Evidence‐based treatment for gynoid lipodystrophy: A review of the recent literature

Resumen La lipodistrofia ginoide (LDG) es una alteracion estructural, inflamatoria y bioquımica del tejido subcutaneo que causa modificaciones topograficas en la piel. Conocida comunmente como “celulitis”, la LDG afecta hasta a 90% de las mujeres, practicamente en todas las etapas de la vida, iniciando en la pubertad. Se trata de una condicion que afecta considerablemente la calidad de vida de quien la padece. Es motivo frecuente de consulta aunque las pacientes recurren a tratamientos empıricos, improvisados, sin bases ni evidencia cientıfica, los cuales desmotivan y producen frustracion no solo por su falta de resultados, sino por complicaciones derivadas de dichos tratamientos. Un grupo de expertos de diversas especialidades involucradas en el manejo de este problema presenta en este artıculo el resultado de una busqueda bibliografica sistematica y de la discusion consensuada de la evidencia obtenida de diversos tratamientos disponibles actualmente. El analisis se dividio en tratamientos topicos, tratamientos sistemicos, tratamientos no invasivos y tratamientos mınimamente invasivos

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients

Background Identifying predictive non-invasive biomarkers of immunotherapy response is crucial to avoid premature treatment interruptions or ineffective prolongation. Our aim was to develop a non-invasive biomarker for predicting immunotherapy clinical durable benefit, based on the integration of radiomics and clinical data monitored through early anti-PD-1/PD-L1 monoclonal antibodies treatment in patients with advanced non-small cell lung cancer (NSCLC).MethodsIn this study, 264 patients with pathologically confirmed stage IV NSCLC treated with immunotherapy were retrospectively collected from two institutions. The cohort was randomly divided into a training (n = 221) and an independent test set (n = 43), ensuring the balanced availability of baseline and follow-up data for each patient. Clinical data corresponding to the start of treatment was retrieved from electronic patient records, and blood test variables after the first and third cycles of immunotherapy were also collected. Additionally, traditional radiomics and deep-radiomics features were extracted from the primary tumors of the computed tomography (CT) scans before treatment and during patient follow-up. Random Forest was used to implementing baseline and longitudinal models using clinical and radiomics data separately, and then an ensemble model was built integrating both sources of information.ResultsThe integration of longitudinal clinical and deep-radiomics data significantly improved clinical durable benefit prediction at 6 and 9 months after treatment in the independent test set, achieving an area under the receiver operating characteristic curve of 0.824 (95% CI: [0.658,0.953]) and 0.753 (95% CI: [0.549,0.931]). The Kaplan-Meier survival analysis showed that, for both endpoints, the signatures significantly stratified high- and low-risk patients (p-value< 0.05) and were significantly correlated with progression-free survival (PFS6 model: C-index 0.723, p-value = 0.004; PFS9 model: C-index 0.685, p-value = 0.030) and overall survival (PFS6 models: C-index 0.768, p-value = 0.002; PFS9 model: C-index 0.736, p-value = 0.023).ConclusionsIntegrating multidimensional and longitudinal data improved clinical durable benefit prediction to immunotherapy treatment of advanced non-small cell lung cancer patients. The selection of effective treatment and the appropriate evaluation of clinical benefit are important for better managing cancer patients with prolonged survival and preserving quality of life

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Anales de Edafología y Agrobiología Tomo 36 Número 11-12

Utilización de antibióticos en el cebo precoz de corderos. Efectos de la Virginiamicina, por E. Zaera Jimeno, J. P. Gómez Ballesteros y V. González González.-- Evaluación de suelos para diferentes usos agrícolas. Un sistema desarrollado para regiones mediterráneas, por D. de la Rosa, F. Cardona y G. Paneque.-- Fraccionamiento de ácidos húmicos de vertisol por geles de Sephadex. Comportamiento según los métodos empleados, por F. J. G. Vila y F. Martín.-- Composición y evolución de la fracción arcilla en suelos hidromorfos de zonas semiáridas. II. Suelos pseugleyzados, por M. Ledesma García y M. Sánchez Camazano.-- Composición y evolución de la fracción arcilla en suelos hidromorfos de zonas semiáridas. III. Suelos de vega, gleyzados y planosuelos, por M. Sánchez Camazo y M. Ledesma García.-- Variations saisonnieres dans l'ultrastructure d'un lichen foliace: Parmclia conspersa S. L., par María Carmen Ascaso et Jesús Galván.-- Evolución de la materia orgánica de un suelo clímax y de un suelo de repoblación de la vertiente norte de la Sierra de Gata (Salamanca}, por M. l. M. González, J. F. Gallardo y J. A. Egido.-- Estudio de los ácidos húmicos de dos fertilizantes orgánicos, sirle y turba, en suelos calizos. III. Aminoácidos, por J. Cegarra y F. Costa.-- Estudio crítico sobre la utilización de Azotobacter y fosfobacterias como fertilizantes microbianos, por J. M. Barca, J. A. Ocampo y E. Montoya.-- Estudio de la acción del boro sobre la absorción y metabolismo de elementos esenciales, rendimientos e índice de calidad en el cultivo de tomate. I. Influencia sobre absorción y metabolismo de elementos esenciales, por N. T. Piñero, C. Cadahia, V. Hernando e l. Bonilla.-- Estudio de la acción del boro sobre la absorción y metabolismo de elementos esenciales, rendimientos e índice de calidad en el cultivo de tomate. II. Influencia sobre rendimientos e índices de calidad nutritiva, por M. T. Piñero, C. Cadahia y V. Hernando.-- Estudio de tierras pardas sobre material calizo del Pirineo. II. Estudio de suelos: comportamiento de los principales constituyentes, por A. Hoyos, A. M. Moreno y J. González Parra.-- de tierras pardas sobre material calizo del Pirineo. III. Estudio de la fracción arcilla, por A. Hoyos, A. M. Moreno y J. González Parra.-- Estudio de la nutrición del aguacate por análisis foliar. l. Hojas procedentes de ramas fructíferas, por Serafín Jaime Palacios y Eduardo Esteban Velasco.—Nota preliminar.-- Determinación analítica del boro asimilable en suelos de cultivo, por A. Aguilar-Ros y A. Aguilar.—Estudios recapitulativos.-- Estudio de los factores ecológicos que afectan a las poblaciones microbianas en la rizosfera, por J. A. Ocampo, J. M. Barca y E. Montoya.-- Notas Prof. Tames Alarcón.-- Fallecimiento del Dr. Claver Aliod.-- El Prof. Mückenhansen cumple 70 años.-- Nombramiento de Presidente de la Comisión Asesora de Investigación Científica y Técnica.-- Nombramiento de Director General de Política Científica El Prof. Suárez cesa en la Dirección General de E. G. B.-- Distinción a D. Julio Rodríguez Martínez.-- El Prof. Balcells, Miembro de Honor de la Société Zoologique de France.-- Visita del Prof. Gómez Gutiérrez a la República Argentina.-- XIII Curso Internacional de Hidrología General y Aplicada y VII Curso Internacional de Ingeniería de Regadíos.-- Tenth International Congress of Sedimentology.-- Conferencia del Prof. H. Vu.-- Fundación del I. N. I. .-- Jornadas Técnicas sobre Ganado Porcino.-- Congreso Internacional de Técnicas Analíticas en la Química Ambiental.-- XV Premio Agrícola AEDOS.-- Sociedad Española de Ciencia del Suelo.-- Aprobación del Reglamento del C. S. I. C.-- BibliografíaPeer reviewe