495 research outputs found

    Organic vs Conventional Suckling Lamb Production: Product Quality and Consumer Acceptance

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    Samples of suckling lambs (n=40) of two breeds reared under conventional and organic conditions were analysed to asses physico-chemical characteristics, including instrumental texture, and nutritional quality in terms of fatty acid composition. Consumer acceptance was also studied using the home-use test. Results revealed that organic suckling lamb meat is healthier as shown by the lower saturated fatty acid levels, the higher polyunsaturated fatty acid contents and the higher 6/3 ratiko. The organic meat had lower instrumental hardness, received higher scores in all sensory parameters, and had statistically better fat sensation and higher ratings for overall liking. These results lend support to the notion among consumers that organic products are healthier and tastier

    Consumer Appreciation of Carcass Quality of Organic vs Conventional Suckling Lamb Production

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    Carcass characteristics of sucking lambs (n= 40) of two breeds reared under conventional and organic conditions were analysed including objective and subjective parameters for fatness and conformation, meat and fat colour. Consumer acceptance was also studied using the home-use test. Results showed that the characteristics of the carcass of suckling lamb were similar for both types of production systems pointing out that organic production system did not affect fatness or muscle development. However, organic meat was darker (higher L* and a* values) probably related with the higher amount of exercise, although fat was not more yellow. In contrast consumers did not consider organic meat darker and there were not significant differences in appearance related with the similar conformation. These results reflect that consumer perceive organic meat as at least as good as conventional production not only regarding environmental quality but also regarding carcass quality

    Caracterización del ecosistema hídrico y su funcionamiento hidráulico Puerto Mazán, Loreto, Perú utilizando SIG

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    Con el propósito de caracterizar el ecosistema hídrico y evaluar el funcionamiento hidráulico de Puerto Mazan, Loreto, Perú, se recuperaron imágenes satelitales del programa espacial Landsat de las bandas MSS, TM y ETM, Bandas 3,4,5; e imágenes satelitales del Google earth; las que fueron procesadas con los software ENVI 5, ERDAS ENGINE, Leowowrks y Arcgis 10. Se determinó que el sistema hídrico en el entorno de Puerto Mazan está caracterizado por: el Río Napo, Islas “AB” y “C”, meandros “2” y “4”, lóbulo “abandonado”-“9”, cauces alivio “7” y 6”, Río Mazán “8”. El funcionamiento hidráulico está definido por: Partidor de flujo en Rio Napo; Grado de libertad representado por meandro “2” y Lóbulo “abandonado”- “9”; cortas “1” previo al meandro “2” y corta “7” en el meandro “2”; aliviadero de demasías “7” que regula la entrada de flujo proveniente de la margen izquierda del Río Napo; Desarenador establecido por el Río Mazán y atenuador de magnitud de la velocidad de flujo proveniente de la margen izquierda del Río Napo

    Automated radiofrequency-based US measurement of common carotid intima-media thickness in RA patients treated with synthetic vs synthetic and biologic DMARDs

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    Objective. To compare the carotid intima-media thickness (IMT) assessed with automated radiofrequency-based US in RA patients treated with synthetic vs synthetic and biologic DMARDs and controls. Methods. Ninety-four RA patients and 94 sex-and age-matched controls were prospectively recruited at seven centres. Cardiovascular (CV) risk factors and co-morbidities, RA characteristics and therapy were recorded. Common carotid artery (CCA)-IMT was assessed in RA patients and controls with automated radiofrequency-based US by the same investigator at each centre. Results. Forty-five (47.9%) RA patients had been treated with synthetic DMARDs and 49 (52.1%) with synthetic and biologic DMARDs. There were no significant differences between the RA patients and controls in demographics, CV co-morbidities and CV disease. There were significantly more smokers among RA patients treated with synthetic and biologic DMARDs (P = 0.036). Disease duration and duration of CS and synthetic DMARD therapy was significantly longer in RA patients treated with synthetic and biologic DMARDs (P<0.0005). The mean CCA-IMT was significantly greater in RA patients treated only with synthetic DMARDs than in controls [591.4 (98.6) vs 562.1 (85.8); P = 0.035] and in RA patients treated with synthetic and biologic DMARDs [591.4 (98.6) vs 558.8 (95.3); P = 0.040). There was no significant difference between the mean CCA-IMT in RA patients treated with synthetic and biologic DMARDs and controls (P = 0.997). Conclusion. Our results suggest that radiofrequency-based measurement of CCA-IMT can discriminate between RA patients treated with synthetic DMARDs vs RA patients treated with synthetic and biologic DMARDs

    Hospital control and multidrug-resistant pulmonary tuberculosis in female patients, Lima, Peru.

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    We examined the prevalence of tuberculosis (TB), rate of multidrug-resistant (MDR) TB, and characteristics of TB on a female general medicine ward in Peru. Of 250 patients, 40 (16%) were positive by sputum culture and 27 (11%) by smear, and 8 (3%) had MDRTB. Thirteen (33%) of 40 culture-positive patients had not been suspected of having TB on admission. Six (46%) of 13 patients whose TB was unsuspected on admission had MDRTB, compared with 2 (7%) of 27 suspected cases (p = 0.009). Five (63%) of 8 MDRTB patients were smear positive and therefore highly infective. In developing countries, hospital control, a simple method of reducing the spread of MDRTB, is neglected

    The implausibility of ‘usual care’ in an open system: sedation and weaning practices in Paediatric Intensive Care Units (PICUs) in the United Kingdom (UK)

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    Background: The power of the randomised controlled trial depends upon its capacity to operate in a closed system whereby the intervention is the only causal force acting upon the experimental group and absent in the control group, permitting a valid assessment of intervention efficacy. Conversely, clinical arenas are open systems where factors relating to context, resources, interpretation and actions of individuals will affect implementation and effectiveness of interventions. Consequently, the comparator (usual care) can be difficult to define and variable in multi-centre trials. Hence outcomes cannot be understood without considering usual care and factors that may affect implementation and impact on the intervention. Methods: Using a fieldwork approach, we describe PICU context, ‘usual’ practice in sedation and weaning from mechanical ventilation, and factors affecting implementation prior to designing a trial involving a sedation and ventilation weaning intervention. We collected data from 23 UK PICUs between June and November 2014 using observation, individual and multi-disciplinary group interviews with staff. Results: Pain and sedation practices were broadly similar in terms of drug usage and assessment tools. Sedation protocols linking assessment to appropriate titration of sedatives and sedation holds were rarely used (9 % and 4 % of PICUs respectively). Ventilator weaning was primarily a medical-led process with 39 % of PICUs engaging senior nurses in the process: weaning protocols were rarely used (9 % of PICUs). Weaning methods were variably based on clinician preference. No formal criteria or use of spontaneous breathing trials were used to test weaning readiness. Seventeen PICUs (74 %) had prior engagement in multi-centre trials, but limited research nurse availability. Barriers to previous trial implementation were intervention complexity, lack of belief in the evidence and inadequate training. Facilitating factors were senior staff buy-in and dedicated research nurse provision. Conclusions: We examined and identified contextual and organisational factors that may impact on the implementation of our intervention. We found usual practice relating to sedation, analgesia and ventilator weaning broadly similar, yet distinctively different from our proposed intervention, providing assurance in our ability to evaluate intervention effects. The data will enable us to develop an implementation plan; considering these factors we can more fully understand their impact on study outcomes

    SISTEMADE INDICADORES DE CALIDAD I

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    The present essay deals with a Quality Indicators System likely to be adopted in organizations implementing their continuous quality and improvement systems. Relations among criterion, indicator and standard and their own examples, as well as the different kinds of indicators is shown.El presente ensayo trata sobre un sistema de indicadores de la calidad que puede ser implantado en organizaciones que están implementando sistemas de calidad y mejora continua, mostrándose la relación entre criterio, indicador y estándar con sus respectivos ejemplos así como los diferentes tipos de indicadores

    Latent Patient Network Learning for Automatic Diagnosis

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    Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN's downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications
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