In vivo laser speckle contrast imaging of 4-aminopyridine- or pentylenetetrazole-induced seizures.

Clinical and preclinical studies on epileptic seizures are closely linked to the study of neurovascular coupling. Obtaining reliable information about cerebral blood flow (CBF) in the area of epileptic activity through minimally invasive techniques is crucial for research in this field. In our studies, we used laser speckle contrast imaging (LSCI) to gather information about the local blood circulation in the area of epileptic activity. We used two models of epileptic seizures: one based on 4-aminopyridine (4-AP) and another based on pentylenetetrazole (PTZ). We verified the duration of an epileptic seizure using electrocorticography (ECoG). We applied the antiepileptic drug topiramate (TPM) to both models, but its effect was different in each case. However, in both models, TPM had an effect on neurovascular coupling in the area of epileptic activity, as shown by both LSCI and ECoG data. We demonstrated that TPM significantly reduced the amplitude of 4-AP-induced epileptic seizures (4-AP+TPM: 0.61 ± 0.13 mV vs 4-AP: 1.08 ± 0.19 mV; p < 0.05), and it also reduced gamma power in ECoG in PTZ-induced epileptic seizures (PTZ+TPM: 38.5% ± 11.9% of the peak value vs PTZ: 59.2% ± 3.0% of peak value; p < 0.05). We also captured the pattern of CBF changes during focal epileptic seizures induced by 4-AP. Our data confirm that the system of simultaneous cortical LSCI and registration of ECoG makes it possible to evaluate the effectiveness of pharmacological agents in various types of epileptic seizures in in vivo models and provides spatial and temporal information on the process of ictogenesis.

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization.

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.

Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.

IoT-based wearable health monitoring device and its validation for potential critical and emergency applications.

The COVID-19 pandemic brought the world to a standstill, posing unprecedented challenges for healthcare systems worldwide. The overwhelming number of patients infected with the virus placed an enormous burden on healthcare providers, who struggled to cope with the sheer volume of cases. Furthermore, the lack of effective treatments or vaccines means that quarantining has become a necessary measure to slow the spread of the virus. However, quarantining places a significant burden on healthcare providers, who often lack the resources to monitor patients with mild symptoms or asymptomatic patients. In this study, we propose an Internet of Things (IoT)-based wearable health monitoring system that can remotely monitor the exact locations and physiological parameters of quarantined individuals in real time. The system utilizes a combination of highly miniaturized optoelectronic and electronic technologies, an anti-epidemic watch, a mini-computer, and a monitor terminal to provide real-time updates on physiological parameters. Body temperature, peripheral oxygen saturation (SpO2), and heart rate are recorded as the most important measurements for critical care. If these three physiological parameters are aberrant, then it could represent a life-endangering situation and/or a short period over which irreversible damage may occur. Therefore, these parameters are automatically uploaded to a cloud database for remote monitoring by healthcare providers. The monitor terminal can display real-time health data for multiple patients and provide early warning functions for medical staff. The system significantly reduces the burden on healthcare providers, as it eliminates the need for manual monitoring of patients in quarantine. Moreover, it can help healthcare providers manage the COVID-19 pandemic more effectively by identifying patients who require medical attention in real time. We have validated the system and demonstrated that it is well suited to practical application, making it a promising solution for managing future pandemics. In summary, our IoT-based wearable health monitoring system has the potential to revolutionize healthcare by providing a cost-effective, remote monitoring solution for patients in quarantine. By allowing healthcare providers to monitor patients remotely in real time, the burden on medical resources is reduced, and more efficient use of limited resources is achieved. Furthermore, the system can be easily scaled to manage future pandemics, making it an ideal solution for managing the health challenges of the future.

Personalized Cancer Therapeutics Using Photoacoustic Imaging-Guided Sonodynamic Therapy.

Sonodynamic therapy (SDT) is a promising approach for cancer treatment that uses sonosensitizers (SNSs) to generate reactive oxygen species (ROS) in the presence of ultrasound (US). However, SDT is oxygen-dependent and requires an imaging tool to monitor the tumor microenvironment and guide treatment. Photoacoustic imaging (PAI) is a noninvasive and powerful imaging tool that offers high spatial resolution and deep tissue penetration. PAI can quantitatively assess tumor oxygen saturation (sO2) and guide SDT by monitoring time-dependent sO2 changes in the tumor microenvironment. Here, we discuss recent advances in PAI-guided SDT for cancer therapy. We discuss various exogenous contrast agents and nanomaterial-based SNSs developed for PAI-guided SDT. Additionally, combining SDT with other therapies, including photothermal (PTT) therapy, can enhance its therapeutic effect. However, the application of nanomaterial-based contrast agents in PAI-guided SDT for cancer therapy remains challenging due to the lack of simple designs, the need for extensive pharmacokinetic studies, and high production costs. Integrated efforts from researchers, clinicians, and industry consortia are necessary for the successful clinical translation of these agents and SDT for personalized cancer therapy. PAI-guided SDT shows the potential to revolutionize cancer therapy and improve patient outcomes, but further research is necessary to realize its full potential.

Revisiting the effects of exercise on cerebral neurovascular functions in rats using multimodal assessment techniques.

Physical activity in the form of aerobic exercise has many beneficial effects on brain function. Here, we aim to revisit the effects of exercise on brain morphology and neurovascular organization using a rat running model. Electrocorticography (ECoG) was integrated with laser speckle contrast imaging (LSCI) and applied to simultaneously detect CSD propagation and the corresponding neurovascular function. In addition, blood oxygenation level-dependent (BOLD) signal in fMRI was used to observe cerebral utilization of oxygen. Results showed significant decrease in somatosensory evoked potentials (SSEPs) and deceleration of CSD propagation in the EXE group. Western blot results in the EXE group showed significant increases in BDNF, GFAP, and NeuN levels and significant decreases in neurodegenerative disease markers. Decreases in SSEP and CSD parameters may result from exercise-induced increases in cerebrovascular system function and increases in the stability and buffering of extracellular ion concentrations and cortical excitability.

An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance.

An essential aspect of controlling and preventing mosquito-borne diseases is to reduce mosquitoes that carry viruses. We designed a smart mosquito trap system to reduce the density of mosquito vectors and the spread of mosquito-borne diseases. This smart trap uses computer vision technology and deep learning networks to identify features of live Aedes aegypti and Culex quinquefasciatus in real-time. A unique mechanical design based on the rotation concept is also proposed and implemented to capture specific living mosquitoes into the corresponding chambers successfully. Moreover, this system is equipped with sensors to detect environmental data, such as CO2 concentration, temperature, and humidity. We successfully demonstrated the implementation of such a tool and paired it with a reliable capture mechanism for live mosquitos without destroying important morphological features. The neural network achieved 91.57% accuracy with test set images. When the trap prototype was applied in a tent, the accuracy rate in distinguishing live Ae. aegypti was 92%, with a capture rate reaching 44%. When the prototype was placed into a BG trap to produce a smart mosquito trap, it achieved a 97% recognition rate and a 67% catch rate when placed in the tent. In a simulated living room, the recognition and capture rates were 90% and 49%, respectively. This smart trap correctly differentiated between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak.

Visible CCD Camera-Guided Photoacoustic Imaging System for Precise Navigation during Functional Rat Brain Imaging.

In photoacoustic (PA) imaging, tissue absorbs specific wavelengths of light. The absorbed energy results in thermal expansion that generates ultrasound waves that are reconstructed into images. Existing commercial PA imaging systems for preclinical brain imaging are limited by imprecise positioning capabilities and inflexible user interfaces. We introduce a new visible charge-coupled device (CCD) camera-guided photoacoustic imaging (ViCPAI) system that integrates an ultrasound (US) transducer and a data acquisition platform with a CCD camera for positioning. The CCD camera accurately positions the US probe at the measurement location. The programmable MATLAB-based platform has an intuitive user interface. In vitro carbon fiber and in vivo animal experiments were performed to investigate the precise positioning and imaging capabilities of the ViCPAI system. We demonstrated real-time capturing of bilateral cerebral hemodynamic changes during (1) forelimb electrical stimulation under normal conditions, (2) forelimb stimulation after right brain focal photothrombotic ischemia (PTI) stroke, and (3) progression of KCl-induced cortical spreading depression (CSD). The ViCPAI system accurately located target areas and achieved reproducible positioning, which is crucial in animal and clinical experiments. In animal experiments, the ViCPAI system was used to investigate bilateral cerebral cortex responses to left forelimb electrical stimulation before and after stroke, showing that the CBV and SO2 in the right primary somatosensory cortex of the forelimb (S1FL) region were significantly changed by left forelimb electrical stimulation before stroke. No CBV or SO2 changes were observed in the bilateral cortex in the S1FL area in response to left forelimb electrical stimulation after stroke. While monitoring CSD progression, the ViCPAI system accurately locates the S1FL area and returns to the same position after the probe moves, demonstrating reproducible positioning and reducing positioning errors. The ViCPAI system utilizes the real-time precise positioning capability of CCD cameras to overcome various challenges in preclinical and clinical studies.

