Multi-Region Microdialysis Imaging Platform Revealed Dorsal Raphe Nucleus Calcium Signaling and Serotonin Dynamics during Nociceptive Pain.

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.

Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO2 system.

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on simple linear regression algorithms that are prone to underfitting the training data. These models rely heavily on experimental and expensive computational quantum mechanical descriptors, which significantly limit their practical use. Herein, we present the application of empirical descriptors and complex machine learning algorithms to the development of high-performance quantitative structure-toxicity relationship (QSTR) models of TiO2 hybridized with multi-metallic (Ag, Au, Pt) alloy nanoparticles (multi-metallic NPs/TiO2). To confirm the viability of empirical descriptors as model input, we selected five distinct machine learning algorithms for predicting the toxicity of multi-metallic alloy NPs/TiO2 system in Chinese hamster ovary cell line. Notably, an empirical descriptor-based QSTR model (kernel ridge regression) revealed a predictive performance that is on par with density functional theory (DFT) descriptor-based counterparts. More specifically, the results indicated that model selection is influenced by descriptor choice, such that complex DFT descriptors worked best with a complex algorithm (random forest regression; RMSET = 0.0954, MAET = 0.0811, RT2 = 0.9411), whereas more straightforward empirical descriptors were most suitable with a simpler algorithm (kernel ridge regression; RMSET = 0.1244, MAET = 0.1106, RT2 = 0.8999). Moreover, our model outperforms existing QSAR models built on the same data set. This study offers a new perspective on using empirical features to develop accurate predictive computational models for the rapid discovery and profiling of safe-by-design nanomaterials.

Ultrasmall compact CMOS imaging system for bioluminescence reporter-based live gene expression analysis.

SIGNIFICANCE: Gene expression analysis is an important fundamental area of biomedical research. However, live gene expression imaging has proven challenging due to constraints in conventional optical devices and fluorescent reporters. AIM: Our aim is to develop smaller, more cost-effective, and versatile imaging capabilities compared with conventional devices. Bioluminescence reporter-based gene expression analysis was targeted due to its advantages over fluorescence-based imaging. APPROACH: We created a small compact imaging system using micro-CMOS image sensors (µCIS). The µCIS model had an improved pixel design and a patterned absorption filter array to detect the low light intensity of bioluminescence. RESULTS: The device demonstrated lower dark current, lower temporal noise, and higher sensitivity compared with previous designs. The filter array enabled us to subtract dark current drift and attain a clearer light signal. These improvements allowed us to measure bioluminescence reporter-based gene expression in living mammalian cells. CONCLUSION: Using our µCIS system for bioluminescence imaging in the future, the device can be implanted in vivo for simultaneous gene expression imaging, behavioral analysis, and optogenetic modulation.

Simultaneous CMOS-Based Imaging of Calcium Signaling of the Central Amygdala and the Dorsal Raphe Nucleus During Nociception in Freely Moving Mice.

Fluorescence imaging devices have been indispensable in elucidating the workings of the brain in living animals, including unrestrained, active ones. Various devices are available, each with their own strengths and weaknesses in terms of many factors. We have developed CMOS-based needle-type imaging devices that are small and lightweight enough to be doubly implanted in freely moving mice. The design also allowed angled implantations to avoid critical areas. We demonstrated the utility of the devices by using them on GCaMP6 mice in a formalin test experiment. Simultaneous implantations to the capsular-lateral central amygdala (CeLC) and dorsal raphe nucleus (DRN) were proven to be safe and did not hinder the execution of the study. Analysis of the collected calcium signaling data, supported by behavior data, showed increased activity in both regions as a result of pain stimulation. Thus, we have successfully demonstrated the various advantages of the device in its application in the pain experiment.