Fungal diversity present in snow sampled in summer in the north-west Antarctic Peninsula and the South Shetland Islands, Maritime Antarctica, assessed using metabarcoding

We assessed the fungal diversity present in snow sampled during summer in the north-west Antarctic Peninsula and the South Shetland Islands, maritime Antarctica using a metabarcoding approach. A total of 586,693 fungal DNA reads were obtained and assigned to 203 amplicon sequence variants (ASVs). The dominant phylum was Ascomycota, followed by Basidiomycota, Mortierellomycota, Chytridiomycota and Mucoromycota. Penicillium sp., Pseudogymnoascus pannorum, Coniochaeta sp., Aspergillus sp., Antarctomyces sp., Phenoliferia sp., Cryolevonia sp., Camptobasidiaceae sp., Rhodotorula mucilaginosa and Bannozyma yamatoana were assessed as abundant taxa. The snow fungal diversity indices were high but varied across the different locations sampled. Of the fungal ASVs detected, only 28 were present all sampling locations. The 116 fungal genera detected in the snow were dominated by saprotrophic taxa, followed by symbiotrophic and pathotrophic. Our data indicate that, despite the low temperature and oligotrophic conditions, snow can host a richer mycobiome than previously reported through traditional culturing studies. The snow mycobiome includes a complex diversity dominated by cosmopolitan, cold-adapted, psychrophilic and endemic taxa. While saprophytes dominate this community, a range of other functional groups are present

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes