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Advances in fluorescence imaging technology have been crucial to the progress of neuroscience. Whether it was specific expression of indicator proteins, detection of neurotransmitters, or miniaturization of fluorescence microscopes, fluorescence imaging has improved upon electrophysiology, the gold standard for monitoring brain activity, and enabled novel methods to sense activity in the brain. Hence, we developed a lightweight and compact implantable CMOS-based lensless Ca2+ imaging device for freely moving transgenic G-CaMP mouse experiments. However, without a lens system, determination of regions of interest (ROI) has proven challenging. Localization of fluorescence activity and separation of signal from noise are difficult. In this study, we report an ROI selection method using a series of adaptive binarizations with a gaussian method and morphological image processing. The parameters for each operation such as the kernel size, sigma and footprint size were optimized. We then validated the utility of the algorithm with simulated data and freely moving nociception experiments using the lensless devices. The device was implanted in the dorsal raphe nucleus to observe pain-related brain activity following a formalin test to stimulate pain. We observed significant increases in fluorescence activity after formalin injection compared to the control group when using the ROI determination algorithm., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Castillo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective : Current neuronal imaging methods mostly use bulky lenses that either impede animal behavior or prohibit multi-depth imaging. To overcome these limitations, we developed a lightweight lensless biophotonic system for neuronal imaging, enabling compact and simultaneous visualization of multiple brain layers. Approach : Our developed 'CIS-NAIST' device integrates a micro-CMOS image sensor, thin-film fluorescence filter, micro-LEDs, and a needle-shaped flexible printed circuit. With this device, we monitored neuronal calcium dynamics during seizures across the different layers of the hippocampus and employed machine learning techniques for seizure classification and prediction. Main results : The CIS-NAIST device revealed distinct calcium activity patterns across the CA1, molecular interlayer, and dentate gyrus. Our findings indicated an elevated calcium amplitude activity specifically in the dentate gyrus compared to other layers. Then, leveraging the multi-layer data obtained from the device, we successfully classified seizure calcium activity and predicted seizure behavior using Long Short-Term Memory and Hidden Markov models. Significance : Taken together, our 'CIS-NAIST' device offers an effective and minimally invasive method of seizure monitoring that can help elucidate the mechanisms of temporal lobe epilepsy., (Creative Commons Attribution license.)

Dopamine (DA)'s relationship with addiction is complex, and the related pathways in the mesocorticolimbic system are used to deliver DA, regulating both behavioral and perceptual actions. Specifically, the mesolimbic pathway connecting the ventral tegmental area (VTA) and the nucleus accumbens (NAc) is crucial in regulating memory, emotion, motivation, and behavior due to its responsibility to modulate dopamine. To better investigate the relationship between DA and addiction, more advanced mapping methods are necessary to monitor its production and propagation accurately and efficiently. In this study, we incorporate dLight1.2 adeno-associated virus (AAV) into our latest CMOS (complementary metal-oxide semiconductor) imaging platform to investigate the effects of two pharmacological substances, morphine and cocaine, in the NAc using adult mice. By implanting our self-fabricated CMOS imaging device into the deep brain, fluorescence imaging of the NAc using the dLight1.2 AAV allows for the visualization of DA molecules delivered from the VTA in real time. Our results suggest that changes in extracellular DA can be observed with this adapted system, showing potential for new applications and methods for approaching addiction studies. Additionally, we can identify the unique characteristic trend of DA release for both morphine and cocaine, further validating the underlying biochemical mechanisms used to modulate dopaminergic activation.

In this research, we combined our ultralight micro-imaging device for calcium imaging with microdialysis to simultaneously visualize neural activity in the dorsal raphe nucleus (DRN) and measure serotonin release in the central nucleus of the amygdala (CeA) and the anterior cingulate cortex (ACC). Using this platform, we observed brain activity following nociception induced by formalin injection in the mouse's hind paw. Our device showed that DRN fluorescence intensity increased after formalin injection, and the increase was highly correlated with the elevation in serotonin release in both the CeA and ACC. The increase in calcium fluorescence intensity occurred during the acute and inflammatory phases, which suggests the biphasic response of nociceptive pain. Furthermore, we found that the increase in fluorescence intensity was positively correlated with mouse licking behavior. Lastly, we compared the laterality of pain stimulation and found that DRN fluorescence activity was higher for contralateral stimulation. Microdialysis showed that CeA serotonin concentration increased only after contralateral stimulation, while ACC serotonin release responded bilaterally. In conclusion, our study not only revealed the inter-regional serotonergic connection among the DRN, the CeA, and the ACC, but also demonstrated that our device is feasible for multi-site implantation in conjunction with a microdialysis system, allowing the simultaneous multi-modal observation of different regions in the brain.

To address the need for low-cost analytical tools for on-site aquaculture water quality monitoring, miniaturized electrochemical sensor systems can be readily fabricated using additive manufacturing technologies such as 3D printing and inkjet printing. In this work, we report the design and fabrication of an additively manufactured electrochemical platform featuring a reusable 3D-printed electrochemical cell with integrated reference and counter electrodes, and a replaceable inkjet-printed Ag (IJP-Ag) working electrode. The electrochemical cell was 3D-printed with acrylonitrile butadiene styrene (ABS) filament and features a 3D-printed ABS-carbon counter electrode and a Ag|AgCl|gel-KCl reference electrode with a 3D-printed porous junction directly integrated along the sides of the sample compartment. The application of the integrated cell is demonstrated with the analysis of nitrate ions on the IJP-Ag electrode, which was modified with electrodeposited nanostructured Ag to enhance sensitivity to nitrate reduction. Linear sweep voltammetry (LSV) was successfully applied to detect nitrate with a LOD of 1.40 ppm and a sensitivity of 0.2086 μA ppm -1 in a background of artificial brackish aquaculture water (pH 8.0). The sensor response showed intra- and inter-electrode reproducibility and no significant interferences to most of the commonly encountered cations and anions in brackish water. The electrochemical sensor system was also applied to nitrate determination in real aquaculture water samples and demonstrated no significant differences with the results obtained using the standard spectrophotometric method at a 95% confidence level. Our results show how additive manufacturing is a promising approach to readily fabricate fit-for-purpose, low-cost miniaturized electrochemical sensor systems for point-of-use applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)