Verification of the expression trend and interaction prediction of innate immune cells and immune-checkpoint molecules in the process of oral mucosal carcinogenesis.

OBJECTIVES: This study aimed to explore the expression trends of innate immune cells and immune-checkpoint molecules validated by data calculation in the process of oral mucosal carcinogenesis, as well as to explore methods of suppressing oral mucosal carcinogenesis based on immunotherapy by predicting their interactions. Me-thods 1) The cancer genome atlas (TCGA) database comprehensively scores immune cells and immune-checkpoint molecules in the process of oral mucosal carcinogenesis and screens out intrinsic immune cells and immune-checkpoint molecules that interfere with tumor immune escape. 2) Clinical patient blood routine data were collected for the statistical analysis of peripheral blood immune cells during the progression of oral mucosal carcinogenesis. Immune cells in peripheral blood that may affect the progression of oral mucosal carcinogenesis were screened. 3) Immunohistochemical staining was performed on intrinsic immune cells and immune-checkpoint molecules validated based on data calculation in various stages of oral mucosal carcinogenesis. 4) Special staining was used to identify innate immune cells in various stages of oral mucosal carcinogenesis based on data-calculation verification. 5) Survival analysis was conducted on intrinsic immune cells and immune-checkpoint molecules validated based on data calculation during the process of oral mucosal carcinogenesis. The association of intrinsic immune cells and immune-checkpoint molecules with the prognosis of oral squamous cell carcinoma was verified. RESULTS: The expression of monocytes and neutrophils increased during the process of oral mucosal carcinogenesis. The expression of eosinophils showed a single peak trend of up and down. The expression of mast cells decreased. In the process of oral mucosal carcinogenesis, the expression of the immune-checkpoint molecules cytotoxic T-lymphocyte-associated protein 4 (CTLA4) and programmed cell death-ligand (PD-L1) increased. The expression trends of monocytes, neutrophils, and eosinophils were positively correlated with those of CTLA4 and PD-L1 immune-checkpoint molecules. The expression trend of mast cells was negatively correlated with the expression of CTLA4 and PD-L1. Monocytes, neutrophils, and eosinophils may promote tumor immune escape mediated by CTLA4 and/or PD-L1, thereby accelerating the progression of oral mucosal carcinogenesis. Mast cells may inhibit tumor immune escape mediated by CTLA4 and/or PD-L1, delaying the progression of oral mucosal carcinogenesis. CONCLUSIONS: Therefore, interference with specific immune cells in innate immunity can regulate the expression of CTLA4 and/or PD-L1 to a certain extent, inhibit tumor immune escape, and delay the progression of oral mucosal carcinogenesis.

Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN.

Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe's action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient's upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method's recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient's rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.

Comprehensive review on sexual dimorphism to improve scalp acupuncture in nervous system disease.

BACKGROUND: With the development of modern medicine, the Traditional Chinese Medicine (TCM) combined with western medicine began to be produced and applied. Scalp acupuncture (SA) as a Chinese medicine based on neurological theory, has a great advantage compared with TCM in the treatment of nervous system diseases. METHOD: In this paper, we analyze the physiological and pathological manifestations of sexual dimorphism (SD) to illustrate the necessity of SD treatment. In addition, we review the factors that can affect SD and analyze in physiological structure, function, and pathological neurons. Diseases (pathological basis, pathological manifestations, and incidence) and factors leading to gender differences, which to analyze the possibility of gender differences in SA. RESULT: Furthermore, we creatively a new insight of SD-SA and provide the complete SD treatment cases on the basis of the existing SA in different kinds of diseases including stroke, migraine, attention deficit hyperactivity disorder (ADHD), and depression. CONCLUSION: In summary, we believe that it is feasible to improve the clinical effectiveness of SA, which is able to promote the development of SA, and then provides an actionable evidence for the promotion of precision medicine in the future.

Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology.

Traditional respiratory rehabilitation training fails to achieve visualization and quantification of respiratory data in improving problems such as decreased lung function and dyspnea in people with respiratory disorders, and the respiratory rehabilitation training process is simple and boring. Therefore, this article designs a biofeedback respiratory rehabilitation training system based on virtual reality technology. It collects respiratory data through a respiratory sensor and preprocesses it. At the same time, it combines the biofeedback respiratory rehabilitation training virtual scene to realize the interaction between respiratory data and virtual scenes. This drives changes in the virtual scene, and finally the respiratory data are fed back to the patient in a visual form to evaluate the improvement of the patient's lung function. This paper conducted an experiment with 10 participants to evaluate the system from two aspects: training effectiveness and user experience. The results show that this system has significantly improved the patient's lung function. Compared with traditional training methods, the respiratory data are quantified and visualized, the rehabilitation training effect is better, and the training process is more active and interesting.

Effects of intervention integrating physical literacy into active school recesses on physical fitness and academic achievement in Chinese children.

Background/Objective: The aim of this study was to examine the effects of school-based intervention integrating physical literacy (PL) into active school recesses (ASR) on physical fitness (i.e., body composition, 20-m shuttle run, 50-m run, rope skipping, sit and reach, handgrip) and academic achievement (i.e., academic result of Chinese and Mathematics) in Chinese children. Methods: A total of 357 children (mean age: 7.8 ± 0.7 years; boys: 50.4%) were recruited from two schools and these two schools were randomly assigned as the intervention group (IG) and the control group (CG), respectively. The IG consisted of 155 children (mean age: 7.9 ± 0.7 years, boys: 51.0%), and 202 children (mean age: 7.8 ± 0.7 years; boys: 50%) were allocated to the CG. Children in the IG received a 10-week intervention integrating PL that was conducted during ASR. In the CG, children's regular school activity was remained during the intervention period. Generalized estimating equation was performed to compare the levels of physical fitness and academic achievement between the IG and the CG after intervention. Results: Regarding physical fitness, there was a significant group × time interaction on 20-m shuttle run (ß = -3.89, 95% CI [-5.08; -2.71], p < 0.001) and handgrip (ß = -0.70, 95% CI [-1.20; -0.20], p = 0.006). After intervention, children in the IG had a greater increase than the CG (p < 0.001) in terms of 20-m shuttle run and handgrip. In addition, the post-test analysis indicated that performance of children in the IG was significantly greater than those in the CG (20-m shuttle run: p < 0.001, handgrip: p = 0.002). There was a significant group × time interaction on academic result of Chinese (ß = -1.21, 95% CI [-1.91; -0.56], p = 0.001) and academic result of Mathematics (ß = 16.71, 95% CI [15.14; 18.143], p < 0.001). Statistically significant positive difference in post-test was observed in academic result of Mathematics between the IG and the CG (p = 0.012). Conclusion: The results of this intervention study indicate that intervention integrating PL into ASR could bring a promising effect on physical fitness and academic achievement in children. It is recommended that future studies are necessary to assess the effects of ASR-based PL intervention on physical and cognitive outcomes using a wide range of sample.

Water-Assisted Programmable Assembly of Flexible and Self-Standing Janus Membranes.

Janus membranes with asymmetric wettability have been considered cutting-edge for energy/environmental-sustainable applications like water/fog harvester, breathable skin, and smart sensor; however, technical challenges in fabrication and accurate regulation of asymmetric wettability limit their development. Herein, by using water-assisted hydrogen-bonded (H-bonded) assembly of small molecules at water/oil interface, a facile strategy is proposed for one-step fabrication of membranes with well-regulable asymmetric wettability. Asymmetric orderly patterns, beneficial for mass transport based on abundant high-permeability sites and large surface area, are constructed on opposite membrane surfaces. Upon tuning water-assisted H-bonding via H-sites/configuration design and temperature/pH modulation, double-hydrophobic, double-hydrophilic, and hydrophobic-hydrophilic membranes are facilely fabricated. The Janus membranes show smart vapor-responsive curling and unidirectional water transport with promising flux of 1158±25 L m-2  h-1 under natural gravity and 31500±670 L·(m-2  h-1  bar-1 ) at negative pressure. This bottom-up approach offers a feasible-to-scalable avenue to precise-manipulation of Janus membranes for advanced applications, providing an effective pathway for developing tailor-made self-assembled nanomaterials.

Exploring psychological and psychosocial correlates of non-suicidal self-injury and suicide in college students using network analysis.

