IUICP

Enhancing mental health literacy in obsessive-compulsive disorder and reducing stigma via smartphone: A randomized controlled trial protocol

Background Obsessive-compulsive disorder (OCD) is a disabling disorder that can be successfully treated. However, individuals with OCD do not seek or delay seeking treatment. This delay may be explained by poor mental health literacy and stigmatizing attitudes toward OCD in community. In order to work on these variables, a gamified mental health mobile application (app) called esTOCma has been developed. The purpose of this study is to describe the protocol for a study to test the efficacy of esTOCma, increasing mental health literacy and help-seeking intention, reducing the stigmatizing attitudes and social distance suffered by people with OCD, as well as the distress associated with obsessive-compulsive symptoms. Methods A randomized controlled trial with a crossover design with two conditions (immediate-use App group versus delayed-use App group) will be conducted on a non-clinical adult sample of the community of a minimum size of 200 participants. Participants in the immediate-use App group will start using the app at baseline until completion (10 days); whereas participants in the delayed-use App group will wait 10 days, and then start using the app until completion (10 days). The outcomes will be measured at four assessment points (baseline; 10 days from baseline; and 20 days from baseline; and after 3 months). The following instruments will be administered: Attribution Questionnaire, General Help-Seeking Questionnaire, Social Distance Scale, Mental Health Literacy, Psychoeducation Questionnaire, Social Desirability Scale, Single-Item Self-esteem Scale, and Obsessive-Compulsive Inventory-Revised. Discussion This protocol presents the first study to describe a randomized control trial of a mental health app focused on changing mental health literacy, stigmatizing attitudes, social distance and help-seeking intention associated with OCD. An app intervention of these characteristics is especially relevant nowadays as the COVID-19 pandemic has increased obsessive-compulsive symptoms and severity. An improvement in general knowledge about OCD and a reduction in stigma could be associated with earlier OCD detection and an increase in help-seeking intention, which could result in greater wellbeing. Moreover, normalizing intrusions and knowledge about the cognitive OCD model could serve as a protective variable in vulnerable individuals

A Practical Beginner's Guide to Raman microscopy

Raman microscopy is a highly suitable and well settled down analytical techniquefor qualitative determination of chemical substances. However, many universityundergraduate chemical degrees do not incorporate its practical training in theexperimental laboratory practises that constitute their curricula. For this reason,this work aimed at designing a practical beginner's guide to Raman microscopyuseful for undergraduate students, teachers or practitioners who need to use it forthe first time. After a brief explanation of the main concepts about Ramanmicroscopy, the methodology development and results interpretation are mainlyexplained using paracetamol (acetaminophen) drugs as example. In addition, thisguide presents an application to the identification of different components withina mixture, which shows the instrumental potential and how to use it effectively.Finally, acetaminophen, ascorbic acid, and sucrose were positively detected usingRaman microscopy on a commercial drug whose major component wasacetaminophen. In fact, the guide shows the detection and unequivocalidentification of different components in the mixtures, even for those lowconcentration components (5-10 % mass ratio). This work clearly proposesdifferent pragmatic criteria at the laboratory for identifying substances in mixturesto promote an easy implementation of the Raman microscopy technique

Electrophoretic fingerprinting of benzodiazepine tablets in spike drinks

Over the last few years, there has been an increase in the reports of drug-facilitated crimes. The list of drugs associated with these crimes is extensive and benzodiazepines constitute one of the groups of substances more commonly used. The sedative properties, which characterize benzodiazepines, are enhanced when such drugs are combined with alcohol, being more attractive for committing these types of crimes. In this work, a capillary electrophoresis method was applied to the analysis of 63 different samples of club drinks spiked with benzodiazepine tablets. The resulting electropherograms were processed and analyzed with the chemometric multivariate techniques: principal component analysis (PCA) and soft independent modeling of class analogies (SIMCA) classification. The PCA results allowed a clear differentiation of each drug class in a 3D plot. In addition, the SIMCA classification model (5% significance level) showed that eight out of nine test samples were automatically assigned by software to their proper sample class. The conflicting sample was correctly classified in the Coomans? plot (95% confidence). This novel approach based on the comparison of electrophoretic profiles of spiked drinks by chemometric tools allows determining the benzodiazepine used for drink spiking without the use of drug standards.Moreover, it provides an opportunity for the forensic laboratories to incorporate the identification capability provided by the electrophoretic fingerprinting of benzodiazepine solutions in existing or new databases

Comparing and Tuning Machine Learning Algorithms to Predict Type 2 Diabetes Mellitus

The main goals of this work is to study and compare machine learning algorithms to predict the development of type 2 diabetes mellitus. Four classifi cation algorithms have been considered, studying and comparing the accuracy of each one to predict the incidence of type 2 diabetes mellitus seven years in advance. Specifically, the techniques studied are: Decision Tree, Random Forest, kNN (k-Nearest Neighbors) and Neural Networks. The study not only involves the comparison among these techniques, but also, the tuning of the meta-parameters in each algorithm. The algorithms have been implemented using the language R. The data base used is obtained from the nation-wide cohort [email protected] study. The conclusions will include the accuracy of each algorithm and therefore the best technique for this problem. The best meta-parameters for each algorithm will be also provided.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec