RESUMEN
This paper proposes design considerations that need to be followed in order to eliminate potential sources of artefact that could distort a recorded neural signal. The artefact that appears in a recorded signal has a combination of potential sources each of which contributes towards its formation. As such, these sources of artefact have been addressed in three main categories: a) electronics artefact, b) encapsulation artefact and c) interface artefact. Each source (component) is analyzed further and appropriate design techniques and considerations are suggested towards its mitigation.
Asunto(s)
Electroencefalografía , Electrónica , Artefactos , HumanosRESUMEN
We develop a system-level approach to modelling optogenetic-neurons firing behaviour in in-vivo conditions. This approach contains three sub-modules: 1) a Mie/Rayleigh scattering mode of light penetration in tissue; 2) a classic likelihood Poisson spiking train model; 3) a 4-state model of the Channelrhodopsin-2 (ChR2) channel added to a CA3 neuron Hodgkin-Huxley model. We first investigate opto-neurons lightto-spike mechanisms in an in-vivo model: the background noise (synaptic currents) play a dominant role in generating spikes rather than light intensities as for in-vitro conditions (Typically the required light intensity is less than 0.3 mW/mm2 for in-vivo). Then the spiking fidelity is analyzed for different background noise levels. Next, by combining light penetration profiles, we show how neuron firing rates decay as tissue distance increases, for a 2D dimensional cross-section. This preliminary data clearly demonstrate that at given light stimulation protocol, the maximum effected distance in-vivo is 250 µm with small frequency decay rates, while for in-vitro is 50µm with considerable frequency decay rates. Therefore, the developed model can be used for designing sensible light stimulation strategies in-vivo and opto-electronics systems.
Asunto(s)
Modelos Neurológicos , Optogenética , Potenciales de Acción , Neuronas , ProbabilidadRESUMEN
Emerging evidence suggests the microbiome may affect a number of diseases, including lung cancer. However, the direct relationship between gut bacteria and lung cancer remains uncharacterized. In this study, we directly sequenced the hypervariable V1-V2 regions of the 16S rRNA gene in fecal samples from patients with lung cancer and healthy volunteers. Unweighted principal coordinate analysis (PCoA) revealed a clear difference in the bacterial community membership between the lung cancer group and the healthy control group. The lung cancer group had remarkably higher levels of Bacteroidetes, Fusobacteria, Cyanobacteria, Spirochaetes, and Lentisphaerae but dramatically lower levels of Firmicutes and Verrucomicrobia than the healthy control group (P < 0.05). Despite significant interindividual variation, eight predominant genera were significantly different between the two groups. The lung cancer group had higher levels of Bacteroides, Veillonella, and Fusobacterium but lower levels of Escherichia-Shigella, Kluyvera, Fecalibacterium, Enterobacter, and Dialister than the healthy control group (P < 0.05). Most notably, correlations between certain specific bacteria and serum inflammatory biomarkers were identified. Our findings demonstrated an altered bacterial community in patients with lung cancer, providing a significant step in understanding the relationship between gut bacteria and lung cancer. To our knowledge, this is the first study to evaluate the correlations between certain specific bacteria and inflammatory indicators. To better understand this relationship, further studies should investigate the underlying mechanisms of gut bacteria in lung cancer animal models.
RESUMEN
Dynamic clamp emerges as an important apparatus to study the intrinsic neuronal properties through close-loop interactions between models and biological neurons. Modelling large-scale neuronal networks in software will result in significant computational delay that becomes a bottleneck to apply dynamic clamp for more complicated systems. In this paper, we present a real-time dynamic clamping system based on field programmable gate arrays (FPGAs) to accelerate the necessary computations. It also provides a flexible platform to reconfigure various model parameters and topologies. Realtime neuronal and synaptic models were implemented in FPGA, and interconnected with the stomatograstric ganglion (STG) nervous system to exemplify the real-time dynamics. Results show that our method can be effectively configured to mimic various biological neural networks and is two orders of magnitude faster than software approach using desktop computer.