Quality of life in adult survivors after paediatric heart transplantation in Australia.

BACKGROUND: Paediatric heart transplantation in Australia is centralised at The Royal Children's Hospital, Melbourne. Survival to adulthood is improving but the ongoing need for complex medical therapy, surveillance, and potential for late complications continues to impact on quality of life. Quality of life in adults who underwent heart transplantation in childhood in Australia has not been assessed. METHODS: Cross-sectional quality of life data were collected from paediatric heart transplant survivors >18 years of age using Rand 36-Item Health Survey. Self-reported raw scores were transformed to a 0-100 scale with higher scores indicating better quality of life. Mean scores were compared to National Health Survey Short Form-36 Population Norms data using the independent sample t-test. RESULTS: A total of 64 patients (64/151) who underwent transplantation at The Royal Children's Hospital between 1988 and 2016 survived to adulthood. In total 51 patients (51/64, 80%) were alive at the time of the study and 27 (53%) responded with a mean age of 25 ± 6 years, being a median of 11 years (interquartile range 7-19) post-transplantation. Most self-reported quality of life subscale scores were not significantly different from the Australian normative population data. However, self-reported 'General Health' was significantly worse than normative data (p = 0.02). Overall, 93% (25/27) reported their general health as being the same or better compared to 1-year ago. CONCLUSION: Adult survivors after paediatric heart transplantation in Australia report good quality of life in multiple domains and demonstrate independence in activities of daily living and employment. However, lifelong medical treatment may affect perceptions of general health.

Now you see it, now you don't: Relevance of threat enhances social anxiety-linked attentional bias to angry faces, but relevance of neutral information attenuates it.

Temporary goals modulate attention to threat. We examined whether attentional bias to angry faces differs depending on whether a temporary background goal is neutral, or threat related, whilst also measuring social anxiety. Participants performed a dot probe task combined with a separate task that induced a temporary goal. Depending on the phase in this goal task, the goal made angry faces or neutral stimuli (i.e., houses) relevant. The dot probe task measured attention to combinations of angry faces, neutral but goal-relevant stimuli (i.e., houses), and neutral control stimuli. Attention was allocated to angry faces when an angry goal was active. This was more pronounced for people scoring high on social phobia. The neutral goal attenuated attention to angry faces and effects of social phobia were no longer apparent. These findings suggest that individual differences in social anxiety interact with current and temporary goals to affect attentional processes.

A brain-plausible neuromorphic on-the-fly learning system implemented with magnetic domain wall analog memristors.

Neuromorphic computing is an approach to efficiently solve complicated learning and cognition problems like the human brain using electronics. To efficiently implement the functionality of biological neurons, nanodevices and their implementations in circuits are exploited. Here, we describe a general-purpose spiking neuromorphic system that can solve on-the-fly learning problems, based on magnetic domain wall analog memristors (MAMs) that exhibit many different states with persistence over the lifetime of the device. The research includes micromagnetic and SPICE modeling of the MAM, CMOS neuromorphic analog circuit design of synapses incorporating the MAM, and the design of hybrid CMOS/MAM spiking neuronal networks in which the MAM provides variable synapse strength with persistence. Using this neuronal neuromorphic system, simulations show that the MAM-boosted neuromorphic system can achieve persistence, can demonstrate deterministic fast on-the-fly learning with the potential for reduced circuitry complexity, and can provide increased capabilities over an all-CMOS implementation.

Cost-effectiveness of the National Pediatric Heart Transplantation Program in Australia.

