Cytokine levels in bronchoalveolar lavage and serum in 3 patients with 2009 influenza A(H1N1)v severe pneumonia.

Introduction: Pandemic Influenza A (H1N1)v pneumonia has led to a notable increase of admissions to intensive care units. A cytokine-mediated inflammatory response has been well documented in pneumonia and acute respiratory distress syndrome. However, few studies have focused on the role of these inflammatory mediators in infections caused by the Influenza A (H1N1)v. In this study, we assess the inflammatory response mediated by cytokines at the local and systemic levels in three cases of severe pneumonia caused by Influenza A (H1N1) virus. Methodology: Serum and bronchoalveolar lavage samples were obtained from three mechanically ventilated patients diagnosed with Influenza A (H1N1) virus pneumonia by bronchoscopic bronchoalveolar lavage. Levels of interleukin 6 (IL-6), interleukin 8 (IL-8), tumour necrosis factor alpha (TNFα) and interleukin 1 beta (IL-1ß) were meassured in these samples by enzyme-linked immunosorbent assay (ELISA). Results: High levels of C Reactive Protein, Procalcitonin below 1 ng/ml and absence of leukocytosis were common findings in all patients. TNF α and IL-1ß were not detected in the serum. IL-6 levels in serum were (94, pg/ml, 77 pg/ml and 84 pg/ml) respectively in the three patients, while IL-8 levels were (30,2 pg/ml, 128 pg/ml and 40,5 pg/ml). In the BAL samples, only one of the analysed cytokines, IL-1ß was present at detectable levels in two patients (21 pg/ml and 11 pg/ml respectively). Conclusions: Our results support previous findings which suggest that high levels of IL-6 and IL-8 in serum somehow participate in the inflammatory response in severe cases of pandemic influenza pneumonia.

Interrupción voluntaria del embarazo : Andalucía 1997 : evolución de la incidencia de I.V.E. 1991-1998 : distribución de las I.V.E. en los distritos sanitarios 1998

En port.: Unidad Estadística. Publicado en la página web de la Consejería de Salud: www.juntadeandalucia.es/salud (Consejería de Salud / Profesionales / Estadísticas Sanitarias / Estadísticas de interrupción voluntaria del embarazo > Acceso a las Estadisticas de Interrupción Voluntaria del Embarazo)

Interrupción voluntaria del embarazo : Andalucía 1996 : evolución de la incidencia de I.V.E. : años 1991-1997 : (1997 datos provisionales]

En port.: Unidad Estadística

Clausurada la V edición del programa Forma Joven en la universidad de Sevilla

Boletín semanal para profesionales sanitarios de la Secretaría General de Salud Pública y Participación Social de la Consejería de Salud

Las buenas prácticas en la atención temprana, a debate en el v encuentro de profesionales celebrado en Punta Umbría (Huelva)

Boletín semanal para profesionales sanitarios de la Secretaría General de Salud Pública y Participación Social de la Consejería de Salud

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).