Objectives: Since diastolic abnormalities are typical findings of cardiac amyloidosis (CA), we hypothesized that speckle-tracking-imaging (STI) derived longitudinal early diastolic strain rate (LSRdias) could predict outcome in CA patients with preserved left ventricular ejection fraction (LVEF >50%). Background: Diastolic abnormalities including altered early filling are typical findings and are related to outcome in CA patients. Reduced longitudinal systolic strain (LSsys) assessed by STI predicts increased mortality in CA patients. It remains unknown if LSRdias also related to outcome in these patients. Methods: Conventional echocardiography and STI were performed in 41 CA patients with preserved LVEF (25 male; mean age 65±9 years). Global and segmental LSsys and LSRdias were obtained in six LV segments from apical 4-chamber views. Results: Nineteen (46%) out of 41 CA patients died during a median of 16 months (quartiles 5–35 months) follow-up. Baseline mitral annular plane systolic excursion (MAPSE, 6±2 vs. 8±3 mm), global LSRdias and basal-septal LSRdias were significantly lower in non-survivors than in survivors (all p<0.05). NYHA class, number of non-cardiac organs involved, MAPSE, mid-septal LSsys, global LSRdias, basal-septal LSRdias and E/LSRdias were the univariable predictors of all-cause death. Multivariable analysis showed that number of non-cardiac organs involved (hazard ratio [HR] = 1.96, 95% confidence interval [CI] 1.17–3.26, P = 0.010), global LSRdias (HR = 7.30, 95% CI 2.08–25.65, P = 0.002), and E/LSRdias (HR = 2.98, 95% CI 1.54–5.79, P = 0.001) remained independently predictive of increased mortality risk. The prognostic performance of global LSRdias was optimal at a cutoff value of 0.85 S−1 (sensitivity 68%, specificity 67%). Global LSRdias <0.85 S−1 predicted a 4-fold increased mortality in CA patients with preserved LVEF. Conclusions: STI-derived early diastolic strain rate is a powerful independent predictor of survival in CA patients with preserved LVEF.

CXCR4 is a G-protein-coupled receptor that mediates recruitment of blood cells toward its ligand SDF-1. In cancer, high CXCR4 expression is frequently associated with tumor dissemination andpoor prognosis. We evaluated the novel CXCR4 probe [\(^{68}\)Ga]Pentixafor for invivo mapping of CXCR4 expression density in mice xenografted with human CXCR4-positive MM cell lines and patients with advanced MM by means of positron emission tomography (PET). [\(^{68}\)Ga]Pentixafor PET provided images with excellent specificity and contrast. In 10 of 14 patients with advanced MM [\(^{68}\)Ga]Pentixafor PET/CT scans revealed MM manifestations, whereas only nine of 14 standard [\(^{18}\)F]fluorodeoxyglucose PET/CT scans were rated visually positive. Assessment of blood counts and standard CD34\(^{+}\) flow cytometry did not reveal significant blood count changes associated with tracer application. Based on these highly encouraging data on clinical PET imaging of CXCR4 expression in a cohort of MM patients, we conclude that [\(^{68}\)Ga]Pentixafor PET opens a broad field for clinical investigations on CXCR4 expression and for CXCR4-directed therapeutic approaches in MM and other diseases.

Lenalidomide is an integral, yet evolving, part of current treatment pathways for both transplant-eligible and transplant-ineligible patients with newly diagnosed multiple myeloma (NDMM). It is approved in combination with dexamethasone as first-line therapy for transplant-ineligible patients with NDMM, and as maintenance treatment following autologous stem cell transplantation (ASCT). Although strong clinical trial evidence has supported the integration of lenalidomide into current treatment paradigms for NDMM, applying those paradigms to individual patients and determining which patients are most likely to benefit from lenalidomide treatment are more complex. In this paper, we utilize the available clinical trial evidence to provide recommendations for patient selection and lenalidomide dosing in both the first-line setting in patients ineligible for ASCT and the maintenance setting in patients who have undergone ASCT. In addition, we provide guidance on management of those adverse events that are most commonly associated with lenalidomide treatment, and consider the optimal selection and sequencing of next-line agents following long-term frontline or maintenance treatment with lenalidomide.

Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - ). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.