Noninvasive blood oxygen, heartbeat rate, and blood pressure parameter monitoring by photoplethysmography signals.

The popularization of long-term invasive tools for continuously monitoring blood pressure remains challenging. However, with the rising popularity of wearable personal health management devices, non-cuff blood pressure measurement technology that applies electrocardiography (ECG) and photoplethysmography (PPG) has gradually received increasing attention. In particular, whether blood pressure can be measured continuously by the PPG signal alone is of great interest. In this study, we aim to develop a device that includes systolic and diastolic blood pressure calculation formulas derived from characteristic waveform points in the PPG time domain and that can measure blood oxygenation and heart rate. This device applies empirical formulas developed by PPG waveforms in the PhysioNet MIMIC-II database to calculate blood pressure. The systolic and diastolic pressures are then compared with the actual blood pressures obtained from invasive blood pressure waveforms to verify the effectiveness and feasibility of the complete developed system. Overall, 263 waveforms with double peaks and 261 waveforms with only a single peak totaling 524 sets of data are used to derive the empirical formulas. The systolic blood pressure estimation result using single peak analysis has an excessively large error exceeding ±40 mmHg, providing no reference value. However, systolic blood pressure estimation is notably better in double peak analysis, with error values reducing to approximately 23 mmHg. Diastolic pressure estimation errors are low with both single (±7 mmHg) and double peak (±4 mmHg) analyses. The error is lower in double-peak analysis than in single-peak analysis for obtaining systolic pressure from PPG waves. We plan to use PPG to detect additional physiological parameters in the future, e.g., respiratory rate, heart rate variability, or irregular heartbeat, to further enhance the functionality of PPG-based wearable devices.

Revisiting the cerebral hemodynamics of awake, freely moving rats with repeated ketamine self-administration using a miniature photoacoustic imaging system.

Significance: Revealing the dynamic associations between brain functions and behaviors is a significant challenge in neurotechnology, especially for awake subjects. Imaging cerebral hemodynamics in awake animal models is important because the collected data more realistically reflect human disease states. Aim: We previously reported a miniature head-mounted scanning photoacoustic imaging (hmPAI) system. In the present study, we utilized this system to investigate the effects of ketamine on the cerebral hemodynamics of normal rats and rats subjected to prolonged ketamine self-administration. Approach: The cortical superior sagittal sinus (SSS) was continuously monitored. The full-width at half-maximum (FWHM) of the photoacoustic (PA) A-line signal was used as an indicator of the SSS diameter, and the number of pixels in PA B-scan images was used to investigate changes in the cerebral blood volume (CBV). Results: We observed a significantly higher FWHM (blood vessel diameter) and CBV in normal rats injected with ketamine than in normal rats injected with saline. For rats subjected to prolonged ketamine self-administration, no significant changes in either the blood vessel diameter or CBV were observed. Conclusions: The lack of significant change in prolonged ketamine-exposed rats was potentially due to an increased ketamine tolerance. Our device can reliably detect changes in the dilation of cortical blood vessels and the CBV. This study validates the utility of the developed hmPAI system in an awake, freely moving rat model for behavioral, cognitive, and preclinical cerebral disease studies.

Assessment of Brain Functional Activity Using a Miniaturized Head-Mounted Scanning Photoacoustic Imaging System in Awake and Freely Moving Rats.

