855 research outputs found

    Obtención de Chalconas, Catalizada por Óxidos Mixtos de Al-Mg Modificadas con Metales Básicos

    Get PDF
    Los hidróxidos de doble capa son materiales pertenecientes a las arcillas aniónicas. Dichas arcillas pueden ser modificadas con determinados metales de manera de impartirles carácter básico para ser utilizadas en reacciones de química fina. En este trabajo se presenta el estudio realizado a los hidróxidos de doble capa incorporados por el método de coprecipitación, con metales como el Zn, Ni, La y Ce. Dichos materiales presentaron buena estructura, área superficial y alta basicidad. En cuanto a la actividad catalítica, se estudió la reacción de condensación para la obtención de chalconas, obteniéndose conversiones por encima de 80% con altas selectividades y pureza del producto deseado

    Quantitative PCR Is Faster, More Objective, and More Reliable Than Immunohistochemistry for the Diagnosis of Cytomegalovirus Gastrointestinal Disease in Allogeneic Stem Cell Transplantation

    Get PDF
    Diagnosis of gastrointestinal (GI) cytomegalovirus (CMV) disease relies on the presence of GI symptoms and detection of CMV, mainly by immunohistochemistry (IHC), in GI biopsy specimens. Thus, in a symptomatic patient, a positive CMV-IHC result is accepted as a diagnosis of CMV disease. However, a positive CMV-PCR in GI tissue is considered "possible" CMV disease. Therefore, it would be very useful if, in practice, both techniques showed equal sensitivity and reliability. This is because PCR has many practical advantages over IHC for detecting CMV. The aim of this study was to compare quantitative PCR with IHC for the diagnosis of GI CMV disease. A total of 186 endoscopic GI biopsy specimens from 123 patients with GI symptoms after an allogeneic stem cell transplantation (allo-SCT; 2004-2017) were analyzed by IHC and PCR on 113 paraffin-embedded and 73 fresh samples. The results were then compared. Of the patients with macroscopic lesions in the mucosa and CMV-IHC-positive biopsy specimens (eg, "proven" CMV disease, n = 28), all but 1 were CMV-PCR positive. Of the patients without macroscopic lesions in the mucosa and CMV-IHC-positive biopsy specimens (eg, probable CMV disease, n = 4), only 1 was CMV-PCR positive. Eight patients had CMV-IHC-negative/CMV-PCR-positive gut biopsy specimens. These cases fall within the current definition of possible CMV disease. In 6 of these 8 cases (75%), the viral load in GI tissue was very high (>10,000 copies/µg). Taken together, the results from the proven and probable cases revealed that CMV-PCR shows the same sensitivity (100%), specificity (98%), and positive (93%) and negative predictive value (100%) as CMV-IHC. Detection of CMV in fresh GI mucosa by quantitative PCR is as useful as IHC for the diagnosis of GI CMV disease. The results show that quantitative PCR has the same sensitivity, specificity, and positive/negative predictive value as IHC

    Association of VAV2 and VAV3 polymorphisms with cardiovascular risk factors

    Get PDF
    Hypertension, diabetes and obesity are cardiovascular risk factors closely associated to the development of renal and cardiovascular target organ damage. VAV2 and VAV3, members of the VAV family proto-oncogenes, are guanosine nucleotide exchange factors for the Rho and Rac GTPase family, which is related with cardiovascular homeostasis. We have analyzed the relationship between the presence of VAV2 rs602990 and VAV3 rs7528153 polymorphisms with cardiovascular risk factors and target organ damage (heart, vessels and kidney) in 411 subjects. Our results show that being carrier of the T allele in VAV2 rs602990 polymorphism is associated with an increased risk of obesity, reduced levels of ankle-brachial index and diastolic blood pressure and reduced retinal artery caliber. In addition, being carrier of T allele is associated with increased risk of target organ damage in males. On the other hand, being carrier of the T allele in VAV3 rs7528153 polymorphism is associated with a decreased susceptibility of developing a pathologic state composed by the presence of hypertension, diabetes, obesity or cardiovascular damage, and with an increased risk of developing altered basal glycaemia. This is the first report showing an association between VAV2 and VAV3 polymorphisms with cardiovascular risk factors and target organ damage

    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

    Habitable Zones in the Universe

    Full text link
    Habitability varies dramatically with location and time in the universe. This was recognized centuries ago, but it was only in the last few decades that astronomers began to systematize the study of habitability. The introduction of the concept of the habitable zone was key to progress in this area. The habitable zone concept was first applied to the space around a star, now called the Circumstellar Habitable Zone. Recently, other, vastly broader, habitable zones have been proposed. We review the historical development of the concept of habitable zones and the present state of the research. We also suggest ways to make progress on each of the habitable zones and to unify them into a single concept encompassing the entire universe.Comment: 71 pages, 3 figures, 1 table; to be published in Origins of Life and Evolution of Biospheres; table slightly revise

    Enforced PGC-1α expression promotes CD8 T cell fitness, memory formation and antitumor immunity.

    Get PDF
    Memory CD8 T cells can provide long-term protection against tumors, which depends on their enhanced proliferative capacity, self-renewal and unique metabolic rewiring to sustain cellular fitness. Specifically, memory CD8 T cells engage oxidative phosphorylation and fatty acid oxidation to fulfill their metabolic demands. In contrast, tumor-infiltrating lymphocytes (TILs) display severe metabolic defects, which may underlie their functional decline. Here, we show that overexpression of proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), the master regulator of mitochondrial biogenesis (MB), favors CD8 T cell central memory formation rather than resident memory generation. PGC-1α-overexpressing CD8 T cells persist and mediate more robust recall responses to bacterial infection or peptide vaccination. Importantly, CD8 T cells with enhanced PGC-1α expression provide stronger antitumor immunity in a mouse melanoma model. Moreover, TILs overexpressing PGC-1α maintain higher mitochondrial activity and improved expansion when rechallenged in a tumor-free host. Altogether, our findings indicate that enforcing mitochondrial biogenesis promotes CD8 T cell memory formation, metabolic fitness, and antitumor immunity in vivo

    Prostate Cancer Susceptibility Loci Identified on Chromosome 12 in African Americans

    Get PDF
    Prostate cancer (PCa) is a complex disease that disproportionately affects African Americans and other individuals of African descent. A number of regions across the genome have been associated to PCa, most of them with moderate effects. A few studies have reported chromosomal changes on 12p and 12q that occur during the onset and development of PCa but to date no consistent association of the disease with chromosome 12 polymorphic variation has been identified. In order to unravel genetic risk factors that underlie PCa health disparities we investigated chromosome 12 using ancestry informative markers (AIMs), which allow us to distinguish genomic regions of European or West African origin, and tested them for association with PCa. Additional SNPs were genotyped in those areas where significant signals of association were detected. The strongest signal was discovered at the SNP rs12827748, located upstream of the PAWR gene, a tumor suppressor, which is amply expressed in the prostate. The most frequent allele in Europeans was the risk allele among African Americans. We also examined vitamin D related genes, VDR and CYP27B1, and found a significant association of PCa with the TaqI polymorphism (rs731236) in the former. Although our results warrant further investigation we have uncovered a genetic susceptibility factor for PCa in a likely candidate by means of an approach that takes advantage of the differential contribution of parental groups to an admixed population
    corecore