Plants have provided humans with useful products since antiquity, but in the last 30 years they have also been developed as production platforms for small molecules and recombinant proteins. This initially niche area has blossomed with the growth of the global bioeconomy, and now includes chemical building blocks, polymers and renewable energy. All these applications can be described as "plant molecular farming" (PMF). Despite its potential to increase the sustainability of biologics manufacturing, PMF has yet to be embraced broadly by industry. This reflects a combination of regulatory uncertainty, limited information on process cost structures, and the absence of trained staff and suitable manufacturing capacity. However, the limited adaptation of plants and plant cells to the requirements of industry-scale manufacturing is an equally important hurdle. For example, the targeted genetic manipulation of yeast has been common practice since the 1980s, whereas reliable site-directed mutagenesis in most plants has only become available with the advent of CRISPR/Cas9 and similar genome editing technologies since around 2010. Here we summarize the applications of new genetic engineering technologies to improve plants as biomanufacturing platforms. We start by identifying current bottlenecks in manufacturing, then illustrate the progress that has already been made and discuss the potential for improvement at the molecular, cellular and organism levels. We discuss the effects of metabolic optimization, adaptation of the endomembrane system, modified glycosylation profiles, programmable growth and senescence, protease inactivation, and the expression of enzymes that promote biodegradation. We outline strategies to achieve these modifications by targeted gene modification, considering case-by-case examples of individual improvements and the combined modifications needed to generate a new general-purpose "chassis" for PMF. Patients with coronavirus disease 2019 (COVID-19) are often elderly, with comorbidities, and receiving polypharmacy, all of which are known factors for potentially severe drug-drug interactions (DDIs) and the prescription of potentially inappropriate medications (PIMs). The aim of this study was to assess the risk of DDIs and PIMs in COVID-19 patients at hospital discharge. Patients with a proven diagnosis of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) infection who were hospitalized between 21 February and 30 April 2020, treated with at least two drugs, and with available information regarding pharmacological treatments upon admission and at discharge were considered. The appropriateness of drug prescriptions was assessed using INTERcheck . A significant increase in the prescription of proton pump inhibitors and heparins was found when comparing admission with hospital discharge (from 24 to 33% [p<0.05] and from 1 to 17% [p<0.01], respectively). The increased prescription of heparins at discharge resulted in a highly significant increase in the potentially severe DDIs mediated by this class of drugs. 51% of COVID-19 patients aged >65 years had at least one PIM upon admission, with an insignificant increment at discharge (58%). An increased number of prescribed drugs was observed in COVID-19 patients discharged from our hospital. The addition of heparins is appropriate according to the current literature, while the use of proton pump inhibitors is more controversial. Particular attention should be paid to the risk of bleeding complications linked to heparin-based DDIs.An increased number of prescribed drugs was observed in COVID-19 patients discharged from our hospital. The addition of heparins is appropriate according to the current literature, while the use of proton pump inhibitors is more controversial. Particular attention should be paid to the risk of bleeding complications linked to heparin-based DDIs.Bedside lung ultrasound (LUS) can play a role in the setting of the SarsCoV2 pneumonia pandemic. To evaluate the clinical and LUS features of COVID-19 in the ED and their potential prognostic role, a cohort of laboratory-confirmed COVID-19 patients underwent LUS upon admission in the ED. LUS score was derived from 12 fields. A prevalent LUS pattern was assigned depending on the presence of interstitial syndrome only (Interstitial Pattern), or evidence of subpleural consolidations in at least two fields (Consolidation Pattern). The endpoint was 30-day mortality. The relationship between hemogasanalysis parameters and LUS score was also evaluated. Out of 312 patients, only 36 (11.5%) did not present lung involvment, as defined by LUS score 13 had a 77.2% sensitivity and a 71.5% specificity (AUC 0.814; p less then 0.001) in predicting mortality. LUS alterations were more frequent (64%) in the posterior lower fields. LUS score was related with P/F (R2 0.68; p less then 0.0001) and P/F at FiO2 = 21% (R2 0.59; p less then 0.0001). The correlation between LUS score and P/F was not influenced by the prevalent ultrasound pattern. LUS represents an effective tool in both defining diagnosis and stratifying prognosis of COVID-19 pneumonia. The correlation between LUS and hemogasanalysis parameters underscores its role in evaluating lung structure and function.During the first outbreak of Coronavirus disease 2019 (COVID-19) Emergency Departments (EDs) were overcrowded. Hence, the need for a rapid and simple tool to support clinical decisions, such as the ROX index (Respiratory rate - OXygenation), defined as the ratio of peripheral oxygen saturation and fraction of inspired oxygen, to respiratory rate. The aim of the study was to evaluate the accuracy of the ROX index in predicting hospitalization and mortality in patients with a diagnosis of COVID-19 in the ED. The secondary outcomes were to assess the number of readmissions and the variations in the ROX index between the first and the second admission. This was an observational prospective monocentric study, carried out in the ED of Sant'Orsola-Malpighi Hospital in Bologna, Italy. OTX015 ic50 Five hundred and fifty-four consecutive patients with COVID-19 were enrolled and the ROX index was calculated. Patients were followed until hospital discharge or death. A ROX index value less then 25.7 was associated with hospitalization (area under the curve [AUC] = 0.

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