OBJECTIVES: Cost data for pediatric heart transplantation are scarce. We examined hospital cost of the national pediatric heart transplantation program in Australia and assessed factors associated with increased costs. METHODS: The hospital cost of all children who underwent heart transplantation at a national referral center between January 2003 and June 2015 and were followed more than 1 year was retrospectively analyzed. Lifetime follow-up costs were adjusted for quality of life and projected to life expectancy. All costs were reported in 2016 US dollars. RESULTS: Of 70 children who underwent heart transplantation in the study period, 61 were followed more than 1 year after transplantation (mean, 4.3 ± 2.5 years). Mean cost of primary heart transplantation was $278,480 (95% confidence interval, 219,282-337,679) and did not change over time. Pretransplant mechanical circulatory support was required in 36% (22/61) of children. On multivariable analysis, greater admission costs were associated with ventricular assist device and pretransplant length of stay. Mean annual follow-up cost after discharge was $55,823 (95% confidence interval, 47,631-64,015) in the first year and $12,119 (95% confidence interval, 8578-15,661) thereafter. Increased first-year follow-up costs were associated with endomyocardial biopsies and length of readmissions. Cost per quality-adjusted life-year gained varied from $29,161 to $44,481 on sensitivity analysis. Freedom from treated rejections was 65.5% at 1 year, 63.2% at 3 years, and 59.5% at 5 years. Endomyocardial biopsies contributed to 52% of first-year follow-up costs. CONCLUSIONS: Primary pediatric heart transplantation in Australia is cost-effective for long-term survivors, even for those supported by ventricular assist device. Surveillance endomyocardial biopsy was a major contributor to post-transplantation costs. Selective targeting of surveillance biopsies may be cost-saving.

Surgical Management of Extensive Perinatal Myocardial Infarction.

Extensive perinatal myocardial infarction caused by coronary artery thrombosis is extremely rare and has a dismal prognosis. We report a 3.5-kg neonate who presented at birth with an extensive myocardial infarction caused by aortic root and left main coronary artery thrombus after an emergency cesarean section. We performed emergency surgical thrombectomy and insertion of extracorporeal membrane oxygenation support. After subsequent conversion to long-term left ventricular assist device with an EXCOR device (Berlin Heart, Berlin, Germany), the patient had no ventricular recovery after 163 days of support. He was successfully bridged to transplantation.

A neuromorphic circuit mimicking biological short-term memory.

Research shows that the way we remember things for a few seconds is a different mechanism from the way we remember things for a longer time. Short-term memory is based on persistently firing neurons, whereas storing information for a longer time is based on strengthening the synapses or even forming new neural connections. Information about location and appearance of an object is segregated and processed by separate neurons. Furthermore neurons can continue firing using different mechanisms. Here, we have designed a biomimetic neuromorphic circuit that mimics short-term memory by firing neurons, using biological mechanisms to remember location and shape of an object. Our neuromorphic circuit has a hybrid architecture. Neurons are designed with CMOS 45nm technology and synapses are designed with carbon nanotubes (CNT).

Neuromorphic circuit modeling directional selectivity in the visual cortex.

We have designed a neuromorphic circuit that models directional selectivity in the visual cortex, where selected neurons fire depending on the direction of object motion, along with the size and orientation of the object. The neuromorphic circuit is biomimetic. It consists of neurons and synapses, and models biological mechanisms. Neurons (including the Axon Hillock and the Dendritic Arbor) are designed with CMOS technology and synapses (both excitatory and inhibitory) are designed with Carbon Nanotube Transistors. We show example simulations of neuromorphic circuits that indicate orientation, direction of movement and size selectivity. In these circuits, the processing capability of the neural network emerges from the way the neurons are connected.

An astrocyte neuromorphic circuit that influences neuronal phase synchrony.

Neuromorphic circuits are designed and simulated to emulate the role of astrocytes in phase synchronization of neuronal activity. We emulate, to a first order, the ability of slow inward currents (SICs) evoked by the astrocyte, acting on extrasynaptic N-methyl-D-aspartate receptors (NMDAR) of adjacent neurons, as a mechanism for phase synchronization. We run a simulation test incorporating two small networks of neurons interacting with astrocytic microdomains. These microdomains are designed using a resistive and capacitive ladder network and their interactions occur through pass transistors. Upon enough synaptic activity, the astrocytic microdomains interact with each other, generating SIC events on synapses of adjacent neurons. Since the amplitude of SICs is several orders of magnitude larger compared to synaptic currents, a SIC event drastically enhances the excitatory postsynaptic potential (EPSP) on adjacent neurons simultaneously. This causes neurons to fire synchronously in phase. Phase synchrony holds for a duration of time proportional to the time constant of the SIC decay. Once the SIC decay has completed, the neurons are able to go back to their natural phase difference, inducing desynchronization of their firing of spikes. This paper incorporates some biological aspects observed by recent experiments showing astrocytic influence on neuronal synchronization, and intends to offer a circuit view on the hypothesis of astrocytic role on synchronous activity that could potentially lead to the binding of neuronal information.

