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Purpose
Over the course of COVID-19 pandemic, evidence has accumulated that SARS-CoV-2 infections may affect multiple organs and have serious clinical sequelae, but on-site clinical examinations with non-hospitalized samples are rare. We, therefore, aimed to systematically assess the long-term health status of samples of hospitalized and non-hospitalized SARS-CoV-2 infected individuals from three regions in Germany.
Methods
The present paper describes the COVIDOM-study within the population-based cohort platform (POP) which has been established under the auspices of the NAPKON infrastructure (German National Pandemic Cohort Network) of the national Network University Medicine (NUM). Comprehensive health assessments among SARS-CoV-2 infected individuals are conducted at least 6 months after the acute infection at the study sites Kiel, Würzburg and Berlin. Potential participants were identified and contacted via the local public health authorities, irrespective of the severity of the initial infection. A harmonized examination protocol has been implemented, consisting of detailed assessments of medical history, physical examinations, and the collection of multiple biosamples (e.g., serum, plasma, saliva, urine) for future analyses. In addition, patient-reported perception of the impact of local pandemic-related measures and infection on quality-of-life are obtained.
Results
As of July 2021, in total 6813 individuals infected in 2020 have been invited into the COVIDOM-study. Of these, about 36% wished to participate and 1295 have already been examined at least once.
Conclusion
NAPKON-POP COVIDOM-study complements other Long COVID studies assessing the long-term consequences of an infection with SARS-CoV-2 by providing detailed health data of population-based samples, including individuals with various degrees of disease severity.
Trial registration
Registered at the German registry for clinical studies (DRKS00023742).
The factors determining gradients of biodiversity are a fundamental yet unresolved topic in ecology. While diversity gradients have been analysed for numerous single taxa, progress towards general explanatory models has been hampered by limitations in the phylogenetic coverage of past studies. By parallel sampling of 25 major plant and animal taxa along a 3.7 km elevational gradient on Mt. Kilimanjaro, we quantify cross-taxon consensus in diversity gradients and evaluate predictors of diversity from single taxa to a multi-taxa community level. While single taxa show complex distribution patterns and respond to different environmental factors, scaling up diversity to the community level leads to an unambiguous support for temperature as the main predictor of species richness in both plants and animals. Our findings illuminate the influence of taxonomic coverage for models of diversity gradients and point to the importance of temperature for diversification and species coexistence in plant and animal communities.
New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.
The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.