Application of AI-assisted MRI for the identification of surgical target areas in pediatric hip and periarticular infections.

OBJECTIVE: To develop an AI-assisted MRI model to identify surgical target areas in pediatric hip and periarticular infections. METHODS: A retrospective study was conducted on the pediatric patients with hip and periarticular infections who underwent Magnetic Resonance Imaging(MRI)examinations from January 2010 to January 2023 in three hospitals in China. A total of 7970 axial Short Tau Inversion Recovery (STIR) images were selected, and the corresponding regions of osteomyelitis (label 1) and abscess (label 2) were labeled using the Labelme software. The images were randomly divided into training group, validation group, and test group at a ratio of 7:2:1. A Mask R-CNN model was constructed and optimized, and the performance of identifying label 1 and label 2 was evaluated using receiver operating characteristic (ROC) curves. Calculation of the average time it took for the model and specialists to process an image in the test group. Comparison of the accuracy of the model in the interpretation of MRI images with four orthopaedic surgeons, with statistical significance set at P < 0.05. RESULTS: A total of 275 patients were enrolled, comprising 197 males and 78 females, with an average age of 7.10 ± 3.59 years, ranging from 0.00 to 14.00 years. The area under curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score for the model to identify label 1 were 0.810, 0.976, 0.995, 0.969, 0.922, and 0.957, respectively. The AUC, accuracy, sensitivity, specificity, precision, and F1 score for the model to identify label 2 were 0.890, 0.957, 0.969, 0.915, 0.976, and 0.972, respectively. The model demonstrated a significant speed advantage, taking only 0.2 s to process an image compared to average 10 s required by the specialists. The model identified osteomyelitis with an accuracy of 0.976 and abscess with an accuracy of 0.957, both statistically better than the four orthopaedic surgeons, P < 0.05. CONCLUSION: The Mask R-CNN model is reliable for identifying surgical target areas in pediatric hip and periarticular infections, offering a more convenient and rapid option. It can assist unexperienced physicians in pre-treatment assessments, reducing the risk of missed and misdiagnosis.

Do as Sonographers Think: Contrast-enhanced Ultrasound for Thyroid Nodules Diagnosis via Microvascular Infiltrative Awareness.

Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 93.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.

Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment.

Background: Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI. Methods: A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages. Results: The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone. Conclusion: Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis.

Solid Additive Dual-Regulates Spectral Response Enabling High-Performance Semitransparent Organic Solar Cells.

Semi-transparent organic solar cells (ST-OSCs) possess significant potential for applications in vehicles and buildings due to their distinctive visual transparency. Conventional device engineering strategies are typically used to optimize photon selection and utilization at the expense of power conversion efficiency (PCE); moreover, the fixed spectral utilization range always imposes an unsatisfactory upper limit to its light utilization efficiency (LUE). Herein, a novel solid additive named 1,3-diphenoxybenzene (DB) is employed to dual-regulate donor/acceptor molecular aggregation and crystallinity, which effectively broadens the spectral response of ST-OSCs in near-infrared region. Besides, more visible light is allowed to pass through the devices, which enables ST-OSCs to possess satisfactory photocurrent and high average visible transmittance (AVT) simultaneously. Consequently, the optimal ST-OSC based on PP2+DB/BTP-eC9+DB achieves a superior LUE of 4.77%, representing the highest value within AVT range of 40-50%, which also correlates with the formation of multi-scale phase-separated morphology. Such results indicate that the ST-OSCs can simultaneously meet the requirements for minimum commercial efficiency and plant photosynthesis when integrated with the roofs of agricultural greenhouses. This work emphasizes the significance of additives to tune the spectral response in ST-OSCs, and charts the way for organic photovoltaics in economically sustainable agricultural development.

A Novel Analytical Model for the IEEE 802.11p/bd Medium Access Control, with Consideration of the Capture Effect in the Internet of Vehicles.

The traditional vehicular ad hoc network (VANET), which is evolving into the internet of vehicles (IoV), has drawn great attention for its enormous potential in road safety improvement, traffic management, infotainment service support, and even autonomous driving. IEEE 802.11p, as the vital standard for wireless access in vehicular environments, has been released for more than one decade and its evolution, IEEE 802.11bd, has also been released for a few months. Since the analytical models for the IEEE 802.11p/bd medium access control (MAC) play important roles in terms of performance evaluation and MAC protocol optimization, a lot of analytical models have been proposed. However, the existing analytical models are still not accurate as a result of ignoring some important factors of the MAC itself and real communication scenarios. Motivated by this, a novel analytical model is proposed, based on a novel two-dimensional (2-D) Markov chain model. In contrast to the existing studies, all the important factors are considered in this proposed model, such as the backoff freezing mechanism, retry limit, post-backoff states, differentiated packet arrival probabilities for empty buffer queue, and queue model of packets in the buffer. In addition, the influence of the capture effect under a Nakagami-m fading channel has also been considered. Then, the expressions of successful transmission, collided transmission, normalized unsaturated throughput, and average packet delay are all meticulously derived, respectively. At last, the accuracy of the proposed analytical model is verified via the simulation results, which show that it is more accurate than the existing analytical models.

Deep Semi-Supervised Ultrasound Image Segmentation by Using a Shadow Aware Network With Boundary Refinement.

Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow regions. Shadow-masked transformer block contains an adaptive shadow attention mechanism that introduces an adaptive mask, which is updated automatically to promote the network training. Additionally, we utilize unlabeled US images to train a missing structure inpainting path with shadow-masked transformer, which further facilitates semi-supervised segmentation. Experiments on two public US datasets demonstrate the superior performance of the SABR-Net over other state-of-the-art semi-supervised segmentation methods. In addition, experiments on a private breast US dataset prove that our method has a good generalization to clinical small-scale US datasets.

Anatomy preserving GAN for realistic simulation of intraoperative liver ultrasound images.

In ultrasound-guided liver surgery, the lack of large-scale intraoperative ultrasound images with important anatomical structures remains an obstacle hindering the successful application of AI to ultrasound guidance. In this case, intraoperative ultrasound (iUS) simulation should be conducted from preoperative magnetic resonance (pMR), which not only helps doctors understand the characteristics of iUS in advance, but also expands the iUS dataset from various imaging positions, thereby promoting the automatic iUS analysis in ultrasound guidance. Herein, a novel anatomy preserving generative adversarial network (ApGAN) framework was proposed to generate simulated intraoperative ultrasound (Sim-iUS) of liver with precise structure information from pMR. Specifically, the low-rank factors based bimodal fusion was first established focusing on the effective information of hepatic parenchyma. Then, a deformation field based correction module was introduced to learn and correct the slight structural distortion from surgical operations. Meanwhile, the multiple loss functions were designed to constrain the simulation of the content, structures, and style. Empirical results of clinical data showed that the proposed ApGAN obtained higher Structural Similarity (SSIM) of 0.74 and Fr´echet Inception Distance (FID) of 35.54 compared to existing methods. Furthermore, the average Hausdorff Distance (HD) error of the liver capsule structure was less than 0.25 mm, and the average relative (Euclidean Distance) ED error for polyps was 0.12 mm, indicating the high-level precision of this ApGAN in simulating the anatomical structures and focal areas.

Precise angle estimation of capsule robot in ultrasound using heatmap guided two-stage network.

PURPOSE: A capsule robot can be controlled inside gastrointestinal (GI) tract by an external permanent magnet outside of human body for finishing non-invasive diagnosis and treatment. Locomotion control of capsule robot relies on the precise angle feedback that can be achieved by ultrasound imaging. However, ultrasound-based angle estimation of capsule robot is interfered by gastric wall tissue and the mixture of air, water, and digestive matter existing in the stomach. METHODS: To tackle these issues, we introduce a heatmap guided two-stage network to detect the position and estimate the angle of the capsule robot in ultrasound images. Specifically, this network proposes the probability distribution module and skeleton extraction-based angle calculation to obtain accurate capsule robot position and angle estimation. RESULTS: Extensive experiments were finished on the ultrasound image dataset of capsule robot within porcine stomach. Empirical results showed that our method obtained small position center error of 0.48 mm and high angle estimation accuracy of 96.32%. CONCLUSION: Our method can provide precise angle feedback for locomotion control of capsule robot.

The ability of Segmenting Anything Model (SAM) to segment ultrasound images.

Accurate ultrasound (US) image segmentation is important for disease screening, diagnosis, and prognosis assessment. However, US images typically have shadow artifacts and ambiguous boundaries that affect US segmentation. Recently, Segmenting Anything Model (SAM) from Meta AI has demonstrated remarkable potential in a wide range of applications. The purpose of this paper was to conduct an initial evaluation of the ability for SAM to segment US images, particularly in the event of shadow artifacts and ambiguous boundaries. We evaluated SAM's performance on three US datasets of different tissues, including multi-structure cardiac tissue, thyroid nodules, and the fetal head. Results indicated that SAM generally performs well with US images with clear tissue structures, but it has limited performance in the event of shadow artifacts and ambiguous boundaries. Thus, creating an improved SAM that considers the characteristics of US images is significant for automatic and accurate US segmentation.

Performance Analysis of IEEE 802.11p MAC with Considering Capture Effect under Nakagami-m Fading Channel in VANETs.

Vehicular ad hoc networks (VANETs) have recently drawn a large amount of attention because of their enormous potential in road safety improvement and traffic management as well as infotainment service support. As the standard of medium access control (MAC) and physical (PHY) layers for VANETs, IEEE 802.11p has been proposed for more than a decade. Though performance analyses of IEEE 802.11p MAC have been performed, the existing analytical methods still need to be improved. In this paper, to assess the saturated throughput and the average packet delay of IEEE 802.11p MAC in VANETs, a two-dimensional (2-D) Markov model is introduced by considering the capture effect under Nakagami-m fading channel. Moreover, the closed-form expressions of successful transmission, collided transmission, saturated throughput, and average packet delay are carefully derived. Finally, the simulation results are demonstrated to verify the accuracy of the proposed analytical model, which also proves that this analytical model is more precise than the existing ones in terms of saturated throughput and average packet delay.

A multiple phenotype imputation method for genetic diversity and core collection in Taiwanese vegetable soybean.

Establishment of vegetable soybean (edamame) [Glycine max (L.) Merr.] germplasms has been highly valued in Asia and the United States owing to the increasing market demand for edamame. The idea of core collection (CC) is to shorten the breeding program so as to improve the availability of germplasm resources. However, multidimensional phenotypes typically are highly correlated and have different levels of missing rate, often failing to capture the underlying pattern of germplasms and select CC precisely. These are commonly observed on correlated samples. To overcome such scenario, we introduced the "multiple imputation" (MI) method to iteratively impute missing phenotypes for 46 morphological traits and jointly analyzed high-dimensional imputed missing phenotypes (EC impu ) to explore population structure and relatedness among 200 Taiwanese vegetable soybean accessions. An advanced maximization strategy with a heuristic algorithm and PowerCore was used to evaluate the morphological diversity among the EC impu . In total, 36 accessions (denoted as CC impu ) were efficiently selected representing high diversity and the entire coverage of the EC impu . Only 4 (8.7%) traits showed slightly significant differences between the CC impu and EC impu . Compared to the EC impu , 96% traits retained all characteristics or had a slight diversity loss in the CC impu . The CC impu exhibited a small percentage of significant mean difference (4.51%), and large coincidence rate (98.1%), variable rate (138.76%), and coverage (close to 100%), indicating the representativeness of the EC impu . We noted that the CC impu outperformed the CC raw in evaluation properties, suggesting that the multiple phenotype imputation method has the potential to deal with missing phenotypes in correlated samples efficiently and reliably without re-phenotyping accessions. Our results illustrated a significant role of imputed missing phenotypes in support of the MI-based framework for plant-breeding programs.

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics.

With the continuous deepening of Artificial Neural Network (ANN) research, ANN model structure and function are improving towards diversification and intelligence. However, the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough. Hence, a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper. Firstly, four classical neural network models are illustrated: Back Propagation (BP) network, Deep Belief Network (DBN), LeNet5 network, and olfactory bionic model (KIII model), and the neuron transmission mode and equation, network structure, and weight updating principle of the models are analyzed qualitatively. The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models, and the LeNet5 network simulates the nervous system in depth. Secondly, evaluation indexes of ANN are constructed from the perspective of bionics in this paper: small-world, synchronous, and chaotic characteristics. Finally, the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics. The experimental results show that the DBN network, LeNet5 network, and BP network have synchronous characteristics. And the DBN network and LeNet5 network have certain chaotic characteristics, but there is still a certain distance between the three classical neural networks and actual biological neural networks. The KIII model has certain small-world characteristics in structure, and its network also exhibits synchronization characteristics and chaotic characteristics. Compared with the DBN network, LeNet5 network, and the BP network, the KIII model is closer to the real biological neural network.

Pair Nodes Clock Synchronization Algorithm Based on Kalman Filter for Underwater Wireless Sensor Networks.

Time synchronization is the basis of many applications. Aiming at the limitations of the existing clock synchronization algorithms in underwater wireless sensor networks, we propose a pairwise synchronization algorithm called K-Sync, which is based on the Kalman filter. The algorithm does not need the assistance of the position sensor or the speed sensor, and the high time synchronization accuracy can be realized only by utilizing the time-stamps information in the process of message exchange. The K-Sync uses the general constraints of the motion characteristics of the sensor nodes to establish the recursive equations of the clock skew, clock offset, relative mobility velocity, and relative distance. At the same time, the time-stamps are viewed as the observation variables and the system observation equation is obtained. The K-Sync estimates the normalized clock skew and offset of the node via the Kalman filter to achieve high-precision clock synchronization between the two nodes. The simulation shows that the K-Sync has obvious advantages in the key indicators such as the estimated accuracy of clock skew and clock offset, convergence speed, etc. In addition, the K-Sync is more robust to a variety of underwater motion scenes.

Microbial profiles associated improving bioelectricity generation from sludge fermentation liquid via microbial fuel cells with adding fruit waste extracts.

This first-attempt study illustrated the microbial cooperative interactions related to bioelectricity generation from the mixture of sludge fermentation liquid (SFL) and fruit waste extracts (FWEs) via microbial fuel cells (MFCs). The optimal output voltages of 0.65 V for SFL-MFCs, 0.51 V for FWEs-MFCs and 0.75 V for mixture-MFCs associated with bioelectricity conversion efficiencies of 1.061, 0.718 and 1.391 kWh/kg COD were reached, respectively. FWEs addition for substrates C/N ratio optimization contributed considerably to increase SFL-fed MFCs performance via triggering a higher microbial diversity, larger relatively abundance of functional genes and microbial synergistic interactions with genera enrichment of Clostridium, Alicycliphilus, Thermomonas, Geobacter, Paludibaculum, Pseudomonas, Taibaiella and Comamonas. Furthermore, a conceptual illustration of co-locating scenario of wastewater treatment plant(s), waste sludge in situ acidogenic fermentation, fruit waste collection/crushing station and MFC plant was proposed for the first time, which provided new thinking for future waste sludge treatment toward maximizing solid reduction and power recovery.

Atypical hemolytic uremic syndrome with C3 mutation: A case report and literature review.

INTRODUCTION: Atypical hemolytic uremic syndrome (aHUS) is characterized by hemolytic anemia, thrombocytopenia, and acute kidney injury. Uncontrolled activation of the complement system induced by single or combined complement gene mutations is one of the mechanisms leading to the pathogenesis of aHUS. CASE PRESENTATION: We report a case of a 26-year-old female with a C3 heterozygous gene mutation (p.Asn153Asn). The patient was found to have low complement H factor (CFH) but normal levels of anti-CFH autoantibody. She was treated primarily with plasma exchange and plasma infusion. The patient did not relapse during a 1-year follow-up. CONCLUSION: This is the first case of a novel C3 mutation (p.Asn153Asn) in a patient with aHUS. Further studies are needed to confirm the association between this mutation and the CFH level.

Burrow ambient temperature influences Helice crab activity and availability for migratory Red-crowned cranes Grus japonensis.

For migratory birds that specialize on particular benthic macroinvertebrate species, the timing of migration is critical since prey availability may be temporally limited and a function of local ambient temperature. Hence, variation in local ambient temperature can influence the diet composition of migrant birds, and, consequently, they may be constrained by which stopover and wintering sites they are able to utilize during periods of colder temperatures. Here, we use fecal analysis, observer-based population counts, digital video recordings, and temperature data to test five predictions regarding the influence of local ambient temperature on the activity and availability of mudflat crabs-a key prey resource at three staging/wintering sites in eastern China, for migratory Red-crowned cranes (Grus japonensis) and how this subsequently influences crane diet and use of wetland sites. Pearson's correlations and generalized linear models revealed that mudflat crabs became significantly more surface active with increasing burrow ambient temperature. Piecewise regression analysis revealed that crab surface activity was largely limited to a burrow ambient temperature threshold between 12 and 13℃ after which activity significantly increased. Crab activity declining temporally during the crane's autumn migration period but increased during spring migration. Crabs accounted for a significant proportion of crane diet at two of three sites; however, the frequency of crab remains was significantly different between sites, and between autumn and spring migration. Analyses of crane count data revealed a degree of congruence between the migration timing of Red-crowned cranes with periods of warmer ambient temperature, and a significant, positive correlation between the percentage of crab remains in crane feces and site ambient temperature. Collectively, our data suggest that temperature-related mudflat crab activity may provide an important time window for migratory Red-crowned cranes to utilize critical stopover sites and the crabs' food resources.

A Stackelberg Game-Based Caching Incentive Scheme for Roadside Units in VANETs.

As a key technology of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs) have been promising to provide safety and infotainment for drivers and passengers. To support different applications about traffic safety, traffic efficiency, autonomous driving and entertainment, it is important to investigate how to effectively deliver content in VANETs. Since it takes resources such as bandwidth and power for base stations (BSs) or roadside units (RSUs) to deliver content, the optimal pricing strategy for BSs and the optimal caching incentive scheme for RSUs need to be studied. In this paper, a framework of content delivery is proposed first, where each moving vehicle can obtain small-volume content files from either the nearest BS or the nearest RSU according to the competition among them. Then, the profit models for both BSs and RSUs are established based on stochastic geometry and point processes theory. Next, a caching incentive scheme for RSUs based on Stackelberg game is proposed, where both competition sides (i.e., BSs and RSUs) can maximize their own profits. Besides, a backward introduction method is introduced to solve the Stackelberg equilibrium. Finally, the simulation results demonstrate that BSs can obtain their own optimal pricing strategy for maximizing the profit as well as RSUs can obtain the optimal caching scheme with the maximum profit during the content delivery.

Fully automated identification and quantification of five polar pesticides in groundwater by isotope dilution-online solid phase extraction coupled with high-performance liquid chromatography-quadrupole Orbitrap high-resolution mass spectrometry.

A fully automated method for identification and quantification of five polar pesticides in groundwater by isotope dilution-online solid-phase extraction (SPE) coupled with high-performance liquid chromatography-quadrupole Orbitrap high-resolution mass spectrometry was developed. After one step of filtration, an aliquot of a 7.5-ml water sample was automatedly preconcentrated and purified on a turbulent Cyclone SPE column. The analytes were eluted in backflush mode, then separated on an analytical column and acquired by full MS/dd-MS2 scan in negative and positive ions mode. The major parameters for sample loading, cleanup, and elution were optimized in detail. Preconcentration and ionization efficiency were highly improved by using 0.1% acid solution in the mobile phase. The method provided good linearity of calibration coefficients (R2 > 0.995), sensitive method limits of detection (0.5-10.0 ng/L), accurate mass spectra (within 5 ppm error), satisfactory matrix spiking recoveries (98.4% to 109%), and high precision (intraday/interday relative standard deviations 1.57-8.90%). The method was successfully applied to analyze large batch groundwater of National Groundwater Monitoring Project and suspect screening of potential pesticides in groundwater. The study provided a practical alternative for a simple, robust, sensitive, and accurate identification and qualification of five polar pesticides in groundwater.

Upregulation of FoxO3a expression through PI3K/Akt pathway attenuates the progression of lupus nephritis in MRL/lpr mice.

FoxO3a plays key roles in inflammation and autoimmunity, and the PI3K-Akt-FoxO3a pathway has been proposed to modulate diverse biological processes. The aim of the present study, using lupus murine models, was to investigate whether FoxO3a contributes to the pathogenesis of lupus nephritis. LY294002 was used as an inhibitor of PI3K/AKT signaling pathway. FoxO3a-targeted small interfering RNA (siRNA) was also used for in vivo intervention. Female MRL/lpr mice were separately injected with LY294002, LY294002+siFoxO3a, and LY294002+siControl for 8 weeks. C57BL/6 mice were normal controls. Disease development, including serum creatinine (CRE), blood urea nitrogen (BUN), proteinuria, and renal pathological changes, was monitored. Levels of anti-dsDNA antibodies and immune complex (IC) deposition in the kidney were also measured. The expression of proteins was evaluated. We found that significant downregulation of FoxO3a was detected in the kidney of MRL/lpr mice as compared with normal control mice. Blockade of p-FoxO3a activation by LY294002 suppressed PI3K/Akt/FoxO3a pathway and the subsequent upregulation of FoxO3a in the nucleus resulting in the severity of inflammation and fibrosis in the kidney of MRL/lpr mice. Also, improved kidney function and decreased circulating anti-dsDNA antibodies were due to the upregulation of FoxO3a. Opposite results were obtained by specific siRNA silencing of Foxo3a in vivo. In conclusion, our research demonstrated that the upregulation of FoxO3a expression through inhibiting PI3K/Akt pathway attenuates murine lupus nephritis (LN). Thus, our results suggest that targeting of FoxO3a can be considered as a novel strategy for the treatment of LN.