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Abstract Background Toxoplasma gondii, an intracellular parasite, is a significant cause of zoonotic disease, with an estimated one-third of the world’s human population believed to be infected. T. gondii is transmitted to humans through the consumption of contaminated water, soil, vegetables, fruits, shellfish or undercooked meat, and can also be passed from human to human through vertical transmission, transplants and blood transfusion. While T. gondii infection typically manifests mild symptoms such as colds among immunocompetent individuals, it can prove lethal for those with weakened immune systems. Methods To summarize the diagnostic methods for Toxoplasma gondii infection, we performed a literature search on PubMed from 1948 to 2023 using the keywords “T. gondii serological diagnosis” or “T. gondii molecular diagnosis”. Results Rapid and accurate diagnosis of T. gondii infection is imperative. Although a diagnostic kit is currently commercially available, there are a number of disadvantages to the validation principles applied to each diagnostic kit. Consequently, multiple diagnostic methods are concurrently employed to offset these limitations. Serological methods for diagnosing T. gondii infection include the Dye Test (DT), Agglutination Test (AT), Modified Agglutination Test (MAT), Latex Agglutination Test (LAT), Enzyme-Linked Immunosorbent Assay (ELISA), and Western Blot. Meanwhile, molecular methods such as polymerase chain reaction (PCR), nested PCR, real-time PCR, loop-mediated isothermal amplification (LAMP), multiplex PCR, and PCR–restriction fragment length polymorphism (PCR–RFLP) are also utilized. Each of these methods possess its own set of advantages and disadvantages. Conclusions By summarizing the advantages and disadvantages of different diagnostic techniques, it is hoped that the epidemiology, prevention, and control of toxoplasmosis will be improved in the future through the use of appropriate technologies.

We present CNNExplorer, a generalized visualization system designed to assist beginners in understanding Convolutional Neural Networks (CNNs). CNNExplorer provides an intuitive interface and detailed visualizations at both the module and layer levels, enabling users to explore and understand the complex structures and operations of CNN models without requiring extensive coding knowledge. The module view abstracts the complexity by grouping layers into modules, while the layer view shows detailed transformations within each module. Our system supports a wide range of CNN models, including those available on Hugging Face, allowing users to visualize and analyze a variety of pre-trained models. To demonstrate the potential effectiveness of CNNExplorer in practical applications, we proposed specific application scenarios. Furthermore, we conducted a user study with participants and found the system is effective in enhancing their understanding of deep learning models and appreciated the interactive features. In future work, we will focus on supporting the latest vision models and expanding visualization capabilities to other deep learning architectures.

Structured light 3D cameras have generally been the preferred choice for depth imaging in case high accuracy and precision of point clouds need to be captured at relatively short working distances. However, in environments featuring wide variations in surface reflectivity and ambient lighting, they face difficulty obtaining point clouds that sufficiently cover all the surface areas of interest without partial omissions. This happens not only with a single exposure but even with multiple exposures when a limited range of exposure settings is available. Although automatic multi-exposure settings have been explored for use in structured light 3D cameras to address this issue, there is yet to be a rigorous optimal solution for automatically setting multiple exposures to determine the minimum number of exposure settings while maximally covering the ill-conditioned surface of interest. In the current study, we present a method for optimal multi-exposure settings that automatically determines the minimum number of exposure settings for capturing the maximum number of 3D points from the surface of interest under a limited range of available exposure. The proposed method, which applies to any extremity in surface reflectivity and ambient light variations, is based on finding the minimum number of exposures that enable the identification of exposure-intensity slopes for as many pixels in the surface of interest as possible under projected light while removing the effect of ambient light. The proposed method was experimentally shown to have superior performance based on a case study and a performance comparison with a state-of-the-art multi-exposure setting practice.

In this paper, we propose a deep reinforcement learning (DRL) based unmanned aerial vehicles (UAV)-assisted trajectory optimization for ultra-dense small cell networks. We assume that each UAV is equipped with a sensing radio to obtain distance information to the UEs and other UAVs in the network which are used to update the UAV’s trajectory. The proposed DRL-based system selects the optimal joint control actions for the UAVs that maximizes the system sum-rate. The simulation results show that the proposed DRL-based UAV controller provides fast UAV placement in the network with a high system performance when compared with the benchmark schemes.

For successful human–robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to facilitate human–robot interaction (HRI) effectively. The evolution of robot intelligence now enables robots to take a proactive role in initiating and sustaining HRI, thereby allowing humans to concentrate more on their primary tasks. In this paper, we introduce a system known as the Robot-Facilitated Interaction System (RFIS), where mobile robots are employed to perform identification, tracking, re-identification, and gesture recognition in an integrated framework to ensure anytime readiness for HRI. We implemented the RFIS on an autonomous mobile robot used for transporting a patient, to demonstrate proactive, real-time, and user-friendly interaction with a caretaker involved in monitoring and nursing the patient. In the implementation, we focused on the efficient and robust integration of various interaction facilitation modules within a real-time HRI system that operates in an edge computing environment. Experimental results show that the RFIS, as a comprehensive system integrating caretaker recognition, tracking, re-identification, and gesture recognition, can provide an overall high quality of interaction in HRI facilitation with average accuracies exceeding 90% during real-time operations at 5 FPS.

Abstract The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge.

Abstract Individuals who are unable to walk independently spend most of the day in a wheelchair. This population is at high risk for developing pressure injuries caused by sitting. However, early diagnosis and prevention of these injuries still remain challenging. Herein, we introduce battery-free, wireless, multimodal sensors and a movable system for continuous measurement of pressure, temperature, and hydration at skin interfaces. The device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity, a temperature sensor for measuring skin temperature, and a galvanic skin response sensor for measuring skin hydration levels. The movable system enables power harvesting, and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body coverage. Experimental evaluations and numerical simulations of the devices, together with clinical trials for wheelchair patients, demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

This paper presents an approach for enhancing indoor localization accuracy using a hybrid quantum deep neural network model (H-QDNN). To improve the accuracy of indoor localization based on contemporary techniques, we employ the combined strengths of quantum computing (QC) and deep neural networks (DNN). The strengths of QC, which accelerates the training process and enables efficient handling of complex data representations through quantum superposition and entanglement, were combined with DNN, known for its ability to extract meaningful features and learn complex patterns from data. The proposed model can be trained using small datasets, reducing the need for extensive data, particularly useful in indoor localization, where data collection can be time-consuming and resource-intensive. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments and comparisons with existing state-of-the-art methods. The results demonstrate that the H-QDNN model significantly improves indoor localization accuracy compared to traditional techniques. Additionally, we provide insights into the factors contributing to enhanced performance, such as the quantum-inspired algorithms utilized and the integration of mixed fingerprints.

Abstract Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen’s kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

Laparoscopic surgery is a technique in which surgeons insert laparoscopic instruments into the body through a small incision and conduct surgical operations. It offers a shorter recovery period, less blood loss, and less postoperative pain than open surgeries. In order to maximize these benefits of this surgery technique, it is important to localize the lesion accurately without palpation, minimizing the surgical area. It is because surgeons cannot palpate the organ during laparoscopic surgeries. For accurate localization, we propose a lesion localization system (LLS). This adopts an endoscopic clip with a small-size magnet as a marker and an improved magnetometer as a probe for measuring the magnetic field from the magnet. The magnetometer, which consists of two Hall sensors, can cancel out the fluctuating magnetic baseline mainly caused by external environments so that it can detect a smaller change in the target magnetic field. As a result, the LLS improves the detection range up to 40 mm while using a weak magnet with a small volume of 17.1 mm3, which is compatible with the use of commercial clip appliers. An error of less than 0.1% is achieved at a distance of 40 mm, and the maximum error is kept below 6% even when the rotation angle is varied to ±90°. This LLS does not cause discomfort to the surgery operation because it uses a marker small enough and does not require external coils.

Pre-swirl system is installed to minimize energy loss between the stationary and rotating parts of turbine secondary air system. Although various optimization studies were conducted to increase the pre-swirl efficiency, most of the studies were focused on a hole type pre-swirl nozzles. In this study, a vane type pre-swirl nozzle was optimized to increase mass flow rate and temperature drop for given boundary conditions. The system performance was analyzed by 3D CFD and the objective functions were used to maximize the discharge coefficient and the adiabatic effectiveness. After sensitivity analysis, seven design variables were chosen on the three planes of nozzle span-wise direction. The OLHD method was used to obtain the initial scattered test points, and the additional ones were supplied by the ALHD method. The Kriging model was constructed as the surrogate one, and refined iteratively until satisfying the convergence criteria between the estimated point and the CFD result. The optimized model improved the span-wise uniformity of the flow path and the discharge coefficient was increased by 2.57%, whilst the adiabatic effectiveness remained nearly constant. The performance was also analyzed for pressure ratio and off-design points, with the optimized model showing better performance at all boundary conditions.

