Quality of life and metabolic status in mildly depressed patients with type 2 diabetes treated with paroxetine: A double-blind randomised placebo controlled 6-month trial

<p>Abstract</p> <p>Background</p> <p>Depression is prevalent in people with type 2 diabetes and affects both glycaemic control and overall quality of life. The aim of this investigator-initiated trial was to evaluate the effect of the antidepressant paroxetine on quality of life, metabolic control, and mental well-being in mildly depressed diabetics aged 50–70 years.</p> <p>Methods</p> <p>We randomised 49 mildly depressed primary care outpatients with non-optimally controlled diabetes to a 6-month double-blind treatment with either paroxetine 20 mg per day or matching placebo. Primary efficacy measurements were quality of life and glycaemic control. The primary global outcome of the study was defined as a 10 points improvement in the SF-36 quality of life score. The primary metabolic outcome of the study was defined as a 0.8%-units decrease in glycosylated haemoglobin A_1c(GHbA_1c). Psychiatric symptoms were assessed with the Hospital Anxiety and Depression Scale.</p> <p>Results</p> <p>Six patients withdrew their consent before starting medication and six dropped out later in the study. We performed analysis of covariance with the baseline value as a covariate. Quality of life and glycaemic control as well as symptoms of depression and anxiety improved in both groups over the 6-month study period. After three months of treatment we found a statistically significant difference between the two treatment groups in GHbA_1c(mean difference = 0.59%-units, p = 0.018) and in SF-36 score (mean difference = 11.0 points, p = 0.039). However, at the end of the study, no statistically significant differences between the treatment groups were observed. No severe adverse events occurred.</p> <p>Conclusion</p> <p>This pragmatic study of primary care patients did not confirm earlier preliminary findings indicating a beneficial effect of paroxetine on glycaemic control. The study indicates that in pragmatic circumstances any possible benefit from administration of paroxetine in diabetic patients with sub-threshold depression is likely to be modest and of short duration. Routine antidepressant prescription for patients with diabetes and sub-threshold depressive symptoms is not indicated.</p> <p>Trial registration</p> <p>Current controlled trials ISRCTN55819922</p

Mapping the ionised gas around the luminous QSO HE 1029-1401: Evidence for minor merger events?

We present VIMOS integral field spectroscopy of the brightest radio-quiet QSO on the southern sky HE 1029-1401 at a redshift of z=0.086. Standard decomposition techniques for broad-band imaging are extended to integral field data in order to deblend the QSO and host emission. We perform a tentative analysis of the stellar continuum finding a young stellar population (<100Myr) or a featureless continuum embedded in an old stellar population (10Gyr) typical for a massive elliptical galaxy. The stellar velocity dispersion of sigma_*=320\pm90 km/s and the estimated black hole mass log(M_BH/M_sun)=8.7\pm0.3 are consistent with the local M_BH-sigma_* relation within the errors. For the first time we map the two-dimensional ionised gas distribution and the gas velocity field around HE 1029-1401. While the stellar host morphology is purely elliptical we find a highly structured distribution of ionised gas out to 16 kpc from the QSO. The gas is highly ionised solely by the QSO radiation and has a significantly lower metallicity than would be expected for the stellar mass of the host, indicating an external origin of the gas most likely due to minor mergers. We find a rotating gas disc around the QSO and a dispersion-dominated non-rotating gas component within the central 3 kpc. At larger distances the velocity field is heavily disturbed, which could be interpreted as another signature of past minor merger events. Alternatively, the arc-like structure seen in the ionised gas might also be indicative of a large-scale expanding bubble, centred on and possibly driven by the active nucleus.Comment: 13 pages, 13 figures, 1 table, accepted for publication in A&

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

Quality of life and metabolic status in mildly depressed women with type 2 diabetes treated with paroxetine: A single-blind randomised placebo controlled trial

BACKGROUND: Depression is prevalent in people with type 2 diabetes and affects both glycemic control and overall quality of life. The aim of this trial was to evaluate the effect of the antidepressant paroxetine on metabolic control, quality of life and mental well-being in mildly depressed women with type 2 diabetes. METHODS: We randomised 15 mildly depressed women with non-optimally controlled type 2 diabetes to a 10-week single-blind treatment with either paroxetine 20 mg per day or placebo. Primary efficacy measurements were glycemic control and quality of life. Glycosylated hemoglobin A(1c )(GHbA(1c)) was used as a measure of glycemic control. Quality of life was evaluated using RAND-36. Mental state was assessed using two clinician-rated scoring instruments, Hamilton's Anxiety Scale (HAM-A) and Montgomery-Åsberg's Depression Rating Scale (MADRS), and a patient-rated scoring instrument, Beck's Depression Inventory (BDI). RESULTS: At the end of the study no significant difference between groups in improvement of quality of life was found. A trend towards a superior improvement in glycemic control was found in the paroxetine group (p = 0.08). A superior increase in sex-hormone-binding-globuline (SHBG) levels was evidenced in the paroxetine group (p = 0.01) as a sign of improved insulin sensitivity. There was also a trend for superior efficacy of paroxetine in investigator-rated anxiety and depression. This notion was supported by a trend for superior decrease of serum cortisol levels in the paroxetine group (p = 0.06). CONCLUSION: Paroxetine has a beneficial effect on measures of insulin sensitivity and may improve glycemic control. Larger studies of longer duration are needed to verify the benefits of paroxetine in type 2 diabetes. While waiting for more conclusive evidence it seems sensible to augment standard care of type 2 diabetes with paroxetine even in patients who do not fulfil routine psychiatric criteria for initiation of antidepressant drug treatment

Aldehyde Dehydrogenase (ALDH) Activity Does Not Select for Cells with Enhanced Aggressive Properties in Malignant Melanoma

Malignant melanoma is an exceptionally aggressive, drug-resistant and heterogeneous cancer. Recently it has been shown that melanoma cells with high clonogenic and tumourigenic abilities are common, but markers distinguishing such cells from cells lacking these abilities have not been identified. There is therefore no definite evidence that an exclusive cell subpopulation, i.e. cancer stem cells (CSC), exists in malignant melanoma. Rather, it is suggested that multiple cell populations are implicated in initiation and progression of the disease, making it of importance to identify subpopulations with elevated aggressive properties.. Furthermore, both subpopulations showed similar sensitivity to the anti-melanoma drugs, dacarbazine and lexatumumab.These findings suggest that ALDH does not distinguish tumour-initiating and/or therapy-resistant cells, implying that the ALDH phenotype is not associated with more-aggressive subpopulations in malignant melanoma, and arguing against ALDH as a “universal” marker. Besides, it was shown that the ability to reestablish tumour heterogeneity is not necessarily linked to the more aggressive phenotype

Whole-exome sequencing and clinical interpretation of FFPE tumor samples to guide precision cancer medicine

Translating whole exome sequencing (WES) for prospective clinical use may impact the care of cancer patients; however, multiple innovations are necessary for clinical implementation. These include: (1) rapid and robust WES from formalin-fixed paraffin embedded (FFPE) tumor tissue, (2) analytical output similar to data from frozen samples, and (3) clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival FFPE tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a “long tail” of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15/16 patients. In one patient, previously undetected findings guided clinical trial enrollment leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements