APLICACIÓN LA METODOLOGÍA 5S PARA SISTEMATIZAR LA FORMULACIÓN DE PRODUCTOS EN UN LABORATORIO COSMÉTICO

se deben principalmente a la confusión que se genera entre las pruebas realizadas, provocando reformulaciones y parámetros de calidad establecidos incorrectamente. Por lo que el objetivo principal es mejorar el proceso de formulación para cada producto y ejecutar un correcto seguimiento a las pruebas realizadas. Como resultado se obtiene una categorización de productos, siendo cinco en total. Se realiza un procedimiento para cada tipo de producto, unificados en un documento. Cada uno tiene 13 actividades, incluyendo una para cada S de la Metodología 5S. Se valida por medio de una encuesta y se determina que todas las personas encargadas de formulación identifican como mejoras en el proceso la disminución de errores y mayor rapidez en la formulación con la implementación de los procedimientos

Efficacy of naloxegol on symptoms and quality of life related to opioid-induced constipation in patients with cancer: a 3-month follow-up analysis

Objectives: Opioid-induced constipation (OIC) can affect up to 63% of all patients with cancer. The objectives of this study were to assess quality of life as well as efficacy and safety of naloxegol, in patients with cancer with OIC. Methods: An observational study was made of a cohort of patients with cancer and with OIC exhibiting an inadequate response to laxatives and treated with naloxegol. The sample consisted of adult outpatients with a Karnofsky performance status score ≥50. The Patient Assessment of Constipation Quality of Life Questionnaire (PAC-QOL) and the Patient Assessment of Constipation Symptoms (PAC-SYM) were applied for 3 months. Results: A total of 126 patients (58.2% males) with a mean age of 61.3 years (range 34-89) were included. Clinically relevant improvements (>0.5 points) were recorded in the PAC-QOL and PAC-SYM questionnaires (p<0.0001) from 15 days of treatment. The number of days a week with complete spontaneous bowel movements increased significantly (p<0.0001) from 2.4 to 4.6 on day 15, 4.7 after 1 month and 5 after 3 months. Pain control significantly improved (p<0.0001) during follow-up. A total of 13.5% of the patients (17/126) presented some gastrointestinal adverse reaction, mostly of mild (62.5%) or moderate intensity (25%). Conclusions: Clinically relevant improvements in OIC-related quality of life, number of bowel movements and constipation-related symptoms were recorded as early as after 15 days of treatment with naloxegol in patients with cancer and OIC, with a good safety profile

Proactive and Reactive Recruitment of Black and Latino Adolescents in a Vaping Prevention Randomized Controlled Trial

The purpose of this study was to assesses the effectiveness of proactive and reactive methods in the recruitment of Black and Latino adolescents into a vaping-prevention randomized controlled trial (RCT). This study also assessed the characteristics of study participants by recruitment method. Proactive recruitment strategies included study presentations at community-based events (e.g., festivals, health fairs), school-based events (e.g., back-to-school events, after-school programs), and recreational centers (e.g., fitness centers, malls). Reactive recruitment strategies included study advertisements via social media (e.g., Facebook posts shared by local community-based organizations), word of mouth, and an academic-based research hub. Using proactive and reactive methods, in a 4-month period, 362 Black and Latino adolescents were successfully enrolled into the RCT. Compared to the proactive method, adolescents screened reactively were equally likely to be eligible but significantly more likely to enroll in the study. However, both proactive and reactive strategies made notable contributions to the overall recruitment effort. Moreover, proactive and reactive methods attracted adolescents with different characteristics (e.g., age, gender, sexual orientation, etc.). These findings suggest that both proactive and reactive recruitment strategies should be implemented for studies interested in recruiting a diverse sample of Black and Latino adolescents

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.