Understanding the relationship between brain function and natural behavior remains a significant challenge in neuroscience because there are very few convincing imaging/recording tools available for the evaluation of awake and freely moving animals. Here, we employed a miniaturized head-mounted scanning photoacoustic imaging (hmPAI) system to image real-time cortical dynamics. A compact photoacoustic (PA) probe based on four in-house optical fiber pads and a single custom-made 48-MHz focused ultrasound transducer was designed to enable focused dark-field PA imaging, and miniature linear motors were included to enable two-dimensional (2D) scanning. The total dimensions and weight of the proposed hmPAI system are only approximately 50 × 64 × 48 mm and 58.7 g (excluding cables). Our ex vivo phantom experimental tests revealed that a spatial resolution of approximately 0.225 mm could be achieved at a depth of 9 mm. Our in vivo results further revealed that the diameters of cortical vessels draining into the superior sagittal sinus (SSS) could be clearly imaged and continuously observed in both anesthetized rats and awake, freely moving rats. Statistical analysis showed that the full width at half maximum (FWHM) of the PA A-line signals (relative to the blood vessel diameter) was significantly increased in the selected SSS-drained cortical vessels of awake rats (0.58 ± 0.17 mm) compared with those of anesthetized rats (0.31 ± 0.09 mm) (p < 0.01, paired t-test). In addition, the number of pixels in PA B-scan images (relative to the cerebral blood volume (CBV)) was also significantly increased in the selected SSS-drained blood vessels of awake rats (107.66 ± 23.02 pixels) compared with those of anesthetized rats (81.99 ± 21.52 pixels) (p < 0.01, paired t-test). This outcome may result from a more active brain in awake rats than in anesthetized rats, which caused cerebral blood vessels to transport more blood to meet the increased nutrient demand of the tissue, resulting in an obvious increase in blood vessel volume. This hmPAI system was further validated for utility in the brains of awake and freely moving rats, showing that their natural behavior was unimpaired during vascular imaging, thereby providing novel opportunities for studies of behavior, cognition, and preclinical models of brain diseases.

An Adjustable Dark-Field Acoustic-Resolution Photoacoustic Imaging System with Fiber Bundle-Based Illumination.

Photoacoustic (PA) imaging has become one of the major imaging methods because of its ability to record structural information and its high spatial resolution in biological tissues. Current commercialized PA imaging instruments are limited to varying degrees by their bulky size (i.e., the laser or scanning stage) or their use of complex optical components for light delivery. Here, we present a robust acoustic-resolution PA imaging system that consists of four adjustable optical fibers placed 90° apart around a 50 MHz high-frequency ultrasound (US) transducer. In the compact design concept of the PA probe, the relative illumination parameters (i.e., angles and fiber size) can be adjusted to fit different imaging applications in a single setting. Moreover, this design concept involves a user interface built in MATLAB. We first assessed the performance of our imaging system using in vitro phantom experiments. We further demonstrated the in vivo performance of the developed system in imaging (1) rat ear vasculature, (2) real-time cortical hemodynamic changes in the superior sagittal sinus (SSS) during left-forepaw electrical stimulation, and (3) real-time cerebral indocyanine green (ICG) dynamics in rats. Collectively, this alignment-free design concept of a compact PA probe without bulky optical lens systems is intended to satisfy the diverse needs in preclinical PA imaging studies.

In Vivo Assessment of Hypoxia Levels in Pancreatic Tumors Using a Dual-Modality Ultrasound/Photoacoustic Imaging System.

Noninvasive anatomical and functional imaging has become an essential tool to evaluate tissue oxygen saturation dynamics in preclinical or clinical studies of hypoxia. Our dual-wavelength technique for photoacoustic (PA) imaging based on the differential absorbance spectrum of oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) can quantify tissue oxygen saturation using the intrinsic contrast property. PA imaging of tissue oxygen saturation can be used to monitor tumor-related hypoxia, which is a particularly relevant functional parameter of the tumor microenvironment that has a strong influence on tumor aggressiveness. The simultaneous acquisition of anatomical and functional information using dual-modality ultrasound (US) and PA imaging technology enhances the preclinical applicability of the method. Here, the developed dual-modality US/PA system was used to measure relative tissue oxygenation using the dual-wavelength technique. Tissue oxygen saturation was quantified in a pancreatic tumor mouse model. The differences in tissue oxygenation were detected by comparing pancreatic samples from normal and tumor-bearing mice at various time points after implantation. The use of an in vivo pancreatic tumor model revealed changes in hypoxia at various stages of tumor growth. The US/PA imaging data positively correlated with the results of immunohistochemical staining for hypoxia. Thus, our dual-modality US/PA imaging system can be used to reliably assess and monitor hypoxia in pancreatic tumor mouse models. These findings enable the use of a combination of US and PA imaging to acquire anatomical and functional information on tumor growth and to evaluate treatment responses in longitudinal preclinical studies.

Design and Implementation of a Multifunction Wearable Device to Monitor Sleep Physiological Signals.

We present a wearable device built on an Adafruit Circuit Playground Express (CPE) board and integrated with a photoplethysmographic (PPG) optical sensor for heart rate monitoring and multiple embedded sensors for medical applications-in particular, sleep physiological signal monitoring. Our device is portable and lightweight. Due to the microcontroller unit (MCU)-based architecture of the proposed device, it is scalable and flexible. Thus, with the addition of different plug-and-play sensors, it can be used in many applications in different fields. The innovation introduced in this study is that with additional sensors, we can determine whether there are intermediary variables that can be modified to improve our sleep monitoring algorithm. Additionally, although the proposed device has a relatively low cost, it achieves substantially improved performance compared to the commercially available Philips ActiWatch2 wearable device, which has been approved by the Food and Drug Administration (FDA). To assess the reliability of our device, we compared physiological sleep signals recorded simultaneously from volunteers using both our device and ActiWatch2. Motion and light detection data from our device were shown to be correlated to data simultaneously collected using the ActiWatch2, with correlation coefficients of 0.78 and 0.89, respectively. For 7 days of continuous data collection, there was only one instance of a false positive, in which our device detected a sleep interval, while the ActiWatch2 did not. The most important aspect of our research is the use of an open architecture. At the hardware level, general purpose input/output (GPIO), serial peripheral interface (SPI), integrated circuit (I2C), and universal asynchronous receiver-transmitter (UART) standards were used. At the software level, an object-oriented programming methodology was used to develop the system. Because the use of plug-and-play sensors is associated with the risk of adverse outcomes, such as system instability, this study heavily relied on object-oriented programming. Object-oriented programming improves system stability when hardware components are replaced or upgraded, allowing us to change the original system components at a low cost. Therefore, our device is easily scalable and has low commercialization costs. The proposed wearable device can facilitate the long-term tracking of physiological signals in sleep monitoring and related research. The open architecture of our device facilitates collaboration and allows other researchers to adapt our device for use in their own research, which is the main characteristic and contribution of this study.

Characterization of a Fiber Bundle-Based Real-Time Ultrasound/Photoacoustic Imaging System and Its In Vivo Functional Imaging Applications.

Photoacoustic (PA) imaging is an attractive technology for imaging biological tissues because it can capture both functional and structural information with satisfactory spatial resolution. Current commercially available PA imaging systems are limited by their bulky size or inflexible user interface. We present a new handheld real-time ultrasound/photoacoustic imaging system (HARP) consisting of a detachable, high-numerical-aperture (NA) fiber bundle-based illumination system integrated with an array-based ultrasound (US) transducer and a data acquisition platform. In this system, different PA probes can be used for different imaging applications by switching the transducers and the corresponding jackets to combine the fiber pads and transducer into a single probe. The intuitive user interface is a completely programmable MATLAB-based platform. In vitro phantom experiments were conducted to test the imaging performance of the developed PA system. Furthermore, we demonstrated (1) in vivo brain vasculature imaging, (2) in vivo imaging of real-time stimulus-evoked cortical hemodynamic changes during forepaw electrical stimulation, and (3) in vivo imaging of real-time cerebral pharmacokinetics in rats using the developed PA system. The overall purpose of this design concept for a customizable US/PA imaging system is to help overcome the diverse challenges faced by medical researchers performing both preclinical and clinical PA studies.

Indexes for three-class classification performance assessment--an empirical comparison.

Assessment of classifier performance is critical for fair comparison of methods, including considering alternative models or parameters during system design. The assessment must not only provide meaningful data on the classifier efficacy, but it must do so in a concise and clear manner. For two-class classification problems, receiver operating characteristic analysis provides a clear and concise assessment methodology for reporting performance and comparing competing systems. However, many other important biomedical questions cannot be posed as "two-class" classification tasks and more than two classes are often necessary. While several methods have been proposed for assessing the performance of classifiers for such multiclass problems, none has been widely accepted. The purpose of this paper is to critically review methods that have been proposed for assessing multiclass classifiers. A number of these methods provide a classifier performance index called the volume under surface (VUS). Empirical comparisons are carried out using 4 three-class case studies, in which three popular classification techniques are evaluated with these methods. Since the same classifier was assessed using multiple performance indexes, it is possible to gain insight into the relative strengths and weakness of the measures. We conclude that: 1) the method proposed by Scurfield provides the most detailed description of classifier performance and insight about the sources of error in a given classification task and 2) the methods proposed by He and Nakas also have great practical utility as they provide both the VUS and an estimate of the variance of the VUS. These estimates can be used to statistically compare two classification algorithms.