Shiga toxin-producing Escherichia coli O157 in beef and chicken burgers, and chicken carcasses in Buenos Aires, Argentina

Fil: Chinen, Isabel. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología. Servicio Fisiopatogenia; Argentina.Fil: Epszteyn, Sergio. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología. Servicio Fisiopatogenia; Argentina.Fil: Melamed, Celia L. Departamento Laboratorio Investigación y Monitoreo, Dirección General de Higiene y Seguridad Alimentaria, Gobierno de la Ciudad de Buenos Aires, Patricias Argentinas 277, (1405) Buenos Aires; Argentina.Fil: Aguerre, Lorena. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología. Servicio Fisiopatogenia; Argentina.Fil: Martínez Espinosa, Estela. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología. Servicio Fisiopatogenia; Argentina.Fil: Motter, Mariana M. Departamento Laboratorio Investigación y Monitoreo, Dirección General de Higiene y Seguridad Alimentaria, Gobierno de la Ciudad de Buenos Aires, Patricias Argentinas 277, (1405) Buenos Aires; Argentina.Fil: Baschkier, Ariela. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología. Servicio Fisiopatogenia; Argentina.Fil: Manfredi, Eduardo. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología. Servicio Fisiopatogenia; Argentina.Fil: Miliwebsky, Elizabeth. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología. Servicio Fisiopatogenia; Argentina.Fil: Rivas, Marta. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología. Servicio Fisiopatogenia; Argentina.We describe the isolation and characterization of Shiga toxin (Stx)-producing Escherichia coli (STEC) O157:H7 from cooked and uncooked beef and chicken burgers and from chicken carcasses collected during sampling procedures in 2001 and 2002 in Buenos Aires City, Argentina. Of the 24 STEC O157:H7 strains isolated, 20 were recovered from 19 (6.8%) out of 279 samples of beef and chicken burgers, and 4 strains from 4 (10.3%) out of 39 chicken carcasses. The samples were analyzed following the USDA/FSIS 2002 method. The prevalent stx genotype was stx(2) and stx(2c) (12 strains, 50%). All strains were characterized as eae and ehxA-positive. By XbaI-PFGE, the strains yielded 10 different patterns. Eighteen out of 24 strains were grouped in four clusters: #1 (4 strains, AREXHX01.0043), #2 (4 strains, AREXHX01.0022), #3 (8 strains, AREXHX01.0139), and #4 (2 strains, AREXHX01.0200). Identical strains by phage typing, stx genotyping and PFGE were detected in uncooked and cooked beef and chicken burgers in different restaurants, which had been collected on the same or different sampling dates. These findings help to underline the importance of STEC O157 detection in meat products, to improve active surveillance, and to define control strategies in order to prevent new cases of STEC infection

Comparison of six commercial systems for the detection of non-O157 STEC in meat and vegetables

Shiga toxin-producing Escherichia coli (STEC) are important pathogens transmitted by food that may cause severe illness in human beings. Thus, systems for STEC detection in food should have increasingly higher sensitivity and specificity. Here we compared six commercial systems for non-O157 STEC detection in meat and vegetables and determined their sensitivity, specificity and repeatability. A total of 46 samples (meat n = 23; chard n = 23) were experimentally contaminated with strains O26:H11, O45:H-, O103:H2, O111:NM, O121:H19 and O145:NM isolated in Argentina. Strain detection was confirmed by isolation according to ISO 13136:2012. Detection of the stx and eae genes in meat samples was highly satisfactory with all commercial kits, but only five had 100% sensitivity and specificity in chard. Of four kits evaluated for serogroup detection, three had 100% sensitivity and specificity, and one had 93.7% sensitivity and 100% specificity. All kits were adequate to analyze meat but not vegetable samples, and were not therefore validated for the latter matrix. The challenge for microbiology laboratories is to identify the advantages and disadvantages of the available kits for STEC detection in food based on a clear knowledge of the particular needs of each laboratory.EEA RafaelaFil: Costa, Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaFil: Sucari, Adriana. Centro Estudios Infectológicos “Dr. Daniel Stamboulian”. División Higiene y Seguridad Alimentaria y Ambiental; ArgentinaFil: Epszteyn, Sergio. Buenos Aires. Dirección General de Higiene y Seguridad Alimentaria. Laboratorio de Investigación y Monitoreo; ArgentinaFil: Oteiza, Juan Martín. Centro de Investigación y Asistencia Técnica a la Industria. Laboratorio de Microbiología de los Alimentos (Neuquén); ArgentinaFil: Gentiluomo, Jimena. Centro Estudios Infectológicos “Dr. Daniel Stamboulian”. División Higiene y Seguridad Alimentaria y Ambiental; ArgentinaFil: Melamed, Celia. Buenos Aires. Dirección General de Higiene y Seguridad Alimentaria. Laboratorio de Investigación y Monitoreo; ArgentinaFil: Figueroa, Yamila. Centro Estudios Infectológicos “Dr. Daniel Stamboulian”. División Higiene y Seguridad Alimentaria y Ambiental; ArgentinaFil: Mingorance, Santiago Emmanuel. Buenos Aires. Dirección General de Higiene y Seguridad Alimentaria. Laboratorio de Investigación y Monitoreo; ArgentinaFil: Grisaro, Agustina. Centro Estudios Infectológicos “Dr. Daniel Stamboulian”. División Higiene y Seguridad Alimentaria y Ambiental; ArgentinaFil: Spioussas, Silvia. Buenos Aires. Dirección General de Higiene y Seguridad Alimentaria. Laboratorio de Investigación y Monitoreo; ArgentinaFil: Buffoni Almeida, Mariana. Centro Estudios Infectológicos “Dr. Daniel Stamboulian”. División Higiene y Seguridad Alimentaria y Ambiental; ArgentinaFil: Caruso, Mailen. Buenos Aires. Dirección General de Higiene y Seguridad Alimentaria. Laboratorio de Investigación y Monitoreo; ArgentinaFil: Pontoni, Andrés. Buenos Aires. Dirección General de Higiene y Seguridad Alimentaria. Laboratorio de Investigación y Monitoreo; ArgentinaFil: Signorini, Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Leotta, Gerardo Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentin

Characteristics and Outcomes of Individuals With Pre-existing Kidney Disease and COVID-19 Admitted to Intensive Care Units in the United States

Rationale & objectiveUnderlying kidney disease is an emerging risk factor for more severe coronavirus disease 2019 (COVID-19) illness. We examined the clinical courses of critically ill COVID-19 patients with and without pre-existing chronic kidney disease (CKD) and investigated the association between the degree of underlying kidney disease and in-hospital outcomes.Study designRetrospective cohort study.Settings & participants4,264 critically ill patients with COVID-19 (143 patients with pre-existing kidney failure receiving maintenance dialysis; 521 patients with pre-existing non-dialysis-dependent CKD; and 3,600 patients without pre-existing CKD) admitted to intensive care units (ICUs) at 68 hospitals across the United States.Predictor(s)Presence (vs absence) of pre-existing kidney disease.Outcome(s)In-hospital mortality (primary); respiratory failure, shock, ventricular arrhythmia/cardiac arrest, thromboembolic events, major bleeds, and acute liver injury (secondary).Analytical approachWe used standardized differences to compare patient characteristics (values>0.10 indicate a meaningful difference between groups) and multivariable-adjusted Fine and Gray survival models to examine outcome associations.ResultsDialysis patients had a shorter time from symptom onset to ICU admission compared to other groups (median of 4 [IQR, 2-9] days for maintenance dialysis patients; 7 [IQR, 3-10] days for non-dialysis-dependent CKD patients; and 7 [IQR, 4-10] days for patients without pre-existing CKD). More dialysis patients (25%) reported altered mental status than those with non-dialysis-dependent CKD (20%; standardized difference=0.12) and those without pre-existing CKD (12%; standardized difference=0.36). Half of dialysis and non-dialysis-dependent CKD patients died within 28 days of ICU admission versus 35% of patients without pre-existing CKD. Compared to patients without pre-existing CKD, dialysis patients had higher risk for 28-day in-hospital death (adjusted HR, 1.41 [95% CI, 1.09-1.81]), while patients with non-dialysis-dependent CKD had an intermediate risk (adjusted HR, 1.25 [95% CI, 1.08-1.44]).LimitationsPotential residual confounding.ConclusionsFindings highlight the high mortality of individuals with underlying kidney disease and severe COVID-19, underscoring the importance of identifying safe and effective COVID-19 therapies in this vulnerable population

Obesity, inflammatory and thrombotic markers, and major clinical outcomes in critically ill patients with COVID‐19 in the US

Objective This study aimed to determine whether obesity is independently associated with major adverse clinical outcomes and inflammatory and thrombotic markers in critically ill patients with COVID‐19. Methods The primary outcome was in‐hospital mortality in adults with COVID‐19 admitted to intensive care units across the US. Secondary outcomes were acute respiratory distress syndrome (ARDS), acute kidney injury requiring renal replacement therapy (AKI‐RRT), thrombotic events, and seven blood markers of inflammation and thrombosis. Unadjusted and multivariable‐adjusted models were used. Results Among the 4,908 study patients, mean (SD) age was 60.9 (14.7) years, 3,095 (62.8%) were male, and 2,552 (52.0%) had obesity. In multivariable models, BMI was not associated with mortality. Higher BMI beginning at 25 kg/m2 was associated with a greater risk of ARDS and AKI‐RRT but not thrombosis. There was no clinically significant association between BMI and inflammatory or thrombotic markers. Conclusions In critically ill patients with COVID‐19, higher BMI was not associated with death or thrombotic events but was associated with a greater risk of ARDS and AKI‐RRT. The lack of an association between BMI and circulating biomarkers calls into question the paradigm that obesity contributes to poor outcomes in critically ill patients with COVID‐19 by upregulating systemic inflammatory and prothrombotic pathways