Dynamic spike threshold and nonlinear dendritic computation for coincidence detection in neuromorphic circuits.

We present an electronic cortical neuron incorporating dynamic spike threshold and active dendritic properties. The circuit is simulated using a carbon nanotube field-effect transistor SPICE model. We demonstrate that our neuron has lower spike threshold for coincident synaptic inputs; however when the synaptic inputs are not in synchrony, it requires larger depolarization to evoke the neuron to fire. We also demonstrate that a dendritic spike is key to precisely-timed input-output transformation, produces reliable firing and results in more resilience to input jitter within an individual neuron.

Synaptic variability in a cortical neuromorphic circuit.

Variable behavior has been observed in several mechanisms found in biological neurons, resulting in changes in neural behavior that might be useful to capture in neuromorphic circuits. This paper presents a neuromorphic cortical neuron with synaptic neurotransmitter-release variability, which is designed to be used in neural networks as part of the Biomimetic Real-Time Cortex project. This neuron has been designed and simulated using carbon nanotube (CNT) transistors, which is one of several nanotechnologies under consideration to meet the challenges of scale presented by the cortex. Some research results suggest that some instances of variability are stochastic, while others indicate that some instances of variability are chaotic. In this paper, both possible sources of variability are considered by embedding either Gaussian noise or a chaotic signal into the neuromorphic or synaptic circuit and observing the simulation results. In order to embed chaotic behavior into the neuromorphic circuit, a chaotic signal generator circuit is presented, implemented with CNT transistors that could be embedded in the electronic neural circuit, and simulated using CNT SPICE models. The circuit uses a chaotic piecewise linear 1-D map implemented by switched-current circuits. The simulation results presented in this paper illustrate that neurotransmitter-release variability plays a beneficial role in the reliability of spike generation. In an examination of this reliability, the precision of spike timing in the CNT circuit simulations is found to be dependent on stimulus (postsynaptic potential) transients. Postsynaptic potentials with low neurotransmitter release variability or without neurotransmitter release variability produce imprecise spike trains, whereas postsynaptic potentials with high neurotransmitter-release variability produce spike trains with reproducible timing.

A directionally-selective neuromorphic circuit based on reciprocal synapses in Starburst Amacrine Cells.

Starburst Amacrine Cells (SACs) play a major role in the detection of directional motion in the biological retina. The starburst amacrine cell has intrinsic electrical mechanisms for producing directional selectivity (DS). GABA transmitter-receptor interactions between two overlapping SACs make DS more robust. We present a compartmentalized CMOS neuromorphic circuit that models a portion of two biological starburst amacrine cells in the retina and includes a simplified model of reciprocal interaction between the dendritic branches of SACs. We demonstrate that a neuromorphic circuit incorporating the reciprocal synapses enhances the responses in the neuromorphic dendritic tip and generates robust directional selectivity.

A carbon nanotube cortical neuron with spike-timing-dependent plasticity.

This paper describes a carbon nanotube synapse circuit that exhibits Spike-Timing Dependant Plasticity (STDP). These synapses are found in cortical (e.g. pyramidal) neurons. Experiments with the synapse in a neuron circuit demonstrate changes in synaptic potential with pre- and post-spiking timing variations. The circuit design is biomimetic and changes in control voltages representing neurotransmitter concentration lead to changes in synaptic strength. The experiments are demonstrated with SPICE simulations using carbon nanotube transistor models.