eHealth interventions for people with chronic kidney disease

Background Chronic kidney disease (CKD) is associated with high morbidity and death, which increases as CKD progresses to end‐stage kidney disease (ESKD). There has been increasing interest in developing innovative, effective and cost‐efficient methods to engage with patient populations and improve health behaviours and outcomes. Worldwide there has been a tremendous increase in the use of technologies, with increasing interest in using eHealth interventions to improve patient access to relevant health information, enhance the quality of healthcare and encourage the adoption of healthy behaviours. Objectives This review aims to evaluate the benefits and harms of using eHealth interventions to change health behaviours in people with CKD. Search methods We searched the Cochrane Kidney and Transplant Register of Studies up to 14 January 2019 through contact with the Information Specialist using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov. Selection criteria Randomised controlled trials (RCTs) and quasi‐RCTs using an eHealth intervention to promote behaviour change in people with CKD were included. There were no restrictions on outcomes, language or publication type. Data collection and analysis Two authors independently assessed trial eligibility, extracted data and assessed the risk of bias. The certainty of the evidence was assessed using GRADE. Main results We included 43 studies with 6617 participants that evaluated the impact of an eHealth intervention in people with CKD. Included studies were heterogeneous in terms of eHealth modalities employed, type of intervention, CKD population studied and outcomes assessed. The majority of studies (39 studies) were conducted in an adult population, with 16 studies (37%) conducted in those on dialysis, 11 studies (26%) in the pre‐dialysis population, 15 studies (35%) in transplant recipients and 1 studies (2%) in transplant candidates We identified six different eHealth modalities including: Telehealth; mobile or tablet application; text or email messages; electronic monitors; internet/websites; and video or DVD. Three studies used a combination of eHealth interventions. Interventions were categorised into six types: educational; reminder systems; self‐monitoring; behavioural counselling; clinical decision‐aid; and mixed intervention types. We identified 98 outcomes, which were categorised into nine domains: blood pressure (9 studies); biochemical parameters (6 studies); clinical end‐points (16 studies); dietary intake (3 studies); quality of life (9 studies); medication adherence (10 studies); behaviour (7 studies); physical activity (1 study); and cost‐effectiveness (7 studies). Only three outcomes could be meta‐analysed as there was substantial heterogeneity with respect to study population and eHealth modalities utilised. There was found to be a reduction in interdialytic weight gain of 0.13kg (4 studies, 335 participants: MD ‐0.13, 95% CI ‐0.28 to 0.01; I2 = 0%) and a reduction in dietary sodium intake of 197 mg/day (2 studies, 181 participants: MD ‐197, 95% CI ‐540.7 to 146.8; I2 = 0%). Both dietary sodium and fluid management outcomes were graded as being of low evidence due to high or unclear risk of bias and indirectness (interdialytic weight gain) and high or unclear risk of bias and imprecision (dietary sodium intake). Three studies reported death (2799 participants, 146 events), with 45 deaths/1000 cases compared to standard care of 61 deaths/1000 cases (RR 0.74, CI 0.53 to 1.03; P = 0.08). We are uncertain whether using eHealth interventions, in addition to usual care, impact on the number of deaths as the certainty of this evidence was graded as low due to high or unclear risk of bias, indirectness and imprecision. Authors' conclusions eHealth interventions may improve the management of dietary sodium intake and fluid management. However, overall these data suggest that current evidence for the use of eHealth interventions in the CKD population is of low quality, with uncertain effects due to methodological limitations and heterogeneity of eHealth modalities and intervention types. Our review has highlighted the need for robust, high quality research that reports a core (minimum) data set to enable meaningful evaluation of the literature

The CoQ oxidoreductase FSP1 acts parallel to GPX4 to inhibit ferroptosis.

Ferroptosis is a form of regulated cell death that is caused by the iron-dependent peroxidation of lipids1,2. The glutathione-dependent lipid hydroperoxidase glutathione peroxidase 4 (GPX4) prevents ferroptosis by converting lipid hydroperoxides into non-toxic lipid alcohols3,4. Ferroptosis has previously been implicated in the cell death that underlies several degenerative conditions2, and induction of ferroptosis by the inhibition of GPX4 has emerged as a therapeutic strategy to trigger cancer cell death5. However, sensitivity to GPX4 inhibitors varies greatly across cancer cell lines6, which suggests that additional factors govern resistance to ferroptosis. Here, using a synthetic lethal CRISPR-Cas9 screen, we identify ferroptosis suppressor protein 1 (FSP1) (previously known as apoptosis-inducing factor mitochondrial 2 (AIFM2)) as a potent ferroptosis-resistance factor. Our data indicate that myristoylation recruits FSP1 to the plasma membrane where it functions as an oxidoreductase that reduces coenzyme Q10 (CoQ) (also known as ubiquinone-10), which acts as a lipophilic radical-trapping antioxidant that halts the propagation of lipid peroxides. We further find that FSP1 expression positively correlates with ferroptosis resistance across hundreds of cancer cell lines, and that FSP1 mediates resistance to ferroptosis in lung cancer cells in culture and in mouse tumour xenografts. Thus, our data identify FSP1 as a key component of a non-mitochondrial CoQ antioxidant system that acts in parallel to the canonical glutathione-based GPX4 pathway. These findings define a ferroptosis suppression pathway and indicate that pharmacological inhibition of FSP1 may provide an effective strategy to sensitize cancer cells to ferroptosis-inducing chemotherapeutic agents

Interactive Multi-Stage Robotic Positioner for Intra-Operative MRI-Guided Stereotactic Neurosurgery

Magnetic resonance imaging (MRI) demonstrates clear advantages over other imaging modalities in neurosurgery with its ability to delineate critical neurovascular structures and cancerous tissue in high-resolution 3D anatomical roadmaps. However, its application has been limited to interventions performed based on static pre/post-operative imaging, where errors accrue from stereotactic frame setup, image registration, and brain shift. To leverage the powerful intra-operative functions of MRI, e.g., instrument tracking, monitoring of physiological changes and tissue temperature in MRI-guided bilateral stereotactic neurosurgery, a multi-stage robotic positioner is proposed. The system positions cannula/needle instruments using a lightweight (203 g) and compact (Ø97 × 81 mm) skull-mounted structure that fits within most standard imaging head coils. With optimized design in soft robotics, the system operates in two stages: i) manual coarse adjustment performed interactively by the surgeon (workspace of ±30°), ii) automatic fine adjustment with precise (<0.2° orientation error), responsive (1.4 Hz bandwidth), and high-resolution (0.058°) soft robotic positioning. Orientation locking provides sufficient transmission stiffness (4.07 N/mm) for instrument advancement. The system's clinical workflow and accuracy is validated with lab-based (<0.8 mm) and MRI-based testing on skull phantoms (<1.7 mm) and a cadaver subject (<2.2 mm). Custom-made wireless omni-directional tracking markers facilitated robot registration under MRI

NLSP Gluino Search at the Tevatron and early LHC

We investigate the collider phenomenology of gluino-bino co-annihilation scenario both at the Tevatron and 7 TeV LHC. This scenario can be realized, for example, in a class of realistic supersymmetric models with non-universal gaugino masses and t-b-\tau Yukawa unification. The NLSP gluino and LSP bino should be nearly degenerate in mass, so that the typical gluino search channels involving leptons or hard jets are not available. Consequently, the gluino can be lighter than various bounds on its mass from direct searches. We propose a new search for NLSP gluino involving multi-b final states, arising from the three-body decay \tilde{g}-> b\bar{b}\tilde{\chi}_1^0. We identify two realistic models with gluino mass of around 300 GeV for which the three-body decay is dominant, and show that a 4.5 \sigma observation sensitivity can be achieved at the Tevatron with an integrated luminosity of 10 fb^{-1}. For the 7 TeV LHC with 50 pb^{-1} of integrated luminosity, the number of signal events for the two models is O(10), to be compared with negligible SM background event.Comment: 14 pages, 4 figures and 3 tables, minor modifications made and accepted for publication in JHE

China's post-coal growth

Slowing GDP growth, a structural shift away from heavy industry, and more proactive policies on air pollution and clean energy have caused China's coal use to peak. It seems that economic growth has decoupled from growth in coal consumption

