New challenges for Conservar Património

The current issue sets the beginning of a new stage of the journal Conservar Património. From its genesis until issue number 31, António João Cruz and Francisca Figueira were the Editor and Associate editor of the journal. We would like to highlight their great commitment and perseverance in the odyssey of setting up a periodic journal dedicated to the con- servation of heritage within the non-profit association ARP – Associação Profissional de Conservadores-restauradores de Portugal, and of being able to raise the journal to a level of recognized relevance and prominence in both national and international contexts

Role of nanotechnology in the management of indoor fungi

Fungi are ubiquitous in the environment and seek to colonize and grow on diverse materials as part of their life cycle. They constitute complex biofilms on surfaces and deteriorate the indoor air quality even under adverse conditions. They adapt well to changing humidity and temperature conditions, resuming their growth in minutes. Their vital activity generates a large number of pollutants that contribute to bioaerosols, which generate major health problems. The reports published in last few decades pointed out that contaminated environments play an important role in the transmission of infections, especially in hospitals. Advances in the field of nanotechnology have resulted in different and diverse applications. Antimicrobial nanomaterials have been found to be eco‐friendly alternatives to be applied in functional paint and coatings. These ?smart? surfaces could face at nanoscale level the approaching of propagules to avoid their attachment, which is the first stage in biofilm development. In this sense, several nanomaterials, including metal, non‐metal, and hybrids, have been discussed in relation to their antifungal activity in this chapter.Fil: Gámez Espinosa, Erasmo Junior. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Tecnología de Pinturas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones en Tecnología de Pinturas; ArgentinaFil: Barberia Roque, Leyanet. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Tecnología de Pinturas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones en Tecnología de Pinturas; ArgentinaFil: Bellotti, Natalia. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Tecnología de Pinturas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones en Tecnología de Pinturas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; Argentin

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024