RESUMENSe consiguieron identificar 23 aislamientos de Gallibacterium anatis, a través de pruebas convencionales de cultivo y bioquímicas, confirmándose su identificación a través del método de PCR. Se les efectuó la prueba de difusión en placa para determinar la resistencia antimicrobiana a los antibióticos más utilizados en el área avícola. Los aislamientos fueron más susceptibles a ceftiofur (73 %) y florfenicol (68 %); todos los aislamientos mostraron resistencia marcada a penicilina, tilosina, lincomicina, ampicilina, enrofloxacina, oxitetraciclina, norfloxacina y cefalexina. La presencia de Gallibacterium anatis se asocia a problemas respiratorios y reproductivos en las poblaciones de gallinas de postura comercial. Los aislamientos mostraron una resistencia marcada a distintos antibióticos, probablemente por la medicación desmedida ante la presencia de este microorganismo. PALABRAS CLAVE: Gallibacterium anatis, PCR, Resistencia, Susceptibilidad, Antimicrobianos. ABSTRACTIt was able to identify 23 isolates of Gallibacterium anatis through conventional culture and biochemical tests, and identification confirmed through PCR method. A disk diffusion test determined antimicrobial resistance to commonly used antibiotics in the poultry area. Isolates were more susceptible to ceftiofur (73 %) and florfenicol (68 %). All isolates showed marked resistance to penicillin, tylosin, lincomycin, ampicillin, enrofloxacin, oxytetracycline, norfloxacin and cephalexin. The presence of Gallibacterium anatis is associated with respiratory and reproductive problems in populations of commercial laying hens. Isolates showed marked resistance to different antibiotics, probably due to excessive medication in the presence of this organism.

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Identification of the predominant circulating biovars is important for controlling Gallibacterium anatis infections in poultry because efficient protection is based on the use of biovar-specific immunogens. Twenty-three isolates of Gallibacterium anatis were recovered from commercial laying hens with respiratory and reproductive clinical signs. Bacteria were identified as G. anatis based on morphology, biochemistry, and PCR testing. Biochemical analyses identified the isolates as biovar 6 which had not previously been reported in Sonora, México. Therefore, the recent finding of biovar type 6 could explain clinical presentations of this disease in immunized commercial laying flocks in Sonora.

Es un libro dirigido a reforzar y poner en práctica los conocimientos adquiridos durante la carrera de medicina veterinaria y zootecnia. Está organizado en 11 prácticas enfocadas a que el alumno adquiera las habilidades y destrezas necesarias para realizar las actividades que se esperan de un clínico de pequeñas especies. Algunas de las actividades son las siguientes: la identificación y acercamiento de los diferentes tipos de dueños de mascotas; conocer y aplicar las distintas formas de sujeción: psicológicas, manuales, físicas y químicas de los animales de compañía; la obtención de información como la anamnesis, la reseña, historia clínica, examen físico general para la elaboración y aplicación del Expediente Clínico Orientado a Problemas (ECOP); la correcta toma y envío de muestras de laboratorio con su posterior análisis e interpretación de resultados; analizar los distintos tipos de vacuna que existen y tomar decisiones sobre la selección de un calendario de vacunación adecuado para las necesidades específicas de sus pacientes; realizar cálculos de terapia de líquidos; conocer los fármacos más utilizados en gatos y perros; realizar órdenes clínicas y terapéuticas con base en la literatura actualizada y finalmente elegir un protocolo anestésico individual para cada paciente tomando en cuenta las consideraciones especiales de pacientes complicados.

Background We sought to discover serum biomarkers of ankylosing spondylitis (AS) for diagnosis and monitoring disease activity. Methods We studied biologic-treatment-naïve AS and healthy control (HC) patients’ sera. Eighty samples matched by age, gender, and race (1:1:1 ratio) for AS patients with active disease, inactive disease, and HC were analyzed with SOMAscan™, an aptamer-based discovery platform. T-tests tests were performed for high/low-disease activity AS patients versus HCs (diagnosis) and high versus low disease activity (Monitoring) in a 2:1 and 1:1 ratio, respectively, to identify differentially expressed proteins (DEPs). We used the Cytoscape Molecular Complex Detection (MCODE) plugin to find clusters in protein–protein interaction networks and Ingenuity Pathway Analysis (IPA) for upstream regulators. Lasso regression analysis was performed for diagnosis. Results Of the 1317 proteins detected in our diagnosis and monitoring analyses, 367 and 167 (317 and 59, FDR-corrected q < .05) DEPs, respectively, were detected. MCODE identified complement, IL-10 signaling, and immune/interleukin signaling as the top 3 diagnosis PPI clusters. Complement, extracellular matrix organization/proteoglycans, and MAPK/RAS signaling were the top 3 monitoring PPI clusters. IPA showed interleukin 23/17 (interleukin 22, interleukin 23A), TNF (TNF receptor-associated factor 3), cGAS-STING (cyclic GMP-AMP synthase, Stimulator of Interferon Gene 1), and Jak/Stat (Signal transducer and activator of transcription 1), signaling in predicted upstream regulators. Lasso regression identified a Diagnostic 13-protein model predictive of AS. This model had a sensitivity of 0.75, specificity of 0.90, a kappa of 0.59, and overall accuracy of 0.80 (95% CI: 0.61–0.92). The AS vs HC ROC curve was 0.79 (95% CI: 0.61–0.96). Conclusion We identified multiple candidate AS diagnostic and disease activity monitoring serum biomarkers using a comprehensive proteomic screen. Enrichment analysis identified key pathways in AS diagnosis and monitoring. Lasso regression identified a multi-protein panel with modest predictive ability.