This paper presents an experimental validation of deep learning-based direction-of-arrival (DoA) estimation by using realistic data collected via universal software radio peripheral (USRP). Deep neural network (DNN) and convolutional neural network (CNN) structures are designed to estimate the DoA. Two types of data are used for training networks. One is the data synthesized by the signal model, and the other is the data collected by USRP. Here, the signal model considers both mutual coupling and multipath signals. Experimental results show that the estimation performance is most accurate when training DNN and CNN with the collected data. Furthermore, the estimation tends to be poor in the indoor environment, which suffers from the strong non-line-of-sight (NLoS) signals.

Background We previously reported that percutaneous dilatational tracheostomy (PDT) can be safely performed 2 cm below the cricothyroid membrane without the aid of a bronchoscope. Although our simplified method is convenient and does not require sophisticated equipment, the precise location for tracheostomy cannot be confirmed. Because it is recommended that tracheostomy be performed at the second tracheal ring, we assessed whether patient characteristics could predict the distance between the cricothyroid membrane and the second tracheal ring. Methods Data from 490 patients who underwent three-dimensional neck computed tomography from January 2012 to December 2015 were analyzed, and the linear distance from the upper part of the cricoid cartilage (CC) to the lower part of the second tracheal ring (2TR) was measured in the sagittal plane. Results The mean CC-to-2TR distance was 25.26 mm (95% CI 25.02–25.48 mm). Linear regression analysis showed that the predicted CC-to-2TR distance could be calculated as −5.73 + 0.2 × height (cm) + 1.22 × sex (male: 1, female: 0) + 0.01 × age (yr) −0.03 × weight (kg) (adj. R2 = 0.55). Conclusions These results suggest that height and sex should be considered when performing PDT without bronchoscope guidance.

Many experiments have been performed that study rectification by exciting electrons to tunnel across a single quantum barrier. Here, the authors take a 2-dimensional approach by combining 2D closed-loop tunneling barriers with broken symmetry to enable geometrically controllable rectification of THz fields.

Cardiovascular diseases, such as stroke, are the most common causes of death in developed countries. Ischemic stroke accounts for 85% of the total cases and is caused by abnormal thrombus formation in the vessels, causing deficient blood and oxygen supply to the brain. Prophylactic treatments include the prevention of thrombus formation, of which the most used is acetylsalicylic acid (ASA); however, it is associated with a high incidence of side effects. Angelica gigas Nakai (AG) is a natural herb used to improve blood circulation via anti-platelet aggregation, one of the key processes involved in thrombus formation. We examined the antithrombotic effects of AGE 232, the ethanol extract of A. gigas Nakai. AGE 232 showed a significant reduction in death or paralysis in mice caused by collagen/epinephrine-induced thromboembolism in a dose-dependent manner and inhibition of collagen-induced human platelet aggregation in a concentration-dependent manner. Additionally, AGE 232-treated mice did not show severe bleeding in the gut compared to ASA-treated mice. AGE 232 resulted in a decrease in the number of neutrophils attached to the human umbilical vein endothelial cells (HUVECs) and lower inhibition of COX-1 in response to bleeding and damage to blood vessels, a major side effect of ASA. Therefore, AGE 232 can prevent thrombus formation and stroke.

Diaphragmatic eventration is a rare anomaly. When patients with this condition undergo general anesthesia, anesthetic management should be performed with particular care owing to the risk of diaphragmatic rupture. Such a rupture can be perioperatively diagnosed using multiple tools including lung ultrasonography. This case report describes the anesthetic management of a male infant with osteochondroma in the distal ulna, presenting with diaphragmatic eventration on the right side.

Successful vaccines against specific pathogens often require multiple immunizations and adjuvant usage. Yet, assessing the protective efficacy of different immunization regimens with adjuvanted Toxoplasma gondii vaccines remains elusive. In this study, we investigated the vaccine efficacy induced by CpG-ODN-adjuvanted T. gondii virus-like particles (VLPs) after challenge infection with T. gondii (ME49) in mice (BALB/c) upon one, two, and three immunizations. Immunization with adjuvanted T. gondii VLPs induced higher levels of T. gondii-specific IgG and/or IgA antibody responses, germinal center (GC) B cells, total B cells, and CD4+ and CD8+ T cells compared with unadjuvanted VLPs. Increasing the number of immunizations was strongly correlated with enhanced protective immunity against T. gondii in mice, with the highest protection being demonstrated in mice thrice-immunized with either adjuvanted T. gondii VLPs or VLPs alone. Notably, lesser bodyweight reductions and cerebral cyst counts were observed in mice receiving multiple immunizations with the adjuvanted VLPs, thereby confirming the effectiveness of adjuvanted boost immunizations. These results demonstrated that multiple immunizations with T. gondii VLPs is an effective approach, and the CpG-ODN can be developed as an effective adjuvant for T. gondii VLP vaccines.

Synechocystis salt-responsive gene 1 (sysr1) was engineered for expression in higher plants, and gene construction was stably incorporated into tobacco plants. We investigated the role of Sysr1 [a member of the alcohol dehydrogenase (ADH) superfamily] by examining the salt tolerance of sysr1-overexpressing (sysr1-OX) tobacco plants using quantitative real-time polymerase chain reactions, gas chromatography-mass spectrometry, and bioassays. The sysr1-OX plants exhibited considerably increased ADH activity and tolerance to salt stress conditions. Additionally, the expression levels of several stress-responsive genes were upregulated. Moreover, airborne signals from salt-stressed sysr1-OX plants triggered salinity tolerance in neighboring wild-type (WT) plants. Therefore, Sysr1 enhanced the interconversion of aldehydes to alcohols, and this occurrence might affect the quality of green leaf volatiles (GLVs) in sysr1-OX plants. Actually, the Z-3-hexenol level was approximately twofold higher in sysr1-OX plants than in WT plants within 1–2 h of wounding. Furthermore, analyses of WT plants treated with vaporized GLVs indicated that Z-3-hexenol was a stronger inducer of stress-related gene expression and salt tolerance than E-2-hexenal. The results of the study suggested that increased C6 alcohol (Z-3-hexenol) induced the expression of resistance genes, thereby enhancing salt tolerance of transgenic plants. Our results revealed a role for ADH in salinity stress responses, and the results provided a genetic engineering strategy that could improve the salt tolerance of crops.

The therapeutic goal for the treatment of posttraumatic stress disorder (PTSD) is to promote extinction and to prevent the relapse of fearful memories. Research has identified pharmacological treatments that may regulate the formation and extinction of fear memories, but not many reagents that block the relapse of extinguished fear are known. Radix Polygalae (RP) is an Asian herb used for sedation, and its ingredients have anxiolytic and antidepressant properties. As various neurological effects have been identified, we tested whether RP affects the relapse of fear. Freezing in response to a conditioned context and cues was used to measure the effects of RP in mice. In cohort 1 (n = 30), consolidation, extinction, and reinstatement were tested during the course of 18 days of treatment. In cohort 2 (n = 30), consolidation, extinction, and renewal were tested during 10 days of treatment. The consolidation, extinction, reinstatement, and possibly the renewal of context-induced freezing were inhibited due to the administration of RP in animal subjects. However, the effects of RP on the freezing responses of subjects elicited by conditioned auditory cues were less obvious. Because it effectively suppresses the consolidation of fear memories, RP may be used for primary and secondary prevention of symptoms in PTSD patients. Additionally, because it effectively suppresses the reinstatement and renewal of fear memories, RP may be applied for the prevention of fear relapse in PTSD patients who have undergone exposure therapy.

Tin disulfide (SnS2) has attracted much attention as a two-dimensional (2D) material. A high-quality, low-temperature process for producing 2D materials is required for future electronic devices. Here, we investigate tin disulfide (SnS2) layers deposited via atomic layer deposition (ALD) using tetrakis(dimethylamino)tin (TDMASn) as a Sn precursor and H2S gas as a sulfur source at low temperature (150° C). The crystallinity of SnS2 was improved by H2S gas annealing. We carried out H2S gas annealing at various conditions (250° C, 300° C, 350° C, and using a three-step method). Angle-resolved X-ray photoelectron spectroscopy (ARXPS) results revealed the valence state corresponding to Sn4+ and S2- in the SnS2 annealed with H2S gas. The SnS2 annealed with H2S gas had a hexagonal structure, as measured via X-ray diffraction (XRD) and the clearly out-of-plane (A1g) mode in Raman spectroscopy. The crystallinity of SnS2 was improved after H2S annealing and was confirmed using the XRD full-width at half-maximum (FWHM). In addition, high-resolution transmission electron microscopy (HR-TEM) images indicated a clear layered structure.

Tin monosulfide (SnS) is a promising p-type semiconductor material for energy devices. To realize the device application of SnS, studies on process improvement and film characteristics of SnS is needed. Thus, we developed a new film process using atomic layer deposition (ALD) to produce SnS films with high quality and various film characteristics. First, a process for obtaining a thick SnS film was studied. An amorphous SnS2 (a-SnS2) film with a high growth rate was deposited by ALD, and a thick SnS film was obtained using phase transition of a-SnS2 film by vacuum annealing. Subsequently, we investigated the effect of seed layer on formation of SnS film to verify the applicability of SnS to various devices. Separately deposited crystalline SnS and SnS2 thin films were used as seed layer. The SnS film with a SnS seed showed small grain size and high film density from the low surface energy of the SnS seed. In the case of the SnS film using a SnS2 seed, volume expansion occurred by vertically grown SnS grains due to a lattice mismatch with the SnS2 seed. The obtained SnS film using the SnS2 seed exhibited a large reactive site suitable for ion exchange.