The EDKB: an established knowledge base for endocrine disrupting chemicals

<p>Abstract</p> <p>Background</p> <p>Endocrine disruptors (EDs) and their broad range of potential adverse effects in humans and other animals have been a concern for nearly two decades. Many putative EDs are widely used in commercial products regulated by the Food and Drug Administration (FDA) such as food packaging materials, ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990’s by the FDA as a resource for the study of EDs. The EDKB database, a component of the project, contains data across multiple assay types for chemicals across a broad structural diversity. This paper demonstrates the utility of EDKB database, an integral part of the EDKB project, for understanding and prioritizing EDs for testing.</p> <p>Results</p> <p>The EDKB database currently contains 3,257 records of over 1,800 EDs from different assays including estrogen receptor binding, androgen receptor binding, uterotropic activity, cell proliferation, and reporter gene assays. Information for each compound such as chemical structure, assay type, potency, etc. is organized to enable efficient searching. A user-friendly interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet and graphical displays for viewing results. The search engine implemented in the EDKB database enables searching by one or more of the following fields: chemical structure (including exact search and similarity search), name, molecular formula, CAS registration number, experiment source, molecular weight, etc. The data can be cross-linked to other publicly available and related databases including TOXNET, Cactus, ChemIDplus, ChemACX, Chem Finder, and NCI DTP. </p> <p>Conclusion</p> <p>The EDKB database enables scientists and regulatory reviewers to quickly access ED data from multiple assays for specific or similar compounds. The data have been used to categorize chemicals according to potential risks for endocrine activity, thus providing a basis for prioritizing chemicals for more definitive but expensive testing. The EDKB database is publicly available and can be found online at <url>http://edkb.fda.gov/webstart/edkb/index.html</url>.</p> <p><b>Disclaimer:</b><it>The views presented in this article do not necessarily reflect those of the US Food and Drug Administration.</it></p

Identifying factors relevant in the assessment of return-to-work efforts in employees on long-term sickness absence due to chronic low back pain: a focus group study

ABSTRACT: BACKGROUND: Efforts undertaken during the return to work (RTW) process need to be sufficient to prevent unnecessary applications for disability benefits. The purpose of this study was to identify factors relevant to RTW Effort Sufficiency (RTW-ES) in cases of sick-listed employees with chronic low back pain (CLBP). METHODS: Using focus groups consisting of Labor Experts (LE's) working at the Dutch Social Insurance Institute, arguments and underlying grounds relevant to the assessment of RTW-ES were investigated. Factors were collected and categorized using the International Classification of Functioning, Disability and Health (ICF model). RESULTS: Two focus groups yielded 19 factors, of which 12 are categorized in the ICF model under activities (e.g. functional capacity) and in the personal (e.g. age, tenure) and environmental domain (e.g. employer-employee relationship). The remaining 7 factors are categorized under intervention, job accommodation and measures. CONCLUSIONS: This focus group study shows that 19 factors may be relevant to RTW-ES in sick-listed employees with CLBP. Providing these results to professionals assessing RTW-ES might contribute to a more transparent and systematic approach. Considering the importance of the quality of the RTW process, optimizing the RTW-ES assessment is essential

Bioconjugates of Glucose Oxidase and Gold Nanorods Based on Electrostatic Interaction with Enhanced Thermostability

Bioconjugates made up of an enzyme and gold nanorods (GNRs) were fabricated by electrostatic interactions (layer-by-layer method, LBL) between anionic glucose oxidase (GOD) and positively charged GNRs. The assembled processes were monitored by UV–Vis spectra, zeta potential measurements, and transmission electron microscopy. The enzyme activity assays of the obtained bioconjugates display a relatively enhanced thermostability behavior in contrast with that of free enzyme. Free GOD in solution only retains about 22% of its relative activity at 90 °C. Unexpectedly, the immobilized GOD on GNRs still retains about 39.3% activity after the same treatment. This work will be of significance for the biologic enhancement using other kinds of anisotropic nanostructure and suggests a new way of enhancing enzyme thermostability using anisotropic metal nanomaterials

Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization

<p>Abstract</p> <p/> <p>This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work.</p> <p>Background</p> <p>Our work can be viewed as an alternative to existing methods to solve the quantitative structure-activity relationship (QSAR) problem. To this end, we review a number approaches both from a methodological and also a performance perspective. In addition to these approaches, we also examined a number of chemical properties that can be used by generic classifier systems, such as feed-forward artificial neural networks. In studying these approaches, we identified a set of interesting benchmark problem sets to which many of the above approaches had been applied. These included: ACE, AChE, AR, BBB, BZR, Cox2, DHFR, ER, FXa, GPB, Therm, and Thr. Finally, we developed our own benchmark set by collecting data on toxicology.</p> <p>Results</p> <p>Our results show that our system performs better than, or comparatively to, the existing methods over a broad range of problem types. Our method does not require the expert knowledge that is necessary to apply the other methods to novel problems.</p> <p>Conclusions</p> <p>We conclude that our success is due to the ability of our system to: 1) encode molecules losslessly before presentation to the learning system, and 2) leverage the design of molecular description languages to facilitate the identification of relevant structural attributes of the molecules over different problem domains.</p

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution