A systematic review of the effectiveness of docetaxel and mitoxantrone for the treatment of metastatic hormone-refractory prostate cancer

A systematic review was performed to evaluate the clinical effectiveness of docetaxel in combination with prednisolone (docetaxel is licensed in the UK for use in combination with prednisone or prednisolone for the treatment of patients with metastatic hormone-refractory prostate cancer. Prednisone is not used in the UK, but it is reasonable to use docetaxel plus prednisone data in this review of docetaxel plus prednisolone) for the treatment of metastatic hormone-refractory prostate cancer. A scoping search identified a trial of docetaxel plus prednisone vs mitoxantrone plus prednisone, but did not identify any trials comparing docetaxel plus prednisolone/prednisone with any other treatments. Therefore, we considered additional indirect evidence that would enable a comparison of docetaxel plus prednisolone/prednisone with other chemotherapy regimens and active supportive care. Systematic searching (upto April 2005) identified seven randomised controlled trials. One large well-conducted trial assessed docetaxel plus prednisone vs mitoxantrone plus prednisone; this showed statistically significant improvements with 3-weekly docetaxel in terms of overall survival, quality of life, pain response and PSA decline. Two other chemotherapy regimens that included docetaxel with estramustine also showed improved outcomes in comparison with mitoxantrone plus prednisone. Three trials that compared mitoxantrone plus corticosteroids with corticosteroids alone were identified and their results for overall survival combined, which showed very little difference between the two groups. The addition of clodronate to mitoxantrone plus prednisone showed no significant differences in comparison with mitoxantrone plus prednisone alone. The evidence suggests that chemotherapy regimens containing 3-weekly docetaxel are superior to mitoxantrone or corticosteroids alone

The impact of predation by marine mammals on Patagonian toothfish longline fisheries

Predatory interaction of marine mammals with longline fisheries is observed globally, leading to partial or complete loss of the catch and in some parts of the world to considerable financial loss. Depredation can also create additional unrecorded fishing mortality of a stock and has the potential to introduce bias to stock assessments. Here we aim to characterise depredation in the Patagonian toothfish (Dissostichus eleginoides) fishery around South Georgia focusing on the spatio-temporal component of these interactions. Antarctic fur seals (Arctocephalus gazella), sperm whales (Physeter macrocephalus), and orcas (Orcinus orca) frequently feed on fish hooked on longlines around South Georgia. A third of longlines encounter sperm whales, but loss of catch due to sperm whales is insignificant when compared to that due to orcas, which interact with only 5% of longlines but can take more than half of the catch in some cases. Orca depredation around South Georgia is spatially limited and focused in areas of putative migration routes, and the impact is compounded as a result of the fishery also concentrating in those areas at those times. Understanding the seasonal behaviour of orcas and the spatial and temporal distribution of “depredation hot spots” can reduce marine mammal interactions, will improve assessment and management of the stock and contribute to increased operational efficiency of the fishery. Such information is valuable in the effort to resolve the human-mammal conflict for resources

Exploring Relations Between BCG and Cluster Properties in the SPectroscopic IDentification of eROSITA Sources Survey from 0.05<z<0.30.05 < z < 0.3

We present a sample of 329 low to intermediate redshift ($0.05 < z < 0.3$) brightest cluster galaxies (BCGs) in X-ray selected clusters from the SPectroscopic IDentification of eRosita Sources (SPIDERS) survey, a spectroscopic survey within Sloan Digital Sky Survey-IV (SDSS-IV). We define our BCGs by simultaneous consideration of legacy X-ray data from ROSAT, maximum likelihood outputs from an optical cluster-finder algorithm and visual inspection. Using SDSS imaging data, we fit S\'ersic profiles to our BCGs in three bands (\textit{g}, \textit{r}, \textit{i}) with \textsc{SIGMA}, a \textsc{GALFIT}-based software wrapper. We examine the reliability of our fits by running our pipeline on ${\sim}10^{4}$ psf-convolved model profiles injected into 8 random cluster fields, we then use the results of this analysis to create a robust subsample of 198 BCGs. We outline three cluster properties of interest: overall cluster X-ray luminosity ($L_{X}$), cluster richness as estimated by \textsc{redMaPPer} ($\lambda$) and cluster halo mass ($M_{200}$), which is estimated via velocity dispersion. In general, there are significant correlations with BCG stellar mass between all three environmental properties, but no significant trends arise with either S\'ersic index or effective radius. There is no major environmental dependence on the strength of the relation between effective radius and BCG stellar mass. Stellar mass therefore arises as the most important factor governing BCG morphology. Our results indicate that our sample consists of a large number of relaxed, mature clusters containing broadly homogeneous BCGs up to $z \sim 0.3$, suggesting that there is little evidence for much ongoing structural evolution for BCGs in these systems

