1. |
|
|
2. |
|
|
3. |
|
|
4. |
- Burrascano, S., et al.
(author)
-
Where are we now with European forest multi-taxon biodiversity and where can we head to?
- 2023
-
In: Biological Conservation. - 0006-3207. ; 284
-
Journal article (peer-reviewed)abstract
- The European biodiversity and forest strategies rely on forest sustainable management (SFM) to conserve forest biodiversity. However, current sustainability assessments hardly account for direct biodiversity indicators. We focused on forest multi-taxon biodiversity to: i) gather and map the existing information; ii) identify knowledge and research gaps; iii) discuss its research potential. We established a research network to fit data on species, standing trees, lying deadwood and sampling unit description from 34 local datasets across 3591 sampling units. A total of 8724 species were represented, with the share of common and rare species varying across taxonomic classes: some included many species with several rare ones (e.g., Insecta); others (e.g., Bryopsida) were repre-sented by few common species. Tree-related structural attributes were sampled in a subset of sampling units (2889; 2356; 2309 and 1388 respectively for diameter, height, deadwood and microhabitats). Overall, multi-taxon studies are biased towards mature forests and may underrepresent the species related to other develop-mental phases. European forest compositional categories were all represented, but beech forests were over-represented as compared to thermophilous and boreal forests. Most sampling units (94%) were referred to a habitat type of conservation concern. Existing information may support European conservation and SFM stra-tegies in: (i) methodological harmonization and coordinated monitoring; (ii) definition and testing of SFM in-dicators and thresholds; (iii) data-driven assessment of the effects of environmental and management drivers on multi-taxon forest biological and functional diversity, (iv) multi-scale forest monitoring integrating in-situ and remotely sensed information.
|
|
5. |
|
|
6. |
- Gunther, G, et al.
(author)
-
Beyond multidrug-resistant tuberculosis in Europe: a TBNET study
- 2015
-
In: The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease. - : International Union Against Tuberculosis and Lung Disease. - 1815-7920. ; 19:12, s. 1524-1527
-
Journal article (peer-reviewed)
|
|
7. |
- Heyckendorf, J, et al.
(author)
-
Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model
- 2021
-
In: The European respiratory journal. - : European Respiratory Society (ERS). - 1399-3003 .- 0903-1936. ; 58:3
-
Journal article (peer-reviewed)abstract
- The World Health Organization recommends standardised treatment durations for patients with tuberculosis (TB). We identified and validated a host-RNA signature as a biomarker for individualised therapy durations for patients with drug-susceptible (DS)- and multidrug-resistant (MDR)-TB.MethodsAdult patients with pulmonary TB were prospectively enrolled into five independent cohorts in Germany and Romania. Clinical and microbiological data and whole blood for RNA transcriptomic analysis were collected at pre-defined time points throughout therapy. Treatment outcomes were ascertained by TBnet criteria (6-month culture status/1-year follow-up). A whole-blood RNA therapy-end model was developed in a multistep process involving a machine-learning algorithm to identify hypothetical individual end-of-treatment time points.Results50 patients with DS-TB and 30 patients with MDR-TB were recruited in the German identification cohorts (DS-GIC and MDR-GIC, respectively); 28 patients with DS-TB and 32 patients with MDR-TB in the German validation cohorts (DS-GVC and MDR-GVC, respectively); and 52 patients with MDR-TB in the Romanian validation cohort (MDR-RVC). A 22-gene RNA model (TB22) that defined cure-associated end-of-therapy time points was derived from the DS- and MDR-GIC data. The TB22 model was superior to other published signatures to accurately predict clinical outcomes for patients in the DS-GVC (area under the curve 0.94, 95% CI 0.9–0.98) and suggests that cure may be achieved with shorter treatment durations for TB patients in the MDR-GIC (mean reduction 218.0 days, 34.2%; p<0.001), the MDR-GVC (mean reduction 211.0 days, 32.9%; p<0.001) and the MDR-RVC (mean reduction of 161.0 days, 23.4%; p=0.001).ConclusionBiomarker-guided management may substantially shorten the duration of therapy for many patients with MDR-TB.
|
|
8. |
|
|