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Abstract Plants and microorganisms have been co-evolving and interacting for billions of years. Prior researchers have explored the plant’s immune system responses and interaction with diverse microbes, but several ambiguities need further explanation. This review provides insight into mechanisms underlying plant–microbe interaction and knowledge dearth domains, along with possibilities to use beneficial microbes to improve plant growth, disease resistance, nutritional value, and productivity. Microorganisms in the phyllosphere and the rhizosphere could be beneficial or pathogenic. Host plants use their innate immune system and the antagonistic competence of plant-growth-promoting microbes against pathogens. The innate immune system of plants has two paramount protection forms involving different types of immune receptors, which assist in recognizing non-self-components. The first group of receptors is membrane-resident pattern recognition receptors (PRRs), which are responsible for sensing microbe-associated molecular patterns (MAMPs) and damage-associated molecular patterns (DAMPs). The second group consists of intracellular immune sensors, specifically resistance (R) proteins, astute in recognizing the structure or function of strain-specific pathogen effectors injected into host plant cells. Plants activate their pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) defense mechanisms to counter the infection. Plants benefit from certain microbes by promoting their growth, disease resistance, and resilience under various stress conditions in exchange for shelter and nutrients. Graphical Abstract

Abstract Background This study explored the significance of consolidation maintenance chemotherapy after concurrent chemoradiotherapy with different regimens in patients with esophageal squamous cell carcinoma. Method A prospective randomized controlled phase III clinical trial was designed and registered in the China Clinical Trials Registry (Registration number: ChiCTR‐TRC‐12002719). Survival data were analyzed in terms of intention‐to‐treat (ITT) and per‐protocol (PP) sets for patients undergoing cisplatin and 5‐fluorouracil (PF) (group A), or cisplatin and paclitaxel (TP) (group B). Results The incidence risk of grade III–IV leukopenia in group B was higher than in group A (49.2% vs. 25.5%, p = 0.012). The survival rates at 1, 2, 3, and 5 years were 83.8%, 62.6%, 53.1%, and 41.3%, respectively. Consolidation chemotherapy after concurrent chemoradiation therapy had no benefit on median progression‐free survival (PFS) (p = 0.95) and overall survival (OS) (p = 0.809). According to the ITT analysis, the median PFS in group A and group B was 28.6 months and 30.3 months (X2 = 0.242, p = 0.623), while the median OS was 31.0 months and 50.3 months (X2 = 1.25，p = 0.263). For the PP analysis, the median PFS in group A and group B were 28.6 months and 30.3 months (p = 0.584), while the median OS was 31.0 months and 50.3 months (p = 0.259), respectively. Patients receiving consolidation chemotherapy did not show significant OS benefits (46.9 months vs. 38.3 months; X2 = 0.059, p = 0.866). Conclusion Similar PFS and OS were found between PF and TP regimens with concurrent chemoradiotherapy. Consolidation chemotherapy did not show any significant OS benefits.

Background: Accurate evaluation of level of disorder of consciousness (DOC) is clinically challenging. Objective: This study aimed to establish a distinctive DOC-related pattern (DOCRP) for assessing disease severity and distinguishing unresponsive wakefulness syndrome (UWS) from minimally conscious state (MCS). Methods: Fifteen patients with DOC and eighteen health subjects with F-18-fluorodeoxyglucose (F-18-FDG) positron emission tomography (PET) were enrolled in this study. All patients were assessed by Coma Recovery Scale-Revised (CRS-R) and all individuals were randomly divided into two cohorts (Cohort A and B). DOCRP was identified in Cohort A and subsequently validated in Cohort B and A+B. We also assessed the discriminatory power of DOCRP between MCS and UWS. Results: The DOCRP was characterized bilaterally by relatively decreased metabolism in the medial and lateral frontal lobes, parieto-temporal lobes, cingulate gyrus and caudate, associated with relatively increased metabolism in the cerebellum and brainstem. DOCRP expression exhibited high accuracy in differentiating DOC patients from controls (P

