Calidad de vida de pacientes trasplantados renales atendidos en el Hospital José Carrasco Arteaga 2007-2016, Cuenca Ecuador

Antecedentes: La ERC afecta tanto el estado de salud como el ámbito económico, emocional y social del paciente. El trasplante renal (TR) es el tratamiento de elección puesto que mejora significativamente la calidad de vida (CV). En el Ecuador solo el año 2016 se realizaron 139 trasplantes renales. Objetivo: Determinar la calidad de vida de pacientes trasplantados renales atendidos en el Hospital José Carrasco Arteaga 2007-2016, Cuenca, Ecuador. Métodos y materiales. Se realizó un estudio observacional descriptivo transversal. Se utilizó el formulario SF-36 para determinar la CV, la encuesta de Estratificación Social del INEC y un cuestionario de recolección de datos sociodemográficos. El análisis fue realizado en Microsoft Office Excel y SPSS 19.0. Se reportan promedios y desviaciones estándar. Resultados: Se incluyeron 89 pacientes con una edad promedio de 46,62 ±13,71 años, de sexo masculino con 61 casos (68.5%), procedentes de zonas urbanas 62 (69.7%). Con >de 5 años de trasplante 46 pacientes (51.6%). Causa principal de ERC la nefroesclerosis en 32 casos (36%). El nivel socio económico medio típico en 43 personas (48,3%). CV en Salud Mental 75,10±1,94. CV en Dolor Corporal 73,68±6,63. CV en Rol Físico 58,99±3,24 y CV en Salud General 58,43±3,45. Conclusión: La CV de la población de TR es muy buena, la salud mental tiene una calidad de vida 75.1. Los pacientes refieren sentirse ALGO MEJOR que el año anterior y esto en la mayoría de pacientes con >5 años de trasplanteObjective: Determine the quality of life of renal transplant patients treated at the José Carrasco Arteaga Hospital from 2007-2016 in Cuenca, Ecuador. Background: CRF affects the health status just as much as the economic, emotional and social environment of the patient. Renal transplantation is the treatment of choice since it significantly improves the quality of life (CV). In Ecuador, 139 kidney transplants (TR) were performed in the year 2016 alone. Methods and materials: A cross-sectional observational study was performed. The SF-36 form was used to determine CV, the Social Stratification Survey of the INEC and a questionnaire for sociodemographic data were applied. The analysis was performed in Microsoft Excel and SPSS 19.0. Averages and standard deviations are reported. Results: Included were 89 patients with a mean age of 46.62 ± 13.71 years, 61 cases with male patients (68.5%), from urban areas 62 (69.7%). With> 5 years of transplantation, 46 patients (51.6%). Principal cause of CNS nephrosclerosis in 32 cases (36%). The average socioeconomic level in 43 people (48.3%). CV in Mental Health 75.10 ± 1.94. CV in Body Pain 73.68 ± 6.63. CV in Physical Role 58.99 ± 3.24 and CV in General Health 58.43 ± 3.45. Conclusion: The CV of the TR population is very good, mental health has a quality of life of 75.1. The patients reported feeling SOMEWHAT BETTER than the previous year and this in the majority of patients with> 5 years of TR.MédicoCuenc

Fenómeno del Niño historia y perspectivas

Ecuador se ubica en zona de riesgo para la llegada del Fenómeno del Niño, por lo que es necesario estar informados sobre este tema y diferenciar que es la Corriente del Niño (corriente cálida del Pacífico Sudamericano) y el Niño-Oscilación del Sur (patrón climático en el que se producen oscilaciones de la temperatura en dos fases: el Fenómeno del Niño y La Niña). En los años 1997-1998 este fenómeno afectó el 60% del total de la población, con un impacto muy alto en la salud de la ciudadanía, así como en la propiedad pública privada y en diversos ecosistemas. Ante la amenaza en el periodo 2015-2016, la Secretaría de Gestión de Riesgos (SGR) planificó tres etapas de acción: Preparación, Respuesta y Rehabilitación. Luego de la revisión los autores recomiendan entre otras cosas: brindar información oportuna sobre los cambios meteorológicos, informar sobre los planes de contingencia, garantizar la seguridad alimentaria y el acceso al agua; y fortalecer la atención integral que proveen los Servicios de SaludEcuador is located in the a danger zone for the arrival of “El Niño” phenomenon, so it is really necessary to be informed about this issue and notice the difference between “El Niño” (South American Pacific warm current) and “El Niño” Southern Oscillation (ENSO) (weather pattern in which temperature fluctuations occur in two phases: “El Niño” and “La Niña”). In 1997-1998 this phenomenon affected the 60% of the total population, with a very high impact on the public health as well as private and public property in different ecosystems. Faced to a possible threat in the period 2015-2016, the Risk Management Secretary (RMS) planned three action stages: Preparation, Response and Rehabilitation. After reviewing the authors recommend: provide timely information about weather changes, report on contingency plans, confirm food security and water access, and strengthen the comprehensive care that provides the Health ServicesCuenc

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.