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Type 1 diabetes mellitus (T1DM) can result in complications such as retinopathy, nephropathy, and peripheral neuropathy, which can lead to brain dysfunction. In this study, we investigated the effects of T1DM on cerebral neurovascular function in mice. Streptozotocin (STZ) is known to induce T1DM in animals; thus, we used an STZ-induced diabetes model to evaluate the effects of hyperglycemia on brain morphology and neurovascular tissue. Neurovascular coupling is the connection between neuronal activity and cerebral blood flow that maintains brain function. The ECoG-LSCI technique combines electrocorticography (ECoG) and laser speckle contrast imaging (LSCI) to detect cortical spreading depression (CSD) as a marker of neurovascular coupling and measure corresponding neurovascular function. Our results suggested that in the STZ group, hyperglycemia affected excitatory neurotransmission and metabolism, leading to reductions in intercellular signaling, somatosensory evoked potential (SSEP) amplitudes, and CSD transmission rates. Western blot data further revealed that brain-derived neurotrophic factor (BDNF) and neuronal nuclear antigen levels were reduced in the STZ group. Abnormalities in glucose metabolism in the brain and increased phosphorylation of AKT and GSK3 are hypothesized to be responsible for these decreases. Overall, this study highlights the importance of glucose metabolism in normal brain physiology and demonstrates that hyperglycemia disrupts neurovascular coupling and affects cerebral neurovascular function and that the degree of CSD is positively correlated with the extent of brain tissue damage. Further research is essential to gain a complete understanding of the related mechanisms and the implications of these findings.

Zebrafish are ideal model organisms for various fields of biological research, including genetics, neural transmission patterns, disease and drug testing, and heart disease studies, because of their unique ability to regenerate cardiac muscle. Tracking zebrafish trajectories is essential for understanding their behavior, physiological states, and disease associations. While 2D tracking methods are limited, 3D tracking provides more accurate descriptions of their movements, leading to a comprehensive understanding of their behavior. In this study, we used deep learning models to track the 3D movements of zebrafish. Videos were captured by two custom-made cameras, and 21,360 images were labeled for the dataset. The YOLOv7 model was trained using hyperparameter tuning, with the top- and side-view camera models trained using the v7x.pt and v7.pt weights, respectively, over 300 iterations with 10,680 data points each. The models achieved impressive results, with an accuracy of 98.7% and a recall of 98.1% based on the test set. The collected data were also used to generate dynamic 3D trajectories. Based on a test set with 3,632 3D coordinates, the final model detected 173.11% more coordinates than the initial model. Compared to the ground truth, the maximum and minimum errors decreased by 97.39% and 86.36%, respectively, and the average error decreased by 90.5%.This study presents a feasible 3D tracking method for zebrafish trajectories. The results can be used for further analysis of movement-related behavioral data, contributing to experimental research utilizing zebrafish.

Drowning, as a leading cause of unintentional injury-related deaths worldwide, is a major public health concern. Swimming pool drowning is the main cause of most drowning incidents, and even with preventive measures such as surveillance cameras and lifeguards, tens of thousands of lives are lost to drowning every year. To address this issue, technology is being utilized to prevent drowning accidents and provide timely alerts for rescue. This paper explores the use of drowning prevention technology in embedded systems within enclosed environments, artificial intelligence (AI), and the Internet of Things (IoT) to decrease the likelihood of drowning incidents. Embedded systems play a critical role in such technology, enabling real-time monitoring, identification of dangerous situations, and prompt alerting. Due to their ease of installation and technical implementation, embedded devices are especially effective as drowning prevention devices. The image recognition capabilities of drowning prevention systems are enhanced through computer vision. Swimming pool drowning situations can be identified with the help of cameras and deep learning technologies, thereby increasing rescue efficiency. Finally, the IoT endows drowning prevention systems with comprehensive intelligence by connecting various devices and communication tools. Real-time alert transmission and analysis have become possible, enabling the early prediction of dangerous situations and the implementation of preventive measures, significantly reducing drowning incidents. In summary, the integration of these three types of drowning prevention technologies represents significant progress. The flexibility, accuracy, and intelligence of drowning prevention systems are enhanced through the incorporation of these technologies, providing robust support for safeguarding human lives and thus potentially saving tens of thousands of lives each year.

Summary: To study neurovascular function in type 2 diabetes mellitus (T2DM), we established a high-fat diet/streptozotocin (HFD/STZ) rat model. Electrocorticography-laser speckle contrast imaging (ECoG-LSCI) revealed that the somatosensory-evoked potential (SSEP) amplitude and blood perfusion volume were significantly lower in the HFD/STZ group. Cortical spreading depression (CSD) velocity was used as a measure of neurovascular function, and the results showed that the blood flow velocity and the number of CSD events were significantly lower in the HFD/STZ group. In addition, to compare changes during acute hyperglycemia and hyperglycemia, we used intraperitoneal injection (IPI) of glucose to induce transient hyperglycemia. The results showed that CSD velocity and blood flow were significantly reduced in the IPI group. The significant neurovascular changes observed in the brains of rats in the HFD/STZ group suggest that changes in neuronal apoptosis may play a role in altered glucose homeostasis in T2DM.

Summary: Ischemic stroke can cause depolarized brain waves, termed peri-infarct depolarization (PID). Here, we evaluated whether topiramate, a neuroprotective drug used to treat epilepsy and alleviate migraine, has the potential to reduce PID. We employed a rat model of photothrombotic ischemia that can reliably and reproducibly induce PID and developed a combined electrocorticography-laser speckle contrast imaging (ECoG-LSCI) platform to monitor neuronal activity and cerebral blood flow (CBF) simultaneously. Topiramate administration after photothrombotic ischemia did not rescue CBF but significantly restored somatosensory evoked potentials in the forelimb area of the primary somatosensory cortex. Moreover, infarct volume was investigated by 2,3,5-triphenyltetrazolium chloride (TTC) staining, and neuronal survival was evaluated by Nissl staining. Mechanistically, the levels of inflammatory markers, such as ED1 (CD68), Iba-1, and GFAP, decreased significantly after topiramate administration, as did BDNF expression, while the expression of NeuN and Bcl-2/Bax increased, which is indicative of reduced inflammation and improved neuroprotection.

Wound monitoring is crucial for effective healing, as nonhealing wounds can lead to tissue ulceration and necrosis. Evaluating wound recovery involves observing changes in angiogenesis. Laser speckle contrast imaging (LSCI) is vital for wound assessment due to its rapid imaging, high resolution, wide coverage, and noncontact properties. When using LSCI equipment, regions of interest (ROIs) must be delineated in lesion areas in images for quantitative analysis. However, patients with serious wounds cannot maintain constant postures because the affected areas are often associated with discomfort and pain. This leads to deviations between the drawn ROI and actual wound position when using LSCI for wound assessment, affecting the reliability of relevant assessments. To address these issues, we used the channel and spatial reliability tracker object tracking algorithm to develop an automatic ROI tracking function for LSCI systems. This algorithm is used to track and correct artificial movements in blood flow images, address the ROI position offset caused by the movement of the affected body part, increase the blood flow analysis accuracy, and improve the clinical applicability of LSCI systems. ROI tracking experiments were performed by simulating wounds, and the results showed that the intraclass correlation coefficient (ICC) ranged from 0.134 to 0.976. Furthermore, the object within the ROI affected tracking performance. Clinical assessments across wound types showed ICCs ranging from 0.798 to 0.917 for acute wounds and 0.628–0.849 for chronic wounds. We also discuss factors affecting tracking performance and propose strategies to enhance implementation effectiveness.

Abstract Background Ischemic stroke-reperfusion (S/R) injury is a crucial issue in the protection of brain function after thrombolysis. The vasodilation induced by ultrasound (US)-stimulated microbubble cavitation has been applied to reduce S/R injury through sonoperfusion. The present study uses oxygen-loaded microbubbles (OMBs) with US stimulation to provide sonoperfusion and local oxygen therapy for the reduction of brain infarct size and neuroprotection after S/R. Methods The murine S/R model was established by photodynamic thrombosis and thrombolysis at the remote branch of the anterior cerebral artery. In vivo blood flow, partial oxygen pressure (pO2), and brain infarct staining were examined to analyze the validity of the animal model and OMB treatment results. The animal behaviors and measurement of the brain infarct area were used to evaluate long-term recovery of brain function. Results The percentage of blood flow was 45 ± 3%, 70 ± 3%, and 86 ± 2% after 60 min stroke, 20 min reperfusion, and 10 min OMB treatment, respectively, demonstrating sonoperfusion, and the corresponding pO2 level was 60 ± 1%, 76 ± 2%, and 79 ± 4%, showing reoxygenation. After 14 days of treatment, a 87 ± 3% reduction in brain infarction and recovery of limb coordination were observed in S/R mice. The expression of NF-κB, HIF-1α, IL-1β, and MMP-9 was inhibited and that of eNOS, BDNF, Bcl2, and IL-10 was enhanced, indicating activation of anti-inflammatory and anti-apoptosis responses and neuroprotection. Our study demonstrated that OMB treatment combines the beneficial effects of sonoperfusion and local oxygen therapy to reduce brain infarction and activate neuroprotection to prevent S/R injury. Graphical Abstract

Abstract Endogenous signals, namely nitric oxide (NO) and electrons, play a crucial role in regulating cell fate as well as the vascular and neuronal systems. Unfortunately, utilizing NO and electrical stimulation in clinical settings can be challenging due to NO's short half‐life and the invasive electrodes required for electrical stimulation. Additionally, there is a lack of tools to spatiotemporally control gas release and electrical stimulation. To address these issues, an “electromagnetic messenger” approach that employs on‐demand high‐frequency magnetic field (HFMF) to trigger NO release and electrical stimulation for restoring brain function in cases of traumatic brain injury is introduced. The system comprises a NO donor (poly(S‐nitrosoglutathione), pGSNO)‐conjugated on a gold yarn‐dynamos (GY) and embedded in an implantable silk in a microneedle. When subjected to HFMF, conductive GY induces eddy currents that stimulate the release of NO from pGSNO. This process significantly enhances neural stem cell (NSC) synapses' differentiation and growth. The combined strategy of using NO and electrical stimulation to inhibit inflammation, angiogenesis, and neuronal interrogation in traumatic brain injury is demonstrated in vivo.

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

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.

Summary: 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 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.

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.

Although radiotherapy is one of the most important curative treatments for cancer, its clinical application is associated with undesired therapeutic effects on normal or healthy tissues. The use of targeted agents that can simultaneously achieve therapeutic and imaging functions could constitute a potential solution. Herein, we developed 2-deoxy-d-glucose (2DG)-labeled poly(ethylene glycol) (PEG) gold nanodots (2DG-PEG-AuD) as a tumor-targeted computed tomography (CT) contrast agent and radiosensitizer. The key advantages of the design are its biocompatibility and targeted AuD with excellent sensitivity in tumor detection via avid glucose metabolism. As a consequence, CT imaging with enhanced sensitivity and remarkable radiotherapeutic efficacy could be attained. Our synthesized AuD displayed linear enhancement of CT contrast as a function of its concentration. In addition, 2DG-PEG-AuD successfully demonstrated significant augmentation of CT contrast in both in vitro cell studies and in vivo tumor-bearing mouse models. In tumor-bearing mice, 2DG-PEG-AuD showed excellent radiosensitizing functions after intravenous injection. Results from this work indicate that 2DG-PEG-AuD could greatly potentiate theranostic capabilities by providing high-resolution anatomical and functional images in a single CT scan and therapeutic capability.

Graphical AbstractThe hybrid nanoplatform that harnesses the tumor microenvironment factors for dual targeted magnetic resonance imaging and assessment of tumor oxygen status by photoacoustic imaging.

Emergency care is a critical area of medicine whose outcomes are influenced by the time, availability, and accuracy of contextual information. The success of critical or emergency care is determined by the quality and accuracy of the information received during the emergency call and the data collected during emergency transportation. The Internet of Things (IoT) consists of many smart devices and components that communicate via their connection to the Internet, which is used to collect data with sensors that obtain personal health parameters. In the past, most health measurement systems were based on a single dedicated orientation, and few systems had multiple devices on the same platform. In addition to traditional health measurement technologies, most such systems use centralized data transmission, which means that health measurement data have become the exclusive intellectual asset of the system developer. Therefore, this study develops an IoT-based message-broker system that is deployed and demonstrated for five health devices: blood oxygen, blood pressure, forehead temperature, body temperature, and body weight sensors. A central controller accessed by radio-frequency identification (RFID) collects clients’ health profiles on the cloud platform. All collected data can be quickly shared, analyzed, and visualized, and the health devices can be changed, added to, and removed reliably when the requirements change. Additionally, following the message queuing telemetry transport (MQTT) protocol, all devices can communicate with each other and be integrated into a higher-level health measurement standard (such as blood pressure plus weight or body temperature plus blood oxygen). We implement a smart healthcare monitoring system (SHMS) and verify its reliability. We use MQTT to establish an open communication format that other organizations can follow to perform individual patient vital sign monitoring in potential applications. The robustness and flexibility of this research can be verified through the addition of other systems. Through this structure, more large-scale health detection devices can be integrated into the method proposed in this research in the future. Personal RFID or health insurance cards can be used for personal services or in medical institutions, and the data can easily be shared through the mechanism of this research. Such information sharing will enable the utilization of medical resources to be maximized.

In the public health domain, healthcare systems are a crucial part of the economy, transportation, education, health infrastructure, and military of any country. In this study, a system is proposed to implement smart people management, monitoring, and tracking processes that can be used in hospitals to automate and organize information management. For disease management in a public setting, measuring forehead temperatures is a standard method of identifying people for further treatment. To prevent disease transmission on campuses or in any public space, daily temperature checks for everyone have been mandated at the entrances to many public spaces. Although this task can be performed in seconds for an individual, substantial human resources are required to perform temperature checks for all people arriving at one or more specified checkpoints each day during an epidemic. As a result, a smart measuring, monitoring, and management system is urgently needed. We propose a complete solution that includes current Internet of Things technology that can be used in hospitals or any public space to automate and organize information management operations. This system offers a cost-effective means of enhancing reliability, privacy, and security for healthcare record management. One attractive feature of the system is its low cost due to the use of off-the-shelf devices and sensors that can be sourced and operated in our region. Recorded measurements of vital sig/ns are presented via a compact, user-friendly interface that can be monitored remotely. Because the proposed solution is based on mature existing hardware modules and software packages, any experienced information technology professional can quickly build an analogous monitoring and management system by following the instructions presented in this paper.

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.

Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.

Users often need to control and monitor the environmental variables of their homes, even when they are not at home. In this paper, we present a multifunctional, low-cost, and flexible system for smart home control and environmental monitoring. This system employs an embedded micro web server based on an Arduino Yún microcontroller with Internet connectivity that allows remote device control. The proposed system can be controlled via the Internet through an Android-based mobile app. To guarantee access regardless of Internet availability, the proposed system can also be controlled via standalone manual operation using a touch display. The proposed system transmits sensor data to a cloud platform and can receive commands from the server, allowing many devices to be automatically controlled. To demonstrate the feasibility and effectiveness of this system, devices such as light switches, power plugs, and various sensors, including temperature, gas, 2.5-μm particulate matter (PM2.5) and motion sensors, were integrated into a prototype of the proposed home control system. Finally, we implemented the prototype in a model home to validate the flexibility, scalability, usability and reliability of the system.

For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and blood oxygen machines for medical applications with the hope that it can be applied in the future. We develop an integrated multiple-biosignal recording system based on a modular design. The developed system monitors and records EEG, ECG, and peripheral oxygen saturation (SpO2) signals for health purposes simultaneously in a single setting. We use a logic level converter to connect the developed EEG module (BR8), ECG module, and SpO2 module to a microcontroller (Arduino). The modular data are then smoothly encoded and decoded through consistent overhead byte stuffing (COBS). This developed system has passed simulation tests and exhibited proper functioning of all modules and subsystems. In the future, the functionalities of the proposed system can be expanded with additional modules to support various emergency or ICU applications.

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.

In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

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.

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.

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.

This study developed a novel system combining a 16-channel micro-electrocorticography (μECoG) electrode array and functional photoacoustic microscopy (fPAM) to examine changes in neurovascular functions following transient ischemic attack (TIA) in rats. To mimic the pathophysiology of TIA, a modified photothrombotic ischemic model was developed by using 3 min illumination of 5 mW continuous-wave (CW) green laser light focusing on a distal branch of the middle cerebral artery (MCA). Cerebral blood volume (CBV), hemoglobin oxygen saturation (SO2), somatosensory evoked potentials (SSEPs) and alpha-to-delta ratio (ADR) were measured pre- and post-ischemia over a focal cortical region (i.e., 1.5 × 1.5 mm2). Unexpectedly, the SO2, peak-to-peak amplitude (PPA) of SSEPs and ADR recovered and achieved levels greater than the baseline values at the 4th hour post-ischemia induction without any intervention, whereas the CBV value only partially recovered. In other words, transient ischemia led to increased neural activity when the relative CBV was reduced, which may further compromise neural integrity or lead to subsequent vascular disease. This novel μECoG-fPAM system complements currently available imaging techniques and represents a promising technology for studying neurovascular coupling in animal models.

To investigate the potential therapeutic effects of peripheral sensory stimulation during the hyperacute phase of stroke, the present study utilized electrophysiology and photoacoustic imaging techniques to evaluate neural and vascular responses of the rat cortex following ischemic insult. We employed a rat model of photothrombotic ischemia (PTI), which targeted the forelimb region of the primary somatosensory cortex (S1FL), due to its high reproducibility in creating localized ischemic injury. We also established a hybrid, dual-modality system, including six-channel electrocorticography (ECoG) and functional photoacoustic microscopy (fPAM), termed ECoG–fPAM, to image brain functional responses to peripheral sensory stimulation during the hyperacute phase of PTI. Our results showed that the evoked cerebral blood volume (CBV) and hemoglobin oxygen saturation (SO2) recovered to 84 ± 7.4% and 79 ± 6.2% of the baseline, respectively, when stimulation was delivered within 2.5 h following PTI induction. Moreover, neural activity significantly recovered, with 77 ± 8.6%, 76 ± 5.3% and 89 ± 8.2% recovery for the resting-state inter-hemispheric coherence, alpha-to-delta ratio (ADR) and somatosensory evoked potential (SSEP), respectively. Additionally, we integrated the CBV or SO2 with ADR values as a recovery indicator (RI) to assess functional recovery after PTI. The RI indicated that 80 ± 4.2% of neurovascular function was preserved when stimulation was delivered within 2.5 h. Additionally, stimulation treatment within this optimal time window resulted in a minimal infarct volume in the ischemic hemisphere (4.6 ± 2.1%). In contrast, the infarct volume comprised 13.7 ± 1.7% of the ischemic hemisphere when no stimulation treatment was applied.

Many commercially available electroencephalography (EEG) sensors, including conventional wet and dry sensors, can cause skin irritation and user discomfort owing to the foreign material. The EEG products, especially sensors, highly prioritize the comfort level during devices wear. To overcome these drawbacks for EEG sensors, this paper designs Societe Generale de Surveillance S · A · (SGS)-certified, silicon-based dry-contact EEG sensors (SBDSs) for EEG signal measurements. According to the SGS testing report, SBDSs extract does not irritate skin or induce noncytotoxic effects on L929 cells according to ISO10993-5. The SBDS is also lightweight, flexible, and nonirritating to the skin, as well as capable of easily fitting to scalps without any skin preparation or use of a conductive gel. For forehead and hairy sites, EEG signals can be measured reliably with the designed SBDSs. In particular, for EEG signal measurements at hairy sites, the acicular and flexible design of SBDS can push the hair aside to achieve satisfactory scalp contact, as well as maintain low skin-electrode interface impedance. Results of this paper demonstrate that the proposed sensors perform well in the EEG measurements and are feasible for practical applications.

Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (PCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, PCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 ± 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 ± 11.73 mm), OLE (20.17 ± 6.43 mm), PCA (19.13 ± 0.75 mm), SFS (22.75 ± 2.01 mm), and NN (16.75 ± 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information.

A brain−computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.

This article provides a guide to design and build a handheld, real-time photoacoustic (PA) imaging system from simulation to realization for animal neurological disease models. A pulsed laser and array-based ultrasound (US) platform were utilized to develop the system for evaluating vascular functions in rats with focal ischemia or subcutaneous tumors. To optimize the laser light delivery, finite element (FE)-based simulation models were developed to provide information regarding light propagation and PA wave generation in soft tissues. Besides, simulations were also conducted to evaluate the ideal imaging resolution of the US system. As a result, a PA C-scan image of a designed phantom in 1% Lipofundin was reconstructed with depth information. Performance of the handheld PA system was tested in an animal ischemia model, which revealed that cerebral blood volume (CBV) changes at the cortical surface could be monitored immediately after ischemia induction. Another experiment on subcutaneous tumors showed the anomalous distribution of the total hemoglobin concentration (HbT) and oxygen saturation (SO2), while 3D and maximum intensity projection (MIP) PA images of the subcutaneous tumors are also presented in this article. Overall, this system shows promise for monitoring disease progression in vascular functional impairments.

In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes.