83 research outputs found
Characterization of a murine model of monocrotaline pyrrole-induced acute lung injury
<p>Abstract</p> <p>Background</p> <p>New animal models of chronic pulmonary hypertension in mice are needed. The injection of monocrotaline is an established model of pulmonary hypertension in rats. The aim of this study was to establish a murine model of pulmonary hypertension by injection of the active metabolite, monocrotaline pyrrole.</p> <p>Methods</p> <p>Survival studies, computed tomographic scanning, histology, bronchoalveolar lavage were performed, and arterial blood gases and hemodynamics were measured in animals which received an intravenous injection of different doses of monocrotaline pyrrole.</p> <p>Results</p> <p>Monocrotaline pyrrole induced pulmonary hypertension in Sprague Dawley rats. When injected into mice, monocrotaline pyrrole induced dose-dependant mortality in C57Bl6/N and BALB/c mice (dose range 6–15 mg/kg bodyweight). At a dose of 10 mg/kg bodyweight, mice developed a typical early-phase acute lung injury, characterized by lung edema, neutrophil influx, hypoxemia and reduced lung compliance. In the late phase, monocrotaline pyrrole injection resulted in limited lung fibrosis and no obvious pulmonary hypertension.</p> <p>Conclusion</p> <p>Monocrotaline and monocrotaline pyrrole pneumotoxicity substantially differs between the animal species.</p
Robust stability of uncertain Markovian jump neural networks with mode-dependent time-varying delays and nonlinear perturbations
Non‐Fragile Extended Dissipativity Control Design for Generalized Neural Networks with Interval Time‐Delay Signals
Non-fragile sampled-data stabilization analysis for linear systems with probabilistic time-varying delays
Global asymptotic stability analysis for neutral-type complex-valued neural networks with random time-varying delays
Delay-range-dependent passivity analysis for uncertain stochastic neural networks with discrete and distributed time-varying delays
Robust dissipativity analysis for uncertain neural networks with additive time-varying delays and general activation functions
New delay dependent robust asymptotic stability for uncertain stochastic recurrent neural networks with multiple time varying delays
Improved stability analysis for Markovian Jump Static Neural Networks with Mode-Dependent Time-Varying Delays
ISSN: 2454-132X Impact factor: 4.295 Exponential Hfiltering design for discrete-time neural networks switched systems with time-varying delay
Abstract: This paper deals with the exponential H filtering problem for discrete-time neural networks switched singular systems with time-varying delays. The main purpose of this paper is to design a linear mode-dependent filter such that the resulting filtering error system is regular, causal, and exponentially stable with a prescribed H-infinity performance bound. In addition, the decay rate of the filtering error dynamics can also be tuned. By constructing an appropriate Lyapunov functional together with some zero inequalities and using the average dwell time scheme, a novel delay-dependent sufficient condition for the solvability of the H-infinity filtering problem is derived. Based on this condition, the desired filter gains can be obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is presented to show the effectiveness of the proposed design method
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