Silver-coated silicon nanowire platform discriminates genomic DNA from normal and malignant human epithelial cells using label-free Raman spectroscopy.

Genomic deoxyribonucleic acid (DNA) stores and carries the information required to maintain and replicate cellular life. While much efforts have been devoted in decoding the sequence of DNA basis to detect the genetic mutations related to cancer disease, it is becoming clear that physical properties, like structural conformation, stiffness and shape, can play an important role to recognize DNA modifications. Here, silver-coated silicon nanowires (Ag/SiNWs) are exploited as Raman spectroscopic platform to easily discriminate healthy and cancer genomic DNA, extracted from human normal skin and malignant melanoma cells, respectively. In particular, aqueous DNA droplets are directly deposited onto a forest of Ag/SiNWs and Raman maps are acquired after sample dehydration. By applying principal component analysis (PCA) to the Raman spectra collected within the droplets, healthy and cancer cell DNA can be distinguished without false negative identifications and with few false positive results (< 2%). The discrimination occurs regardless the analysis of specific DNA sequencing, but through Raman bands strictly related to the interfacing of the DNA and the NWs. The observed phenomenon can be ascribed to conformational differences and/or diverse charge properties between healthy and cancer cell DNA determining a different arrangement of the molecules adsorbed onto the NWs upon water evaporation. The unique interaction with DNA and facile fabrication technology make Ag/SiNWs an effective platform for a robust, rapid and label-free cancer diagnosis, as well as a potential tool to investigate physical properties of DNA.

A Glial-Silicon Nanowire Electrode Junction Enabling Differentiation and Noninvasive Recording of Slow Oscillations from Primary Astrocytes.

The correct human brain function is dependent on the activity of non-neuronal cells called astrocytes. The bioelectrical properties of astrocytes in vitro do not closely resemble those displayed in vivo and the former are incapable of generating action potential; thus, reliable approaches in vitro for noninvasive electrophysiological recording of astrocytes remain challenging for biomedical engineering. Here it is found that primary astrocytes grown on a device formed by a forest of randomly oriented gold coated-silicon nanowires, resembling the complex structural and functional phenotype expressed by astrocytes in vivo. The device enables noninvasive extracellular recording of the slow-frequency oscillations generated by differentiated astrocytes, while flat electrodes failed on recording signals from undifferentiated cells. Pathophysiological concentrations of extracellular potassium, occurring during epilepsy and spreading depression, modulate the power of slow oscillations generated by astrocytes. A reliable approach to study the role of astrocytes function in brain physiology and pathologies is presented.

An ultra-compact integrated system for brain activity recording and stimulation validated over cortical slow oscillations in vivo and in vitro.

The understanding of brain processing requires monitoring and exogenous modulation of neuronal ensembles. To this end, it is critical to implement equipment that ideally provides highly accurate, low latency recording and stimulation capabilities, that is functional for different experimental preparations and that is highly compact and mobile. To address these requirements, we designed a small ultra-flexible multielectrode array and combined it with an ultra-compact electronic system. The device consists of a polyimide microelectrode array (8 µm thick and with electrodes measuring as low as 10 µm in diameter) connected to a miniaturized electronic board capable of amplifying, filtering and digitalizing neural signals and, in addition, of stimulating brain tissue. To evaluate the system, we recorded slow oscillations generated in the cerebral cortex network both from in vitro slices and from in vivo anesthetized animals, and we modulated the oscillatory pattern by means of electrical and visual stimulation. Finally, we established a preliminary closed-loop algorithm in vitro that exploits the low latency of the electronics (<0.5 ms), thus allowing monitoring and modulating emergent cortical activity in real time to a desired target oscillatory frequency.

Highly Disordered Array of Silicon Nanowires: an Effective and Scalable Approach for Performing and Flexible Electrochemical Biosensors.

The direct integration of disordered arranged and randomly oriented silicon nanowires (SiNWs) into ultraflexible and transferable electronic circuits for electrochemical biosensing applications is proposed. The working electrode (WE) of a three-electrode impedance device, fabricated on a polyimide (PI) film, is modified with SiNWs covered by a thin Au layer and functionalized to bind the sensing element. The biosensing behavior is investigated through the ligand-receptor binding of biotin-avidin system. Impedance measurements show a very efficient detection of the avidin over a broad range of concentrations from hundreds of micromolar down to the picomolar values. The impedance response is modeled through a simple equivalent circuit, which takes into account the unique WE morphology and its modification with successive layers of biomolecules. This approach of exploiting highly disordered SiNW ensemble in biosensing proves to be very promising for the following three main reasons: first, the system morphology allows high sensing performance; second, these nanostructures can be built via scalable and transferable fabrication methodology allowing an easy integration on non-conventional substrates; third, reliable modeling of the sensing response can be developed by considering the morphological and surface characteristics over an ensemble of disordered NWs rather than over individual NWs.

Stable odor recognition by a neuro-adaptive electronic nose.

Sensitivity, selectivity and stability are decisive properties of sensors. In chemical gas sensors odor recognition can be severely compromised by poor signal stability, particularly in real life applications where the sensors are exposed to unpredictable sequences of odors under changing external conditions. Although olfactory receptor neurons in the nose face similar stimulus sequences under likewise changing conditions, odor recognition is very stable and odorants can be reliably identified independently from past odor perception. We postulate that appropriate pre-processing of the output signals of chemical sensors substantially contributes to the stability of odor recognition, in spite of marked sensor instabilities. To investigate this hypothesis, we use an adaptive, unsupervised neural network inspired by the glomerular input circuitry of the olfactory bulb. Essentially the model reduces the effect of the sensors' instabilities by utilizing them via an adaptive multicompartment feed-forward inhibition. We collected and analyzed responses of a 4 × 4 gas sensor array to a number of volatile compounds applied over a period of 18 months, whereby every sensor was sampled episodically. The network conferred excellent stability to the compounds' identification and was clearly superior over standard classifiers, even when one of the sensors exhibited random fluctuations or stopped working at all.

Understanding odor information segregation in the olfactory bulb by means of mitral and tufted cells.

Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer. To this end, we built a detailed neural model of the glomerular layer based on its known anatomical connections and conducted two simulated odor experiments. In the first experiment, the model was exposed to an odor stimulus dataset composed of six different odorants, each one dosed at six different concentrations. In the second experiment, we conducted an odor morphing experiment where a sequence of binary mixtures going from one odor to another through intermediate mixtures was presented to the model. The results of the experiments were visualized using principal components analysis and analyzed with hierarchical clustering to unveil the structure of the high-dimensional output space. Additionally, Fisher's discriminant ratio and Pearson's correlation coefficient were used to quantify odor identity and odor concentration information respectively. Our results showed that the architecture of the glomerular layer was able to mediate the segregation of odor information obtaining output spiking sequences of the principal neurons, namely the mitral and external tufted cells, strongly correlated with odor identity and concentration, respectively. An important conclusion is also that the morphological difference between the principal neurons is not key to achieve odor information segregation.

An investigation on the role of spike latency in an artificial olfactory system.

Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time.