Retinal electrostimulation is promising a successful therapy to restore functional vision. However, a narrow stimulating current range exists between retinal neuron excitation and inhibition which may lead to misperformance of visual prostheses. As the conveyance of representation of complex visual scenes may require neighbouring electrodes to be activated simultaneously, electric field summation may contribute to reach this inhibitory threshold. This study used three approaches to assess the implications of relatively high stimulating conditions in visual prostheses: (1) in vivo, using a suprachoroidal prosthesis implanted in a feline model, (2) in vitro through electrostimulation of murine retinal preparations, and (3) in silico by computing the response of a population of retinal ganglion cells. Inhibitory stimulating conditions led to diminished cortical activity in the cat. Stimulus-response relationships showed non-monotonic profiles to increasing stimulating current. This was observed in vitro and in silico as the combined response of groups of neurons (close to the stimulating electrode) being inhibited at certain stimulating amplitudes, whilst other groups (far from the stimulating electrode) being recruited. These findings may explain the halo-like phosphene shapes reported in clinical trials and suggest that simultaneous stimulation in retinal prostheses is limited by the inhibitory threshold of the retinal ganglion cells.

The bypassing of degenerated photoreceptors using retinal neurostimulators is helping the blind to recover functional vision. Researchers are investigating new ways to improve visual percepts elicited by these means as the vision produced by these early devices remain rudimentary. However, several factors are hampering the progression of bionic technologies: the charge injection limits of metallic electrodes, the mechanical mismatch between excitable tissue and the stimulating elements, neural and electric crosstalk, the physical size of the implanted devices, and the inability to selectively activate different types of retinal neurons. Electrochemical and mechanical limitations are being addressed by the application of electromaterials such as conducting polymers, carbon nanotubes and nanocrystalline diamonds, among other biomaterials, to electrical neuromodulation. In addition, the use of synthetic hydrogels and cell-laden biomaterials is promising better interfaces, as it opens a door to establishing synaptic connections between the electrode material and the excitable cells. Finally, new electrostimulation approaches relying on the use of high-frequency stimulation and field overlapping techniques are being developed to better replicate the neural code of the retina. All these elements combined will bring bionic vision beyond its present state and into the realm of a viable, mainstream therapy for vision loss.

Hexapolar stimulation reduces electric cross-talk between neighboring sites and represents a technique to reduce interference between individual stimulation sites. In contrast, concurrent monopolar stimulation produces a reduction of the activation threshold of stimuli delivered nearby. Thus, a single source of subthreshold monopolar charge injection can provide benefit in the form of significant threshold reduction simultaneously at multiple stimulation sites.

Retinal implants have proven their ability to restore visual sensation to people with degenerative retinopathy, characterized by photoreceptor cell death and the retina's inability to sense light. Retinal bionics operate by electrically stimulating the surviving neurons in the retina, thus triggering the transfer of visual sensory information to the brain. Suprachoroidal implants were first investigated in Australia in the 1950s. In this approach, the neuromodulation hardware is positioned between the sclera and the choroid, thus providing significant surgical and safety benefits for patients, with the potential to maintain residual vision combined with the artificial input from the device. Here we review the latest advances and state of the art devices for suprachoroidal prostheses, highlight future technologies and discuss challenges and perspectives towards improved rehabilitation of vision.

Gastroesophageal monitoring is limited to 96 hours by the current technology. This work presents a computational model to investigate symptom association in gastroesophageal reflux disease with larger data samples proving important deficiencies of the current methodology that must be taking into account in clinical evaluation. A computational model based on Monte Carlo analysis was implemented to simulate patients with known statistical characteristics Thus, sets of 2000 10-day-long recordings were simulated and analyzed using the symptom index (SI), the symptom sensitivity index (SSI), and the symptom association probability (SAP). Afterwards, linear regression was applied to define the dependency of these indexes with the number of reflux, the number of symptoms, the duration of the monitoring, and the probability of association. All the indexes were biased estimators of symptom association and therefore they do not consider the effect of chance: when symptom and reflux were completely uncorrelated, the values of the indexes under study were greater than zero. On the other hand, longer recording reduced variability in the estimation of the SI and the SSI while increasing the value of the SAP. Furthermore, if the number of symptoms remains below one-tenth of the number of reflux episodes, it is not possible to achieve a positive value of the SSI. A limitation of this computational model is that it does not consider feeding and sleeping periods, differences between reflux episodes or causation. However, the conclusions are not affected by these limitations. These facts represent important limitations in symptom association analysis, and therefore, invasive treatments must not be considered based on the value of these indexes only until a new methodology provides a more reliable assessment.

Optical spectroscopic techniques have been commonly used to detect the presence of biofilm-forming pathogens (bacteria and fungi) in the agro-food industry. Recently, near-infrared (NIR) spectroscopy revealed that it is also possible to detect the presence of viruses in animal and vegetal tissues. Here we report a platform based on visible and NIR (VNIR) hyperspectral imaging for non-contact, reagent free detection and quantification of laboratory-engineered viral particles in fluid samples (liquid droplets and dry residue) using both partial least square-discriminant analysis and artificial feed-forward neural networks. The detection was successfully achieved in preparations of phosphate buffered solution and artificial saliva, with an equivalent pixel volume of 4 nL and lowest concentration of 800 TU·$$\upmu$$ μ L−1. This method constitutes an innovative approach that could be potentially used at point of care for rapid mass screening of viral infectious diseases and monitoring of the SARS-CoV-2 pandemic.

Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.

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