A clinician’s guide to management of intra-abdominal hypertension and abdominal compartment syndrome in critically ill patients

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901

Método híbrido para categorización de texto basado en aprendizaje y reglas

En este artículo se presenta un nuevo método híbrido de categorización automática de texto, que combina un algoritmo de aprendizaje computacional, que permite construir un modelo base de clasificación sin mucho esfuerzo a partir de un corpus etiquetado, con un sistema basado en reglas en cascada que se emplea para filtrar y reordenar los resultados de dicho modelo base. El modelo puede afinarse añadiendo reglas específicas para aquellas categorías difíciles que no se han entrenado de forma satisfactoria. Se describe una implementación realizada mediante el algoritmo kNN y un lenguaje básico de reglas basado en listas de términos que aparecen en el texto a clasificar. El sistema se ha evaluado en diferentes escenarios incluyendo el corpus de noticias Reuters-21578 para comparación con otros enfoques, y los modelos IPTC y EUROVOC. Los resultados demuestran que el sistema obtiene una precisión y cobertura comparables con las de los mejores métodos del estado del arte

VENTX induces expansion of primitive erythroid cells and contributes to the development of acute myeloid leukemia in mice

Homeobox genes are key regulators in normal and malignant hematopoiesis. The human Vent-like homeobox gene VENTX, a putative homolog of the Xenopus laevis Xvent-2 gene, was shown to be highly expressed in normal myeloid cells and in patients with acute myeloid leukemia. We now demonstrate that constitutive expression of VENTX suppresses expression of genes responsible for terminal erythroid differentiation in normal CD34(+) stem and progenitor cells. Transplantation of bone marrow progenitor cells retrovirally engineered to express VENTX caused massive expansion of primitive erythroid cells and partly acute erythroleukemia in transplanted mice. The leukemogenic potential of VENTX was confirmed in the AML1-ETO transplantation model, as in contrast to AML1-ETO alone co-expression of AML1-ETO and VENTX induced acute myeloid leukemia, partly expressing erythroid markers, in all transplanted mice. VENTX was highly expressed in patients with primary human erythroleukemias and knockdown of VENTX in the erythroleukemic HEL cell line significantly blocked cell growth. In summary, these data indicate that VENTX is able to perturb erythroid differentiation and to contribute to myeloid leukemogenesis when co-expressed with appropriate AML oncogenes and point to its potential significance as a novel therapeutic target in AML

Multi-omics approaches to study platelet mechanisms

Platelets are small anucleate cell fragments (2-4 µm in diameter) in the blood, which play an essential role in thrombosis and hemostasis. Genetic or acquired platelet dysfunctions are linked to bleeding, increased risk of thromboembolic events and cardiovascular diseases. Advanced proteomic approaches may pave the way to a better understanding of the roles of platelets in hemostasis, and pathophysiological processes such as inflammation, metastatic spread and thrombosis. Further insights into the molecular biology of platelets are crucial to aid drug development and identify diagnostic markers of platelet activation. Platelet activation is known to be an extremely rapid process and involves multiple post-translational mechanisms at sub second time scale, including proteolysis and phosphorylation. Multi-omics technologies and biochemical approaches can be exploited to precisely probe and define these posttranslational pathways. Notably, the absence of a nucleus in platelets significantly reduces the number of present proteins, simplifying mass spectrometry-based proteomics and metabolomics approaches

1168P Tumor PD-L1 predicts the outcome of PD-1-based immunotherapy in metastatic melanoma depending on the type of tissue examined [Abstract]

Background PD-1-based immune checkpoint inhibition (ICI) is the major backbone of current melanoma therapy. Tumor PD-L1 expression represents one of few biomarkers predicting ICI therapy outcome. Here, we systematically investigated whether the type of tumor tissue examined for PD-L1 expression has an impact on ICI therapy outcome prediction. Methods Pre-treatment tumor tissue obtained before 1st ICI therapy for non-resectable stage III/IV metastatic melanoma was prospectively collected within the DeCOG multicenter study Tissue Registry in Melanoma. Stratified by tissue type, best overall response (BOR), progression-free survival (PFS), and overall survival (OS) were correlated with tumor PD-L1 expression (cutoff ≥5%). Results Of 448 patients, tumor PD-L1 was determined on 95 primary tumors (PT; 36.8% positivity), 153 skin (34.0% positivity), 115 lymph node (LN; 50.4% positivity), and 85 organ (40.8% positivity) metastases. Skin metastases were significantly more often classified as PD-L1 negative than LN metastases (OR=0.751; 95%CI=0.599-0.956; P=0.007). PD-L1 positivity was predictive for BOR if determined on LN (CR/PR 37.5% versus 16.1%; OR=0.319; 95%CI=0.138-0.76; P=0.010), but not on skin metastases (CR/PR 36.0% versus 28.0%; OR=0.778; 95%CI=0.379-1.554; P=0.49), translating into favorable survival for PD-L1 positivity determined on LN metastases (median PFS 22.0 versus 3.5 months, HR=0.490; 95%CI=0.310-0.775; P=0.002; median OS 68.9 versus 16.6 months, HR=0.519; 95%CI=0.307-0.880P=0.014). PD-L1 positivity determined on PT (PFS= HR=0.757; 95%CI=0.467-1.226; P=0.27; OS= HR=0.528; 95%CI=0.305-0.913; P=0.032) was predictive to a lesser extent. No relevant survival differences were detected by PD-L1 determined on skin metastases. Multivariate analysis revealed tumor PD-L1 determined on LN metastases as independent predictive factor for PFS (HR=0.43; 95%CI=0.24-0.75; P=0.003) and OS (HR=0.51; 95%CI=0.27-0.96; P=0.037). Conclusions For outcome prediction of PD-1-based immunotherapy in melanoma, tumor PD-L1 determined on LN metastases was more reliable than that assessed on PT. PD-L1 determined on skin metastases showed no predictive value and cannot be recommended for clinical use