Estudio de necesidades de información de los estudiantes de bachillerato (jornada mañana) del Colegio Técnico Palermo respecto a la biblioteca institucional

The research addresses a study of the information needs of high school students (day tomorrow) of the Technical College of Palermo. To achieve this, the theoretical concepts of the school library were approached from the UNESCO / IFLA manifestation for the school library, the possibility of characterizing the school library from history, function, services and others. Likewise, the state of the art of the publications of the studies of the school libraries in Colombia and Latin America was identified, thanks to the revision it has been possible to identify methodologies of this type of studies that served as reference of the investigation. Subsequently, from the theoretical framework of the user studies it was possible to model the methodology of the research. Finally, after the implementation of the users' study, an analysis of the results was carried out to characterize the information needs of high school students, based on this information, the conclusions and recommendations were presented where the proposals for the Strengthening of information services of the library using information and communication technologies (ICT)

Impact of measurable residual disease by decentralized flow cytometry: a PETHEMA real-world study in 1076 patients with acute myeloid leukemia

The role of decentralized assessment of measurable residual disease (MRD) for risk stratification in acute myeloid leukemia (AML) remains largely unknown, and so it does which methodological aspects are critical to empower the evaluation of MRD with prognostic significance, particularly if using multiparameter flow cytometry (MFC). We analyzed 1076 AML patients in first remission after induction chemotherapy, in whom MRD was evaluated by MFC in local laboratories of 60 Hospitals participating in the PETHEMA registry. We also conducted a survey on technical aspects of MRD testing to determine the impact of methodological heterogeneity in the prognostic value of MFC. Our results confirmed the recommended cutoff of 0.1% to discriminate patients with significantly different cumulative-incidence of relapse (-CIR- HR:0.71, P < 0.001) and overall survival (HR: 0.73, P = 0.001), but uncovered the limited prognostic value of MFC based MRD in multivariate and recursive partitioning models including other clinical, genetic and treatment related factors. Virtually all aspects related with methodological, interpretation, and reporting of MFC based MRD testing impacted in its ability to discriminate patients with different CIR. Thus, this study demonstrated that “real-world” assessment of MRD using MFC is prognostic in patients at first remission, and urges greater standardization for improved risk-stratification toward clinical decisions in AML.This study was supported by the Centro de Investigación Biomédica en Red – Área de Oncología - del Instituto de Salud Carlos III (CIBERONC; CB16/12/00369, CB16/12/00233, CB16/12/00284 and CB16/12/00400), Instituto de Salud Carlos III/Subdirección General de Investigación Sanitaria (FIS No. PI16/01661, PI16/00517 and PI18/01946), Gerencia Regional de Salud de CyL (GRS 1346/A/16) and the Plan de Investigación de la Universidad de Navarra (PIUNA 2014-18). This study was supported internationally by the Cancer Research UK, FCAECC and AIRC under the Accelerator Award Program EDITOR

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts