Attaining decent work among Chinese rural migrants: Exploring the roles of psychological ownership and proactive personality within the psychology of working theory.

Testing an adapted psychology of working theory (PWT) model (PWT; Duffy et al., 2016), this study examined how and when contextual constraints affect the ability of Chinese rural migrant workers () to attain decent work. Surveying a sample of 510 employed Chinese rural migrants at two time points, this study revealed that economic constraints and daily discrimination were indirectly related to attaining decent work through the mediating factors of work volition and psychological ownership over work. In addition, proactive personality moderated the relationships between daily discrimination and economic constraints and the two mediators. The results contribute to better understanding Chinese rural migrant workers' work experience, refining the PWT and enriching the literature on the underlying mechanisms and moderating factors associated with decent work attainment. The research results call for policy and social change to lessen economic constraints and discrimination at the macro level and organizational commitment to employee-oriented human resource management and counseling interventions to improve the quality of the work lives of rural migrant workers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hypoxia Aggravates Neuron Ferroptosis in Early Brain Injury Following Subarachnoid Hemorrhage via NCOA4-Meditated Ferritinophagy.

The occurrence of early brain injury (EBI) significantly contributes to the unfavorable prognosis observed in patients with subarachnoid hemorrhage (SAH). During the process of EBI, a substantial quantity of iron permeates into the subarachnoid space and brain tissue, thereby raising concerns regarding its metabolism. To investigate the role and metabolic processes of excessive iron in neurons, we established both in vivo and in vitro models of SAH. We substantiated that ferritinophagy participates in iron metabolism disorders and promotes neuronal ferroptosis using an in vivo model, as detected by key proteins such as ferritin heavy chain 1, glutathione peroxidase 4, autophagy related 5, nuclear receptor coactivator 4 (NCOA4), LC3B, and electron microscopy results. By interfering with NCOA4 expression in vitro and in vivo, we confirmed the pivotal role of elevated NCOA4 levels in ferritinophagy during EBI. Additionally, our in vitro experiments demonstrated that the addition of oxyhemoglobin alone did not result in a significant upregulation of NCOA4 expression. However, simultaneous addition of oxyhemoglobin and hypoxia exposure provoked a marked increase in NCOA4 expression and heightened ferritinophagy in HT22 cells. Using YC-1 to inhibit hypoxia signaling in in vitro and in vitro models effectively attenuated neuronal ferroptosis. Collectively, we found that the hypoxic microenvironment during the process of EBI exaggerates iron metabolism abnormalities, leading to poor prognoses in SAH. The findings also offer a novel and potentially effective foundation for the treatment of SAH, with the aim of alleviating hypoxia.

[A method of mental disorder recognition based on visibility graph].

The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.

Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection.

In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial-temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial-temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial-temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD. Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields. To this end, we propose a computer-aided MDD detection framework based on spatial-temporal EEG fusion using neural network, as shown in Fig. 1. The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial-temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial-temporal EEG fusion.

Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition.

BACKGROUND AND OBJECTIVE: In view of the depression characteristics such as high prevalence, high disability rate, high fatality rate, and high recurrence rate, early identification and early intervention are the most effective methods to prevent irreversible damage of brain function over time. The traditional method of depression recognition based on questionnaires and interviews is time-consuming and labor-intensive, and heavily depends on the doctor's subjective experience. Therefore, accurate, convenient and effective recognition of depression has important social value and scientific significance. METHODS: This paper proposes a depression recognition framework based on feature-level fusion of spatial-temporal pervasive electroencephalography (EEG). Time series EEG data were collected by portable three-electrode EEG acquisition instrument, and mapped to a spatial complex network called visibility graph (VG). Then temporal EEG features and spatial VG metric features were extracted and selected. Based on the correlation between features and categories, the differences in contribution of individual feature are explored, and different contribution coefficients are assigned to different features as the data basis of feature-level fusion to ensure the diversity of data. A cascade forest model based on three different decision forests is designed to realize the efficient depression recognition using spatial-temporal feature-level fusion data. RESULTS: Experimental data were obtained from 26 depressed patients and 29 healthy controls (HC). The results of multiple control experiments show that compared with single type feature, feature-level fusion without contribution coefficient, and independent classifiers, the feature-level method with contribution coefficient of spatial-temporal has a stronger recognition ability of depression, and the highest accuracy is 92.48%. CONCLUSION: Feature-level fusion method provides an effective computer-aided tool for rapid clinical diagnosis of depression.

The multiscale 3D convolutional network for emotion recognition based on electroencephalogram.

Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion recognition. Motivated by the studies that multiple smaller scale kernels could increase non-linear expression than a larger scale, we propose a 3D convolutional neural network model with multiscale convolutional kernels to recognize emotional states based on EEG signals. We select more suitable time window data to carry out the emotion recognition of four classes (low valence vs. low arousal, low valence vs. high arousal, high valence vs. low arousal, and high valence vs. high arousal). The results using EEG signals in the DEAP and SEED-IV datasets show accuracies for our proposed emotion recognition network model (ERN) of 95.67 and 89.55%, respectively. The experimental results demonstrate that the proposed approach is potentially useful for enhancing emotional experience in BCI.

Computer-Aided Recognition Based on Decision-Level Multimodal Fusion for Depression.

Aiming at the problem of depression recognition, this paper proposes a computer-aided recognition framework based on decision-level multimodal fusion. In Song Dynasty of China, the idea of multimodal fusion was contained in "one gets different impressions of a mountain when viewing it from the front or sideways, at a close range or from afar" poetry. Objective and comprehensive analysis of depression can more accurately restore its essence, and multimodal can represent more information about depression compared to single modal. Linear electroencephalography (EEG) features based on adaptive auto regression (AR) model and typical nonlinear EEG features are extracted. EEG features related to depression and graph metric features in depression related brain regions are selected as the data basis of multimodal fusion to ensure data diversity. Based on the theory of multi-agent cooperation, the computer-aided depression recognition model of decision-level is realized. The experimental data comes from 24 depressed patients and 29 healthy controls (HC). The results of multi-group controlled trials show that compared with single modal or independent classifiers, the decision-level multimodal fusion method has a stronger ability to recognize depression, and the highest accuracy rate 92.13% was obtained. In addition, our results suggest that improving the brain region associated with information processing can help alleviate and treat depression. In the field of classification and recognition, our results clarify that there is no universal classifier suitable for any condition.

MAP3K1 Variant Causes Hyperactivation of Wnt4/ß-Catenin/FOXL2 Signaling Contributing to 46,XY Disorders/Differences of Sex Development.

Background: 46,XY disorders/differences of sex development (46,XY DSD) are congenital conditions that result from abnormal gonadal development (gonadal dysgenesis) or abnormalities in androgen synthesis or action. During early embryonic development, several genes are involved in regulating the initiation and maintenance of testicular or ovarian-specific pathways. Recent reports have shown that MAP3K1 genes mediate the development of the 46,XY DSD, which present as complete or partial gonadal dysgenesis. Previous functional studies have demonstrated that some MAP3K1 variants result in the gain of protein function. However, data on possible mechanisms of MAP3K1 genes in modulating protein functions remain scant. Methods: This study identified a Han Chinese family with the 46,XY DSD. To assess the history and clinical manifestations for the 46,XY DSD patients, the physical, operational, ultra-sonographical, pathological, and other examinations were performed for family members. Variant analysis was conducted using both trio whole-exome sequencing (trio WES) and Sanger sequencing. On the other hand, we generated transiently transfected testicular teratoma cells (NT2/D1) and ovary-derived granular cells (KGN), with mutant or wild-type MAP3K1 gene. We then performed functional assays such as determination of steady-state levels of gender related factors, protein interaction and luciferase assay system. Results: Two affected siblings were diagnosed with 46,XY DSD. Our analysis showed a missense c.556A > G/p.R186G variant in the MAP3K1 gene. Functional assays demonstrated that the MAP3K1R186G variant was associated with significantly decreased affinity to ubiquitin (Ub; 43-49%) and increased affinity to RhoA, which was 3.19 ± 0.18 fold, compared to MAP3K1. The MAP3K1R186G led to hyperphosphorylation of p38 and GSK3ß, and promoted hyperactivation of the Wnt4/ß-catenin signaling. In addition, there was increased recruitment of ß-catenin into the nucleus, which enhanced the expression of pro-ovarian transcription factor FOXL2 gene, thus contributing to the 46,XY DSD. Conclusion: Our study identified a missense MAP3K1 variant associated with 46,XY DSD. We demonstrated that MAP3K1R186G variant enhances binding to the RhoA and improves its own stability, resulting in the activation of the Wnt4/ß-catenin/FOXL2 pathway. Taken together, these findings provide novel insights into the molecular mechanisms of 46,XY DSD and promotes better clinical evaluation.

[Research on depression recognition based on brain function network].

Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of "small world" and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.

Maintaining manganese in tumor to activate cGAS-STING pathway evokes a robust abscopal anti-tumor effect.

Radiotherapy (RT)-induced DNA damage leaked into cytosol can elicit host antitumor immune response. However, such response rate is unpromising due to limited cyclic GMP-AMP synthase (cGAS) recognition of cytosolic DNA, which could be digested inherently by host DNases. Here we show that synchronizing Mn2+ delivery with accumulated cytosolic DNA after RT can promote the activation of cGAS-STING pathway, thereby enhancing RT-induced antitumor immunity. Intratumoral Mn2+ injection immediately after RT cannot enhance RT, while intratumoral Mn2+ injection 24 h after RT can. Direct-injected Mn2+ can be metabolized out from tumor in minutes while RT-induced DNA damage need cells mitotic progression for up to 24 h to accumulate into cytosol. Alginate can maintain Mn2+ in tumor for up to 24 h due to it can chelate divalent cations. When the release profile of Mn2+ is controlled by alginate (Alg) and synchronized with the accumulation of RT-induced DNA damage, over 90% inhibition rate can be obtained even in the unirradiated tumor, and survival time is significantly extended. This synchronizing strategy provides a simple and novel approach to effectively activate cGAS-STING pathway in tumor and promote RT-induced immunity.

Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification.

If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.

Spectral characteristics and optical temperature sensing properties of Er3+/Yb3+-co-doped phosphate glasses with GeO2 modification.

Er3+/Yb3+-co-doped phosphate glasses with GeO2 modification (PLAZGs) were successfully prepared by the melt-quenching method. The phenomenological intensity parameters Ωt (t=2, 4, 6) of the PLAZGs have been calculated by the Judd-Ofelt theory. Based on the phenomenological intensity parameters, the spectroscopic parameters of Er3+ and fluorescence intensity ratio (FIR) of green upconversion emissions were estimated. It was observed that, under 980 nm excitation, all samples exhibit green and red upconversion emissions of Er3+. The 10 mol% GeO2 modified phosphate glass has the strongest upconversion emission. Additionally, the fluorescence decays of the 2F5/2→2F7/2 transition of Yb3+ ions were measured to evaluate the energy transfer efficiency from Yb3+ to Er3+ ions. Finally, the optical temperature sensing properties based on upconversion emissions were investigated at temperatures from 150 K to 600 K. The maximum absolute temperature sensitivity S value of 6.0×10-3K-1 at 400 K is obtained, which indicates that the glass is promising for temperature sensing application based on the FIR technology.

A Novel Compound Heterozygous CYP17A1 Variant Causes 17α-Hydroxylase/17, 20-Lyase Deficiency.

Background: Congenital adrenal hyperplasia (CAH) encompasses a group of autosomal recessive diseases characterized by enzyme deficiencies, within steroid hormone anabolism, which lead to disorders in cortisol synthesis. The 17α-hydroxylase/17,20-lyase deficiency (17-OHD) is an uncommon form of CAH caused by variants in the CYP17A1 gene. Aims: We report a novel compound heterozygous CYP17A1 variant and its association with the pathogenesis of 17-OHD. Methods: The patient was assessed for medical history, clinical manifestations, physical examination, laboratory examination, karyotype analysis, and adrenal computed tomography. Mutation screening was conducted using whole-exome sequencing (WES) and Sanger sequencing. The wild-type and mutant CYP17A1 complementary DNAs (cDNAs) were amplified and cloned into a pcDNA3.1(+) vector. These plasmids were transfected transiently into HEK-293T cells. Quantitative PCR and Western blotting analysis were performed to measure the expression level of P450c17. An enzymatic activity assay was conducted to measure the content of 17-hydroxyprogesterone (17-OHP) and dehydroepiandrosterone (DHEA) in medium using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results: The proband was characterized by 17-OHD with rhabdomyolysis, hypokalemia, and adrenal insufficiency. Novel compound heterozygous variants of the CYP17A1 gene (c.1304T > C/p.Phe435Ser and c.1228delG/p.Asp410Ilefs*9) were identified. The enzymatic activity assay revealed that this variant resulted in a complete deficiency of 17α-hydroxylase and 17,20-lyase activity. This was consistent with the hormonal characteristics of the proband's blood. Conclusions: These results suggest that the compound heterozygous variant of c.1304T > C and c.1228delG of the CYP17A1 gene can lead to 17-OHD. Our findings thus provide a novel insight into the clinical evaluations and molecular basis of 17-OHD.

Classification of high dimensional biomedical data based on feature selection using redundant removal.

High dimensional biomedical data contain tens of thousands of features, accurate and effective identification of the core features in these data can be used to assist diagnose related diseases. However, there are often a large number of irrelevant or redundant features in biomedical data, which seriously affect subsequent classification accuracy and machine learning efficiency. To solve this problem, a novel filter feature selection algorithm based on redundant removal (FSBRR) is proposed to classify high dimensional biomedical data in this paper. First of all, two redundant criteria are determined by vertical relevance (the relationship between feature and class attribute) and horizontal relevance (the relationship between feature and feature). Secondly, to quantify redundant criteria, an approximate redundancy feature framework based on mutual information (MI) is defined to remove redundant and irrelevant features. To evaluate the effectiveness of our proposed algorithm, controlled trials based on typical feature selection algorithm are conducted using three different classifiers, and the experimental results indicate that the FSBRR algorithm can effectively reduce the feature dimension and improve the classification accuracy. In addition, an experiment of small sample dataset is designed and conducted in the section of discussion and analysis to clarify the specific implementation process of FSBRR algorithm more clearly.

Mitochondrial dysfunction caused by m.2336T>C mutation with hypertrophic cardiomyopathy in cybrid cell lines.

Hypertrophic cardiomyopathy (HCM), affecting approximately 1 in 500 in the general population, is the most prominent cause of sudden heart disease-related mortality in the young. Mitochondrial DNA (mtDNA) mutations are among the primary causes of HCM. We previously identified a novel m.2336T>C homoplasmic mutation in the mitochondrial 16S rRNA gene (MT-RNR2) in a Chinese maternally inherited HCM family. However, the molecular mechanisms by which m.2336T>C mutation contributes to HCM remain elusive. Here we generated transferring mitochondria cell lines (cybrids) with a constant nuclear background by transferring mitochondria from immortalized lymphoblastoid cell lines carrying the HCM-associated m.2336T>C mutation into human mtDNA-less (ρ°) cells. Functional assays showed a decreased stability for 16S rRNA and the steady-state levels of its binding proteins in the mutant cybrids. This mutation impaired the mitochondrial translation capacity and resulted in many mitochondrial dysfunctions, including elevation of ROS generation, reduction of ATP production and impairment of mitochondrial membrane potential. Moreover, the mutant cybrids had poor physiological status and decreased survival ability. These results confirm that the m.2336T>C mutation leads to mitochondrial dysfunction and strongly suggest that this mutation may play a role in the pathogenesis of HCM.

EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

Structural origin of hardness decrease in irradiated sodium borosilicate glass.

Mechanical properties such as hardness and modulus of sodium borosilicate (NBS) glasses in irradiation conditions were studied extensively in recent years. With irradiation of heavy ions, a trend that the hardness of NBS glasses decreased and then stabilized with increase of dose has been reported. Variations in network structures were suggested for the decrease of hardness after irradiation. However, details of these variations in a network of glass are not clear yet. In this paper, molecular dynamics was applied to simulate the network variations in a type of NBS glass and the changes in hardness after xenon irradiation. The simulation results indicated that hardness variation decreased with fluence in an exponential law, which was consistent with experimental results. The origin of hardness decrease after irradiation might be attributed to the break of Biv-O links that could be derived from the (1) decrease of average coordinate number of boron, (2) decrease of Si-O-Biv bonds, and (3) increase of non-bridging oxygen.

[Comparison of bulbar conjunctival microcirculation in rabbits of five subtypes of blood stasis syndrome].

OBJECTIVE: To observe changes of bulbar conjunctival microcirculation in rabbits of five kinds subtypes of blood stasis syndrome (BSS), and to analyze their different properties. METHODS: Totally 60 Japanese big-ear rabbits were randomly divided into six groups, i.e., qi deficiency blood stasis group, qi stagnation blood stasis group, cold coagulation blood stasis group, heat toxin blood stasis group, external injury blood stasis group, and the normal control group, 10 in each group. Changes of rabbit bulbar conjunctiva microcirculation were observed before and after modeling. RESULTS: Compared with the normal control group, the total integral of bulbar conjunctiva microcirculation obviously increased in the 5 BSS groups (P < 0.05). There was no statistical difference among the 5 BSS groups (P > 0.05). But there was statistical difference in any concrete integral among the 5 BSS groups (P < 0.05). Thickening blood vessels and errhysis of vascular walls were dominant in the heat toxin blood stasis group. Ischemia, partial cystic dilatation, vascular engorgement and twist were dominant in the qi deficiency blood stasis group. Partial vascular buckling, aneurysmal changes, flow velocity slowed down were dominant in the qi stagnation blood stasis group. Vascular buckling, hyperemia, vascular engorgement, blood flow slowed down were dominant in the external injury blood stasis group. Vascular buckling, ischemia, dark color were dominant in the cold coagulation blood stasis group. CONCLUSION: Changes of bulbar conjunctival microcirculation were different in 5 kinds of BSS types, which could reflect their various features.

Optimization of subcritical water extraction of polysaccharides from Grifola frondosa using response surface methodology.

BACKGROUND: This research is among the few that has been conducted on the feasibility of subcritical water extraction (SWE) as a rapid and efficient extraction tool for polysaccharides. OBJECTIVE: The aim of the study was to extractand optimize the parameter conditions of SWE of polysaccharides from Grifola frondosa using response surface methodology. MATERIALS AND METHODS: In the study, SWEwas applied to extractbioactive compounds from G. frondosa. A preliminary analysis was made on the physical properties and content determination of extracts using SWE and hot water extraction (HWE). Analysis of the sample residues and antioxidant activities of the polysaccharides extracted by SWE and HWE were then evaluated. RESULTS: THE OPTIMAL EXTRACTION CONDITIONS INCLUDE: extraction temperature of 210°C, extraction time of 43.65 min and the ratio of water to raw material of 26.15:1. Under these optimal conditions, the experimental yield of the polysaccharides (25.1 ± 0.3%) corresponded with the mean value predicted by the model and two times more than the mean value obtained by the traditional HWE. The antioxidant activities of polysaccharides extracted by SWE were generally higher than those extracted by HWE. From the study, the SWE technology could be a time-saving, high yield, and bioactive technique for production of polysaccharides.