Abstract Background Increasing evidence has indicated that long non-coding RNAs (lncRNAs) have been proven to regulate esophageal cancer progression. The lncRNA protein disulfide isomerase family A member 3 pseudogene 1 (PDIA3P1) has been shown to promote cancer stem cell properties; however, its mechanism of action remains unclear. In this study, we investigated the regulation of esophageal cancer stem cell properties by the interaction of PDIA3P1 with proteins. Methods The GEPIA2 and Gene Expression Omnibus databases were used to analyze gene expression. PDIA3P1 expression in human esophageal squamous cell carcinoma (ESCC) tissues and cell lines was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Loss-of-function experiments were performed to determine the effects of PDIA3P1 on ESCC cell proliferation, migration, and invasion. The sphere formation assay, number of side population cells, and CD271 + /CD44 + cells were detected by flow cytometry to identify the cancer stem cell properties. RNA immunoprecipitation (RIP), RNA pull-down, co-immunoprecipitation (co-IP), dual luciferase reporter, and cleavage under targets and tagmentation (CUT&Tag) assays were performed to elucidate the underlying molecular mechanisms. Results PDIA3P1 expression was upregulated in ESCC cell lines and tissues. Functionally, higher PDIA3P1 expression promoted cell proliferation, invasion, and metastasis and inhibited apoptosis in esophageal cancer. Importantly, PDIA3P1 promoted cancer stem cell properties in ESCC. Mechanistically, PDIA3P1 interacted with and stabilized octamer-binding transcription factor 4 (OCT4) by eliminating its ubiquitination by the ubiquitinating enzyme WW domain-containing protein 2 (WWP2). Moreover, as a transcription factor, OCT4 bound to the PDIA3P1 promoter and promoted its transcription. Conclusions Our research revealed a novel mechanism by which a positive feedback loop exists between PDIA3P1 and OCT4. It also demonstrated that the PDIA3P1-WWP2-OCT4 loop is beneficial for promoting the cancer stem cell properties of ESCC. Owing to this regulatory relationship, the PDIA3P1-WWP2-OCT4-positive feedback loop might be used in the diagnosis and prognosis, as well as in the development of novel therapeutics for esophageal cancer.

Background: Autophagy is involved in the pathophysiological process of sepsis. This study was designed to identify autophagy-related key genes in sepsis, analyze their correlation with immune cell signatures, and search for new diagnostic and prognostic biomarkers.Methods: Whole blood RNA datasets GSE65682, GSE134347, and GSE134358 were downloaded and processed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify autophagy-related key genes in sepsis. Then, key genes were analyzed by functional enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene and competing endogenous RNA (ceRNA) network analysis. Subsequently, key genes with diagnostic efficiency and prognostic value were identified by receiver operating characteristic (ROC) curves and survival analysis respectively. The signatures of immune cells were estimated using CIBERSORT algorithm. The correlation between significantly different immune cell signatures and key genes was assessed by correlation analysis. Finally, key genes with both diagnostic and prognostic value were verified by RT-qPCR.Results: 14 autophagy-related key genes were identified and their TF-gene and ceRNA regulatory networks were constructed. Among the key genes, 11 genes (ATIC, BCL2, EEF2, EIF2AK3, HSPA8, IKBKB, NLRC4, PARP1, PRKCQ, SH3GLB1, and WIPI1) had diagnostic efficiency (AUC > 0.90) and 5 genes (CAPN2, IKBKB, PRKCQ, SH3GLB1 and WIPI1) were associated with survival prognosis (p-value < 0.05). IKBKB, PRKCQ, SH3GLB1 and WIPI1 had both diagnostic and prognostic value, and their expression were verified by RT-qPCR. Analysis of immune cell signatures showed that the abundance of neutrophil, monocyte, M0 macrophage, gamma delta T cell, activated mast cell and M1 macrophage subtypes increased in the sepsis group, while the abundance of resting NK cell, resting memory CD4+ T cell, CD8+ T cell, naive B cell and resting dendritic cell subtypes decreased. Most of the key genes correlated with the predicted frequencies of CD8+ T cells, resting memory CD4+ T cells, M1 macrophages and naive B cells.Conclusion: We identified autophagy-related key genes with diagnostic and prognostic value in sepsis and discovered associations between key genes and immune cell signatures. This work may provide new directions for the discovery of promising biomarkers for sepsis.

The extensive existence of high-brightness ice and snow underlying surfaces in polar regions presents notable complexities for cloud detection in remote sensing imagery. To elevate the accuracy of cloud detection in polar regions, a novel polar cloud detection algorithm is proposed in this paper. Employing the MOD09 surface reflectance product, we compiled a database of monthly composite surface reflectance in the shortwave infrared bands specific to polar regions. Through the forward simulation of the correlation between the apparent reflectance and surface reflectance across diverse conditions using the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model, we established a dynamic cloud detection model for the shortwave infrared channels. In contrast to a machine learning algorithm and the widely used MOD35 cloud product, the algorithm introduced in this study demonstrates enhanced congruence with the authentic cloud distribution within cloud products. It precisely distinguishes between the cloudy and clear-sky pixels, achieving rates surpassing 90% for both, while maintaining an error rate and a missing rate each under 10%. The algorithm yields positive results for cloud detection in polar regions, effectively distinguishing between ice, snow, and clouds. It provides robust support for comprehensive and long-term cloud detection efforts in polar regions.

Abstract Background Early rehabilitation is the foundation for recovery for those admitted to an intensive care unit. Appropriate assessment of consciousness is needed before any rehabilitative intervention begins. Methods This prospective study compared the validity, reliability and applicability of the sedation-agitation scale, the Richmond Agitation-Sedation Scale, the motor activity assessment scale and the Glasgow Coma Scale in a working neurological intensive care unit. Eighty-three stroke patients were assessed with the four scales by the same 3 raters acting independently: a senior physician, a senior therapist and a trainee. That generated 996 assessment records for comparison. Results Good agreement (r=0.98–0.99) was found among the sedation-agitation scale, the Richmond Agitation-Sedation Scale, the motor activity assessment scale scores, but the Glasgow Coma Scale ratings correlated less well (r=0.72–0.76) with the others. Consistent results were also found among the three raters. After stratification of the ratings by age, gender, level of consciousness and Acute Physiology and Chronic Health Evaluation score, the scales reported significant differences among the levels of consciousness and among those with different Acute Physiology and Chronic Health Evaluation results, but not with different age or gender strata. Conclusions The four instruments tested are all reliable enough and feasible for use as a tool for consciousness screening in a neurological intensive care unit.

Abstract Background Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups. Conclusions In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792 . Registered November 20th, 2017.

Haze may affect the quality of optical remote sensing images, thus limiting the scope of their application. Remote sensing image dehazing has become important in remote sensing image preprocessing, promoting the use of remote sensing data and the precision of target recognition. Existing remote sensing dehazing methods based on simplified atmospheric degradation models are not suitable for the removal of heterogeneous haze that exist in remote sensing images. For this purpose, this study proposes an end-to-end convolutional neural network based on attention mechanism, in which the residual block structure combines both channel and spatial attention mechanisms, and establishes a synthetic high-resolution haze image dataset for full training. Thus, it obtains the desired dehazing model. Finally, this study investigates the dehazing model using a GF-1 image and compares it with existing dehazing methods. The results show that the proposed method improved the image similarity, color authenticity, and haze residue level.

ABSTRACT: Objectives: Effective antifungal therapy is important to reduce mortality in patients with invasive fungal infections (IFIs). Numerous factors affect pharmacokinetic/pharmacodynamic (PK/PD) parameters in critically-ill patients. To guide individualised administration in critically-ill patients, it is of great significance to determine the population pharmacokinetics of caspofungin. Methods: A prospective study in 42 ICU patients with IFIs was conducted in China. A population pharmacokinetic model of caspofungin was established using a non-linear mixed-effects model, which was utilised to investigate the effects of demographic indices, liver function and kidney function on pharmacokinetics. Additionally, appropriate dosages of caspofungin under various scenarios were determined based on MICs and probability of target attainment (PTA) at specific dosages. Results: In critically-ill Chinese patients, clearance (CL), volume of distribution (V) and area under the curve at steady-state (AUCss) of caspofungin were 0.32 L/h, 6.77 L and 135.47 mg•h/L, respectively. Blood albumin and total bilirubin levels were factors affecting CL, while body weight was the only factor affecting V among Chinese people with relatively low weight compared with other populations. A maintenance dose of 50 mg caspofungin achieved a high PTA for treating IFIs caused by Candida albicans (MIC ≤ 0.06 mg/L) and Candida glabrata (MIC ≤ 0.125 mg/L). The maintenance dose of caspofungin should be adjusted to 70–200 mg for IFIs caused by C. albicans (MIC, 0.06–0.125 mg/L). For IFIs caused by Candida parapsilosis, an MIC > 0.03 mg/L is associated with a very low PTA, but higher doses of caspofungin or alternative antifungals need to be further studied. Conclusion: The population pharmacokinetic model established here described well the PK/PD characteristics of caspofungin in critically-ill Chinese patients. These results could guide the formulation of individualised caspofungin dosing regimens for critically-ill patients.

The application of mobile robots and artificial intelligence technology has shown great application prospects in many fields. The ability of intelligent obstacle avoidance is the basis for the deep application of mobile robots. However, there are often more or less uncertain factors in the actual operating environment of the robot, such as people or objects that are not updated in time or temporarily appear. Therefore, it is an important step to complete the automatic learning of obstacle avoidance for mobile robots. In a nondeterministic environment, a mobile robot intelligent obstacle avoidance algorithm based on an improved fuzzy neural network with self-learning is firstly proposed. The mobile robot intelligent obstacle avoidance system is constructed through the reaction layer, the deliberation layer, and the supervision layer. Through the analysis of sensor performance, model accuracy, path obstacle avoidance optimization, and obstacle avoidance simulation, the following conclusions are drawn. First, through network training, the accuracy rate of the test set is stable at 98%, and the loss of the function value has also been reduced from the original 0.79 to 0.08, which is 10 times smaller. Second, the traditional single sensor cannot meet the obstacle avoidance requirements of robots, and mobile robots must combine multipurpose technology. Third, the algorithm in this paper encounters the following. When there are obstacles, the path is dominated by straight lines, obstacle avoidance planning is optimal, and the distance is shorter. Fourth, the larger N : M, the larger the solution space, indicating that this algorithm gradually improves the search efficiency to the greatest extent and can handle any form of medium and large scale task allocation problem.

Background: The society is aging in China, and the cognitive level of elderly post-stroke patients gradually declines. Face-to-face cognitive functional training is no longer sufficient. Immersive virtual reality (IVR) is a promising rehabilitation training device. In this study, we developed an IVR-based puzzle game to explore its effectiveness, feasibility, and safety in elderly stroke patients with cognitive dysfunction. Methods: A total of 30 patients with mild post-stroke cognitive impairment after stroke were randomly assigned to a control or IVR group. Patients in both groups received routine rehabilitation therapy. Patients in the control group received traditional cognitive training, and those in the IVR group received IVR-based puzzle game therapy. Before and after treatment, Montreal cognitive assessment (MOCA), trail-making test-A (TMT-A), digit symbol substitution test (DSST), digital span test (DST), verbal fluency test (VFT), and modified Barthel index (MBI) were evaluated in both groups. In addition, the IVR group was administered a self-report questionnaire to obtain feedback on user experience. Results: There was no significant difference in the baseline data between the two groups. After six weeks of treatment, the cognitive assessment scores were improved in both groups. Moreover, the IVR group showed more improvements than the control group in the DSST (Z = 2.203, p = 0.028 < 0.05, η2 = 0.16); MOCA (T = 1.186, p = 0.246 > 0.05, d = 0.44), TMT-A (T = 1.791, p = 0.084 > 0.05, d = 0.65), MBI (T = 0.783, p = 0.44 > 0.05, d = 0.28), FDST (Z = 0.78, p = 0.435 > 0.05, η2 = 0.02), BDST (Z = 0.347, p = 0.728 > 0.05, η2 = 0.004), and VFT(Z = 1.087, p = 0.277 > 0.05, η2 = 0.039) did not significantly improve. The significant difference in DSST represents an improvement in executive function and visual–spatial cognitive characteristics. The other assessment scores did not show such features. Therefore, we did not observe significant differences through this measure. According to the results of the self-report questionnaire, most of the patients were satisfied with the equipment stability and training content. Several individuals reported mild adverse reactions. Conclusions: This pilot study suggests that IVR-based puzzle games are a promising approach to improve post-stroke cognitive function, especially executive cognitive function, and visual–spatial attention in older adults.

The non-uniform haze distribution in remote sensing images, together with the complexity of the ground information, brings many difficulties to the dehazing of remote sensing images. In this paper, we propose a multi-input convolutional neural network based on an encoder–decoder structure to effectively restore remote sensing hazy images. The proposed network can directly learn the mapping between hazy images and the corresponding haze-free images. It also effectively utilizes the strong haze penetration characteristic of the Infrared band. Our proposed network also includes the attention module and the global skip connection structure, which enables the network to effectively learn the haze-relevant features and better preserve the ground information. We build a dataset for training and testing our proposed method. The dataset consists of remote sensing images with two different resolutions and nine bands, which are captured by Sentinel-2. The experimental results demonstrate that our method outperforms traditional dehazing methods and other deep learning methods in terms of the final dehazing effect, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).

Understanding the patterns of brain glucose metabolism and connectivity in hypoxic-ischemic encephalopathy (HIE) patients with prolonged disorders of consciousness (DOC) may be of positive significance to the accurate assessment of consciousness and the optimization of neuromodulation strategy. We retrospectively analyzed the brain glucose metabolism pattern and its correlation with clinical Coma Recovery Scale-Revised (CRS-R) score in six HIE patients with prolonged DOC who had undergone 18F-deoxyglucose brain positron emission tomography scanning (FDG-PET). We also compared the differences in global metabolic connectivity patterns and the characteristics of several brain networks between HIE patients and healthy controls (HC). The metabolism of multiple brain regions decreased significantly in HIE patients, and the degree of local metabolic preservation was correlated with CRS-R score. The internal metabolic connectivity of occipital lobe and limbic system in HIE patients decreased, and their metabolic connectivity with frontal lobe, parietal lobe and temporal lobe also decreased. The metabolic connectivity patterns of default mode network, dorsal attention network, salience network, executive control network and subcortex network of HIE also changed compared with HC. The present study suggested that pattern of cerebral glucose metabolism and network connectivity of HIE patients with prolonged DOC were significantly different from those of healthy people.

By using the C-shaped metal lead shell and copper shell to wrap the detonating cord, the restraint of the explosion energy is improved, and the simple charge structure is used to achieve the purpose of effectively separating the carbon fiber composite woven plate in a limited and small space. The commercial software AUTODYN was used for numerical simulation, The monitoring points of stress, velocity, and specific internal energy were set up, combined with Tsai-Wu tensor strength criterion, the plate failure and the process of shell metal reflecting and absorbing energy when the plate was separated under the shell constraint were observed, respectively. Through further analysis of the experimental phenomena and calculated data, it is concluded that the main reason for the fracture failure of the plate is the opposing compression of the shock waves on both sides of the plate, and the layer cracks are caused by the tension of the stress wave in the later stage. The charge structure of the detonating cord wrapped by the metal shell can effectively use its inertia to restrain the explosion energy and improve the separation ability during the process of explosively separating the carbon fiber composite woven plate.

Purpose: Developing a risk prediction model for invasive fungal disease based on an analysis of the disease-related risk factors in critically ill patients in the intensive care unit (ICU) to diagnose the invasive fungal disease in the early stages and determine the time of initiating early antifungal treatment. Methods: Data were collected retrospectively from 141 critically ill adult patients with at least 4 days of general ICU stay at Sun Yat-sen Memorial Hospital, Sun Yat-sen University during the period from February 2015 to February 2016. Logistic regression was used to develop the risk prediction model. Discriminative power was evaluated by the area under the receiver operating characteristics (ROC) curve (AUC). Results: Sequential organ failure assessment (SOFA) score, antibiotic treatment period, and positive culture of Candida albicans other than normally sterile sites are the three predictors of invasive fungal disease in critically ill patients in the ICU. The model performs well with an ROC-AUC of .73. Conclusion: The risk prediction model performs well to discriminate between critically ill patients with or without invasive fungal disease. Physicians could use this prediction model for early diagnosis of invasive fungal disease and determination of the time to start early antifungal treatment of critically ill patients in the ICU. Keywords: critical illness, invasive fungal infections, logistic models, risk, risk factors

Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through the use of swarm intelligence paradigm, a solution approach is accordingly proposed by employing an efficient Jaya algorithm, called PA-Jaya, to deal with the power allocation problem in cognitive OFDM radio networks for IoT. Because of the algorithm-specific parameter-free feature in the proposed PA-Jaya algorithm, a satisfactory computational performance could be achieved in the handling of this problem. For this optimization problem with some constraints, the simulation results show that compared with some popular algorithms, the efficiency of spectrum utilization could be further improved by using PA-Jaya algorithm with faster convergence speed, while maximizing the total transmission rate.

OBJECTIVE: Treadmill pre-training can ameliorate blood brain barrier (BBB) dysfunction in ischemia-reperfusion injury, however, its role in ischemic brain edema remains unclear. This study assessed the neuroprotective effects induced by treadmill pre-training, particularly on brain edema in transient middle cerebral artery occluded model. METHODS: Transient middle cerebral artery occlusion to induce stroke was performed on rats after 2 weeks of treadmill pre-training. Magnetic resonance imaging (MRI) was used to evaluate the dynamic impairment of cerebral edema after ischemia-reperfusion injury. In addition, measurements of wet and dry brain weight, Evans Blue assay and Garcia scores were performed to investigate the cerebral water content, BBB permeability and neurologic deficit, respectively. Moreover, during ischemia-reperfusion injury, the expression of Aquaporin 4 (AQP4) was detected using immunofluorescence and Western bloting analyses. RESULTS: Treadmill pre-training improved the relative apparent diffusion coefficient (rADC) loss in the ipsilateral cortex and striatum at 1 hour and 2.5 hours after cerebral ischemia. In the treadmill pre-training group, T2W1 values of the ipsilateral cortex and striatum increased less at 7.5 hours, 1 day, and 2 days after stroke while the brain water content decreased at 2 days after ischemia. Regarding the BBB permeability, the semi-quantitative amount of contrast agent leakage of treadmill pre-training group significantly decreased. Less Evans Blue exudation was also observed in treadmill pre-training group at 2 days after stroke. In addition, treadmill pre-training mitigated the Garcia score deficits at 2 days after stroke. Immunofluorescence staining and Western blotting results showed a significant decrease in the expression of AQP4 after treadmill ischemia following pre-training. CONCLUSIONS: Treadmill pre-training may reduce cerebral edema and BBB dysfunction during cerebral ischemia/reperfusion injury via the down-regulation of AQP4.