Predictors of Gains During Inpatient Rehabilitation in Patients with Stroke- A Review.

Stroke remains a major cause of disability. The cost of stroke rehabilitation is substantial. Understanding the factors that predict response to inpatient stroke rehabilitation may be useful, for example, to best individualize the content of therapy, or to maximize the efficiency with which resources are directed. This review reviewed the literature and found that numerous variables were associated with outcome after inpatient stroke rehabilitation. The strongest evidence exists for factors such as age, stroke subtype, nutritional status, psychosocial factors such as living with family prior to stroke or presence of a caregiver. Functional status on admission, urinary incontinence, post-stroke infection, and aphasia each can also impact prognosis. Strengths and weaknesses of cited studies are considered in an attempt to inform design of future studies examining the factors that predict response to inpatient rehabilitation after stroke

The cytoplasmic zinc finger protein ZPR1 accumulates in the nucleolus of proliferating cells

The zinc finger protein ZPR1 translocates from the cytoplasm to the nucleus after treatment of cells with mitogens. The function of nuclear ZPR1 has not been defined. Here we demonstrate that ZPR1 accumulates in the nucleolus of proliferating cells. The role of ZPR1 was examined using a gene disruption strategy. Cells lacking ZPR1 are not viable. Biochemical analysis demonstrated that the loss of ZPR1 caused disruption of nucleolar function, including preribosomal RNA expression. These data establish ZPR1 as an essential protein that is required for normal nucleolar function in proliferating cells

Applicability of the Threshold of Toxicological Concern (TTC) approach to cosmetics – preliminary analysis

This report describes the application of chemoinformatic methods to explore the applicability of the Threshold of Toxicological Concern (TTC) approach to cosmetic ingredients. For non-cancer endpoints, the most widely used TTC approach is the Cramer classification scheme, which categorises chemicals into three classes (I, II and III) depending on their expected level of concern for oral systemic toxicity (low, medium, high, respectively). The chemical space of the Munro non-cancer dataset was characterised to assess whether this underlying TTC dataset is representative of the “world” of cosmetic ingredients, as represented by the COSMOS Cosmetics Inventory. In addition, the commonly used Cramer-related Munro threshold values were applied to a toxicological dataset of cosmetic ingredients, the COSMOS TTC dataset, to assess the degree of protectiveness resulting from the application of the Cramer classification scheme. This analysis is considered preliminary, since the COSMOS TTC dataset and Cosmetics Inventory are subject to an ongoing process of extension and quality control within the COSMOS project. The results of this preliminary analysis show that the Munro dataset is broadly representative of the chemical space of cosmetics, although certain structural classes are missing, notably organometallics, silicon-containing compounds, and certain types of surfactants (non-ionic and cationic classes). Furthermore, compared with the Cosmetics Inventory, the Munro dataset has a higher prevalence of reactive chemicals and a lower prevalence of larger, long linear chain structures. The COSMOS TTC dataset, comprising repeat dose toxicity data for cosmetics ingredients, shows a good representation of the Cosmetics Inventory, both in terms of physicochemical property ranges, structural features and chemical use categories. Thus, this dataset is considered to be suitable for investigating the applicability of the TTC approach to cosmetics. The results of the toxicity data analysis revealed a number of cosmetic ingredients in Cramer Class I with No Observed Effect Level (NOEL) values lower than the Munro threshold of 3000 µg/kg bw/day. The prevalence of these “false negatives” was less than 5%, which is the percentage expected by chance resulting from the use of the 5th percentile of cumulative probability distribution of NOELs in the derivation of TTC values. Furthermore, the majority of these false negatives do not arise when structural alerts for DNA-binding are used to identify potential genotoxicants, to which a lower TTC value of 0.0025 µg/kg bw/day is typically applied. Based on these preliminary results, it is concluded that the current TTC approach is broadly applicable to cosmetics, although a number of improvements can be made, through the quality control of the underlying TTC datasets, modest revisions / extensions of the Cramer classification scheme, and the development of explicit guidance on how to apply the TTC approach.JRC.I.5-Systems Toxicolog

Discussion: Potential for carfree development in the UK

Discussion: Potential for carfree development in the U

A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures

Exposure to chemicals generally occurs in the form of mixtures. However, the great majority of the toxicity data, upon which chemical safety decisions are based, relate only to single compounds. It is currently unfeasible to test a fully representative proportion of mixtures for potential harmful effects and, as such, in silico modelling provides a practical solution to inform safety assessment. Traditional methodologies for deriving estimations of mixture effects, exemplified by principles such as concentration addition (CA) and independent action (IA), are limited as regards the scope of chemical combinations to which they can reliably be applied. Development of appropriate quantitative structure-activity relationships (QSARs) has been put forward as a solution to the shortcomings present within these techniques – allowing for the potential formulation of versatile predictive tools capable of capturing the activities of a full contingent of possible mixtures. This review addresses the current state-of-the-art as regards application of QSAR towards mixture toxicity, discussing the challenges inherent in the task, whilst considering the strengths and limitations of existing approaches. Forty studies are examined within – through reference to several characteristic elements including the nature of the chemicals and endpoints modelled, the form of descriptors adopted, and the principles behind the statistical techniques employed. Recommendations are in turn provided for practices which may assist in further advancing the field, most notably with regards to ensuring confidence in the acquired predictions.publishedVersio

understanding the consequences of changes in the production frontiers for roots tubers and bananas

Abstract The widely recognized role of roots, tubers and bananas (RT&Bs) in achieving food security and providing income opportunities in the world's poorest regions will be challenged by socioeconomic and climate related drivers. These will affect demand and production patterns and increase pressure on farming systems. Foresight results presented in this paper show that the importance of RT&B crops for food security will likely increase by 2050 despite these challenges. Furthermore, investments targeted at yield growth appear to be more effective than marketing improvements in alleviating production constraints and in strengthening the role of RT&B crops in future food systems

A Mechanistic Framework for Integrating Chemical Structure and High-Throughput Screening Results to Improve Toxicity Predictions

Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. As the point within the AOP where chemicals directly interact with the biology, a detailed understanding of the MIE itself, and the proximal events that occur following this perturbation, provide the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program coupled with chemical structural information to generate chemical clusters using three similarity methods pertaining to the MIEs within an AOP network for hepatic steatosis (fatty liver disease). Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating both biological and chemical data. Evaluation of the chemical clusters that activate the glucocorticoid receptor (GR) identified activity differences in chemicals within a chemical cluster. Comparison of the estrogen receptor (ER) results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies highlight the value provided by AOP-informed chemical clusters when attempting to determine the activity of chemicals for which limited toxicity data exist