Methods of the systematic review We identified appropriate studies based on a comprehensive literature search. We began by conducting three queries in Scopus: 1 forest AND (displace* OR substitut*) AND carbon AND product* 2 lca AND wood AND substit* 3 compar* and LCA and wood These queries identified 488 studies, 87 and 377 studies, respectively. We screened the documents for relevance based on information provided in titles, abstracts, keywords and results, and shortened the list to 81 studies in the first query, 22 studies in the second query and 12 studies in the third query. We also reviewed the reference lists of the identified studies to identify additional relevant references and considered the studies by Rüter et al. (2016) and Valada et al. (2016). Reviewed studies were limited to those published in English, Swedish, Finnish, German or French.

Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87–0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88–0.95) on Seville patients, 0.87 (95% CI, 0.84–0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76–0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.

The freshwater green microalga Haematococcus pluvialis is the richest source of natural astaxanthin. Astaxanthin is a high-value red carotenoid pigment commonly used in the food, feed and cosmetics industries due to its well-known antioxidant, anti-inflammatory and antitumour properties. This study assesses the environmental impacts associated with the production of natural astaxanthin from H. pluvialis at both lab and pilot scale. Closed airlift photobioreactors with artificial illumination, typically used for the production of high value products to avoid contamination risks and allow controlled lighting conditions, were considered. The study extends 2 from the production of the different inputs to the system to microalgal production, harvesting and further extraction of the carotenoid. The life cycle assessment was performed following the ISO 14040 and ten impact categories were considered in the study: abiotic depletion, acidification, eutrophication, global warming, ozone layer depletion, human toxicity, fresh water aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity and photochemical oxidant formation. According to the results, electricity requirements represented the major contributor to the environmental burdens among the activities involved in the production of astaxanthin. For the lab-scale process, the air supply and the production of chemicals and lab materials were also significant contributors in several categories. In the pilot-scale production, the relative environmental impacts were greatly reduced, partially due to changes implemented in the system as a result of lab-scale environmental assessment. However, the production of electricity still dominated the impacts in all categories, particularly due to the cultivation stage. For this reason, a sensitivity assessment was proposed in order to identify alternative photobioreactor configurations for astaxanthin production. Two of the evaluated options, based on the use of sunlight instead of artificial illumination, presented significant reductions of impact. However, the improvements observed in these cases were limited by the decrease in biomass productivity associated with sunlight culture systems. Therefore, a two flat-panel photobioreactor system with artificial illumination is proposed as a suitable option, allowing reductions between 62% and 79% of the impact depending on the considered category.