Validation of the modified Fresno Test: assessing physical therapists' evidence based practice knowledge and skills

<p>Abstract</p> <p>Background</p> <p>Health care educators need valid and reliable tools to assess evidence based practice (EBP) knowledge and skills. Such instruments have yet to be developed for use among physical therapists. The Fresno Test (FT) has been validated only among general practitioners and occupational therapists and does not assess integration of research evidence with patient perspectives and clinical expertise. The purpose of this study was to develop and validate a modified FT to assess EBP knowledge and skills relevant to physical therapist (PT) practice.</p> <p>Methods</p> <p>The FT was modified to include PT-specific content and two new questions to assess integration of patient perspectives and clinical expertise with research evidence. An expert panel reviewed the test for content validity. A cross-sectional cohort representing three training levels (EBP-novice students, EBP-trained students, EBP-expert faculty) completed the test. Two blinded raters, not involved in test development, independently scored each test. Construct validity was assessed through analysis of variance for linear trends among known groups. Inter and intra-rater reliability, internal consistency, item discrimination index, item total correlation, and difficulty were analyzed.</p> <p>Results</p> <p>Among 108 participants (31 EBP-novice students, 50 EBP-trained students, and 27 EBP-expert faculty), there was a statistically significant (p < 0.0001) difference in total score corresponding to training level. Total score reliability and psychometric properties of items modified for discipline-specific content were excellent [inter-rater (ICC (2,1)] = 0.91); intra-rater (ICC (2,1)] = 0.95, 0.96)]. Cronbach's α was 0.78. Of the two new items, only one had strong psychometric properties.</p> <p>Conclusions</p> <p>The 13-item modified FT presented here is a valid, reliable assessment of physical therapists' EBP knowledge and skills. One new item assesses integration of patient perspective as part of the EBP model. Educators and researchers may use the 13-item modified FT to evaluate PT EBP curricula and physical therapists' EBP knowledge and skills.</p

The search for transient astrophysical neutrino emission with IceCube-DeepCore

We present the results of a search for astrophysical sources of brief transient neutrino emission using IceCube and DeepCore data acquired between 2012 May 15 and 2013 April 30. While the search methods employed in this analysis are similar to those used in previous IceCube point source searches, the data set being examined consists of a sample of predominantly sub-TeV muon-neutrinos from the Northern Sky (-5 degrees < delta < 90 degrees) obtained through a novel event selection method. This search represents a first attempt by IceCube to identify astrophysical neutrino sources in this relatively unexplored energy range. The reconstructed direction and time of arrival of neutrino events are used to search for any significant self-correlation in the data set. The data revealed no significant source of transient neutrino emission. This result has been used to construct limits at timescales ranging from roughly 1 s to 10 days for generic soft-spectra transients. We also present limits on a specific model of neutrino emission from soft jets in core-collapse supernovae

Search for the Decays B^0 -> D^{(*)+} D^{(*)-}

Using the CLEO-II data set we have searched for the Cabibbo-suppressed decays B^0 -> D^{(*)+} D^{(*)-}. For the decay B^0 -> D^{*+} D^{*-}, we observe one candidate signal event, with an expected background of 0.022 +/- 0.011 events. This yield corresponds to a branching fraction of Br(B^0 -> D^{*+} D^{*-}) = (5.3^{+7.1}_{-3.7}(stat) +/- 1.0(syst)) x 10^{-4} and an upper limit of Br(B^0 -> D^{*+} D^{*-}) D^{*\pm} D^\mp and B^0 -> D^+ D^-, no significant excess of signal above the expected background level is seen, and we calculate the 90% CL upper limits on the branching fractions to be Br(B^0 -> D^{*\pm} D^\mp) D^+ D^-) < 1.2 x 10^{-3}.Comment: 12 page postscript file also available through http://w4.lns.cornell.edu/public/CLNS, submitted to Physical Review Letter

Observation of the Decay Ds+→ωπ+D_{s}^{+}\to \omega\pi^{+}

Using e+e- annihilation data collected by the CLEO~II detector at CESR, we have observed the decay Ds+ to omega pi+. This final state may be produced through the annihilation decay of the Ds+, or through final state interactions. We find a branching ratio of [Gamma(Ds+ to omega pi+)/Gamma(Ds+ to eta pi+)]=0.16+-0.04+-0.03, where the first error is statistical and the second is systematic.Comment: 9 pages, postscript file also available through http://w4.lns.cornell.edu/public/CLN

