Conventional polymer multielectrode arrays (MEAs) have limitations resulting from a high Young’s modulus, including low conformability and gaps between the electrodes and neurons. These gaps are not a problem in soft tissues such as the brain, due to the repopulation phenomenon. However, gaps can result in signal degradation when recording from a fiber bundle, such as the spinal cord. Methods: We propose a method for fabricating flexible polydimethylsiloxane (PDMS)-based MEAs featuring plateau-shaped microelectrodes. The proposed fabrication technique enables the electrodes on the surface of MEAs to make a tight connection to the neurons, because the wire of the MEA is fabricated to be plateau-shaped, as the Young’s modulus of PDMS is similar to soft tissues and PDMS follows the curvature of the neural tissue due to its high conformability compared to the other polymers. Injury caused by the movement of the MEAs can therefore be minimized. Each electrode has a diameter of 130 μm and the 8-channel array has a center-to-center electrode spacing of 300 μm. The signal-to-noise ratio of the plateau-shaped electrodes was larger than that of recessed electrodes because there was no space between the electrode and neural cell. Reliable neural recordings were possible by adjusting the position of the electrode during the experiment without trapping air under the electrodes. Simultaneous multi-channel neural recordings were successfully achieved from the spinal cord of rodents. We describe the fabrication technique, electrode 3D profile, electrode impedance, and MEA performance in in vivo experiments in rodents.

Background: Mini-screws are widely used as temporary anchorages in orthodontic treatment, but have the disadvantage of showing a high failure rate of about 10%. Therefore, orthodontic mini-screws should have high biocompatibility and retention. Previous studies have demonstrated that the retention of mini-screws can be improved by imparting bioactivity to the surface. The method for imparting bioactivity proposed in this paper is to sequentially perform anodization, periodic pre-calcification, and heat treatments with a Ti–6Al–4V ELI alloy mini-screw. Materials and methods: A TiO₂ nanotube-structured layer was formed on the surface of the Ti–6Al–4V ELI alloy mini-screw through anodization in which a voltage of 20 V was applied to a glycerol solution containing 20 wt% H₂O and 1.4 wt% NH₄F for 60 min. Fine granular calcium phosphate precipitates of HA and octacalcium phosphate were generated as clusters on the surface through the cyclic pre-calcification and heat treatments. The cyclic pre-calcification treatment is a process of immersion in a 0.05 M NaH₂PO₄ solution and a saturated Ca(OH)₂ solution at 90 °C for 1 min each. Results: It was confirmed that the densely structured protrusions were precipitated, and Ca and P concentrations, which bind and concentrate endogenous bone morphogenetic proteins, increased on the surface after simulated body fluid (SBF) immersion test. In addition, the removal torque of the mini-screw fixed into rabbit tibias for 4 weeks was measured to be 8.70 ± 2.60 N cm. Conclusions: A noteworthy point in this paper is that the Ca and P concentrations, which provide a scaffold suitable for endogenous bone formation, further increased over time after SBF immersion of the APH group specimens. The other point is that our mini-screws have a significantly higher removal torque compared to untreated mini-screws. These results represent that the mini-screw proposed in this paper can be used as a mini-screw for orthodontics.

Background: Dental diseases can be prevented through the management of dental plaques. Dental plaque can be identified using the light-induced fluorescence (LIF) technique that emits light at 405 nm. The LIF technique is more convenient than the commercial technique using a disclosing agent, but the result may vary for each individual as it still requires visual identification. Objective: The objective of this study is to introduce and validate a deep learning–based oral hygiene monitoring system that makes it easy to identify dental plaques at home. Methods: We developed a LIF-based system consisting of a device that can visually identify dental plaques and a mobile app that displays the location and area of dental plaques on oral images. The mobile app is programmed to automatically determine the location and distribution of dental plaques using a deep learning–based algorithm and present the results to the user as time series data. The mobile app is also built with convergence of naive and web applications so that the algorithm is executed on a cloud server to efficiently distribute computing resources. Results: The location and distribution of users’ dental plaques could be identified via the hand-held LIF device or mobile app. The color correction filter in the device was developed using a color mixing technique. The mobile app was built as a hybrid app combining the functionalities of a native application and a web application. Through the scrollable WebView on the mobile app, changes in the time series of dental plaque could be confirmed. The algorithm for dental plaque detection was implemented to run on Amazon Web Services for object detection by single shot multibox detector and instance segmentation by Mask region-based convolutional neural network. Conclusions: This paper shows that the system can be used as a home oral care product for timely identification and management of dental plaques. In the future, it is expected that these products will significantly reduce the social costs associated with dental diseases.

Polymer-based micro-electrode arrays (MEAs) are gaining attention as an essential technology to understand brain connectivity and function in the field of neuroscience. However, polymer based MEAs may have several challenges such as difficulty in performing the etching process, difficulty of micro-pattern generation through the photolithography process, weak metal adhesion due to low surface energy, and air pocket entrapment over the electrode site. In order to compensate for the challenges, this paper proposes a novel MEA fabrication process that is performed sequentially with (1) silicon mold preparation; (2) PDMS replica molding, and (3) metal patterning and parylene insulation. The MEA fabricated through this process possesses four arms with electrode sites on the convex microstructures protruding about 20 μm from the outermost layer surface. The validity of the convex microstructure implementation is demonstrated through theoretical background. The electrochemical impedance magnitude is 204.4 ± 68.1 kΩ at 1 kHz. The feasibility of the MEA with convex microstructures was confirmed by identifying the oscillation in the beta frequency band (13–30 Hz) in the electrocorticography signal of a rat olfactory bulb during respiration. These results suggest that the MEA with convex microstructures is promising for applying to various neural recording and stimulation studies.

BACKGROUND Dental diseases can be prevented through the management of dental plaques. Dental plaque can be identified using the light-induced fluorescence (LIF) technique that emits light at 405 nm. The LIF technique is more convenient than the commercial technique using a disclosing agent, but the result may vary for each individual as it still requires visual identification. OBJECTIVE The objective of this study is to introduce and validate a deep learning–based oral hygiene monitoring system that makes it easy to identify dental plaques at home. METHODS We developed a LIF-based system consisting of a device that can visually identify dental plaques and a mobile app that displays the location and area of dental plaques on oral images. The mobile app is programmed to automatically determine the location and distribution of dental plaques using a deep learning–based algorithm and present the results to the user as time series data. The mobile app is also built with convergence of naive and web applications so that the algorithm is executed on a cloud server to efficiently distribute computing resources. RESULTS The location and distribution of users’ dental plaques could be identified via the hand-held LIF device or mobile app. The color correction filter in the device was developed using a color mixing technique. The mobile app was built as a hybrid app combining the functionalities of a native application and a web application. Through the scrollable WebView on the mobile app, changes in the time series of dental plaque could be confirmed. The algorithm for dental plaque detection was implemented to run on Amazon Web Services for object detection by single shot multibox detector and instance segmentation by Mask region-based convolutional neural network. CONCLUSIONS This paper shows that the system can be used as a home oral care product for timely identification and management of dental plaques. In the future, it is expected that these products will significantly reduce the social costs associated with dental diseases.