1,779 research outputs found

    Involving people with diabetes and the wider community in diabetes research: a realist review protocol.

    Get PDF
    BACKGROUND: Patient and public involvement in diabetes research is now actively encouraged in different countries because it is believed that involving people with experience of the condition will improve the quality and relevance of the research. However, reviews of patient involvement have noted that inadequate resources, patients' and communities' lack of research knowledge, and researchers' lack of skills to involve patients and communities in research may present significant contextual barriers. Little is known about the extent of patient/community involvement in designing or delivering interventions for people with diabetes. A realist review of involvement will contribute to assessing when, how and why involvement works, or does not work, to produce better diabetes interventions. METHODS/DESIGN: This protocol outlines the process for conducting a realist review to map how patients and the public have been involved in diabetes research to date. The review questions ask the following: How have people with diabetes and the wider community been involved in diabetes research? What are the characteristics of the process that appear to explain the relative success or failure of involvement? How has involvement (or lack of involvement) in diabetes research influenced the development and conduct of diabetes research? The degree of support in the surrounding context will be assessed alongside the ways in which people interact in different settings to identify patterns of interaction between context, mechanisms and outcomes in different research projects. The level and extent of the involvement will be described for each stage of the research project. The descriptions will be critically reviewed by the people with diabetes on our review team. In addition, researchers and patients in diabetes research will be asked to comment. Information from researcher-patient experiences and documents will be compared to theories of involvement across a range of disciplines to create a mid-range theory describing how involvement (or lack of involvement) in diabetes research influences the development and conduct of diabetes research

    Dissolution dominating calcification process in polar pteropods close to the point of aragonite undersaturation

    Get PDF
    Thecosome pteropods are abundant upper-ocean zooplankton that build aragonite shells. Ocean acidification results in the lowering of aragonite saturation levels in the surface layers, and several incubation studies have shown that rates of calcification in these organisms decrease as a result. This study provides a weight-specific net calcification rate function for thecosome pteropods that includes both rates of dissolution and calcification over a range of plausible future aragonite saturation states (Omega_Ar). We measured gross dissolution in the pteropod Limacina helicina antarctica in the Scotia Sea (Southern Ocean) by incubating living specimens across a range of aragonite saturation states for a maximum of 14 days. Specimens started dissolving almost immediately upon exposure to undersaturated conditions (Omega_Ar,0.8), losing 1.4% of shell mass per day. The observed rate of gross dissolution was different from that predicted by rate law kinetics of aragonite dissolution, in being higher at Var levels slightly above 1 and lower at Omega_Ar levels of between 1 and 0.8. This indicates that shell mass is affected by even transitional levels of saturation, but there is, nevertheless, some partial means of protection for shells when in undersaturated conditions. A function for gross dissolution against Var derived from the present observations was compared to a function for gross calcification derived by a different study, and showed that dissolution became the dominating process even at Omega_Ar levels close to 1, with net shell growth ceasing at an Omega_Ar of 1.03. Gross dissolution increasingly dominated net change in shell mass as saturation levels decreased below 1. As well as influencing their viability, such dissolution of pteropod shells in the surface layers will result in slower sinking velocities and decreased carbon and carbonate fluxes to the deep ocean

    Stochastic population growth in spatially heterogeneous environments

    Full text link
    Classical ecological theory predicts that environmental stochasticity increases extinction risk by reducing the average per-capita growth rate of populations. To understand the interactive effects of environmental stochasticity, spatial heterogeneity, and dispersal on population growth, we study the following model for population abundances in nn patches: the conditional law of Xt+dtX_{t+dt} given Xt=xX_t=x is such that when dtdt is small the conditional mean of Xt+dtiXtiX_{t+dt}^i-X_t^i is approximately [xiμi+j(xjDjixiDij)]dt[x^i\mu_i+\sum_j(x^j D_{ji}-x^i D_{ij})]dt, where XtiX_t^i and μi\mu_i are the abundance and per capita growth rate in the ii-th patch respectivly, and DijD_{ij} is the dispersal rate from the ii-th to the jj-th patch, and the conditional covariance of Xt+dtiXtiX_{t+dt}^i-X_t^i and Xt+dtjXtjX_{t+dt}^j-X_t^j is approximately xixjσijdtx^i x^j \sigma_{ij}dt. We show for such a spatially extended population that if St=(Xt1+...+Xtn)S_t=(X_t^1+...+X_t^n) is the total population abundance, then Yt=Xt/StY_t=X_t/S_t, the vector of patch proportions, converges in law to a random vector YY_\infty as tt\to\infty, and the stochastic growth rate limtt1logSt\lim_{t\to\infty}t^{-1}\log S_t equals the space-time average per-capita growth rate \sum_i\mu_i\E[Y_\infty^i] experienced by the population minus half of the space-time average temporal variation \E[\sum_{i,j}\sigma_{ij}Y_\infty^i Y_\infty^j] experienced by the population. We derive analytic results for the law of YY_\infty, find which choice of the dispersal mechanism DD produces an optimal stochastic growth rate for a freely dispersing population, and investigate the effect on the stochastic growth rate of constraints on dispersal rates. Our results provide fundamental insights into "ideal free" movement in the face of uncertainty, the persistence of coupled sink populations, the evolution of dispersal rates, and the single large or several small (SLOSS) debate in conservation biology.Comment: 47 pages, 4 figure

    Brief Report: Theatre as Therapy for Children with Autism Spectrum Disorder

    Get PDF
    The pilot investigation evaluated a theatrical intervention program, Social Emotional NeuroScience Endocrinology (SENSE) Theatre, designed to improve socioemotional functioning and reduce stress in children with autism spectrum disorder (ASD). Eight children with ASD were paired with typically developing peers that served as expert models. Neuropsychological, biological (cortisol and oxytocin), and behavioral measures were assessed in a pretest–posttest design. The intervention was embedded in a full musical theatrical production. Participants showed some improvement in face identification and theory of mind skills. The intervention shows potential promise in improving the socioemotional functioning in children with ASD through the utilization of peers, video and behavioral modeling, and a community-based theatrical setting

    Two specific mutations are prevalent causes of recessive retinitis pigmentosa in North American patients of Jewish ancestry.

    Get PDF
    PURPOSE: Retinitis pigmentosa is a Mendelian disease with a very elevated genetic heterogeneity. Most mutations are responsible for less than 1% of cases, making molecular diagnosis a multigene screening procedure. In this study, we assessed whether direct testing of specific alleles could be a valuable screening approach in cases characterized by prevalent founder mutations. METHODS: We screened 275 North American patients with recessive/isolate retinitis pigmentosa for two mutations: an Alu insertion in the MAK gene and the p.Lys42Glu missense in the DHDDS gene. All patients were unrelated; 35 reported Jewish ancestry and the remainder reported mixed ethnicity. RESULTS: We identified the MAK and DHDDS mutations homozygously in only 2.1% and 0.8%, respectively, of patients of mixed ethnicity, but in 25.7% and 8.6%, respectively, of cases reporting Jewish ancestry. Haplotype analyses revealed that inheritance of the MAK mutation was attributable to a founder effect. CONCLUSION: In contrast to most mutations associated with retinitis pigmentosa-which are, in general, extremely rare-the two alleles investigated here cause disease in approximately one-third of North American patients reporting Jewish ancestry. Therefore, their screening constitutes an alternative procedure to large-scale tests for patients belonging to this ethnic group, especially in time-sensitive situations.Genet Med 17 4, 285-290

    Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining

    Full text link
    [EN] Background: Public health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes. Objective: Health care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients. Methods: This paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise. Results: The method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner. Conclusions: By discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.This paper was partially funded by the National Commission for Scientific and Technological Research, the Formation of Advanced Human Capital Program and the National Fund for Scientific and Technological Development (CONICYT-PCHA/Doctorado Nacional/2016-21161705 and CONICYT-FONDECYT/1150365; Chile). The authors would like to thank Ancora UC primary health care centers for their help with this research. The founding sponsors had no role in the design of the study in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.Conca, T.; Saint Pierre, C.; Herskovic, V.; Sepulveda, M.; Capurro, D.; Prieto, F.; Fernández Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. JOURNAL OF MEDICAL INTERNET RESEARCH. 20(4). https://doi.org/10.2196/jmir.8884S204Chen, C.-C., Tseng, C.-H., & Cheng, S.-H. (2013). Continuity of Care, Medication Adherence, and Health Care Outcomes Among Patients With Newly Diagnosed Type 2 Diabetes. Medical Care, 51(3), 231-237. doi:10.1097/mlr.0b013e31827da5b9International Diabetes FederationIDF20152018-03-19IDF Diabetes Atlas 7th Edition (2015) https://www.idf.org/e-library/epidemiology-research/diabetes-atlas/13-diabetes-atlas-seventh-edition.htmlMinisterio de Salud de Chileminsal.cl20102018-03-23Encuesta Nacional de Salud ENS Chile 2009-2010 http://www.minsal.cl/estudios_encuestas_salud/Ministerio de Salud de Chileminsal.cl20102018-03-20Guía Clinica Diabetes Mellitus Tipo 2 http://www.minsal.cl/portal/url/item/72213ed52c3e23d1e04001011f011398.pdfSapunar Z., J. (2016). EPIDEMIOLOGÍA DE LA DIABETES MELLITUS EN CHILE. Revista Médica Clínica Las Condes, 27(2), 146-151. doi:10.1016/j.rmclc.2016.04.003World Health Organizationwho.int2018-03-20Global Report on Diabetes http://www.who.int/diabetes/global-report/en/Poblete, F., Glasinovic, A., Sapag, J., Barticevic, N., Arenas, A., & Padilla, O. (2015). Apoyo social y salud cardiovascular: adaptación de una escala de apoyo social en pacientes hipertensos y diabéticos en la atención primaria chilena. Atención Primaria, 47(8), 523-531. doi:10.1016/j.aprim.2014.10.010Tuligenga, R. H., Dugravot, A., Tabák, A. G., Elbaz, A., Brunner, E. J., Kivimäki, M., & Singh-Manoux, A. (2014). Midlife type 2 diabetes and poor glycaemic control as risk factors for cognitive decline in early old age: a post-hoc analysis of the Whitehall II cohort study. The Lancet Diabetes & Endocrinology, 2(3), 228-235. doi:10.1016/s2213-8587(13)70192-xGamiochipi, M., Cruz, M., Kumate, J., & Wacher, N. H. (2016). Effect of an intensive metabolic control lifestyle intervention in type-2 diabetes patients. Patient Education and Counseling, 99(7), 1184-1189. doi:10.1016/j.pec.2016.01.017Wagner, E. H. (2001). Effect of Improved Glycemic Control on Health Care Costs and Utilization. JAMA, 285(2), 182. doi:10.1001/jama.285.2.182McDonald, J., Jayasuriya, R., & Harris, M. F. (2012). The influence of power dynamics and trust on multidisciplinary collaboration: a qualitative case study of type 2 diabetes mellitus. BMC Health Services Research, 12(1). doi:10.1186/1472-6963-12-63Gucciardi, E., Espin, S., Morganti, A., & Dorado, L. (2016). Exploring interprofessional collaboration during the integration of diabetes teams into primary care. BMC Family Practice, 17(1). doi:10.1186/s12875-016-0407-1Caron, F., Vanthienen, J., Vanhaecht, K., Limbergen, E. V., De Weerdt, J., & Baesens, B. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44, 88-96. doi:10.1016/j.compbiomed.2013.10.015Rothman, A. A., & Wagner, E. H. (2003). Chronic Illness Management: What Is the Role of Primary Care? Annals of Internal Medicine, 138(3), 256. doi:10.7326/0003-4819-138-3-200302040-00034Organisation for Economic Co-operation and DevelopmentOECD20162018-03-20OECD Health Policy Overview: Health Policy in Chile http://www.oecd.org/els/health-systems/health-policy-in-your-country.htmRojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Mans, R. S., van der Aalst, W. M. P., & Vanwersch, R. J. B. (2015). Process Mining in Healthcare. SpringerBriefs in Business Process Management. doi:10.1007/978-3-319-16071-9Van der Aalst, W. M. P. (2011). Process Mining. doi:10.1007/978-3-642-19345-3Kim, E., Kim, S., Song, M., Kim, S., Yoo, D., Hwang, H., & Yoo, S. (2013). Discovery of Outpatient Care Process of a Tertiary University Hospital Using Process Mining. Healthcare Informatics Research, 19(1), 42. doi:10.4258/hir.2013.19.1.42Harper, P. R., Sayyad, M. G., de Senna, V., Shahani, A. K., Yajnik, C. S., & Shelgikar, K. M. (2003). A systems modelling approach for the prevention and treatment of diabetic retinopathy. European Journal of Operational Research, 150(1), 81-91. doi:10.1016/s0377-2217(02)00787-7Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Ferreira, D., Zacarias, M., Malheiros, M., & Ferreira, P. (2007). Approaching Process Mining with Sequence Clustering: Experiments and Findings. Business Process Management, 360-374. doi:10.1007/978-3-540-75183-0_26Cheong, L. H., Armour, C. L., & Bosnic-Anticevich, S. Z. (2013). Multidisciplinary collaboration in primary care: through the eyes of patients. Australian Journal of Primary Health, 19(3), 190. doi:10.1071/py12019Boyle, E., Saunders, R., & Drury, V. (2016). A qualitative study of patient experiences of Type 2 Diabetes care delivered comparatively by General Practice Nurses and Medical Practitioners. Journal of Clinical Nursing, 25(13-14), 1977-1986. doi:10.1111/jocn.13219UddinSHossainLEffects of Physician Collaboration Network on Hospital Outcomes2012Fifth Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2012)2012Melbourne, AustraliaBorgermans, L., Goderis, G., Van Den Broeke, C., Verbeke, G., Carbonez, A., Ivanova, A., … Grol, R. (2009). Interdisciplinary diabetes care teams operating on the interface between primary and specialty care are associated with improved outcomes of care: findings from the Leuven Diabetes Project. BMC Health Services Research, 9(1). doi:10.1186/1472-6963-9-179Bosch, M., Dijkstra, R., Wensing, M., van der Weijden, T., & Grol, R. (2008). Organizational culture, team climate and diabetes care in small office-based practices. BMC Health Services Research, 8(1). doi:10.1186/1472-6963-8-180Counsell, S. R., Callahan, C. M., Clark, D. O., Tu, W., Buttar, A. B., Stump, T. E., & Ricketts, G. D. (2007). Geriatric Care Management for Low-Income Seniors. JAMA, 298(22), 2623. doi:10.1001/jama.298.22.2623Anderson, J. G. (2002). Evaluation in health informatics: social network analysis. Computers in Biology and Medicine, 32(3), 179-193. doi:10.1016/s0010-4825(02)00014-8Gray, J. E., Davis, D. A., Pursley, D. M., Smallcomb, J. E., Geva, A., & Chawla, N. V. (2010). Network Analysis of Team Structure in the Neonatal Intensive Care Unit. PEDIATRICS, 125(6), e1460-e1467. doi:10.1542/peds.2009-2621Mian, O., Koren, I., & Rukholm, E. (2012). Nurse practitioners in Ontario primary healthcare: Referral patterns and collaboration with other healthcare professionals. Journal of Interprofessional Care, 26(3), 232-239. doi:10.3109/13561820.2011.650300Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social Network Analysis for Ego-Nets. doi:10.4135/9781473911871Ministerio de Salud de Chile2018-03-20Fondo Nacional de Salud https://www.fonasa.cl/sites/fonasa/beneficiariosGoldstein, D. E., Little, R. R., Lorenz, R. A., Malone, J. I., Nathan, D., Peterson, C. M., & Sacks, D. B. (2004). Tests of Glycemia in Diabetes. Diabetes Care, 27(7), 1761-1773. doi:10.2337/diacare.27.7.1761Meduru, P., Helmer, D., Rajan, M., Tseng, C.-L., Pogach, L., & Sambamoorthi, U. (2007). Chronic Illness with Complexity: Implications for Performance Measurement of Optimal Glycemic Control. Journal of General Internal Medicine, 22(S3), 408-418. doi:10.1007/s11606-007-0310-5Vermeire, E., Hearnshaw, H., Van Royen, P., & Denekens, J. (2001). Patient adherence to treatment: three decades of research. A comprehensive review. Journal of Clinical Pharmacy and Therapeutics, 26(5), 331-342. doi:10.1046/j.1365-2710.2001.00363.xKarter, A. J., Parker, M. M., Moffet, H. H., Ahmed, A. T., Ferrara, A., Liu, J. Y., & Selby, J. V. (2004). Missed Appointments and Poor Glycemic Control. Medical Care, 42(2), 110-115. doi:10.1097/01.mlr.0000109023.64650.73World Health Organization20032018-03-20Adherence to long-term therapies: evidence for action http://www.who.int/chp/knowledge/publications/adherence_report/en/Toth, E. L., Majumdar, S. R., Guirguis, L. M., Lewanczuk, R. Z., Lee, T. K., & Johnson, J. A. (2003). Compliance with Clinical Practice Guidelines for Type 2 Diabetes in Rural Patients: Treatment Gaps and Opportunities for Improvement. Pharmacotherapy, 23(5), 659-665. doi:10.1592/phco.23.5.659.32203Melnikow, J., & Kiefe, C. (1994). Patient compliance and medical research. Journal of General Internal Medicine, 9(2), 96-105. doi:10.1007/bf02600211Fernandez-Llatas, C., Valdivieso, B., Traver, V., & Benedi, J. M. (2014). Using Process Mining for Automatic Support of Clinical Pathways Design. Data Mining in Clinical Medicine, 79-88. doi:10.1007/978-1-4939-1985-7_5Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Wishah, R. A., Al-Khawaldeh, O. A., & Albsoul, A. M. (2015). Impact of pharmaceutical care interventions on glycemic control and other health-related clinical outcomes in patients with type 2 diabetes: Randomized controlled trial. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 9(4), 271-276. doi:10.1016/j.dsx.2014.09.00

    Methods designed for the identification and characterization of in vitro and in vivo chromatin assembly mutants in Saccharomyces cerevisiae

    Get PDF
    Assembly of DNA into chromatin allows for the formation of a barrier that protects naked DNA from protein and chemical agents geared to degrade or metabolize DNA. Chromatin assembly occurs whenever a length of DNA becomes exposed to the cellular elements, whether during DNA synthesis or repair. This report describes tools to study chromatin assembly in the model system Saccharomyces cerevisiae. Modifications to an in vitro chromatin assembly assay are described that allowed a brute force screen of temperature sensitive (ts) yeast strains in order to identify chromatin assembly defective extracts. This screen yielded mutations in genes encoding two ubiquitin protein ligases (E3s): RSP5, and a subunit of the Anaphase Promoting Complex (APC), APC5. Additional modifications are described that allow for a rapid analysis and an in vivo characterization of yeast chromatin assembly mutants, as well as any other mutant of interest. Our analysis suggests that the in vitro and in vivo chromatin assembly assays are responsive to different cellular signals, including cell cycle cues that involve different molecular networks

    Strong interface-induced spin-orbit coupling in graphene on WS2

    Get PDF
    Interfacial interactions allow the electronic properties of graphene to be modified, as recently demonstrated by the appearance of satellite Dirac cones in the band structure of graphene on hexagonal boron nitride (hBN) substrates. Ongoing research strives to explore interfacial interactions in a broader class of materials in order to engineer targeted electronic properties. Here we show that at an interface with a tungsten disulfide (WS2) substrate, the strength of the spin-orbit interaction (SOI) in graphene is very strongly enhanced. The induced SOI leads to a pronounced low-temperature weak anti-localization (WAL) effect, from which we determine the spin-relaxation time. We find that spin-relaxation time in graphene is two-to-three orders of magnitude smaller on WS2 than on SiO2 or hBN, and that it is comparable to the intervalley scattering time. To interpret our findings we have performed first-principle electronic structure calculations, which both confirm that carriers in graphene-on-WS2 experience a strong SOI and allow us to extract a spin-dependent low-energy effective Hamiltonian. Our analysis further shows that the use of WS2 substrates opens a possible new route to access topological states of matter in graphene-based systems.Comment: Originally submitted version in compliance with editorial guidelines. Final version with expanded discussion of the relation between theory and experiments to be published in Nature Communication
    corecore