Risk assessment of failure during transitioning from in-centre to home haemodialysis

Background: Introducing a de-novo home haemodialysis (HHD) program often raises safety concerns as errors could potentially lead to serious adverse events. Despite the complexity of performing haemodialysis at home without the supervision of healthcare staff, HHD has a good safety record. We aim to pre-emptively identify and reduce the risks to our new HHD program by risk assessment and using failure mode and effects analysis (FMEA) to identify potential defects in the design and planning of HHD. Methods: We performed a general risk assessment of failure during transitioning from in-centre to HHD with a failure mode and effects analysis focused on the highest areas of failure. We collaborated with key team members from a well-established HHD program and one HHD patient. Risk assessment was conducted separately and then through video conference meetings for joint deliberation. We listed all key processes, sub-processes, step and then identified failure mode by scoring based on risk priority numbers. Solutions were then designed to eliminate and mitigate risk. Results: Transitioning to HHD was found to have the highest risk of failure with 3 main processes and 34 steps. We identified a total of 59 areas with potential failures. The median and mean risk priority number (RPN) scores from failure mode effect analysis were 5 and 38, with the highest RPN related to vascular access at 256. As many failure modes with high RPN scores were related to vascular access, we focussed on FMEA by identifying the risk mitigation strategies and possible solutions in all 9 areas in access-related medical emergencies in a bundled- approach. We discussed, the risk reduction areas of setting up HHD and how to address incidents that occurred and those not preventable. Conclusions: We developed a safety framework for a de-novo HHD program by performing FMEA in high-risk areas. The involvement of two teams with different clinical experience for HHD allowed us to successfully pre-emptively identify risks and develop solutions

Early Exposure to Soy Isoflavones and Effects on Reproductive Health: A Review of Human and Animal Studies

Soy isoflavones are phytoestrogens with potential hormonal activity due to their similar chemical structure to 17-β-estradiol. The increasing availability of soy isoflavones throughout the food supply and through use of supplements has prompted extensive research on biological benefits to humans in chronic disease prevention and health maintenance. While much of this research has focused on adult populations, infants fed soy protein based infant formulas are exposed to substantial levels of soy isoflavones, even when compared to adult populations that consume a higher quantity of soy-based foods. Infant exposure, through soy formula, primarily occurs from birth to one year of life, a stage of development that is particularly sensitive to dietary and environmental compounds. This has led investigators to study the potential hormonal effects of soy isoflavones on later reproductive health outcomes. Such studies have included minimal human data with the large majority of studies using animal models. This review discusses key aspects of the current human and animal studies and identifies critical areas to be investigated as there is no clear consensus in this research field

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

14-3-3 Amplifies Androgen Receptor Actions in Prostate Cancer

Androgen receptor (AR) abundance and AR-regulated gene expression in castration-recurrent prostate cancer (CaP) are indicative of AR activation in the absence of testicular androgen. AR transactivation of target genes in castration-recurrent CaP occurs in part through mitogen signaling that amplifies the actions of AR and its coregulators. Herein we report on the role of 14-3-3η in AR action

Chronic kidney disease in public renal practices in Queensland, Australia, 2011–2018

Aim: To describe adults with (non-dialysis) chronic kidney disease (CKD) in nine public renal practice sites in the Australian state of Queensland. Methods: 7,060 persons were recruited to a CKD Registry in May 2011 and until start of kidney replacement therapy (KRT), death without KRT or June 2018, for a median period of 3.4 years. Results: The cohort comprised 7,060 persons, 52% males, with a median age of 68 yr; 85% had CKD stages 3A to 5, 45.4% were diabetic, 24.6% had diabetic nephropathy, and 51.7% were obese. Younger persons mostly had glomerulonephritis or genetic renal disease, while older persons mostly had diabetic nephropathy, renovascular disease and multiple diagnoses. Proportions of specific renal diagnoses varied >2-fold across sites. Over the first year, eGFR fell in 24% but was stable or improved in 76%. Over follow up, 10% started KRT, at a median age of 62 yr, most with CKD stages 4 and 5 at consent, while 18.8% died without KRT, at a median age of 80 yr. Indigenous people were younger at consent and more often had diabetes and diabetic kidney disease and had higher incidence rates of KRT. Conclusion: The spectrum of characteristics in CKD patients in renal practices is much broader than represented by the minority who ultimately start KRT. Variation in CKD by causes, age, site and Indigenous status, the prevalence of obesity, relative stability of kidney function in many persons over the short term, and differences between those who KRT and die without KRT are all important to explore

Mechanism of androgen receptor corepression by CKβBP2/CRIF1, a multifunctional transcription factor coregulator expressed in prostate cancer

The transcription factor coregulator Casein kinase IIβbinding protein 2 or CR6-interacting factor 1 (CKβBP2/CRIF1) binds the androgen receptor (AR) in prostate cancer cells and in response to dihydrotestosterone localizes with AR on the prostate-specific antigen gene enhancer, but does not bind DNA suggesting CKβBP2/CRIF1 localization in chromatin is determined by AR. In this study we show also that CKβBP2/CRIF1 inhibits wild-type AR and AR N-terminal transcriptional activity, binds to the AR C-terminal region, inhibits interaction of the AR N- and C-terminal domains (N/C interaction) and competes with p160 coactivator binding to the AR C-terminal domain, suggesting CKβBP2/CRIF1 interferes with AR activation functions 1 and 2. CKβBP2/CRIF1 is expressed mainly in stromal cells of benign prostatic hyperplasia and in stroma and epithelium of prostate cancer. CKβBP2/CRIF1 protein is increased in epithelium of androgen-dependent prostate cancer compared to benign prostatic hyperplasia and decreased slightly in castration recurrent epithelium compared to androgen-dependent prostate cancer. The multifunctional CKβBP2/CRIF1 is a STAT3 interacting protein and reported to be a coactivator of STAT3. CKβBP2/CRIF1 is expressed with STAT3 in prostate cancer where STAT3 may help to offset the AR repressor effect of CKβBP2/CRIF1 and allow AR regulation of prostate cancer growth

What is Learned from Longitudinal Studies of Advertising and Youth Drinking and Smoking? A Critical Assessment

This paper assesses the methodology employed in longitudinal studies of advertising and youth drinking and smoking behaviors. These studies often are given a causal interpretation in the psychology and public health literatures. Four issues are examined from the perspective of econometrics. First, specification and validation of empirical models. Second, empirical issues associated with measures of advertising receptivity and exposure. Third, potential endogeneity of receptivity and exposure variables. Fourth, sample selection bias in baseline and follow-up surveys. Longitudinal studies reviewed include 20 studies of youth drinking and 26 studies of youth smoking. Substantial shortcomings are found in the studies, which preclude a causal interpretation

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio