¿Qué queda de mí?

Este libro es una reclamación a quienes hemos sido, somos o seremos docentes. A quienes no hemos respetado a las personas que se han puesto junto a nosotros y nosotras, confiando su bien más preciado: la libertad. Estas páginas denuncian cada vez que convertimos una visión en la visión, una emoción en la emoción, un saber en el saber, un comportamiento en el comportamiento. Es un grito contra la imposición, la normalización, la neutralización y la universalización de una perspectiva particular. Una pugna contra cada proceso que no se ha conectado con las vidas de los aprendices. Un texto colaborativo realizado por alumnado de Educación y Cambio Social en el Grado en Educación Infantil de la Universidad de Málaga y coordinado por Ignacio Calderón Almendros

Durvalumab consolidation in patients with unresectable stage III non-small cell lung cancer with driver genomic alterations.

INTRODUCTION: Durvalumab is the standard-of-care as consolidation therapy after chemo-radiotherapy in stage III unresectable non-small cell lung cancer (NSCLC); however, its activity across patients with NSCLC harbouring driver genomic alterations (dGA) is poorly characterised. MATERIAL AND METHODS: Multicentre retrospective study including patients with stage III unresectable NSCLC treated with durvalumab after chemo-radiotherapy between April 2015 and October 2020 at 26 centres in Europe and America. Clinical and biological data were collected; dGA included: EGFR/BRAF/KRAS mutations (m) and ALK/ROS1 rearrangements (r). We evaluated progression-free survival (PFS) and overall survival (OS) based on dGA. RESULTS: Out of 323 patients included, 43 patients had one dGA: KRASm (n = 26; 8 G12C), EGFRm (n = 8; 6 del19/ex21), BRAFm (n = 5; 4 V600E) and ALKr (n = 4). The median age was 66 years [39-84], gender ratio 1:1, with 98% performance status (PS) 0-1 and 19% non-smokers; 88% had adenocarcinoma. PD-L1 was positive in 85% (n = 4 missing). In the whole cohort, the median PFS was 17.5 months (mo.) (95% CI, 13.2-24.9) and median OS 47 mo (95%CI, 47-not reached [NR]). No statistically significant differences in terms of the median PFS were observed between patients with dGA vs. non-dGA: 14.9 mo (95% CI, 8.1-NR) vs. 18 mo. (95% CI, 13.4-28.3) (P = 1.0); however, when analysed separately: the median PFS was NR (11.3-NR) in the KRASm G12C vs. 8.1 mo (5.8-NR) in the EGFRm del19/ex21 vs. 7.8 mo (7.7-NR) in the BRAFm V600E/ALKr (P = 0.02). CONCLUSIONS: We observed limited activity of durvalumab consolidation in patients with stage III unresectable NSCLC with EGFR/BRAFm and ALKr but not for those harbouring KRASm. Larger prospective studies are needed to confirm these findings

Association Between Lung Immune Prognostic Index and Durvalumab Consolidation Outcomes in Patients With Locally Advanced Non‐Small‐Cell Lung Cancer

Introduction The LIPI, based on pretreatment derived neutrophils/[leukocytes-neutrophils] ratio (dNLR) and LDH, is associated with immune checkpoint inhibitors (ICI) outcomes in advanced non–small-cell lung cancer (NSCLC). We aimed to assess baseline LIPI correlation with durvalumab consolidation outcomes in the locally advanced setting. Material and Methods Multicentre retrospective study (330 patients) with stage III unresectable NSCLC treated with durvalumab after chemo-radiotherapy between April 2015 and December 2020; 65 patients treated with chemo-radiotherapy only. Baseline LIPI characterized 3 groups: good (dNLR≤3+LDH≤ULN), intermediate (dNLR>3/LDH>ULN) and poor (dNLR>3+LDH>ULN). Primary endpoint was overall survival (OS). Results In the durvalumab cohort, median age was 67 years, 95% smokers, 98% with a performance status of 0-1; 60% had nonsquamous histology and 16% a PD-L1 expression <1%. Radiotherapy was delivered concurrently in 81%. LIPI was evaluable in 216 patients: 66% good, 31% intermediate, 3% poor. LIPI significantly correlated with median OS (median follow-up: 19 months): 18.1 months vs. 47.0 months vs. not reached in poor, intermediate and good LIPI groups, respectively (P = .03). A trend between objective response rate and LIPI groups was observed: 0% vs. 41% vs. 45%, respectively (P = .05). The pooled intermediate/poor LIPI group was associated with shorter OS (HR 1.97; P = .03) and higher risk of progressive disease (OR 2.68; P = .047). Survivals and response were not influenced in the control cohort. Conclusion Baseline LIPI correlated with outcomes in patients with locally advanced NSCLC treated with durvalumab consolidation, but not in those who only received chemo-radiotherapy, providing further evidence of its prognostic and potential predictive role of ICI benefit in NSCLC

Association Between Lung Immune Prognostic Index and Durvalumab Consolidation Outcomes in Patients With Locally Advanced Non-Small-Cell Lung Cancer

The LIPI, based on pretreatment derived neutrophils/[leukocytes-neutrophils] ratio (dNLR) and LDH, is associated with immune checkpoint inhibitors (ICI) outcomes in advanced non-small-cell lung cancer (NSCLC). We aimed to assess baseline LIPI correlation with durvalumab consolidation outcomes in the locally advanced setting. Multicentre retrospective study (330 patients) with stage III unresectable NSCLC treated with durvalumab after chemo-radiotherapy between April 2015 and December 2020; 65 patients treated with chemo-radiotherapy only. Baseline LIPI characterized 3 groups: good (dNLR≤3+LDH≤ULN), intermediate (dNLR>3/LDH>ULN) and poor (dNLR>3+LDH>ULN). Primary endpoint was overall survival (OS). In the durvalumab cohort, median age was 67 years, 95% smokers, 98% with a performance status of 0-1; 60% had nonsquamous histology and 16% a PD-L1 expression <1%. Radiotherapy was delivered concurrently in 81%. LIPI was evaluable in 216 patients: 66% good, 31% intermediate, 3% poor. LIPI significantly correlated with median OS (median follow-up: 19 months): 18.1 months vs. 47.0 months vs. not reached in poor, intermediate and good LIPI groups, respectively (P = .03). A trend between objective response rate and LIPI groups was observed: 0% vs. 41% vs. 45%, respectively (P = .05). The pooled intermediate/poor LIPI group was associated with shorter OS (HR 1.97; P = .03) and higher risk of progressive disease (OR 2.68; P = .047). Survivals and response were not influenced in the control cohort. Baseline LIPI correlated with outcomes in patients with locally advanced NSCLC treated with durvalumab consolidation, but not in those who only received chemo-radiotherapy, providing further evidence of its prognostic and potential predictive role of ICI benefit in NSCLC

Latin America: situation and preparedness facing the multi-country human monkeypox outbreak

Fundación Universitaria Autónoma de las Américas. Faculty of Medicine. Grupo de Investigación Biomedicina. Pereira, Risaralda, Colombia / Universidad Científica del Sur. Master of Clinical Epidemiology and Biostatistics. Lima, Peru / Latin American network of Monkeypox Virus Research. Pereira, Risaralda, ColombiaUniversity of Buenos Aires. Cátedra de Enfermedades Infecciosas. Buenos Aires, Argentina.Hospital Britanico de Buenos Aires. Servicio de Infectología. Buenos Aires, Argentina.University of Buenos Aires. Cátedra de Enfermedades Infecciosas. Buenos Aires, Argentina / Hospital de Enfermedades Infecciosas F. J. Muniz. Buenos Aires, Argentina.University of Buenos Aires. Cátedra de Enfermedades Infecciosas. Buenos Aires, Argentina / Hospital de Enfermedades Infecciosas F. J. Muniz. Buenos Aires, Argentina.Hospital Clínico Viedma. Cochabamba, Bolivia.Gobierno Autonomo Municipal de Cochabamba. Secretaría de Salud. Centros de Salud de Primer Nivel. Direction. Cochabamba, Bolivia.Franz Tamayo University. National Research Coordination. La Paz, Bolivia.Paulista State University Júlio de Mesquita Filho. Botucatu Medical School. Infectious Diseases Department. São Paulo, SP, Brazil / Brazilian Society for Infectious Diseases. Sãao Paulo, SP, Brazil.Universidade de São Paulo. Faculdade de Saúde Pública. Departamento de Epidemiologia. São Paulo, SP, Brazil.Institute of Infectious Diseases Emilio Ribas. São Paulo, Brazil.Ministério da Saúde. Secretaria de Ciência, Tecnologia, Inovação e Insumos Estratégicos. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Centro de Referencia de Salud Dr. Salvador Allende Gossens. Policlínico Neurología. Unidad Procedimientos. Santiago de Chile, Chile.Pontificia Universidad Católica de Chile. School of Medicine. Department of Pediatric Infectious Diseases and Immunology. Santiago de Chile, Chile.Universidad Austral de Chile. Facultad de Medicina. Instituto de Salud Publica. Valdivia, Chile.Ministerio de Salud. Hospital de San Fernando. San Fernando, VI Region, Chile.Fundación Universitaria Autónoma de las Américas. Faculty of Medicine. Grupo de Investigación Biomedicina. Pereira, Risaralda, Colombia.Universidad Nacional de Colombia. Department of Pediatrics. Bogota, DC, Colombia / Hospital Pediatrico La Misericordia. Division of Infectious Diseases. Bogota, DC, Colombia.Hemera Unidad de Infectología IPS SAS. Bogota, Colombia.Hospital San Vicente Fundacion. Rionegro, Antioquia, Colombia.Clinica Imbanaco Grupo Quironsalud. Cali, Colombia / Universidad Santiago de Cali. Cali, Colombia / Clinica de Occidente. Cali, Colombia / Clinica Sebastian de Belalcazar. Valle del Cauca, Colombia.National Institute of Gastroenterology. Epidemiology Unit. La Habana, CubaHospital Salvador Bienvenido Gautier. Santo Domingo, Dominican Republic.Pontificia Universidad Catolica Madre y Maestra. Santiago, Dominican Republic.International University of Ecuador. School of Medicine. Quito, Ecuador.Universidad Tecnica de Ambato. Ambato, Ecuador.Hospital Roosevelt. Guatemala City, Guatemala.Universidad Nacional Autonoma de Honduras. Faculty of Medical Sciences. School of Medical. Unit of Scientific Research. Tegucigalpa, Honduras.Hospital Infantil de Mexico. Federico Gomez, Mexico City, Mexico.Hospital General de Tijuana. Departamento de Infectología. Tijuana, Mexico.Hospital General de Tijuana. Departamento de Infectología. Tijuana, Mexico.Asociacion de Microbiólogos y Químicos Clínicos de Nicaragua. Managua, Nicaragua.Hospital Santo Tomas. Medicine Department-Infectious Diseases Service. Panama City, Panama / Instituto Oncologico Nacional. Panama city, Panama.University of Arizona College of Medicine-Phoenix. Division of Endocrinology. Department of Medicine. Phoenix, AZ, USA / Indian School Rd. Phoenix, AZ, USA.Dirección Nacional de Vigilancia Sanitaria. Dirección de Investigación. Asunción, Paraguay.Universidad Nacional de Asuncion. Faculty of Medical Sciences. Division of Dermatology. Asuncion, Paraguay.Instituto Nacional de Salud del Nino San Borja. Infectious Diseases Division. Lima, Peru / Universidad Privada de Tacna. Facultad de Ciencias de la Salud. Tacna, Peru.Universidad San Juan Bautista. Lima, Peru.Universidad San Ignacio de Loyola. Vicerrectorado de Investigación. Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud. Lima, Peru.Hospital Evangelico de Montevideo. Montevideo, Uruguay.Icahn School of Medicine at Mount Sinai. Molecular and Cell-based Medicine. Department of Pathology. Molecular Microbiology Laboratory. New York, USA / Universidad del Rosario. Facultad de Ciencias Naturales. Centro de Investigaciones en Microbiología y Biotecnología-UR. Bogota, Colombia.Hospital Evangélico de Montevideo. Montevideo, Uruguay / Venezuelan Science Incubator and the Zoonosis and Emerging Pathogens Regional Collaborative Network. Infectious Diseases Research Branch. Cabudare, Lara, Venezuela.Universidad Central de Venezuela. Faculty of Medicine. Caracas, Venezuela.Universidad Central de Venezuela. Faculty of Medicine. Caracas, Venezuela / Biomedical Research and Therapeutic Vaccines Institute. Ciudad Bolivar, Venezuela.Universidad Central de Venezuela. Tropical Medicine Institute, Infectious Diseases Section. Caracas, Venezuela.Instituto Conmemorativo Gorgas de Estudios de la Salud. Clinical Research Department. Investigador SNI Senacyt Panama. Panama City, Panama

Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey

Background: Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes. Methods: An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile. Results: 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. Discussion: The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI

Time for a paradigm shift in shared decision-making in trauma and emergency surgery? Results from an international survey

Background Shared decision-making (SDM) between clinicians and patients is one of the pillars of the modern patient-centric philosophy of care. This study aims to explore SDM in the discipline of trauma and emergency surgery, investigating its interpretation as well as the barriers and facilitators for its implementation among surgeons. Methods Grounding on the literature on the topics of the understanding, barriers, and facilitators of SDM in trauma and emergency surgery, a survey was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was sent to all 917 WSES members, advertised through the society’s website, and shared on the society’s Twitter profile. Results A total of 650 trauma and emergency surgeons from 71 countries in five continents participated in the initiative. Less than half of the surgeons understood SDM, and 30% still saw the value in exclusively engaging multidisciplinary provider teams without involving the patient. Several barriers to effectively partnering with the patient in the decision-making process were identified, such as the lack of time and the need to concentrate on making medical teams work smoothly. Discussion Our investigation underlines how only a minority of trauma and emergency surgeons understand SDM, and perhaps, the value of SDM is not fully accepted in trauma and emergency situations. The inclusion of SDM practices in clinical guidelines may represent the most feasible and advocated solutions

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.

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)