Search for Gravitational Waves from Primordial Black Hole Binary Coalescences in the Galactic Halo

We use data from the second science run of the LIGO gravitational-wave detectors to search for the gravitational waves from primordial black hole (PBH) binary coalescence with component masses in the range 0.2--$1.0 M_\odot$. The analysis requires a signal to be found in the data from both LIGO observatories, according to a set of coincidence criteria. No inspiral signals were found. Assuming a spherical halo with core radius 5 kpc extending to 50 kpc containing non-spinning black holes with masses in the range 0.2--$1.0 M_\odot$, we place an observational upper limit on the rate of PBH coalescence of 63 per year per Milky Way halo (MWH) with 90% confidence.Comment: 7 pages, 4 figures, to be submitted to Phys. Rev.

Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics

[EN] Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two "pre-diabetic behaviours" (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm.This study has been funded by Instituto de Salud Carlos III through the project PI17/00856 (Co-funded by the European Regional Development Fund, A way to make Europe). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Colás, A.; Vigil, L.; Vargas, B.; Cuesta Frau, D.; Varela, M. (2019). Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics. PLoS ONE. 14(12):1-15. https://doi.org/10.1371/journal.pone.0225817S1151412Goldstein, B., Fiser, D. H., Kelly, M. M., Mickelsen, D., Ruttimann, U., & Pollack, M. M. (1998). Decomplexification in critical illness and injury: Relationship between heart rate variability, severity of illness, and outcome. Critical Care Medicine, 26(2), 352-357. doi:10.1097/00003246-199802000-00040Varela, M. (2008). The route to diabetes: Loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Volume 1, 3-11. doi:10.2147/dmso.s3812Vikman, S., Mäkikallio, T. H., Yli-Mäyry, S., Pikkujämsä, S., Koivisto, A.-M., Reinikainen, P., … Huikuri, H. V. (1999). Altered Complexity and Correlation Properties of R-R Interval Dynamics Before the Spontaneous Onset of Paroxysmal Atrial Fibrillation. Circulation, 100(20), 2079-2084. doi:10.1161/01.cir.100.20.2079Wang, H., Naghavi, M., Allen, C., Barber, R. M., Bhutta, Z. A., Carter, A., … Coates, M. M. (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1459-1544. doi:10.1016/s0140-6736(16)31012-1Saudek, C. D., Derr, R. L., & Kalyani, R. R. (2006). Assessing Glycemia in Diabetes Using Self-monitoring Blood Glucose and Hemoglobin A1c. JAMA, 295(14), 1688. doi:10.1001/jama.295.14.1688Monnier, L., Colette, C., & Owens, D. R. (2008). Glycemic Variability: The Third Component of the Dysglycemia in Diabetes. Is it Important? How to Measure it? Journal of Diabetes Science and Technology, 2(6), 1094-1100. doi:10.1177/193229680800200618Abdul-Ghani, M. A., Tripathy, D., & DeFronzo, R. A. (2006). Contributions of  -Cell Dysfunction and Insulin Resistance to the Pathogenesis of Impaired Glucose Tolerance and Impaired Fasting Glucose. Diabetes Care, 29(5), 1130-1139. doi:10.2337/dc05-2179(2017). 2. Classification and Diagnosis of Diabetes:Standards of Medical Care in Diabetes—2018. Diabetes Care, 41(Supplement 1), S13-S27. doi:10.2337/dc18-s002Tabák, A. G., Herder, C., Rathmann, W., Brunner, E. J., & Kivimäki, M. (2012). Prediabetes: a high-risk state for diabetes development. The Lancet, 379(9833), 2279-2290. doi:10.1016/s0140-6736(12)60283-9DeFronzo, R. A., Banerji, M. A., Bray, G. A., Buchanan, T. A., Clement, S., … Tripathy, D. (2009). Determinants of glucose tolerance in impaired glucose tolerance at baseline in the Actos Now for Prevention of Diabetes (ACT NOW) study. Diabetologia, 53(3), 435-445. doi:10.1007/s00125-009-1614-2Nathan, D. M., Davidson, M. B., DeFronzo, R. A., Heine, R. J., Henry, R. R., Pratley, R., & Zinman, B. (2007). Impaired Fasting Glucose and Impaired Glucose Tolerance: Implications for care. Diabetes Care, 30(3), 753-759. doi:10.2337/dc07-9920Ogata, H., Tokuyama, K., Nagasaka, S., Tsuchita, T., Kusaka, I., Ishibashi, S., … Yamamoto, Y. (2012). The lack of long-range negative correlations in glucose dynamics is associated with worse glucose control in patients with diabetes mellitus. Metabolism, 61(7), 1041-1050. doi:10.1016/j.metabol.2011.12.007Kohnert, K.-D. (2015). Utility of different glycemic control metrics for optimizing management of diabetes. World Journal of Diabetes, 6(1), 17. doi:10.4239/wjd.v6.i1.17García Maset, L., González, L. B., Furquet, G. L., Suay, F. M., & Marco, R. H. (2016). Study of Glycemic Variability Through Time Series Analyses (Detrended Fluctuation Analysis and Poincaré Plot) in Children and Adolescents with Type 1 Diabetes. Diabetes Technology & Therapeutics, 18(11), 719-724. doi:10.1089/dia.2016.0208Service, F. J., O’Brien, P. C., & Rizza, R. A. (1987). Measurements of Glucose Control. Diabetes Care, 10(2), 225-237. doi:10.2337/diacare.10.2.225Goldberger, A. L., Amaral, L. A. N., Hausdorff, J. M., Ivanov, P. C., Peng, C.-K., & Stanley, H. E. (2002). Fractal dynamics in physiology: Alterations with disease and aging. Proceedings of the National Academy of Sciences, 99(Supplement 1), 2466-2472. doi:10.1073/pnas.012579499Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism, 101(4), 1490-1497. doi:10.1210/jc.2015-4035Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043Pincus, S. M., Gladstone, I. M., & Ehrenkranz, R. A. (1991). A regularity statistic for medical data analysis. Journal of Clinical Monitoring, 7(4), 335-345. doi:10.1007/bf01619355Richman, J. S. (2007). Sample Entropy Statistics and Testing for Order in Complex Physiological Signals. Communications in Statistics - Theory and Methods, 36(5), 1005-1019. doi:10.1080/03610920601036481Platiša, M. M., Bojić, T., Pavlović, S. U., Radovanović, N. N., & Kalauzi, A. (2016). Generalized Poincaré Plots-A New Method for Evaluation of Regimes in Cardiac Neural Control in Atrial Fibrillation and Healthy Subjects. Frontiers in Neuroscience, 10. doi:10.3389/fnins.2016.00038García-Puig, J., Ruilope, L. M., Luque, M., Fernández, J., Ortega, R., & Dal-Ré, R. (2006). Glucose Metabolism in Patients with Essential Hypertension. The American Journal of Medicine, 119(4), 318-326. doi:10.1016/j.amjmed.2005.09.010Lepot, M., Aubin, J.-B., & Clemens, F. (2017). Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment. Water, 9(10), 796. doi:10.3390/w9100796Eke, A., Hermán, P., Bassingthwaighte, J., Raymond, G., Percival, D., Cannon, M., … Ikrényi, C. (2000). Physiological time series: distinguishing fractal noises from motions. Pflügers Archiv - European Journal of Physiology, 439(4), 403-415. doi:10.1007/s004249900135Eke, A., Herman, P., Kocsis, L., & Kozak, L. R. (2002). Fractal characterization of complexity in temporal physiological signals. Physiological Measurement, 23(1), R1-R38. doi:10.1088/0967-3334/23/1/201King, A. B., Philis-Tsimikas, A., Kilpatrick, E. S., Langbakke, I. H., Begtrup, K., & Vilsbøll, T. (2017). A Fixed Ratio Combination of Insulin Degludec and Liraglutide (IDegLira) Reduces Glycemic Fluctuation and Brings More Patients with Type 2 Diabetes Within Blood Glucose Target Ranges. Diabetes Technology & Therapeutics, 19(4), 255-264. doi:10.1089/dia.2016.0405Colas, A., Vigil, L., Rodríguez de Castro, C., Vargas, B., & Varela, M. (2018). New insights from continuous glucose monitoring into the route to diabetes. Diabetes/Metabolism Research and Reviews, 34(5), e3002. doi:10.1002/dmrr.3002Henriques, T., Munshi, M. N., Segal, A. R., Costa, M. D., & Goldberger, A. L. (2014). «Glucose-at-a-Glance». Journal of Diabetes Science and Technology, 8(2), 299-306. doi:10.1177/1932296814524095Hinton, P. R. (2004). Statistics Explained. doi:10.4324/9780203496787Van Cauter, E., Blackman, J. D., Roland, D., Spire, J. P., Refetoff, S., & Polonsky, K. S. (1991). Modulation of glucose regulation and insulin secretion by circadian rhythmicity and sleep. Journal of Clinical Investigation, 88(3), 934-942. doi:10.1172/jci115396Qian, J., & Scheer, F. A. J. L. (2016). Circadian System and Glucose Metabolism: Implications for Physiology and Disease. Trends in Endocrinology & Metabolism, 27(5), 282-293. doi:10.1016/j.tem.2016.03.00