BACKGROUND: Non-suicidal self-injury (NSSI) and suicide are a serious health concern in young people. Remarkable progress has been made in understanding the correlates and risk factors for suicidality. However, it remains unclear the complex interplay between different factors and which factors are most saliently associated with NSSI and suicide risk and should be targeted for interventions. METHODS: This study utilized network analysis to examine the interrelationship between NSSI, suicide and a variety of psychological (e.g., depression, psychotic-like experiences) and psychosocial (e.g., childhood maltreatment, family dysfunction, being bullied and social support) correlates in a sample of Chinese first year college students (n = 2328). RESULTS: The severity of depressive symptoms was the only factor connected to both NSSI and suicide. Other psychiatric factors like psychotic experiences could only act in an indirect way via the bridging effect of depression. Emotional abuse, compared with other forms of childhood adversities, was found to be the most influential maltreatment form associated with suicidal ideation and attempts. Finally, social support showed the potential to lower the risk of self-injury and suicide in young people. LIMITATIONS: Only a subset of risk and resilience factors of NSSI and suicide were included. Causal inference was impossible due to the cross-sectional design. CONCLUSIONS: Our findings highlighted the importance of identifying risk groups with mental illnesses or negative childhood events, and providing additional support during the key time of transitioning into higher education.

Associations between waist circumference and executive function among Chinese Tibetan adolescents living at high altitude.

Background: Associations between body composition and execution function (EF) were currently studied in low altitude (LA) areas. However, the research on the correlation between waist circumference (WC) and EF among adolescents living at high altitude (HA) was limited. Objective: We sought to explore the association between WC and EF in Chinese Tibetan adolescents aged 13-18 years in HA areas. Methods: After excluding invalid data and extreme values, 1,228 participants (583 boys and 645 girls) were eventually included. The areas of Lhasa (average elevation of 3650 m), Nagqu (4500 m), Qamdo (3500 m), and Nyingchi (3100 m) in China were chosen as study sites. Participants completed tasks to measure inhibitory control, working memory, and cognitive flexibility. The predictive association between WC and EF was explored by One-way ANOVA, Pearson correlation, and linear regression analysis. Results: After controlling for concomitant variables, the reaction time (RT) of responding to inhibitory control (difference incongruent and congruent), working memory (1-back, 2-back), and cognitive flexibility (heterogeneous, difference in heterogeneous and homogeneous) stimuli in subjects with WC ≥ 85th percentile was longer than that in those with WC of the 15th percentile or below [by 1.785 ms (95% CI: 0.078, 3.491), 208.734 ms (95% CI: 96.886, 320.582), 106.679 ms (95% CI: 16.485, 196.873), 82.307 ms (95% CI: 19.171, 145.442), and 58.397 ms (95% CI: 0.343,116.452), respectively], (P < 0.05). Conclusion: After adjustment for concomitant variables, WC was significantly positively associated with the RT of inhibitory control, working memory, and cognitive flexibility among Chinese Tibetan adolescents in HA areas.

Purification and characterization of a chicory polysaccharide and its application in stabilizing genistein for cancer therapy.

Genistein is an isoflavone with chemopreventive and therapeutic effects on various types of cancers. Apparently, in contrast to the advantages of multi-target therapy, the poor water solubility of this molecule is a major obstacle to its clinical application. In this work, zein/chicory polysaccharide nanoparticles (G-zein-P NPs) were prepared by pH-induced antisolvent precipitation method for the encapsulation of genistein. Firstly, an acidic polysaccharide (CIP70-2) with a molecular weight of 66.7 kDa was identified from the roots of chicory (Cichorium intybus). This natural macromolecule was identified as a plant pectin, for which the structure included RG-I (rhamnogalacturonan I) and HG (homogalacturonan) regions. Using this polysaccharide, G-zein-P NPs were prepared, in which the water solubility of genistein was improved by encapsulation. The encapsulation efficiency and loading efficiency of genistein by composite nanoparticles reached 99.0 % and 6.96 %, respectively. In vitro tumor inhibition experiments showed that the inhibitory effect of G-zein-P NPs on HepG2 cells was twice that of unencapsulated genistein. Moreover, the significant inhibition of tumor development and metastasis by G-zein-P NPs was observed in zebrafish xenograft models. The results suggested that zein/chicory polysaccharide nanoparticles may be a promising delivery carrier for genistein application in cancer prevention and therapy.

Ionic-Liquid-Assisted One-Step Construction of Mesoporous Metal-Organic Frameworks.

The synthesis of ionic-mesoporous-metal-organic frameworks (ionic-meso-MOFs) has received considerable interest in the fields of macromolecular adsorption, acid-base catalysis, ionic conductivity, etc.; yet, their synthesis still presents significant difficulties. In this study, functionalized mesoporous MIL-101-ILs (Cr) was facilely constructed via an in situ self-assembly method by using aromatic-anion-functionalized ionic liquids (ILs) as competitive ligands. It has been demonstrated that the inclusion of an aromatic moiety into an IL improves the coordination ability and is advantageous for the anchoring of ILs on Cr3+ via amino-metal coordination. Thus, ionic-meso-MOFs with a specific surface area of 441.9-624.9 cm2/g and an average pore diameter of 5.5 to 8.4 nm were successfully synthesized. Because of the presence of open Lewis acidic metal sites on the MOFs and basic active sites on the ILs, the resulting ionic-meso-MOFs demonstrated both an acid-base cooperative effect and a mesoporous structure, indicating a high potential for acid-base catalysis. This in situ synthesis procedure for ionic mesoporous MOFs offers a simple method for developing and fabricating multifunctional mesoporous materials.

The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources.

OBJECTIVE: To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization. METHODS: Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality. RESULTS: Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01). CONCLUSION: This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.

Baicalin prevents fibrosis of human trabecular meshwork cells via inhibiting the MyD88/NF-κB pathway.

Trabecular meshwork fibrosis contributes to increased aqueous humor outflow resistance, leading to elevated intraocular pressure in primary open-angle glaucoma. Baicalin, an extract from Scutellaria baicalensis Georgi, has shown anti-fibrotic effects in liver, lung, and kidney diseases. However, its anti-fibrotic effect on human trabecular meshwork (HTM) cells has not yet been clarified. In this study, we investigated its effects on TGF-ß2-induced HTM fibrosis as well as the underlying regulatory mechanisms. HTM cells were pretreated with baicalin, TAK-242, and baicalin + TAK-242 for 2 h followed by treatment with or without 5 ng/mL TGF-ß2 for 48 h. Cell viability was assayed using cell counting Kit-8 and fibronectin (FN), laminin (LN), and α-smooth muscle actin (α-SMA) were assessed by western blotting, reverse transcription-polymerase chain reaction (RT-PCR), and immunocytochemistry. Further, the protein and gene expression levels of the TLR4/MyD88/NF-κB pathway (TLR4, MyD88, and NF-κB p65) were also examined by western blotting and RT-PCR, respectively. Thus, we observed that high doses of baicalin (40 µM) decreased (p < 0.1) HTM cell viability and 20 µM baicalin pretreatment was identified as the optimum pretreatment concentration. TGF-ß2 upregulated (p < 0.5) the expression of FN, LN, α-SMA, MyD88, NF-κB p65 proteins and mRNA in HTM cells, and these effects were inhibited by baicalin and TAK-242 (p < 0.5). However, western blot analysis showed that baicalin did not repress TLR4 expression in HTM cells. Therefore, our findings suggested that baicalin could prevent TGF-ß2-induced extracellular matrix (FN, LN) deposition and α-SMA expression in HTM cells by inhibiting the MyD88/NF-κB pathway.

ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method.

Aging is a significant contributing factor to degenerative diseases such as cancer. The extent of DNA methylation in human cells indicates the aging process and screening for age-related methylation sites can be used to construct epigenetic clocks. Thereby, it can be a new aging-detecting marker for clinical diagnosis and treatments. Predicting the biological age of human individuals is conducive to the study of physical aging problems. Although many researchers have developed epigenetic clock prediction methods based on traditional machine learning and even deep learning, higher prediction accuracy is still required to match the clinical applications. Here, we proposed an epigenetic clock prediction method based on a Resnet neuro networks model named ResnetAge. The model accepts 22,278 CpG sites as a sample input, supporting both the Illumina 27K and 450K identification frameworks. It was trained using 32 public datasets containing multiple tissues such as whole blood, saliva, and mouth. The Mean Absolute Error (MAE) of the training set is 1.29 years, and the Median Absolute Deviation (MAD) is 0.98 years. The Mean Absolute Error (MAE) of the validation set is 3.24 years, and the Median Absolute Deviation (MAD) is 2.3 years. Our method has higher accuracy in age prediction in comparison with other methylation-based age prediction methods.

Bilateral Sacrospinous Hysteropexy Versus Bilateral Sacrospinous Ligament Fixation with Vaginal Hysterectomy for Apical Uterovaginal Prolapse.

PURPOSE: The aim of this retrospective study was to compare the anatomical and functional outcomes between bilateral sacrospinous hysteropexy (BSHP) and bilateral sacrospinous ligament fixation with vaginal hysterectomy (BSLF/VH) in women with apical-predominant uterovaginal prolapse. METHODS: Clinical data from patients with symptomatic Pelvic Organ Prolapse-Quantification (POP-Q) stage 2 or higher uterovaginal prolapse who underwent either BSHP (48 patients) or BSLF/VH (69 patients) between January 2014 and December 2018 were reviewed retrospectively. The primary outcome was the subjective satisfaction rate evaluated by Patient Global Impression of Improvement, and the secondary outcomes included objective anatomical success rates, impact on disease-specific quality of life evaluated by the Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire-12, Pelvic Floor Distress Inventory-Short Form 20, and Pelvic Floor Impact Questionnaire 7, and surgical complications. RESULTS: After a median follow-up of 35 months (range, 25-58 months), all patients in both groups demonstrated significant postoperative improvements in anatomical and functional outcomes (P<0.001). There were no significant differences in postoperative subjective and objective results, sexual satisfaction outcomes, or disease-specific quality of life between the BSHP and BSLF/VH groups, and similar incidence rates of intraoperative and postoperative complications were also recorded. CONCLUSION: The uterus-sparing BSHP procedure yielded noninferior anatomical and functional outcomes compared to the BSLF/VH procedure and could be adopted as an alternative to conventional hysterectomy-based native-tissue repair modalities for symptomatic apical-predominant uterovaginal prolapse.

Automatic Segmentation of Standing Trees from Forest Images Based on Deep Learning.

Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have shortcomings such as low segmentation accuracy and manual intervention. To achieve accurate segmentation of standing tree images effectively, SEMD, a lightweight network segmentation model based on deep learning, is proposed in this article. DeepLabV3+ is chosen as the base framework to perform multi-scale fusion of the convolutional features of the standing trees in images, so as to reduce the loss of image edge details during the standing tree segmentation and reduce the loss of feature information. MobileNet, a lightweight network, is integrated into the backbone network to reduce the computational complexity. Furthermore, SENet, an attention mechanism, is added to obtain the feature information efficiently and suppress the generation of useless feature information. The extensive experimental results show that using the SEMD model the MIoU of the semantic segmentation of standing tree images of different varieties and categories under simple and complex backgrounds reaches 91.78% and 86.90%, respectively. The lightweight network segmentation model SEMD based on deep learning proposed in this paper can solve the problem of multiple standing trees segmentation with high accuracy.

Controllable Construction of Amino-Functionalized Dynamic Covalent Porous Polymers for High-Efficiency CO2 Capture from Flue Gas.

The design of high-efficiency CO2 adsorbents with low cost, high capacity, and easy desorption is of high significance for reducing carbon emissions, which yet remains a great challenge. This work proposes a facile construction strategy of amino-functional dynamic covalent materials for effective CO2 capture from flue gas. Upon the dynamic imine assembly of N-site rich motif and aldehyde-based spacers, nanospheres and hollow nanotubes with spongy pores were constructed spontaneously at room temperature. A commercial amino-functional molecule tetraethylenepentamine could be facilely introduced into the dynamic covalent materials by virtue of the dynamic nature of imine assembly, thus inducing a high CO2 capacity (1.27 mmol·g-1) from simulated flue gas at 75 °C. This dynamic imine assembly strategy endowed the dynamic covalent materials with facile preparation, low cost, excellent CO2 capacity, and outstanding cyclic stability, providing a mild and controllable approach for the development of competitive CO2 adsorbents.

Associations between socioeconomic status and physical activity: A cross-sectional analysis of Chinese children and adolescents.

Objectives: Although socioeconomic status (SES) has been shown to be an important determinant of physical activity (PA) in adults, the association in children and adolescents remains less consistent based on evidence from western developed countries. The purpose of this study, therefore, is to investigate associations between SES and PA among Chinese children and adolescents. Methods: A cross-sectional study was conducted with a self-reported questionnaire in China. The multi-stage stratified cluster sampling method was used, and 2,955 children and adolescents (8-17 years old, 53.4% girls) were enrolled in this study. SES was assessed by measuring parental education levels, perceived family wealth, and Family Affluence Scale II (FAS II). PA was assessed by reliable and valid items from the Health Behavior School-aged Children Surveillance questionnaires. Descriptive statistics were used to report sample characteristics, including demographic characteristics, levels of PA, and SES. The Chi-square test was applied to analyze the differences of PA in gender and school period. Binary logistic regression was used to analyze the association between SES and PA among children and adolescents. Results: Overall, 10.4% of children and adolescents accumulated moderate-to-vigorous physical activity (MVPA) at least 60 min per day. Boys were more physically active than girls (14.1% vs. 7.2%, p < 0.001). Higher socioeconomic status was positively associated with higher physical activity levels among children and adolescents, especially using FAS and mother education level as SES measures. The association between SES and PA varied by gender and grade group, and the effects of SES are also different on weekdays and weekends. Conclusion: This study found socioeconomic disparities in PA among Chinese children and adolescents, and showed the importance of targeting children and adolescents with low SES intervention priority. Based on these research findings, it is suggested that SES, especially for FAS and mother education level, should be considered when designing and implementing the promotion of regular PA in children and adolescents. Health policymakers may use this information to develop interventions to reduce health inequalities among children and adolescents in the future.

Multimodal Feature Fusion Method for Unbalanced Sample Data in Social Network Public Opinion.

With the wide application of social media, public opinion analysis in social networks has been unable to be met through text alone because the existing public opinion information includes data information of various modalities, such as voice, text, and facial expressions. Therefore multi-modal emotion analysis is the current focus of public opinion analysis. In addition, multi-modal emotion recognition of speech is an important factor restricting the multi-modal emotion analysis. In this paper, the emotion feature retrieval method for speech is firstly explored and the processing method of sample disequilibrium data is then analyzed. By comparing and studying the different feature fusion methods of text and speech, respectively, the multi-modal feature fusion method for sample disequilibrium data is proposed to realize multi-modal emotion recognition. Experiments are performed using two publicly available datasets (IEMOCAP and MELD), which shows that processing multi-modality data through this method can obtain good fine-grained emotion recognition results, laying a foundation for subsequent social public opinion analysis.

Respiratory Prediction Based on Multi-Scale Temporal Convolutional Network for Tracking Thoracic Tumor Movement.

Radiotherapy is one of the important treatments for malignant tumors. The precision of radiotherapy is affected by the respiratory motion of human body, so real-time motion tracking for thoracoabdominal tumors is of great significance to improve the efficacy of radiotherapy. This paper aims to establish a highly precise and efficient prediction model, thus proposing to apply a depth prediction model composed of multi-scale enhanced convolution neural network and temporal convolutional network based on empirical mode decomposition (EMD) in respiratory prediction with different delay times. First, to enhance the precision, the unstable original sequence is decomposed into several intrinsic mode functions (IMFs) by EMD, and then, a depth prediction model of parallel enhanced convolution structure and temporal convolutional network with the characteristics specific to IMFs is built, and finally training on the respiratory motion dataset of 103 patients with malignant tumors is conducted. The prediction precision and time efficiency of the model are compared at different levels with those of the other three depth prediction models so as to evaluate the performance of the model. The result shows that the respiratory motion prediction model determined in this paper has superior prediction performance under different lengths of input data and delay time, and, furthermore, the network update time is shortened by about 60%. The method proposed in this paper will greatly improve the precision of radiotherapy and shorten the radiotherapy time, which is of great application value.

LncRNA MALAT1 promotes decidualization of endometrial stromal cells via sponging miR-498-3p and targeting histone deacetylase 4.

Decidualization of human endometrial stromal cells (hESCs) is important for the maintenance of a successful pregnancy. Histone deacetylase 4 (HDAC4) was reported to be involved in the dysfunction of decidua-derived mesenchymal stem cells. However, the role of HDAC4 underlying decidualization of hESCs remains unclear. We intended to explore the function and molecular mechanism of HDAC4 in hESCs. In vitro expansion of hESCs using a serum-free medium was used to confirm the characteristics of hESCs. Gene expression in hESCs was evaluated by reverse transcription-quantitative polymerase chain reaction. CCK-8 assay, TUNEL staining, flow cytometry analysis, and Western blot analysis were performed to test the effects of HDAC4 and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) on hESCs. RNA pull-down and luciferase reporter assays were performed to validate the relationship between genes. In this study, the characteristics of hESCs were sustained in serum-free medium during a process of propagation. HDAC4 knockdown suppressed hESCs viability and promoted hESCs apoptosis. HDAC4 was targeted by miR-498-3p in hESCs. MALAT1 bound with miR-498-3p in hESCs. HDAC4 expression was positively regulated by MALAT1 and negatively regulated by miR-498-3p in hESCs. HDAC4 upregulation countervailed the effects of MALAT1 silencing on hESCs proliferation, apoptosis, and decidualization of hESCs. Overall, MALAT1 facilitated the decidualization of hESCs via binding with miR-498-3p and upregulating HDAC4, which might provide a new direction for the maintenance of a successful pregnancy.