WNT signaling regulates self-renewal and differentiation of prostate cancer cells with stem cell characteristics

Prostate cancer cells with stem cell characteristics were identified in human prostate cancer cell lines by their ability to form from single cells self-renewing prostaspheres in non-adherent cultures. Prostaspheres exhibited heterogeneous expression of proliferation, differentiation and stem cell-associated makers CD44, ABCG2 and CD133. Treatment with WNT inhibitors reduced both prostasphere size and self-renewal. In contrast, addition of Wnt3a caused increased prostasphere size and self-renewal, which was associated with a significant increase in nuclear Β-catenin, keratin 18, CD133 and CD44 expression. As a high proportion of LNCaP and C4-2B cancer cells express androgen receptor we determined the effect of the androgen receptor antagonist bicalutamide. Androgen receptor inhibition reduced prostasphere size and expression of PSA, but did not inhibit prostasphere formation. These effects are consistent with the androgen-independent self-renewal of cells with stem cell characteristics and the androgen-dependent proliferation of transit amplifying cells. As the canonical WNT signaling effector Β-catenin can also associate with the androgen receptor, we propose a model for tumour propagation involving a balance between WNT and androgen receptor activity. That would affect the self-renewal of a cancer cell with stem cell characteristics and drive transit amplifying cell proliferation and differentiation. In conclusion, we provide evidence that WNT activity regulates the self-renewal of prostate cancer cells with stem cell characteristics independently of androgen receptor activity. Inhibition of WNT signaling therefore has the potential to reduce the self-renewal of prostate cancer cells with stem cell characteristics and improve the therapeutic outcome.Peer reviewe

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Instrument development, data collection, and characteristics of practices, staff, and measures in the Improving Quality of Care in Diabetes (iQuaD) Study

Peer reviewedPublisher PD

Detecting sulphate aerosol geoengineering with different methods

Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. We investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr−1 of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection when no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed

Differential cross sections and spin density matrix elements for the reaction gamma p -> p omega

High-statistics differential cross sections and spin density matrix elements for the reaction gamma p -> p omega have been measured using the CLAS at Jefferson Lab for center-of-mass (CM) energies from threshold up to 2.84 GeV. Results are reported in 112 10-MeV wide CM energy bins, each subdivided into cos(theta_CM) bins of width 0.1. These are the most precise and extensive omega photoproduction measurements to date. A number of prominent structures are clearly present in the data. Many of these have not previously been observed due to limited statistics in earlier measurements

Streptomyces aridus sp. nov., isolated from a high altitude Atacama Desert soil and emended description of Streptomyces noboritoensis Isono et al. 1957.

A polyphasic study was undertaken to determine the taxonomic status of a Streptomyces strain which had been isolated from a high altitude Atacama Desert soil and shown to have bioactive properties. The strain, isolate H9(T), was found to have chemotaxonomic, cultural and morphological properties that place it in the genus Streptomyces. 16S rRNA gene sequence analyses showed that the isolate forms a distinct branch at the periphery of a well-delineated subclade in the Streptomyces 16S rRNA gene tree together with the type strains of Streptomyces crystallinus, Streptomyces melanogenes and Streptomyces noboritoensis. Multi-locus sequence analysis (MLSA) based on five house-keeping gene alleles showed that isolate H9(T) is closely related to the latter two type strains and to Streptomyces polyantibioticus NRRL B-24448(T). The isolate was distinguished readily from the type strains of S. melanogenes, S. noboritoensis and S. polyantibioticus using a combination of phenotypic properties. Consequently, the isolate is considered to represent a new species of Streptomyces for which the name Streptomyces aridus sp. nov. is proposed; the type strain is H9(T) (=NCIMB 14965(T)=NRRL B65268(T)). In addition, the MLSA and phenotypic data show that the S. melanogenes and S. noboritoensis type strains belong to a single species, it is proposed that S. melanogenes be recognised as a heterotypic synonym of S. noboritoensis for which an emended description is given. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10482-017-0838-2) contains supplementary material, which is available to authorized users