Silicon nitride (SiNx) thin films using 1,3-di-isopropylamino-2,4-dimethylcyclosilazane (CSN-2) and N2 plasma were investigated. The growth rate of SiNx thin films was saturated in the range of 200−500 °C, yielding approximately 0.38 Å/cycle, and featuring a wide process window. The physical and chemical properties of the SiNx films were investigated as a function of deposition temperature. As temperature was increased, transmission electron microscopy (TEM) analysis confirmed that a conformal thin film was obtained. Also, we developed a three-step process in which the H2 plasma step was introduced before the N2 plasma step. In order to investigate the effect of H2 plasma, we evaluated the growth rate, step coverage, and wet etch rate according to H2 plasma exposure time (10−30 s). As a result, the side step coverage increased from 82% to 105% and the bottom step coverages increased from 90% to 110% in the narrow pattern. By increasing the H2 plasma to 30 s, the wet etch rate was 32 Å/min, which is much lower than the case of only N2 plasma (43 Å/min).

Rhoptry organelle proteins (ROPs) secreted by Toxoplasma gondii (T. gondii) play a critical role during parasite invasion into host cells. In this study, virus-like particles (VLPs) vaccines containing ROP4 and/or ROP13 together with influenza M1 were generated. ROP4+ROP13 VLPs were produced by combining ROP4 VLPs with ROP13 VLPs, and ROP(4 + 13) VLPs by co-infecting insect cells with recombinant baculovirus expressing ROP4 or ROP13. Mice intranasally immunized with ROP(4 + 13) VLPs showed significantly higher levels of IgG, IgG1, IgG2a and IgA antibody responses in sera compared to ROP4+ROP13VLPs. Upon challenge infection by oral route, mice immunized with ROP(4 + 13) VLPs elicited higher levels of IgG and IgA antibody responses in fecal, urine, intestine and vaginal samples as well as CD4+ T, CD8+ T cells, and germinal center B cell responses compared to other type of vaccines, ROP4 VLPs, ROP13 VLPs, and ROP4+ROP13 VLPs. ROP(4 + 13) VLPs vaccination showed a significant decrease in the size and number of cyst in the brain and less body weight loss compared to combination ROP4+ROP13 VLPs upon challenge infection with T. gondii ME49. These results indicated that the ROP(4 + 13) VLPs vaccination provided enhanced protection against T. gondii infection compared to ROP4+ROP13 VLPs, providing an important insight into vaccine design strategy for T. gondii VLPs vaccines.

Radix Polygalae (the root of Polygala tenuifolia) is a herb widely used in traditional Asian medicine that is thought to exert a variety of neuropsychiatric effects. Radix Polygalae extract can protect against N-methyl D-aspartate (NMDA) neurotoxicity and induce brain-derived neurotrophic factor (BDNF) expression, suggesting modulatory roles at glutamatergic synapses and possible antidepressant action. In accordance with this hypothesis, Radix Polygalae extract demonstrated antidepressant-like effects in 8-week-old male C57Bl/6 mice by decreasing behavioral despair in the forced swim and tail suspension tasks and increasing hedonic-like behavior in the female urine sniffing test 30 minutes after a single oral administration of 0.1 mg/kg. Reduced latency to acquire a food pellet in the novely suppressed feeding paradigm, without change in anxiety-like behaviors suggested a rapid-onset nature of the antidepressant-like effect. In addition, it decreased the number of failed escapes in the learned helplessness paradigm after two oral administrations 24 hours and 30 minutes before the first test. Finally, it reversed anhedonia as measured by saccharin preference in mice exposed to the chronic stress model after two administrations of 0.1 mg/kg, in contrast to the repeated administration generally needed for similar effect by monoamergic antidepressants. Immobility reduction in tail suspension task was blocked by the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor antagonist NBQX, a pattern previously demonstrated by ketamine and other ketamine-like rapid-onset antidepressants. Also similarly to ketamine, Radix Polygalae appeared to acutely decrease phosphorylation of GluR1 serine-845 in the hippocampus while leaving the phosphorylation of hippocampal mTOR serine 2448 unchanged. These findings serve as preclinical evidence that Radix Polygalae extract exerts rapid-onset antidepressant effects by modulating glutamatergic synapses in critical brain circuits of depression and may be worthy of further evaluation as a safe substitute to other rapid-onset antidepressants known to have unacceptable side effects.