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 & Cancer. The organization has 37488 authors who have published 76057 publications receiving 3704104 citations. The organization is also known as: Mount Sinai School of Medicine.

Enhanced long-term potentiation and impaired learning in mice with mutant postsynaptic density-95 protein

Abstract: Specific patterns of neuronal firing induce changes in synaptic strength that may contribute to learning and memory. If the postsynaptic NMDA (N-methyl-D-aspartate) receptors are blocked, long-term potentiation (LTP) and long-term depression (LTD) of synaptic transmission and the learning of spatial information are prevented. The NMDA receptor can bind a protein known as postsynaptic density-95 (PSD-95), which may regulate the localization of and/or signalling by the receptor. In mutant mice lacking PSD-95, the frequency function of NMDA-dependent LTP and LTD is shifted to produce strikingly enhanced LTP at different frequencies of synaptic stimulation. In keeping with neural-network models that incorporate bidirectional learning rules, this frequency shift is accompanied by severely impaired spatial learning. Synaptic NMDA-receptor currents, subunit expression, localization and synaptic morphology are all unaffected in the mutant mice. PSD-95 thus appears to be important in coupling the NMDA receptor to pathways that control bidirectional synaptic plasticity and learning.

Haematopoietic stem cell release is regulated by circadian oscillations

Abstract: Haematopoietic stem cells (HSCs) circulate in the bloodstream under steady-state conditions, but the mechanisms controlling their physiological trafficking are unknown. Here we show that circulating HSCs and their progenitors exhibit robust circadian fluctuations, peaking 5 h after the initiation of light and reaching a nadir 5 h after darkness. Circadian oscillations are markedly altered when mice are subjected to continuous light or to a 'jet lag' (defined as a shift of 12 h). Circulating HSCs and their progenitors fluctuate in antiphase with the expression of the chemokine CXCL12 in the bone marrow microenvironment. The cyclical release of HSCs and expression of Cxcl12 are regulated by core genes of the molecular clock through circadian noradrenaline secretion by the sympathetic nervous system. These adrenergic signals are locally delivered by nerves in the bone marrow, transmitted to stromal cells by the beta(3)-adrenergic receptor, leading to a decreased nuclear content of Sp1 transcription factor and the rapid downregulation of Cxcl12. These data indicate that a circadian, neurally driven release of HSC during the animal's resting period may promote the regeneration of the stem cell niche and possibly other tissues.

Exploiting lymphatic transport and complement activation in nanoparticle vaccines.

Abstract: Antigen targeting and adjuvancy schemes that respectively facilitate delivery of antigen to dendritic cells and elicit their activation have been explored in vaccine development. Here we investigate whether nanoparticles can be used as a vaccine platform by targeting lymph node-residing dendritic cells via interstitial flow and activating these cells by in situ complement activation. After intradermal injection, interstitial flow transported ultra-small nanoparticles (25 nm) highly efficiently into lymphatic capillaries and their draining lymph nodes, targeting half of the lymph node-residing dendritic cells, whereas 100-nm nanoparticles were only 10% as efficient. The surface chemistry of these nanoparticles activated the complement cascade, generating a danger signal in situ and potently activating dendritic cells. Using nanoparticles conjugated to the model antigen ovalbumin, we demonstrate generation of humoral and cellular immunity in mice in a size- and complement-dependent manner.

Functional atlas of the integrin adhesome

Abstract: A detailed depiction of the 'integrin adhesome', consisting of a complex network of 156 components linked together and modified by 690 interactions is presented. Different views of the network reveal several functional 'subnets' that are involved in switching on or off many of the molecular interactions within the network, consequently affecting cell adhesion, migration and cytoskeletal organization. Examination of the adhesome network motifs reveals a relatively small number of key motifs, dominated by three-component complexes in which a scaffolding molecule recruits both a signalling molecule and its downstream target. We discuss the role of the different network modules in regulating the structural and signalling functions of cell-matrix adhesions.

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Abstract: Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.