Role of hypoxia inducible factor-1α in remote limb ischemic preconditioning.

Remote ischemic preconditioning (RIPC) has emerged as a feasible and attractive therapeutic procedure for heart protection against ischemia/reperfusion (I/R) injury. However, its molecular mechanisms remain poorly understood. Hypoxia inducible factor-1α (HIF-1α) is a transcription factor that plays a key role in the cellular adaptation to hypoxia and ischemia. This study\u27s aim was to test whether RIPC-induced cardioprotection requires HIF-1α upregulation to be effective. In the first study, wild-type mice and mice heterozygous for HIF1a (gene encoding the HIF-1α protein) were subjected to RIPC immediately before myocardial infarction (MI). RIPC resulted in a robust HIF-1α activation in the limb and acute cardioprotection in wild-type mice. RIPC-induced cardioprotection was preserved in heterozygous mice, despite the low HIF-1α expression in their limbs. In the second study, the role of HIF-1α in RIPC was evaluated using cadmium (Cd), a pharmacological HIF-1α inhibitor. Rats were subjected to MI (MI group) or to RIPC immediately prior to MI (R-MI group). Cd was injected 18 0min before RIPC (Cd-R-MI group). RIPC induced robust HIF-1α activation in rat limbs and significantly reduced infarct size (IS). Despite Cd\u27s inhibition of HIF-1α activation, RIPC-induced cardioprotection was preserved in the Cd-R-MI group. RIPC applied immediately prior to MI increased HIF-1α expression and attenuated IS in rats and wild-type mice. However, RIPC-induced cardioprotection was preserved in partially HIF1a-deficient mice and in rats pretreated with Cd. When considered together, these results suggest that HIF-1α upregulation is unnecessary in acute RIPC

A balanced capacity implementation of DSM for DSL systems

This paper investigates a fair resource allocation method in crosstalk limited DSL systems. As a measure of fairness, the concept of Balanced Capacity (BC), introduced in [1], is used. BC corresponds to a point of the boundary of the capacity region where each user experiences the same relative loss with respect to his maximum capacity. Our goal is to propose algorithms to compute the corresponding point in DSL (Digital Subscriber Line) systems where some users suffer from a near-far situation and for constraints on individual power spectrum densities. Results are reported for typical crosstalk limited cases and are compared to a resource allocation optimized for the sum rate. © 2006 IEEE

RISK and SAFE signaling pathway involvement in apolipoprotein A-I-induced cardioprotection.

Recent findings indicate that apolipoprotein A-I (ApoA-I) may be a protective humoral mediator involved in remote ischemic preconditioning (RIPC). This study sought to determine if ApoA-I mediates its protective effects via the RISK and SAFE signaling pathways implicated in RIPC. Wistar rats were allocated to one of the following groups.rats were subjected to myocardial ischemia/reperfusion (I/R) without any further intervention; RIPC: four cycles of limb I/R were applied prior to myocardial ischemia; ApoA-I: 10 mg/Kg of ApoA-I were intravenously injected prior to myocardial ischemia; ApoA-I + inhibitor: pharmacological inhibitors of RISK/SAFE pro-survival kinase (Akt, ERK1/2 and STAT-3) were administered prior to ApoA-I injection. Infarct size was significantly reduced in the RIPC group compared to CONTROL. Similarly, ApoA-I injection efficiently protected the heart, recapitulating RIPC-induced cardioprotection. The ApoA-I protective effect was associated with Akt and GSK-3β phosphorylation and substantially inhibited by pretreatment with Akt and ERK1/2 inhibitors. Pretreatment with ApoA-I in a rat model of I/R recapitulates RIPC-induced cardioprotection and shares some similar molecular mechanisms with those of RIPC-involved protection of the heart

Constrained Laplacian Score for Semi-supervised Feature Selection

International audienceIn this paper, we address the problem of semi-supervised feature selection from high-dimensional data. It aims to select the most discriminative and informative features for data analysis. This is a recent addressed challenge in feature selection research when dealing with small labeled data sampled with large unlabeled data in the same set. We present a filter based approach by constraining the known Laplacian score. We evaluate the relevance of a feature according to its locality preserving and constraints preserving ability. The problem is then presented in the spectral graph theory framework with a study of the complexity of the proposed algorithm. Finally, experimental results will be provided for validating our proposal in comparison with other known feature selection methods