A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

BACKGROUND: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. METHODS: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. FINDINGS: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%. INTERPRETATION: This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics. FUNDING: European Union's Horizon 2020 research and innovation programme, the European Union's Seventh Framework Programme (EUCLIDS), Imperial Biomedical Research Centre of the National Institute for Health Research, the Wellcome Trust and Medical Research Foundation, Instituto de Salud Carlos III, Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Grupos de Refeencia Competitiva, Swiss State Secretariat for Education, Research and Innovation

Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: A Mediterranean assessment

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth observation (EO) data offer a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of land cover (LC) and/or land use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems. Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies—CORINE Land Cover, the Food and Agricultural Organisation (FAO) land cover classification system (LCCS) and the International Geosphere-Biosphere Programme to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for landscape monitoring—a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a result

Generation of Up to Date Land Cover Maps for Central Asia

Human activity and climate variability has always changed the Earth’s surface and both will mainly contribute to future alteration in land cover and land use changes. In this chapte we demonstrate a land cover and land use classification approach for Central Asia addressing regional characteristics of the study area. With the aim of regional classification map for Central Asia a specific classification scheme based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organisation of the United Nations Environment Programme (FAO-UNEP) was developed. The classification was performed by using a supervised classification method applied on metrics, which were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data with 250 m spatial resolution. The metrics wer derived from annual time-series of red and nearinfrared reflectance as well as from Normalized Difference Vegetation Index (NDVI) and thus reflect the temporal behavior of different land cover types. Reference data required for a supervised classification approach were collected from several high resolution satellite imagery distributed all over the study area. The overall accuracy results for performed classification of the year 2001 and 2009 are 91.2 and 91.3 %. The comparison of both classification maps shows significant alterations for different classes. Water bodies such as Shardara Water Reservoir and Aral Sea have changed in their extent. Whereby, the size of the Shardara Water Reservoir is very dynamic from year to year due to water management and the eastern lobe of southern Aral Sea has decreased because of the lack of inflow from Amu Darja. Furthermore, some large scale changes were detected in sparsely vegetated areas in Turkmenistan, where spring precipitation mainly affects the vegetation density. In the north of Kazakhstan significant forest losses caused by forest fires and logging were detected. The presented classification approach is a suitable tool for monitoring land cover and land use in Central Asia. Such independent information is important for accurate assessment of water and land recourses

Liver involvement in congenital disorders of glycosylation (CDG). A systematic review of the literature

Congenital disorders of glycosylation (CDG) are a rapidly growing family of genetic diseases caused by defects in glycosylation. Nearly 100 CDG types are known so far. Patients present a great phenotypic diversity ranging from poly- to mono-organ/system involvement and from very mild to extremely severe presentation. In this literature review, we summarize the liver involvement reported in CDG patients. Although liver involvement is present in only a minority of the reported CDG types (22 %), it can be debilitating or even life-threatening. Sixteen of the patients we collated here developed cirrhosis, 10 had liver failure. We distinguish two main groups: on the one hand, the CDG types with predominant or isolated liver involvement including MPI-CDG, TMEM199-CDG, CCDC115-CDG, and ATP6AP1-CDG, and on the other hand, the CDG types associated with liver disease but not as a striking, unique or predominant feature, including PMM2-CDG, ALG1-CDG, ALG3-CDG, ALG6-CDG, ALG8-CDG, ALG9-CDG, PGM1-CDG, and COG-CDG. This review aims to facilitate CDG patient identification and to understand CDG liver involvement, hopefully leading to earlier diagnosis, and better management and treatment