Role of viral coinfection in post-pandemic invasive Group A streptococcal infections in adults, a nation-wide cohort study (iGASWISS) - S. Musumeci; A. MacPhail; M. Weisser Rohacek; A. Erba; D. Goldenberger; C. Lang; E. Colin-Benoit; S. L. Leib; P. Sendi; C. Chuard; V. Erard; C. Fournier; et al. (European Journal of Clinical Microbiology & Infectious Diseases, 2025)
Objective During the winter season of 2022–2023, numerous countries experienced a surge in invasive Streptococcus pyogenes (iGAS) infections. The role of viral coinfections in the post-COVID surge has not been elucidated. We report nation-wide data describing clinical presentation, microbiological characteristics, and associations with viral infection in adults during this period. Methods A multicenter retrospective cohort study was conducted across 10 hospitals in Switzerland, including adults (> 16 years old) with iGAS infection from November 2022 to February 2023. Descriptive analysis was performed. A multivariable logistic regression model was fitted to assess the impact of viral coinfection. In addition, genetic analysis was performed in available isolates. Results A total of 194 patients were included, with a median age of 50 years (interquartile range [IQR]: 37–69). 17/194 (8.8%) were immunosuppressed and 40/194 (20.6%) exhibited concomitant viral infections, predominantly Influenza A (21/40, 53%). Illness severity was high: 65/194 (33.5%) of cases necessitated admission to an intensive care unit (ICU), and the 30-day mortality was 4% ( n = 8). Among the available strains for genetic analysis ( n = 48), heterogeneity was found although ST28- emm 1 isolates (also known as M1UK) was predominant (22/48). Conclusion The post-COVID iGAS surge in Switzerland was associated with high levels of morbidity and mortality in immunocompetent adults. M1UK was predominant within the iGAS strains circulating in Switzerland during the study period and viral coinfection was a predictor for ICU admission and mortality.

TRY plant trait database – enhanced coverage and open access - Jens Kattge; Gerhard Bönisch; Sandra Díaz; Sandra Lavorel; Iain Colin Prentice; Paul Leadley; Susanne Tautenhahn; Gijsbert D. A. Werner; Tuomas Aakala; Mehdi Abedi; Alicia T. R. Acosta; George C. Adamidis; et al. (Global Change Biology, 2020)
Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.