"Bioinformática con Ñ v1.0": a collaborative project of young Spanish scientists to write a complete book about Bioinformatics

Here we present a project aiming to provide specialized educational bibliography on Bioinformatics for Spanish speakers. The idea of writing a book in Spanish language covering the most important topics in the field of Bioinformatics was born in the XIth Spanish Symposium on Bioinformatics in Barcelona two years ago. Different scientists have been involved in the project, from senior scientists to PhD students from different countries. The book intends to be the beginning of an open project, where all the chapters are susceptible of being updated and new topics can be incorporated in future versions. Current book version can be accessed online at http://goo.gl/UYG0o7.Peer Reviewe

An observational, prospective, open-label, multicentre evaluation of aliskiren in treated, uncontrolled patients: a real-life, long-term, follow-up, clinical practice in Italy.

Introduction: The addition of the direct renin inhibitor aliskiren is demonstrated to improve blood pressure (BP) control rate and reduce progression of organ damage in treated hypertensive patients in clinical trials with a relatively short follow-up period. Aim: The objective of this study was to assess the effectiveness, safety and tolerability of aliskiren as an addon antihypertensive therapy in high-risk, treated, hypertensive patients, who were not controlled with concomitant treatment with at least two antihypertensive drugs under 'real-life' conditions, during a planned observation and treatment period of at least 12 months in Italy. Methods: Clinical data were derived from medical databases of treated, uncontrolled, hypertensive patients followed by specialized physicians operating in different clinical settings (hospital divisions or outpatient clinics) in Italy. Aliskiren was added to stable antihypertensive treatment, including at least two drug classes (independently of class or dosage) and unable to achieve BP control. Follow-up visits for measuring clinic BP levels and collecting data on drug safety and tolerability were planned at time intervals of 1, 6 and 12 months. At each predefined follow-up visit, aliskiren could be up-titrated from 150 to 300 mg daily if BP control was not achieved. Results: From May 2009 to June 2011, a total of 1186 treated, uncontrolled, hypertensive patients (46.3% female, aged 65.2 ± 11.7 years, mean duration of hypertension 13.2 ± 9.3 years, mean clinic BP levels 156.5 ± 15.9/90.3 ± 9.5mmHg) were enrolled. Systolic and diastolic BP levels were 141.1/82.4, 134.9/79.8 and 133.6/78.9 mmHg at 1-, 6- and 12-month follow-up visits, respectively (p < 0.0001 vs baseline for all comparisons). These effects were consistent in all predefined subgroups, including those with left ventricular hypertrophy, renal disease, diabetes mellitus, coronary artery disease or cerebrovascular disease. Reduced levels of microalbuminuria were also reported, without affecting other renal and electrolyte parameters. Overall, compliance to study medication was high (93.0%), with a very low proportion of patients experiencing adverse events leading to drug discontinuation (3.6%). Conclusions: In this observational, prospective, open-label, multicentre study, we reported the 12-month clinical effectiveness, safety and tolerability of adding aliskiren to treated, uncontrolled, hypertensive patients in a 'real-life' setting in Italy. This strategy leads to a significantly improved BP control rate and low incidence of drug-related side effects or discontinuations. © 2012 Springer International Publishing AG. All rights reserved

COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

Background: Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results: Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered model