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Institute
Fanconi anaemia (FA) is an inherited disease with bone marrow failure, variable congenital and developmental abnormalities, and cancer predisposition. With improved survival, non-haematological manifestations of FA become increasingly important for long-term management. While renal abnormalities are recognized, detailed data on patterns and frequency and implications for long-term management are sparse. We reviewed clinical course and imaging findings of FA patients with respect to renal complications in our centre over a 25-year period to formulate some practical suggestions for guidelines for management of renal problems associated with FA. Thirty patients including four sibling sets were reviewed. On imaging, 14 had evidence of anatomical abnormalities of the kidneys. Two cases with severe phenotype, including renal abnormalities, had chronic kidney disease (CKD) at diagnosis. Haematopoietic stem cell transplantation was complicated by significant acute kidney injury (AKI) in three cases. In three patients, there was CKD at long-term follow-up. All patients had normal blood pressure. Evaluation of renal anatomy with ultrasound imaging is important at diagnostic workup of FA. While CKD is uncommon at diagnosis, our data suggests that the incidence of CKD increases with age, in particular after haematopoietic stem cell transplantation. Monitoring of renal function is essential for management of FA. Based on these long-term clinical observations, we formulate some practical guidelines for assessment and management of renal abnormalities in FA.
Objective: To assess patterns and impact of small nerve fiber dysfunction and pathology in patients with fibromyalgia syndrome (FMS).
Methods: One hundred seventeen women with FMS underwent neurological examination, questionnaire assessment, neurophysiology assessment, and small fiber tests: skin punch biopsy, corneal confocal microscopy, microneurography, quantitative sensory testing including C-tactile afferents, and pain-related evoked potentials. Data were compared with those of women with major depressive disorder and chronic widespread pain (MD-P) and healthy women.
Results: Intraepidermal nerve fiber density (IENFD) was reduced at different biopsy sites in 63% of FMS patients (MDP: 10%, controls: 18%; p < 0.001 for each). We found 4 patterns of skin innervation in FMS: normal, distally reduced, proximally reduced, and both distally and proximally reduced (p < 0.01 for each compared to controls). Microneurography revealed initial activity-dependent acceleration of conduction velocity upon low frequencies of stimulation in 1A fibers, besides 1B fiber spontaneous activity and mechanical sensitization in FMS patients. FMS patients had elevated warm detection thresholds (p < 0.01), impaired C-tactile afferents (p < 0.05), and reduced amplitudes (p < 0.001) of pain-related evoked potentials compared to controls. Compared to FMS patients with normal skin innervation, those with generalized IENFD reduction had higher pain intensity and impairment due to pain, higher disease burden, more stabbing pain and paresthesias, and more anxiety (p < 0.05 for each). FMS patients with generalized IENFD reduction also had lower corneal nerve fiber density (p < 0.01) and length (p < 0.05).
Interpretation: The extent of small fiber pathology is related to symptom severity in FMS. This knowledge may have implications for the diagnostic classification and treatment of patients with FMS.
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.
Motivation
The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included
The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
Spatial location and grain
BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).
Time period and grain
BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
Major taxa and level of measurement
BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format
.csv and .SQL.