Icahn School of Medicine at Mount Sinai

About: Icahn School of Medicine at Mount Sinai is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Medicine. The organization has 37488 authors who have published 76057 publications receiving 3704104 citations. The organization is also known as: Mount Sinai School of Medicine.

Mechanisms of hepatic stellate cell activation

Abstract: Activation of hepatic stellate cells (HSCs) in liver injury is the primary driver of hepatic fibrosis. In this Review, Tsuchida and Friedman detail the varied intracellular and extracellular signalling pathways leading to HSC activation, as well as the role of HSCs in liver fibrosis resolution and as therapeutic targets. Hepatic fibrosis is a dynamic process characterized by the net accumulation of extracellular matrix resulting from chronic liver injury of any aetiology, including viral infection, alcoholic liver disease and NASH. Activation of hepatic stellate cells (HSCs) — transdifferentiation of quiescent, vitamin-A-storing cells into proliferative, fibrogenic myofibroblasts — is now well established as a central driver of fibrosis in experimental and human liver injury. Yet, the continued discovery of novel pathways and mediators, including autophagy, endoplasmic reticulum stress, oxidative stress, retinol and cholesterol metabolism, epigenetics and receptor-mediated signals, reveals the complexity of HSC activation. Extracellular signals from resident and inflammatory cells including macrophages, hepatocytes, liver sinusoidal endothelial cells, natural killer cells, natural killer T cells, platelets and B cells further modulate HSC activation. Finally, pathways of HSC clearance have been greatly clarified, and include apoptosis, senescence and reversion to an inactivated state. Collectively, these findings reinforce the remarkable complexity and plasticity of HSC activation, and underscore the value of clarifying its regulation in hopes of advancing the development of novel diagnostics and therapies for liver disease.

Mutations in PTPN11, encoding the protein tyrosine phosphatase SHP-2, cause Noonan syndrome.

Abstract: Noonan syndrome (MIM 163950) is an autosomal dominant disorder characterized by dysmorphic facial features, proportionate short stature and heart disease (most commonly pulmonic stenosis and hypertrophic cardiomyopathy). Webbed neck, chest deformity, cryptorchidism, mental retardation and bleeding diatheses also are frequently associated with this disease. This syndrome is relatively common, with an estimated incidence of 1 in 1,000-2,500 live births. It has been mapped to a 5-cM region (NS1) [corrected] on chromosome 12q24.1, and genetic heterogeneity has also been documented. Here we show that missense mutations in PTPN11 (MIM 176876)-a gene encoding the nonreceptor protein tyrosine phosphatase SHP-2, which contains two Src homology 2 (SH2) domains-cause Noonan syndrome and account for more than 50% of the cases that we examined. All PTPN11 missense mutations cluster in interacting portions of the amino N-SH2 domain and the phosphotyrosine phosphatase domains, which are involved in switching the protein between its inactive and active conformations. An energetics-based structural analysis of two N-SH2 mutants indicates that in these mutants there may be a significant shift of the equilibrium favoring the active conformation. This implies that they are gain-of-function changes and that the pathogenesis of Noonan syndrome arises from excessive SHP-2 activity.

Deep learning for healthcare: review, opportunities and challenges.

Abstract: Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

Second symposium on the definition and management of anaphylaxis: summary report--Second National Institute of Allergy and Infectious Disease/Food Allergy and Anaphylaxis Network symposium.

Abstract: There is no universal agreement on the definition of anaphylaxis or the criteria for diagnosis. In July 2005, the National Institute of Allergy and Infectious Disease and Food Allergy and Anaphylaxis Network convened a second meeting on anaphylaxis, which included representatives from 16 different organizations or government bodies, including representatives from North America, Europe, and Australia, to continue working toward a universally accepted definition of anaphylaxis, establish clinical criteria that would accurately identify cases of anaphylaxis with high precision, further review the evidence on the most appropriate management of anaphylaxis, and outline the research needs in this area.