Enantioselective Disposition of 2-Aryipropionic Acid Nonsteroidal Anti-Inflammatory Drugs. Ill. Fenoprofen Disposition'

ABSTRACT The formation of the acylglucuronides of clofibric acid (Odum and Orton, 1983) and diflunisal (Faed et al., 1984) has also been reported to be induced by phenobarbital. Based on the above observations we have examined the effect ofphenobarbital on fenoprofen disposition and its enantiomeric consequences. ABBREVIATIONS: Cl5

Enantioselective Disposition of 2-Aryipropionic Acid Nonsteroidal Anti-Inflam matory Drugs . IV . Ketoprofen Disposition1

ABSTRACT The disposition of ketoprofen enantiomers has been studied in 1 2 rabbits with normal renal function (control) and in 6 of these rabbits with renal dysfunction. In control animals a mean (S.E.M.) of 0.09 (0.01) of R-ketoprofen was inverted to its S-enantiomer. The mean distribution volumes for A-and S-ketoprofen were 114 (7.4) and 29

A Comparison of Plasma Methylprednisolone Concentrations Following Intra-Articular Injection in Patients with Rheumatoid Arthritis and Osteoarthritis

Plasma concentrations of methylprednislone following intra-articular injection were measured in rheumatoid arthritis and osteoarthritis patients. While substantial plasma concentrations were seen in both groups of patients, there was so significant difference in the rate or extent of absorption from osteoarthritic or rheumatoid knees. This study suggests that it is the dissolution rate of the steroid formulation rather than the characteristics of the synovial membrane which determine rate and extent of systemic absorption of methylprednislone after intra-articular injection

Ranitidine Disposition in Patients with Renal Impairment

Ranitidine disposition has been studied in 12 patients with renal impairment following 50mg given intravenously and 150mg by mouth on separate occasions. The clearance of ranitidine from plasma (y) was correlated with creatinine clearance (x):y = 10.47 + 0.289x,r2=0.751, but there was no significant correlation of creatinine clearance with distribution volume or bioavailability. The mean (s.e. mean) distribution volume was 1.62 (0.08) l/kg and the mean bioavailability 0.81 (0.05). these data suggest that in order to obtain similar ranitidine plasma concentrations in anephric patients and patients with normal renal function, the maintenance dose in the anephric patients should be 25-30% of that for patients with normal renal function

Homeostatic Scaling of Excitability in Recurrent Neural Networks

Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity

Soft-bound synaptic plasticity increases storage capacity

Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses

Growth Rules for the Repair of Asynchronous Irregular Neuronal Networks after Peripheral Lesions

© 2021 Sinha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License. https://creativecommons.org/licenses/by/4.0/Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.Peer reviewe

Computational modeling with spiking neural networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed