The role of sex in the pathophysiology of pulmonary hypertension

Pulmonary arterial hypertension (PAH) is a progressive disease characterised by increased pulmonary vascular resistance and pulmonary artery remodelling as result of increased vascular tone and vascular cell proliferation, respectively. Eventually, this leads to right heart failure. Heritable PAH is caused by a mutation in the bone morphogenetic protein receptor-II (BMPR-II). Female susceptibility to PAH has been known for some time, and most recent figures show a female-to-male ratio of 4:1. Variations in the female sex hormone estrogen and estrogen metabolism modify FPAH risk, and penetrance of the disease in BMPR-II mutation carriers is increased in females. Several lines of evidence point towards estrogen being pathogenic in the pulmonary circulation, and thus increasing the risk of females developing PAH. Recent studies have also suggested that estrogen metabolism may be crucial in the development and progression of PAH with studies indicating that downstream metabolites such as 16α-hydroxyestrone are upregulated in several forms of experimental pulmonary hypertension (PH) and can cause pulmonary artery smooth muscle cell proliferation and subsequent vascular remodelling. Conversely, other estrogen metabolites such as 2-methoxyestradiol have been shown to be protective in the context of PAH. Estrogen may also upregulate the signalling pathways of other key mediators of PAH such as serotonin

A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques

This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network