The Aedes aegypti Toll Pathway Controls Dengue Virus Infection

Aedes aegypti, the mosquito vector of dengue viruses, utilizes its innate immune system to ward off a variety of pathogens, some of which can cause disease in humans. To date, the features of insects' innate immune defenses against viruses have mainly been studied in the fruit fly Drosophila melanogaster, which appears to utilize different immune pathways against different types of viruses, in addition to an RNA interference–based defense system. We have used the recently released whole-genome sequence of the Ae. aegypti mosquito, in combination with high-throughput gene expression and RNA interference (RNAi)-based reverse genetic analyses, to characterize its response to dengue virus infection in different body compartments. We have further addressed the impact of the mosquito's endogenous microbial flora on virus infection. Our findings indicate a significant role for the Toll pathway in regulating resistance to dengue virus, as indicated by an infection-responsive regulation and functional assessment of several Toll pathway–associated genes. We have also shown that the mosquito's natural microbiota play a role in modulating the dengue virus infection, possibly through basal-level stimulation of the Toll immune pathway

Nonlinear complexity analysis of brain fMRI signals in schizophrenia

Peer reviewedPublisher PD

Maternal anaemia and duration of zidovudine in antiretroviral regimens for preventing mother-to-child transmission: a randomized trial in three African countries

Background: Although substantiated by little evidence, concerns about zidovudine-related anaemia in pregnancy have influenced antiretroviral (ARV) regimen choice for preventing mother-to-child transmission of HIV-1, especially in settings where anaemia is common. Methods: Eligible HIV-infected pregnant women in Burkina Faso, Kenya and South Africa were followed from 28 weeks of pregnancy until 12–24 months after delivery (n = 1070). Women with a CD4 count of 200-500cells/mm3 and gestational age 28–36 weeks were randomly assigned to zidovudine-containing triple-ARV prophylaxis continued during breastfeeding up to 6-months, or to zidovudine during pregnancy plus single-dose nevirapine (sd-NVP) at labour. Additionally, two cohorts were established, women with CD4 counts: \u3c200 cells/mm3 initiated antiretroviral therapy, and \u3e500 cells/mm3 received zidovudine during pregnancy plus sd-NVP at labour. Mild (haemoglobin 8.0-10.9 g/dl) and severe anaemia (haemoglobin \u3c 8.0 g/dl) occurrence were assessed across study arms, using Kaplan-Meier and multivariable Cox proportional hazards models. Results: At enrolment (corresponded to a median 32 weeks gestation), median haemoglobin was 10.3 g/dl (IQR = 9.2-11.1). Severe anaemia occurred subsequently in 194 (18.1%) women, mostly in those with low baseline haemoglobin, lowest socio-economic category, advanced HIV disease, prolonged breastfeeding (≥6 months) and shorter ARV exposure. Severe an- aemia incidence was similar in the randomized arms (equivalence P-value = 0.32). After 1–2 months of ARV’s, severe anaemia was significantly reduced in all groups, though remained highest in the low CD4 cohort. Conclusions: Severe anaemia occurs at a similar rate in women receiving longer triple zidovudine-containing regimens or shorter prophylaxis. Pregnant women with pre-existing anaemia and advanced HIV disease require close monitoring