Despite the high burden of malnutrition in the country, there is a lack of a simple and valid tool to screen elders in Ethiopia. The Mini Nutritional Assessment (MNA) tool has been validated for comprehensive geriatric assessment to identify malnutrition in other countries. However, there is a lack of evidence on the potential validity and reliability of the tool for institutionalized elders in Ethiopia. This study was aimed at determining the validity and predictive performance of MNA tool for malnutrition among Ethiopian institutionalized elderly.  A facility-based survey was conducted on randomly selected 164 elders in geriatric centers to evaluate the validity, reliability, and predictive performance of full MNA against hemoglobin (Hgb) and ideal body weight (IBW) measured under standard procedures. The data was presented in ROC graphs, and reliability was evaluated with Cronbach alpha. The receiver-operating characteristic curve (ROC) analysis was used to assess the predictive performance of the tool. The area under the curve (AUC) with its 95% CI was reported. The Youden index, at maximum sensitivity and specificity, was used to obtain optimal cutoff points. α = 0.80). The full MNA score can better predict Hgb  and percentage of IBW  at specified cutoff points. A full MNA can predict malnutrition or risk of malnutrition based on percentage IBW at a sensitivity and specificity of 97.3% and 72.2%, respectively.  The internal consistency of the tool was good ( The full MNA has the potential to be a reliable and valid nutritional assessment tool for institutional elders. Due to access to health care and epidemiologic transitions, the current population age structure is changing, with an increasing aged population. In sub-Saharan Africa, the estimated old age segment of the population will reach above 34 million by 2030 . In EthiThe Mini Nutritional Assessment (MNA) is one of the most widely used and studied nutrition screening/assessment tools . It is aThe recent rapid rise in the elderly population will undoubtedly pose several challenges. The implications of aging are multiple and multifaceted, and most of the time they go unrecognized. As age advances, there will be a steady decline in physical and biological functions. The magnitude of malnutrition in the elderly is underreported , which nSo far, the MNA tool has been tested for validity and reliability in different research and settings –13. The ″N 41°′52″E with an elevation of 1204 meters above sea level. Based on the 2019/2020 population projection by the Central Statistical Agency of Ethiopia, Dire Dawa administration has a total population of 506,609 . Thus, the current number of elderly people in the administration is estimated to be about 16,700 .  were measured under standard procedures. A point-of-care Hgb meter using HemoCue machine  was used to determine the Hgb level under standard laboratory procedures. This method uses a small blood sample, with little skills and higher feasibility in field settings –24. StudThe outcome variable of this study is the MNA score, while demographic characteristics , alcohol drinking, hemoglobin level, anthropometric variables , and other important variables were considered independent variables.In this study, undernutrition is defined as a composite indicator of malnutrition (MNA score < 17 points) and at risk of malnutrition (MNA score from 17 to 23.5) based on the full MNA score cutoff point . Anemia SPSS version 25.0 and MedCalc V. 19 were used to analyze the data. The information was summarized and described using frequency, means, standard deviations, tables, graphs, and other methods. In addition, the ideal body weight (IBW), which is the target healthy body weight , was estα. A Cronbach's α value of 0.60, 0.70, and 0.80 was considered acceptable, adequate, and good, respectively [As a measure of the reliability of the MNA, homogeneity was computed using Cronbach's ectively . The intectively . SensitiP value below 5% was used to declare statistical significance level.We used a receiver-operating characteristic (ROC) analysis to identify optimal threshold values for predicting malnutrition and evaluate predictive performance of the MNA tool against anemia or percentage of IBW for screening malnutrition among elderly. It allows to fit a model for evaluating the diagnostic accuracy of a particular tool against a relatively valid tool. The area under the curve (AUC) reported with a value of 1.0 indicates perfect discrimination . AUC witThe tool was prepared in English and translated into local languages by English-language professionals. Before actual data collection, training was given to data collectors and supervisors on the study procedure, tools, and how to collect data. Extensive hands-on training was given for weight, height, calf circumference, MUAC, and other anthropometric measurements. The Hgb measurement was done using a valid and reliable HemoCue machine under standard operating procedures. A pretest was conducted on 5% of the sample outside of the sampling population. Anthropometric reliability assessment using technical error of measurement (TEM) was calculated for each anthropometric measurement. The intra and interobserver TEM were calculated to pick reliable measurers. Questionnaire completeness, accuracy, and consistency were checked on a daily basis.Ethical approval was obtained from Dire Dawa University's Institutional Ethical Review committee. A written informed consent was obtained from each participant or their care giver if applicable. Confidentiality was kept, and the information will not be shared.In this study, 164 elderly people participated in the study where 81 (49.4%) were men. The age of respondents ranges from 65 to 92 years and mean age of 74.6 (±7.9 SD) years. Among the study participants, 93 (56.7%) were unable to read and write, while 80 (48.8%) of study subjects were widowed with their partner died .Regarding lifestyle characteristics, 19 (11.6%) smoked cigarettes. Compared to women (5.3%), higher proportion of men 9.47% smoked cigarettes. In addition, 35 (21.3%) of the elderly drink alcohol. Among the majority of men, 33 (94.3%) were involved in alcohol drinking more than women (25.7%).Based on the malnutrition screening, 40.9% (33.8 to 48.4%) and 40.2% (32.7 to 47.7%) of the elderly were well-nourished and malnourished, respectively, while 18.9% (3.30 to 24.9%) of the elderly (scored between 17 and 23.5) were at risk of malnutrition. The prevalence of malnutrition and the risk of malnutrition among men was 55.3%, which was higher than among women (40.2%) . Based oThe internal consistency of the MNA tool as measured by Cronbach's coefficient was found to be 0.797. Omitting calf circumference from MNA items significantly lowered the Cronbach's value, and the reliability of the tool became 0.772, 0.773, and 0.777. This shows that CC is the most important item in the tool to assess the nutritional status of the elderly. Conversely, deleting live independently, the value of Cronbach's increased to 0.801. Therefore, living independently has contributed to increasing the overall reliability of the MNA tool in this study .r = 0.766, P < 0.001 and r = 0.693, P < 0.001). Live independently and pressure sores, protein intake, and more than three prescription drugs showed poor correlation to the total MNA score . As a result, poor correlation of these four parameters was observed, and they all showed a higher Cronbach's α if they were omitted from the tool  and a lower correlation against Hgb , respectively. Concurrent validity of the instrument showed the correlation between total MNA scores and the patients' view of their own nutritional status of participants. There was a positive correlation which resulted in r = 0.604 (P < 0.001)  .P < 0.0001) and 89.9   and percent of IBW cutoff point (less than 90%) was 0.85  . This inThe sensitivity, specificity, positive predictive value, and negative predictive value of the full MNA according to its established cutoff points for predicting the percentile of IBW were 97.3%, 72.2%, 73.6%, and 87.0%, respectively. Using, the Youden index, the best cutoff point to detect malnourished and at risk of malnutrition in the present study was ≤23.5, which resulted in similar sensitivity, specificity, positive predictive value, and negative predictive value.According to its established cutoff points for predicting hemoglobin , the MNA's sensitivity, specificity, positive predictive value, and negative predictive value were 87.36%, 72.73%, 78.4%, and 84%, respectively. Using the Youden index, the best cutoff point to detect malnourished and at risk of malnutrition in the present study was ≤19.5, resulting in a considerable decrease in sensitivity and positive predictive value of 81.6% by 5.76% and an increase in specificity and negative predictive value by 8.87%, respectively .This study was conducted with the objective of evaluating the MNA tool to identify malnutrition and risk for malnutrition among the elderly population in a geriatric center. The majority, 40.9% (95% CI: 33.8%-48.42%) and 40.2% (95% CI: 32.7%–47.7%), were found to be well nourished and malnourished, while 18.9% (95% CI: 13.30%–24.89%) were at risk of malnutrition according to full MNA score. Based on a percentage of IBW and Hgb cutoff points, 31.7% and 59.2% were malnourished.α coefficient (0.80), indicating acceptable consistency. This showed better reliability as compared to studies in Hawassa and Iran, which had a 0.65 and 0.6, respectively [The reliability of the tool was checked using Cronbach's ectively , 14. Theectively , 35. Ther = 0.70) and Hgb (r = 0.43), which are valid and reliable assessments of nutritional status. This finding suggests that the tool was valid and can identify the nutritional status of an elderly population with better predictive ability for IBW and Hgb. The MNA score can predict low Hgb (AUC = 0.85), which makes the MNA tool to have a better predictive performance. Similarly, study from Ethiopia showed that the MNA can predict malnutrition based on BMI (AUC = 0.85) [The validity of the MNA tool was validated against percent of IBW ( = 0.85) . These fIn addition, the MNA has a better predictive performance for percent of IBW (AUC = 0.90), which shows excellent predictive performance or agreement. Other studies also showed a better predictive power for malnutrition among elders (AUC = 0.91) .The performance of the tool for predicting IBW at an established cutoff point was great . Validations at the start also reported a higher sensitivity (96%) and specificity (98%) than the current study  and consThe MNA score ≤ 19.5 and those less than 23.5 can identify malnutrition and those at risk of malnutrition among the elderly in Ethiopia. This cutoff point for Hgb is slightly higher than nestle cutoff point (17), while the cutoff point for IBW is almost similar to nestle ranking. Considering the rigorous analysis and the validity of the tool so far, the MNA can be used to screen elderly people for identifying risk and established malnutrition with the current cutoff point .A few studies were conducted based on the validity of MNA tool in Ethiopia. It is easily administered by peripheral or primary level health staff, without the need for specific training in nutrition. In Ethiopia, primary level health personnel, with little skills, are responsible for clinical care, where MNA can help them easily assess the nutritional status of the elderly in resource limited settings.This study is first of its kind to pinpoint and assess the validity and reliability of the MNA tool using valid and reliable nutritional assessment methods (Hgb and % IBW), which allows to pick the outcome more accurately. However, the results of this study should be thought in the light of smaller sample size and the survey nature of the data that may affect the result.n = 164) and being among institutionalized older adults might limit the generalizability of the result.The findings of this study shall be thought considering some limitations of the study. Anthropometric measurements in older adults might introduce some slight measurement bias. The point-of-care Hgb testing may not be hundred percent valid and might under or overestimate the Hgb measurement compared to laboratory testing. The small number of the target population (The full MNA tool is a reliable and valid nutrition assessment method for identifying malnutrition and those at risk of malnutrition among the institutionalized elderly in Ethiopia. The Nestle established cutoff point for at risk and established malnutrition can predict nutritional status and applied in Ethiopia.


Ultra-high-definition (UHD) video has brought new challenges to objective video quality assessment (VQA) due to its high resolution and high frame rate. Most existing VQA methods are designed for non-UHD videos—when they are employed to deal with UHD videos, the processing speed will be slow and the global spatial features cannot be fully extracted. In addition, these VQA methods usually segment the video into multiple segments, predict the quality score of each segment, and then average the quality score of each segment to obtain the quality score of the whole video. This breaks the temporal correlation of the video sequences and is inconsistent with the characteristics of human visual perception. In this paper, we present a no-reference VQA method, aiming to effectively and efficiently predict quality scores for UHD videos. First, we construct a spatial distortion feature network based on a super-resolution model (SR-SDFNet), which can quickly extract the global spatial distortion features of UHD videos. Then, to aggregate the spatial distortion features of each UHD frame, we propose a time fusion network based on a reinforcement learning model (RL-TFNet), in which the actor network continuously combines multiple frame features extracted by SR-SDFNet and outputs an action to adjust the current quality score to approximate the subjective score, and the critic network outputs action values to optimize the quality perception of the actor network. Finally, we conduct large-scale experiments on UHD VQA databases and the results reveal that, compared to other state-of-the-art VQA methods, our method achieves competitive quality prediction performance with a shorter runtime and fewer model parameters. UHD video can bring viewers a better visual experience because of its high resolution, high frame rate, high dynamic range and wide color gamut. However, the quality of UHD video is subject to various distortions and noises during the processes of signal capturing, encoding, storing, transmission and displaying. To better monitor and control the video quality in each stage, it is essential to develop an accurate and efficient UHD video-quality-assessment algorithm.Video quality assessment (VQA) is a technology that can automatically measure video quality. Based on whether the reference video is accessible, VQA can be categorized into three types: full-reference VQA (FR-VQA) ,2, reducNR-VQA is also known as blind VQA ,4. SinceFirst, the VQA task is closely correlated with the perception of distortion information. However, the current BVQA methods ,10,11 usIn contrast, the video super-resolution task is more related to the VQA task and both of them aim at how to better extract the features related to video quality. The process of video super-resolution involves dealing with the content feature and distortion feature. The content feature is extracted and used to generate high-quality and high-resolution videos, while the distortion information is not used in the super-resolution task. Because the super-resolution model can convert low-resolution video into high-resolution video, the distortion information hidden in the low-resolution video can reflect the distortion distribution of the high-resolution video well. Therefore, we can use the distortion information hidden in the low-resolution video to predict the UHD video quality by embedding the super-resolution model into the VQA model. Second, due to the limitation of computing power, most VQA models are unable to continuously handle the temporal features of UHD videos with high frame rates. Instead, these models usually have to segment the video into multiple segments, predict the quality score of each segment, and then average these segment quality scores to obtain the video quality score, as shown in According to the above analysis, we propose a blind video quality assessment method for UHD video based on super-resolution and deep reinforcement learning. First, we construct a spatial distortion feature network based on a super-resolution model (SR-SDFNet) to extract the global spatial distortion features of UHD frames. Then, a time fusion network based on reinforcement learning (RL-TFNet) is employed to aggregate the spatial distortion features of each UHD frame to obtain the UHD video quality score. For the SR-SDFNet, as shown in the green box in We make three main contributions:(1) We propose a global spatial distortion feature network based on a super-resolution model (SR-SDFNet) to efficiently and effectively extract the global spatial distortion features from the UHD frames.(2) We propose a time fusion network based on reinforcement learning (RL-TFNet) to continuously fuse the global spatial distortion features of each frame to obtain the whole video quality score, which can ensure the continuity of the video temporal domain and improve the prediction accuracy for the UHD video quality.(3) By combining the SR-SDFNet with the RL-TFNet, we present an NR-VQA model BVQA-SR&DRL for UHD videos. Our model is driven by distorted UHD videos without human subjective scores. We evaluate our model on two public UHD-VQA databases. The result demonstrates that our model achieves state-of-the-art performance compared with existing BIQA methods.The current UHD video quality assessment methods usually use the VQA model designed for low-resolution video, apart from the above two methods P.1204.5 . Most ofDRL  imitatesZhang et al.  proposedHowever, to our knowledge, RL has never been used for video quality assessment tasks. Existing VQA methods usually divide the entire video into multiple video segments and then process them. We argue that such segmentation is inconsistent with the actual subjective perception process. Therefore, our method attempts to process the entire video with an RL model, which predicts the quality fluctuations that each video frame brings to the entire video, thereby preserving complete and continuous video temporal features.We propose a BVQA method BVQA-SR&DRL that aims to efficiently and accurately predict the quality score of the UHD distorted video. It consists of two components: a spatial distortion feature network based on a super-resolution model (SR-SDFNet) and a time fusion network based on reinforcement learning (RL-TFNet). Second, the proposed RL-TFNet observes the spatial features extracted by SR-SDFNet, as well as the historical quality score To extract high-resolution spatial distortion features quickly and completely, we employ the generator network SRResNet used in the super-resolution method ESRGAN  to constIn the task of SR, the content features In detail, we first downsample the UHD video frame To train SR-SDFNET, we first train the super-resolution model ESRGAN based on the VQA database. The inputs to ESRGAN are the distorted frames which are downsampled by a factor of four, and the corresponding reference frames are used as the ground truth of the ESRGAN model. After the losses of ESRGAN stop falling, we use the feature network To continuously integrate the spatial quality features extracted by SR-SDFNet in the temporal dimension, we formulate the video quality assessment task as a Markov decision process (MDP) and propose the RL-TFNet model, which simulates the process of human visual perception of video quality. As shown in Since the subjective quality scores of each video frame used as the ground truth are hard to obtain, the training of RL-TFNet is guided by a reward generated from VAMF-4K , which iIn order for our model to continuously fuse long-range UHD video quality features, we define the state vector as For the n-th video frame The reward is an essential metric for the agent to learn which action performs better in the environment. Considering the frame After defining the above components of MDP, we design and update RL-TFNet via a deep deterministic policy gradient (DDPG) . RL-TFNeAlgorithm 1 RL-TFNet AlgorithmInitialize copy network weights Initial quality score 1:fordo2: Set 3: Select action 4: Execute action on quality score 5: Set 6: Store transition 7: Set 8: Minimize the loss to optimize critic network: 9: Optimize actor network by the cost function: 10: Update the copy network weights:  11:end forRandomly set the initialize weights of the actor network In our method, we first extract global spatial distortion features To train the two networks (SR-SDFNet and RL-TFNet) stably, we first train the super-resolution model ESRGAN, the distorted frames are downsampled by a factor of four as the input of ESRGAN, and the corresponding reference frames are used as the ground truth of the super-resolution model. After the ESRGAN is trained to convergence, we use the feature network To train the proposed model, we collect 50 UHD (3840 × 2160 pixels and 50 fps) video sequences from the source materials of the UHD TV programs. These source materials are only slightly compressed, so the video quality is high enough to be used as reference videos. The collected video sequences cover a wide range of content, including outdoor, indoor, night scenery, buildings, people, sports games and so on. The duration of each sequence is 10 s to 15 s. We manually add three types of compression distortion  to each of these UHD sequences;each type has four levels, which are set according to the MCML  databaseIn order to evaluate the performance of our method, two UHD VQA databases are used for testing: the AVT-VQDB-UHD-1  and the We chose Spearman’s rank order correlation coefficient (SROCC), the linear correlation coefficient (PLCC) and root-mean-square error (RMSE) to measure the VQA model performance. SROCC describes the rank consistency of the predicted score and the subjective score. Where N is the total number of distorted videos, and PLCC describes the linear correlation between the predicted score and the subjective score, where RMSE is mainly used to measure the relative error, where Following Zhang et al.  and Ma e−4 and decays by a factor of 0.5 every 10,000 iterations. We choose the model with the largest reward value as the best model. In the testing session, each test video is frame sampled every 30 frames intervals for score prediction.In the training session, the SR model ESRGAN is first trained on the training database according to . The disIn this section, we evaluate the performances of our model and various other BVQA models on two UHD-VQA databases. The main models being compared have all been designed for video quality assessment in recent years, and include VSFA , TLVQM , VIDEVALIt can be observed from To our surprise, the proposed model is weaker than HFR-BVQA for TEST_4 on the AVT-VQDB-UHD-1 database. One reason for this is that the TEST_4 of AVT mainly contains distorted videos with different frame rates. Compared with our method, HFR-BVQA is specially designed for low-frame-rate videos. In response to this phenomenon, we will make special optimizations for distorted videos with different frame rates in the future.To evaluate the time and spatial complexity of the proposed model, our model is compared with the other nine BVQA models: VSFA, TLVQM, VIDEVAL, GSTVQA, RAPIQUE, ChipQA, NOFU, HEKE and HFR-BVQA. These models are implemented on CPU or GPU, as requested by the authors. The hardware platform used for testing includes an NVIDIA TITAN X GPU processor and Core i7-5930K CPU @ 3.5 GHz. We summarize the runtime and network parameter size in As shown in In order to visually verify the effectiveness of our model, we output the feature map from the spatial distortion feature network (SR-SDFNet) and show the fluctuation of video quality over time through a line graph, as shown in In this section, we investigate the functionalities of SR-SDFNet and RL-TFNet. All experiments are evaluated on the AVT-VQDB-UHD-1 and MCML databases.To verify the effectiveness of the proposed SR-SDFNet, we evaluate the performance of different spatial feature extractors. The analysis includes five kinds of networks, Resnet-50 , VGG16 , MobilenFurthermore, We use two time-process strategies to explore the effectiveness of the proposed RL-TFNet. In the first strategy, we first extract spatial features through SR-SDFNet and then regress the features of each frame to a quality score through two fully connected layers. In testing, we average the prediction scores for multiple frames as the score of the entire video. In the second strategy, the spatial features from multiple frames are fed into an LSTM network  to prediIn this paper, we present a fast and accurate blind quality assessment algorithm for UHD videos. It consists of two components: a spatial distortion feature network based on a super-resolution model (SR-SDFNet) and a time fusion network based on reinforcement learning (RL-TFNet) in which SR-SDFNet can employ a super-resolution model to efficiently extract the spatial distortion features and RL-TFNet is used to adjust the quality score in the continuous time dimension. Extensive experimental results demonstrate that the proposed model outperforms all compared competing BVQA methods on UHD quality databases. Furthermore, our model has a smaller parameter size and runs faster than other methods, which makes it more suitable for practical applications in UHD VQA.


Lupus nephritis (LN) is a crucial complication of systemic lupus erythematosus (SLE). IKZF2 was identified as a lupus susceptibility locus, while its exact molecular function in LN is unknown. We aimed to explore the relationship between IKZF2 and LN based on multi-omics data. In our study, we carried out a meta-analysis of publicly available data, including not only tubulointerstitium, but also glomerulus tissue samples from LN patients and controls. Based on the common differentially expressed genes (co-DEGs) and previous researches, we selected IKZF2 for further analysis. Then, we analyzed potential molecular mechanisms of co-DEGs and IKZF2 in LN. To explore the possible targets of IKZF2, protein–protein interaction network (PPI) network and ceRNA network of IKZF2 were also constructed. Moreover, we performed immune infiltration analysis and evaluated clinical value of IKZF2. A total of 26 co-DEGs were observed in the integration of the above DEGs coming from the four sets of data, of which IKZF2 was selected for further analysis. Functional enrichment analysis from IKZF2 and related PPI network confirmed the tight relationship between IKZF2 and the immune reaction. Moreover, immune filtration analysis revealed the significant correlation between IKZF2 and naïve B cell, NK cell activation, NK cell rest and other immune cells. Receiver operating characteristic (ROC) analysis showed that the areas under the ROC curves were 0.721, 0.80, 0.682, and 0.859 for IKZF2 in four datasets, which demonstrated the clinical value of IKZF2. Our study revealed that IKZF2 may play an essential role in the molecular function and development of LN, and might be a potential biomarker for distinguishing LN patients and healthy ones. LN is a major driver of mortality of SLE patients2, and up to 20% of SLE patients who have been afflicted by LN will finally develop end-stage kidney disease (ESKD) within the first decade of the disease course3. However, on the one hand, the clinical detection of early disease can be challenging because patients often lack overt signs of kidney disease4, and the conventional parameters such as proteinuria and serum creatinine are insensitive and non-specific5. On the other hand, current therapies are not sufficiently efficacious to control LN and not all patients show adequate treatment responses6. Thus, it is critical and necessary to search and identify novel candidate biomarkers or therapeutic targets for LN.Systemic lupus erythematosus (SLE) is a multifactorial and multisystem autoimmune disease that commonly affects the kidneys7. Genome-wide association studies (GWASs) in SLE have revealed more than 80 susceptibility loci10. Nevertheless, the identified risk variants could only explain a small portion of heritability of SLE11. Besides, gene expression, as an intermediate phenotype between DNA and disease phenotypic variation, can provide us with a new perspective to our understanding of the molecular mechanisms leading to SLE12. IKZF2, also known as HELIOS, was initially thought to be restricted to Treg cells14. In Treg cells, it was claimed to control IL2 expression by promote binding of Foxp3 to the IL2 promoter15. It is expressed in other immune cells, epithelial tissues of the gut, the respiratory tract, and the tubules of the kidney13 etc. Interestingly, it was also identified as a lupus susceptibility locus by GWASs16, and a recent research found it was enriched to regulating the related gene for T cells, B cells, and peripheral blood cells (PBCs) in SLE17. However, its exact molecular function in LN is still unknown18.Big data genomic assays have advanced our understanding of the molecular heterogeneity of SLE10 and previous researches18. Then, we analyzed potential molecular mechanisms of co-DEGs and IKZF2 in LN. Protein–protein interaction network (PPI) network and ceRNA network of IKZF2 were also constructed to explore the possible targets of IKZF2. Moreover, we performed immune infiltration analysis and evaluated clinical value of IKZF2. Over all, this is the first study applying multiple datasets to understand the molecular mechanism of IKZF2 in LN in detail.In our study, we carried out a meta-analysis of publicly available data including not only tubulointerstitium but also glomerulus tissue samples from LN patients and pre-transplant healthy living donors (LD), and we identified common differentially expressed genes (co-DEGs) for each dataset. Among 26 co-DEGs, we selected IKZF2 for further analysis based on the GWASs19 to find publicly available gene expression datasets for LN including both tubulointerstitium or glomerulus tissue samples. The datasets of GSE3259120 and GSE11334221 were selected and downloaded by the R package GEOquery22. Each dataset was divided into two groups of tubulointerstitium (TUB) or glomerulus (GLOM) for subsequent analysis. For details of the LN cases and LD included in the two gene expression profiles, see Table We mined the Gene Expression Omnibus (GEO) database23 in the two gene expression profiles for the tubulointerstitium and glomerulus tissue type, respectively. The criteria of DEGs was defined with adjust P-value < 0.05, and up/down-regulated DEGs was defined with adjust p-value < 0.05 and |logFC|> 1. Then, the co-DEGs was the intersection of the four gene sets. Thus,Differentially expressed genes (DEGs) between LN cases and LD were identified by R package Limma24, one meta-analysis method, in order to make the result robust. In all, the results of DEGs were visualized by the R package ggplot225, pheatmap26, and VennDiagram27.The DEGs of each dataset were also integrated with R package “RobustRankAggreg”28. Kyoto Encyclopedia of Genes and Genomes  pathway is a database regarding diseases, genomes, biological pathways, chemical and drugs substances29. Disease Ontology (DO)30 annotates genes based on human diseases, helping to transform molecular discovery from high-throughput data into clinical relevance. We used the R package cluster Profiler31 to perform GO and KEGG analysis, and used the R package DOSE32 to conduct DO annotation. P-value < 0.05 was considered to be statistically significant for enrichment analysis.Gene Ontology (GO) analysis is commonly conducted for large-scale functional enrichment study33 is a public data source of known and predicted protein–protein interactions (PPI), from which we extracted the PPI of IKZF2-associated genes and used cytoscape34 to perform visualization. Then, we applied cytoscape plugin Molecular Complex Detection (MCODE)35 to identified highly interconnected clusters in the IKZF2-associated PPI network, and annotated GO terms of the specific cluster by ClueGO36 plug-in. Besides, the Database for Annotation, Visualization, and Integrate Discovery (DAVID)37and Gene Set Enrichment Analysis (GSEA)38 were also used to understand biological significance underlying the IKZF2-associated PPI network.STRING39 network related to IKZF2 including the mRNA, miRNA and lncRNA. The prediction of IKZF2-related miRNA targets was obtained from the starBase (http://starbase.sysu.edu.cn/index.php)40, miRDB (http://www.mirdb.org/)41, Targetscan42 (http://www.targetscan.org/vert_72/), TarBase.v.8 (http://carolina.imis.athena-innovation.gr/)43. miRNA-related lncRNA targets were predicted by miRNet (https://www.mirnet.ca)44. Based on miRNA-mRNA and miRNA-LncRNA, a ceRNA network was established and visualized using Cytoscape.To further analyze the potential targets and biological function of IKZF2 in LN, we constructed a competing endogenous RNA (ceRNA)45, which was based linear support vector regression to deconvolute relative abundance of 22 infiltrated immune cells. Principal components analysis (PCA) was applied to illustrate the differences in immune infiltrating cells between LN cases and LD, and the stromal/immune scores of each sample were shown by the R package pheatmap26. Besides, the correlation between the IKZF2 expression and the types of 22 immune cell was determined by Spearman’s correlation test46.For the co-DEGs between LN cases and LD of the tubulointerstitium or glomerulus group in the two datasets, immune infiltration analysis was performed using CIBERSORT46 was adopted to explore the correlation between IKZF2 expression and clinical features. Receiver operating characteristic (ROC) analysis was applied to evaluate whether the expression of IKZF2 could distinguish LN tissues from normal tissues. R package pROC47 was used to calculate the area under the ROC curve and plot ROC curve.Spearman’s correlation testP-value < 0.05, and |logFC|> 1 . The 26 co-DEGs were also mainly enriched in a number of signal pathways, which were highly related to cell proliferation and cell senescence including NF − kappa B signaling pathway, MAPK signaling pathway, Th17 cell differentiation and Toll − like receptor signaling pathway 51 and Fc receptors53. More than 80 genes have been identified associated with SLE by GWAS. Multiple signaling pathways were involved including type I interferon production 54, nuclear factor-kappa B (NF-κB) signaling pathway55 and effector and regulatory CD4( +) T cells especially Th 17 cells56. People has focused on the possibility that viruses may trigger SLE for decades. It suggested that CD8 + T cells are unable to control Epstein–Barr virus (EBV)-infected B cells in SLE patients, because of the higher viral load and faster seroconversion to infection and the molecular similarity between EBV nuclear antigen 1 and the common lupus autoantigen Ro59.The tissue injury or clinical manifestations are influenced by genetic, epigenetic, environmental, hormonal, and immunoregulatory factorsIn the current study, gene expression analysis was applied to identify co-DEGs and possible predictor factors between LN patients with healthy controls. A total of 26 co-DEGs, 717 miRNA and 10,125 lncRNA were identified and the PPI network was conducted. Most of the variable gene and pathways involved have been previously described in previous researches. Based on the analysis of co-DEGs expression, the IKZF2 was found to be down-regulated in the GLOM group and TUB group of the disease group. In order to explore the potential function of IKZF2 in LN patients, we analyzed the abundance of co-DEGs in 22 types of immune cells in LN tissues and normal tissues. The IKZF2 was correlated with immune cells including initial B cells and NK cell. The result showed the excellent value of IKZF2 for the AUC and we can substantially distinguish LN and healthy controls tissues.60. IKZF2, also known as HELIOS, a transcription factor expressing in 60–70% Foxp3þ Treg cells, recently has been demonstrated that it played a role in controlling certain aspects of Treg-suppressive function, differentiation, and survival61. Treg cells play an important role in maintaining immune self-tolerance by suppressing effector T and B cells. And it’s believed that abnormalities of Treg cells may lead to T and B cell hyperactivity and contribute to the pathogenesis of SLE. It’s reported that Tregs expressing IKZF2 have stronger immunosuppressive capacity62. In vitro experiments, it was found that IKZF2 deficient mice would acquire an auto-inflammatory phenotype similar to rheumatoid arthritis. And activated CD4 + and CD8 + T-cells, T-follicular helper cells and germinal center B-cells were noticed increased leading to production of autoantibody in circulation63. In recent study, CD4 + CD25 − Foxp3 + T cells major expressing IKZF2 were indicated significantly increased in active SLE patients. Detailed cohort analysis revealed increased proportions of CD4 + CD25 − Foxp3 + T cells in SLE patients especially LN patients. And the same type T cell was detected in urine sediment samples of patients with active glomerulonephritis and correlated with the extent of proteinuria64. Related data suggested that association between the percentage of FoxP3 + IKZF2 + Treg cells and the SLEDAI score is positive. There is a higher percentage of FoxP3 + IKZF2 + Treg cells in patients with more active disease compared to the patients with inactive disease or healthy donors62. Thus, it’s assumed that CD4 + CD25 − Foxp3 + T cells might serve as an important tool to recognize and monitor SLE patients with renal involvement. However, IKZF2 is considered as a marker of T cell activation and proliferation65. It’s possible that IKZF2 might become a new index for prediction and evaluation of LN.The IKZF2 gene encodes a member of the Ikaros family of zinc-finger proteins including Ikaros, Aiolos and HELIOS, which are hematopoietic-specific transcription factors involved in the regulation of lymphocyte developmentOur study is the first study to explore the molecular mechanisms of IKZF2 in LN by multiple datasets and applying the immune infiltration analysis. Moreover, the AUC of IKZF2 expression had very high diagnostic value in each dataset based on ROC curves, revealing it might be a potential biomarker for distinguishing LN patients and healthy ones. However, due to the ethical and traumatic reasons, SLE patients without nephritis may not be punctured by the kidney. We cannot compare patients with nephritis against patients without nephritis. Nevertheless, we tried our best to obtain all available data and also analyzed clinical characteristics correlation and relative risk assessment. In addition, we valued IKZF2 expression in IgA Nephropathy and ANCA associated vasculitis, verifying our conclusion in a broader range. Besides, further experimental verifications are necessary to illuminate the biological functions of IKZF2 in LN. Subsequently, we would like to perform more careful examinations of the function of IKZF2 in LN by combining in vivo and in vitro experiments. Therefore, we consider that our study may help to investigate the progress of LN, and that IKZF2 may become biomarkers and therapeutic targets of LN in the future.The absence of our patient cohort makes it difficult to verify the results. However, previous researches focused on the expression of IKZF2 in peripheral blood to evaluate the prognosis of the disease, and our study provided some evidence and new ideas about new sights for prediction and evaluation of LN. Our bioinformatics analysis identified the gene IKZF2 may be critical in the prediction and evaluation of disease activity. We hope this study may provide some potential evidence and ideas for the future treatment and monitoring of LN from new insights.Supplementary Information.


Converging evidence suggests a considerable plasticity of self-representation and self-other boundaries. But what are the factors controlling this plasticity? Here we explored how changes in an individual’s affective state impact his/her self-other representation. Participants watched short videos to elicit happiness or sadness before rating unfamiliar faces with happy or sad expressions. After watching the happy video, participants showed more self-other integration of happy than sad faces, while watching the sad video reduced integration for both happy and sad faces equally. This finding suggests the interaction of two processes: Positive mood biases metacontrol toward flexibility, which fosters the processing of features in which self and other might overlap, and possible overlap increases self-other integration. Negative mood, in turn, biases metacontrol toward persistence, which focuses processing on strictly task-relevant feature dimensions, so that possible overlap is less likely to have an impact. When we reach for a cup of water, we do not need to search for our hands first—we in some sense know who we are. This kind of “self-evidence” raises a fundamental philosophical and psychological question: how do we feel and know our bodies? The answer to this lies in how people represent and recognize themselves. The relationship between my body and “I” is very different from the relationship between my body and others, and the individual’s ability to recognize that s/he is different from the outside marks the formation of self-awareness. Human infants can only pass the mirror self-recognition task after a certain age, and Self-recognition presupposes a distinction between the self and others, which, in turn, is based on how individuals represent these two. In other words, self-representation is the basis of self-recognition and the perceived ownership over one’s body . The rubObservations that the self-other distinction is not fixed but variable raise the question when and how this distinction changes and which principles these changes may underlie. There is a consensus that perceiving the states of others facilitates the activation of personal, affective, and conceptual representation . The preThe second mechanism that might render emotions and affective states relevant for self-other integration is metacontrol. This mechanism refers to the fact that adaptive behavior requires a continuous balance between cognitive persistence and flexibility . AccordiThe present study sought to test these two predictions by combining two experimental factors. For one, we attempted to induce positive or negative mood by presenting participants with what we considered happy or sad videos, and confirmed the successful induction by means of affect ratings. Second, we presented participants with unfamiliar faces showing happy or sad facial expressions and asked them to rate the closeness between themselves and this face by means of the Inclusion of Other in the Self (IOS) Scale. Since its development, the IOS scale has been used effectively in various studies regarding relationships and can assess self-other closeness as good as or even better than other more complex and lengthy measures . Our firSD = 2.197, range 19–28) from Hangzhou Dianzi University, unfamiliar with psychological experiments, participated in exchange for course credit or pay. All participants were physically healthy and had no history of mental health problems. Ethical approval for this study was obtained from the local Psychology Research Ethics Committee, and written informed consent was obtained from all participants.Eighty student volunteers  and facial expression . The experiment consisted of two blocks. Each participant watched the happy video and the sad video sequentially . After each viewing, they would see a happy face and a sad face and needed to rate the overlap between themselves and the pictures (the computer randomly assigned the order of the happy and sad expressions).M = 7.78, SD = 2.006) and one with the highest level of sadness  were selected as the emotional primes for this experiment.We used two different videos as emotion priming materials, as indicated by a meta-analysis showing that film scenes are the most effective way of priming mood . First, Facial expression pictures were made by composing multiple photos of real people and processing them with face morphing software, so to disguise their identity. We first invited volunteers to the lab for the photoshoot, and the models were asked to maintain as neutral a facial expression as possible. We took photos of 16 male and 16 female faces with neutral expressions in this process. The photos were then synthesized in groups of 8 photos of the same gender by the synthesis software (FaceFusion) to obtain four synthesized photos of the faces (there were two different pictures for each gender). To process the synthesized faces with neutral expressions, we recruited thirty volunteers and asked them to complete the following task: moving the degree bar to get an expression that they thought naturally represented happiness or sadness with the same software. We then averaged the results and used them as a criterion to generate the corresponding happy/sad expressions.”) to pleasant feelings (“9”), and the vertical axis indicates the level of arousal, ranging from sleepiness (“1”) to high arousal (“9”). Participants were required to assess their emotional state after watching the video twice during the experiment. To avoid the interference of numerical or alphabetic information on their choices, we presented them with a blank grid with explanations on horizontal and vertical coordinates.We adopted the Affect Grid AG;  to assesThe Including other in the self (IOS) scale is a pictorial measure of the relationship between self and other, in which “self” and “other” are symbolized by two circles, respectively. Participants are asked to select the arrangement of the two circles that best describes their relationship between themselves and the specific “other” . These aThe experiment was performed on a computer, and all data  were recorded automatically in the database. Participants sat in front of the computer screen and performed the appropriate actions with the mouse. Before the experiment started, they had enough time to read the instructions and decide whether to continue or withdraw from the experiment. After confirming the start of the experiment, the participants selected their gender to ensure that they would see a face of the same gender as their own. After a “+” lasting for 500 ms, a composite photo with a neutral facial expression and the same gender as the participants appeared on the computer screen. Below the photo, there was the IOS scale, and the participants needed to select the most appropriate option according to their current perceived self-other overlap. Afterward, the participants watched a happy or sad short video of 30 s duration. They were allowed to control the starting time. After the video, the AG would be presented on the screen. Participants were instructed to click on the corresponding position in the grid according to their current affective state. Then, the same face but with different facial expressions appeared again with the requirement to judge the overlap degree. The photo was presented in animation, starting with a neutral face for 2 s (the same as the face the participants had seen before), and then the neutral expression gradually changed to a happy facial expression or a sad expression within 5 s. Once the animation was over, the IOS scale appeared again, and the participants could select the corresponding number to indicate their felt overlap degree with the new expression. This process was repeated twice, separated by a “+” lasting for 500 ms, with the expressions reversed, and random parameters of the program controlled the order of presentation. Specifically, after watching the video, the participants either saw the happy expression, then the sad expression, or vice versa.The first half of the experiment ended after two consecutive overlap degree selections. In order to reduce the effect of experience on the subsequent experiments, the participants were asked to solve some simple calculation problems between two parts of the experiment, which lasted for 5 min. The program recorded the number and percentage of correct answers and gave feedback on the correctness during the experiment to attract the participants’ attention to the arithmetic task. The video in the second half conveyed the opposite mood of the first, but the procedure was the same. The whole experimental procedure is shown in During the experiment, we thus collected participants’ basic information (age and gender), IOS scores toward neutral faces before watching the videos, AG scores after watching the video, and IOS scores for happy and sad faces after watching the videos. The IOS scores for neutral faces were the baseline of the self-other overlap degree, and the results obtained by subtracting the baseline from the IOS scores toward happy and sad faces after watching the videos represent how self-other overlap changed. Specifically, we reorganized the data and obtained arousal and valence scores after watching the happy/sad videos, which were used to validate the emotional priming effects. Also, IOS changes  before and after watching the videos were calculated. The reason for choosing IOS changes instead of raw scores as the dependent variable was to consider the individual differences between the subjective judgments of different participants; therefore, the degree of change would better reflect the interaction between watching happy/sad videos and the assessment of self-other integration.F = 13.053, p = 0.001, ηp2 = 0.142 , and valence, F = 220.394, p < 0.001, ηp2 = 0.736 , which indicates that the videos successfully primed the participants’ affective states as intended.As a manipulation check, we first analyzed the priming effect of video type on participants’ affective states by means of a univariate ANOVA. The effect of video type was significant for both arousal, F = 20.862, p < 0.001, ηp2 = 0.209, due to a drop of IOS for sad faces as compared to happy faces, and a significant interaction, F = 25.211, p < 0.001, ηp2 = 0.242, indicating that the facial-expression effect was more pronounced after watching the happy video than the sad video. The main effect of video type was far from significant, F < 1. Two-tailed paired samples t-tests further revealed that, after watching the happy video, IOS scores increased significantly for the happy expression, t(79) = 2.489, p = 0.015, d = 0.278, and decreased significantly for the sad expression, t(79) = 4.899, p < 0.001, d = 0.548, and these two IOS changes differed significantly, t(79) = 6.724, p < 0.001, d = 0.752. In contrast, after watching the sad video, IOS dropped significantly for both happy and sad expressions, t(79) = 3.086, p = 0.003, d = 0.345, and t(79) = 2.474, p = 0.016, d = 0.277, respectively, and these two IOS changes did not differ, t(79) = 0.773, p = 0.442, d = 0.086. To test our hypotheses, we then submitted the IOS rating changes  to 2 × 2 ANOVA with the factors video type (happy vs. sad video) and facial expression (happy vs. sad face). There was a significant main effect of facial expression, This study aimed to investigate the principles underlying people’s tendency to include others into their self-representation. We specifically focused on the role of affective state and made two predictions. The first was based on TECs feature-overlap principle, which suggests that people would include unfamiliar others more into their self-representation if they are sharing features with them. This predicts that IOS scores should be higher if the expression shown by an unfamiliar face matches (vs. mismatches) the participant’s affective state as induced by the video. The second was based on metacontrol theory, suggesting that people should tend to include others into their self-representation more if they are in a happy mood. We primed happy and sad affective states by means of happy and sad videos, respectively, and measured the degree of self-other inclusion through IOS. Our manipulation checks confirmed that the mood induction worked as expected , suggesting that the videos were effective in inducing happy and sad states in our participants.happy faceM = 3.38, sad faceM = 3.55). Such a phenomenon might be explained under the umbrella of metacontrol theory, however (see below).The first prediction was confirmed. It amounts to the prediction of an interaction between video type and facial expression, which was indeed significant. The pattern also confirmed our expectations: IOS scores were higher if the video-induced affective state matched the expression shown by the unfamiliar face. This provides evidence for the importance of the feature-overlap principle of TEC for predicting self-other integration. However, the prediction was more successful for positive-mood induction than it was for negative-mood induction. Indeed, the predicted feature-overlap effect was significant for positive-mood induction only, whereas the negative-mood induction resulted in an overall drop and an only numerically higher IOS score for the sad face—far from any conventional significance level. This might have been due to a greater efficiency of the positive-mood induction than of the negative-mood induction, but given the very low valence scores after the sad video (2.25) this seems rather unlikely. Another possibility relates to theoretical suggestions that happy mood facilitates global processing, whereas sad mood boosts local processing . AccordiThe second prediction was not confirmed. It amounts to predicting a main effect of video type, which was far from significance. At the same time, however, the metacontrol-inspired prediction accounts for three of the four data points obtained. The prediction worked perfectly well for the sad video, which indeed reduced IOS scores for both kinds of faces. It also worked for the happy faces after the happy video, for which IOS scores increased significantly. The only exception refers to the sad faces after the happy video, for which IOS scores are much lower than for sad faces after the sad video. This suggests that the mechanisms behind our two predictions might not be functionally independent, in the sense that the impact of mood on metacontrol might have repercussions for how mood impacts the processing of feature overlap between self and other. Indeed, this would fit with our observation that the success of the feature-overlap prediction depended on the induced mood.Taken altogether, this suggests that sharing an affective state with an unfamiliar face increases self-other integration if, and only if, metacontrol is biased toward flexibility, like when being in good mood. It is assumed that flexibility is accompanied by an integrative processing mode that does not strongly distinguish between strictly task-relevant and task-irrelevant information, in contrast to the strong focus on task-relevant information under a persistence bias . Given tM = 3.894, SD = 1.654), indicating a certain overlap between the participants and the synthetic photos they saw. Such a result may be due to the fact that the photos we used in the experiment were synthesized from real photos (each synthesized face was composed of eight different photos with the same gender). Although different individuals look very different, there may be some similarities between these differences, which could be one of the bases for the ability of human beings to empathize with others. However, this remains a speculation that calls for future research.As an aside, we found that participants’ baseline level of IOS ratings before watching the video was close to moderate , National Social Science Fund of China (20FZXB017), as well as China Scholarship Council (CSC).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The six-item Positive And Negative Syndrome Scale (PANSS-6) is short psychometric valid scale quantifying the severity of core schizophrenia symptoms. Using PANSS-6 to guide treatment decision-making requires that staff members’ ratings are valid and reliable.The objective of the study was to evaluate whether such valid and reliable PANSS-6 ratings can be obtained through a video-based training program.One-hundred-and-four staff members from Aarhus University Hospital - Psychiatry, Denmark participated in the training. Participants conducted baseline PANSS-6 ratings based on a video of a patient being interviewed using the Simplified Positive And Negative Symptoms interview (SNAPSI). Subsequently, a theoretical introduction video was displayed followed by five SNAPSI patient interviews. After each SNAPSI video, individual ratings were performed before a video providing the gold standard scores was displayed. The validity of ratings was estimated by calculating the proportion of participants not deviating from the gold standard scores with >2 points on individual items or >6 points on the PANSS-6 total score. Reliability was evaluated after each step in the training by means of Gwet’s Agreement Coefficient (Gwet).By the end of the training, 72% of the participants rated within the acceptable deviations of the gold standard, ranging from 60% (nurses) to 91% . The reliability improved (Gwet baseline vs. endpoint) for all PANSS-6 items, except for Blunted affect.The majority of the staff members conducted valid PANSS-6 ratings after a brief standardized training program, supporting the implementation of PANSS-6 in clinical settings to facilitate measurement-based care.Dr. Opler is a full-time employee of MedAvante-ProPhase Inc. Dr. Correll has been a consultant and/or advisor to or have received honoraria from: Acadia, Alkermes, Allergan, Angelini, Axsome, Gedeon Richter, Gerson Lehrman Group, Indivior, IntraCellular T


Even though video games have been present among children for many years, children are using them more continuously and in an abusive and indiscriminate way nowadays because of the “technological boom”. It is affecting the behavior of children and adolescents. This is the reason why we are carrying out this systematic review. The main objective of this article is to investigate literature that directly connects the continuous and undifferentiated use of video games with the emergence of behavioral disorders in children and young people. The PRISMA statement was followed in the process of this article. We used SCOPUS, Web of Science and PubMed as databases, moreover, we searched studies with a scoping review. The results indisputably supported six out of seven of our hypotheses. We find that the excessive use of video games causes addiction to technology, aggressive behaviors, sleep disorders, and poor school performance. In addition, it hinders social relationships and the development of emotional intelligence. To conclude, it is necessary to correctly use video games in particular, and technologies in general, adapting their content to children’s age, as well as the amount of time that they dedicate to use them. New technologies are blameworthy for the significant changes that society must face. We are in the digital age and technologies are progressing at an exponential rate, building our current situation . But whaDue to the COVID-19 pandemic, the average time children and adolescents spend playing video games or connected to the Internet has increased , as thesChildren and teenagers are the main groups that play video games . These gRegarding primary education, some basic cognitive changes, which technologies could influence, can be highlighted. Students foster their chosen capacity consciously, try to achieve their objectives, their memory and attention capacities improve, and thinking becomes logical and flexible . MoreoveOn the other hand, students in secondary education experience different cognitive changes in comparison to primary school students. This stage represents a period of big changes . In factStudies have revealed that there is a relationship between the playing style, the intensity of use, and the player’s personality . HonestyIn this sense, children and adolescents with low self-esteem, honesty, and/or humility choose to play violent games. They show psychopathic traits, and their gaming behaviors are aggressive . For itsChildren and adolescents with high conscientiousness scores choose challenging and team-based games . They acOn the other side, users with extroverted personalities participate more in online environments and prefer survivor or daredevil games . Thus, gFinally, children who are more anxious and dependent participate more in working on in-video games and professions because there is a lack of risk and failure . MoreoveVideo games are also used as an online tool to escape loneliness . HoweverIn the technological environment, we can find video games. They have an essential role in children’s and adolescents’ life because they modify substantially their way of understanding, interacting, and communicating .The video game market is the leading audiovisual and interactive leisure industry in our country, in fact, four out of five families have a gadget to play with at home . These dChildren and adolescents seem to have a great fascination for and attraction to games. The reason could be because videogames are more than entertainment . Video gBefore talking about the negative effects of excessive use of video games, we must differentiate between two concepts : “internIAD is understood as “the excessive use of the Internet in an uncontrolled and time-consuming manner that leads to timelessness and disruption of people’s lives”  (p. 1). IGD is included as a disorder in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5), but IAD is not included. On the contrary, both concepts cause negative impacts and provoke behaviors that could lead to psychological, social, and personal adversities .Despite the fact that young people are considered digital natives , they arExcessive participation in video games is associated with undesired behaviors such as stress, emotional changes, aggressive behaviors, hyperactivity, and impulsivity . FurtherWe can summarize the negative effects of excessive use of video games as the following: reduced attention, emotional and social intelligence deterioration, social isolation, and sleep disorders .In addition, gaming disorders are associated with deregulation of the dopamine reward system , poor loEven some recent studies show that children may experience cardiac arrhythmia during electronic games due to adrenergic stimulation related to the emotionally charged video game environment .Regarding the importance of video games for young people, we focus on the adverse consequences in behavior because of violent and inadequate video games.However, it is also necessary to consider the benefits of video, with the objective of understanding how video games impact the development of children and adolescents. The nature of video games has changed in recent years, becoming increasingly realistic and diverse . For thiIn respect of the cognitive domain, playing video games, especially action games, promotes a wide range of cognitive skills, such as faster and more accurate attention , higher Regarding the motivational domain, video games provide players with immediate feedback that rewards effort and keeps players within what Vygotsky  called tRespecting the emotional domain, previous studies show a causal relationship between playing video games and improving mood or increasing positive emotions. Children describe playing video games as a rewarding experience that provokes a sense of control, thus increasing their self-esteem . Finally, on the subject of the social domain, the virtual social communities created in video games make children quickly learn social skills and prosocial behavior. In this sense, effective cooperation, support, and helping behaviors are developed . SubsequThe overarching objective of this study is to investigate literature that directly relates the continuous and undifferentiated use of video games with the emergence of behavioral disorders in children and young people.Specifying this aim, some hypotheses were raised:1H.Playing video games constantly is a risk factor; it could provoke technology addiction disorders.2H.Use of technology and video games affect sleep.3H.Aggressive video games foster aggressive behaviors in real life.4H.Excessive use of video games makes social relationships more difficult. 5H.Excessive use of video games may provoke a lower academic performance. 6H.Excessive use of technologies and video games hinders emotional intelligence development.7H.Playing video games provokes attention deficit and hyperactivity disorder (ADHD).In this systematic review, the statement of the preferred reporting items for systematic review and meta-analyses 2020 (PRISMA statement)  was follPubMed, Web of Science, and SCOPUS were the databases chosen. The searches were performed from September to November 2022, with the objective of compiling the largest list of studies we could. We looked for relevant articles about the effects of playing video games on behavioral disorders. Additionally, manual searches were carried out on the references of the studies selected and we identified potential studies .Two Boolean operators were used: AND/OR. The descriptor words were “behavioral disorders”, “behavioral problems”, “video games”; they were used in different ways depending on the database. We did not apply any filters or limitations because we wanted to identify the greatest number of appropriate studies . It is tIn Firstly, 113 articles were identified in an automatic search. They were imported to a Word file to identify duplicate articles. Out of 113, there were 5 duplicated. Secondly, we found relevant studies by abstract, keywords, and title. This phase was screened by two independent researchers to eliminate irrelevant articles, which resulted in 70 articles. They used the following exclusion and inclusion criteria:Articles that included our keywords  were accepted. We refused those articles which did not talk about our topic.Articles from scientific specialized journals were included. We refused studies in book chapters, doctoral theses and grey literature in general.Articles with open access were included. We refused articles to which we did not have complete access.Articles were required to be completed works; we refused in-process articles.Participants were required to be children or young people; adults’ samples were refused.A total of 38 articles were selected using these criteria.Thirdly, the same two independent researchers assessed the articles chosen for eligibility by reading all the text. They applied the same criteria and found 17 relevant articles which were included in our systematic review. In Seventeen studies were analyzed to extract data from them. In a table, two independent researchers compiled the names of authors, publication date, objective(s), the instrument used, sample, age of the sample, country, and outcomes. A third researcher validated the collected information. When some required information was not available, we asked the corresponding author to obtain it; however, sometimes we did not receive any reply. The instruments used included the variables measured and the age of the sample included the mean.Descriptors were discussed between the authors who reached an agreement. Then, the selection process was carried out by two authors independently. Any disagreement between the two reviewers was solved by consensus. Dividing the number of agreements by the total number of disagreements plus agreements, we calculated the inter-rater reliability. Then we multiplied by 100 to get the percent. The inter-rater reliability was 94%.A total of seventeen articles were analyzed in detail. They were carried out across several countries around the world. Four were from the United States ,72,73,74The age range was very large, from 6 months to 23 years old. However, the majority of the studies had a population aged from 9 to 15 years old. Regarding the instrument, they were varied. Questionnaires and interviews conducted by researchers of the study were the instruments used most  ,82,83,84Before starting to analyze the influences on children of playing video games, we want to highlight the importance of considering the different types of games and devices that are included in the articles selected. In this sense, it is also necessary to specify the age-appropriateness, the use frequency, the timetable, or the time they spend gaming. For these reasons, we analyzed each article in detail.This information is mentioned roughly in sociodemographic data in most of the articles; there are only a few that explain it in an explicit way. Moreover, Yousef et al. specify that the limitation of their work is there is no evaluation of the TV programs and video games. , AccordiNevertheless, data found in some of the studies reveal important findings about the content of games and aggression or delinquent behaviors. For instance, Wei showed that playing violent video games is positively correlated with violence tolerance and aggressivity .Regarding the age adequacy of each game used, Yilmaz et al. had a sample aged 9–10 years old and most of the games included in their investigation are for teenagers over 13 years old . In thisSimilarly, the use frequency and the time children spend gaming are essential to determining a limit between adequate use and excessive use. Accordingly, Cheung et al. affirmed that more frequent use of digital screens provokes less sleep time. In numeric data, each additional hour of tablet use was associated with 15.6 min less total sleep . FinallyWe find a positive correlation between the use of video games and technology addiction ,84. ThisWe observed in We find a close relationship between the number of hours playing video games per day and the occurrence of aggressive and delinquent behaviors ,75,80,82We find a negative correlation between academic performance and hours playing video games . LikewisWe observe a close relationship between anxiety symptoms in adolescents and abusive use of video games; however, there are no significant differences in children . On the We find a negative relationship between playing time and sustained attention . In addiIt is observed that video games predispose players to hyperactivity . NeverthDespite the fact that social relationships were not in our minds when we started to investigate video games and technologies, several results emerge from the articles selected .We find a positive correlation between time spent playing video games and social problems . In addiThis systematic review has given an analysis of the effects of video games in several areas of children’s and teenagers’ lives. Through this epigraph, we analyze the hypothesis we raised before.1 “playing video games constantly is a risk factor, it could provoke technology addiction disorders” is confirmed by Teng et al. [In this way, we can observe that Hg et al.  and Salig et al. , whose rg et al. . Not onlg et al. . Moreoveg et al. ,76. Thisg et al. ,91,92. Tg et al. . Consideg et al. .The Interaction of Person–Affect–Cognition–Execution (I-PACE) explains the predisposing variables for problematic internet behavior  . It inclCOVID-19 has impacted aspects of human life in general, but one of the key research priorities was mental health . Chronic2, we found a major number of articles that studied the relationship between the use of technology or video games and sleep disorders [Regarding Hisorders ,84,86,87isorders ,78,81,86isorders ,81 when isorders ,103,104.isorders  disagreeisorders .3 is related to aggressive behaviors. The American Psychiatric Association affirms that scientific research has demonstrated a close relation between violent video games and increases in aggressive cognitions, behaviors, and affect. [H affect. . Our fin affect. ,73,80,82 affect. ,84. Hasa affect.  confirm  affect. , and Gre affect.  say thatSociety frames aggression in negative terms . It is dFurthermore, significant differences were found between genders. Boys enjoyed playing video games more than girls . Females4) is corroborated by our findings [These behaviors provoke difficulties in social relationships. Our hypothesis . At first, it was rejected because we found that ADHD was the cause of video game addiction [Finally, Hddiction . Howeverddiction ,85,86. Mddiction . In thisddiction ,127. In ddiction . All of ddiction . Some main ADHD symptoms predict these types of disorders, such as “being easily bored” or “having an aversion for delayed rewards” . These pRelating the two concepts, Weiss et al.  affirm tTechnologies have a strong impact on people’s lives, in general; however, the groups of people who are more affected by them are children and teenagers. The main objective of this study was to find the effects of using video games in a continuous and undifferentiated way on behavioral disorders in children and young people. As we verified, using video games cause a lot of problems, not only in addition to the Internet or technologies but also in children and young people’s behaviors, social relationships, academic performance, sleep disorders, and so on.PRISMA methodology is a good method in social science research because it makes studies transparent and open to suggestions and comments. However, our review has one limitation. We refused grey literature, such as other systematic reviews. We selected exclusively articles from specialized journals because we wanted scientific rigor in our article.Finally, this article would be ideal for families and teachers of primary and secondary education, so they can understand what happens with their children if they do not control the use and type of video games. In this sense, they can use parental controls to avoid excessive use of these devices and inappropriate video games. Parents and guardians can control what children and adolescents are doing without being present. The Entertainment Software Ratings Board website has different manuals explaining how to restrict video game use, depending on the types of devices and software . Blockin


Soundscape has been valued and practiced in classical Chinese garden designs. Some authentic patterns were even mentioned and used in gardening books hundreds of years ago. Though these patterns are well-known, how they work in a classic Chinese garden is still unclear. In this study, we chose one of the most famous soundscapes called Tingyuxuan  in Zhuozhengyuan (Humble Administrator’s Garden), Suzhou as the object. A video of the Tingyuxuan was captured on a rainy day, along with its sound. Twenty-four participants were asked to view this video twice . Eye-movement data and the subjective evaluation of participants were collected. The results showed that the participants’ visual attention is influenced by the sound of rain and helps them identify and observe the main element of the soundscape. Furthermore, participants experienced more positive feelings when viewing the video with the audio on. Yuan Ye is the only book on classical Chinese gardens and was written by Jicheng in the Ming Dynasty. He outlined the methods of using soundscapes to design classical gardens, such as “the rain falling on Musa basjoo at night,” “the breeze blowing through the willow branches,” and “the sound of the bluster of the wind”  in Zhuozhengyuan in Suzhou, China. Zhuozhengyuan has a 500-year history, and this garden is considered by some to be one of the finest gardens in southern China, in fact, it is one of UNESCO’s World Heritage Sites. The Rain Hall is in one corner of the garden, surrounded by 2 m-high walls. Thus, this scenery is relatively isolated from other parts of the garden. The isolation ensures that this portion is not influenced by other spots of the garden. Since Chinese classic gardens usually overlap multiple scene spot to enhance their performance, this place is suitable for the present study. Based on a classification of 62 soundscape sites in traditional Chinese gardens, the site for this experimental video was designed to emphasize the sound of rain through as built . The plahis spot . The souenturies . SpecifiMusa basjoo. There were no visitors around this place when the video was taken. The sound of the video was analyzed in terms of spectrum and pitch using Adobe Audition 2020, which is a digital audio workstation developed by Adobe Computer Software Company . The three feature times at which the leaves shake after the rain falls on the Musa basjoo  were matched using the software in Musa basjoo leaf had identifiable characteristics in a medium rain environment . The video was taken on a moderately rainy day, with no wind and no visitor was around, at 7:00 a.m. The vantage point was inside the pavilion, with the camera facing the pond and  Company . In ordeironment .via microphone.The video was then replayed in a Windows computer with a 1920×1080 px, 16:9, 22-inch monitor. A two-speaker system was used , with the speakers placed on either side of the monitor to simulate a real experience in the experimental room  , specifit-test, given α = 0.05, two tails, effect size dz = 0.6, non-centrality parameter sigma σ = 2.94, critical t = 2.07. Our sample of 24 was sufficiently powered, with actual power being always greater than 0.8.Twenty-four students from the Jiaxing University  voluntarily participated in the study. They were all physically healthy with good eyesight  and normal hearing. The expectation of space, which is based on memory, is an important factor in soundscape perception. Therefore, only those who never visited the room and garden were included in this experiment. Thus, we could focus on the effect and interaction of audio-visual multisensory stimulation. Human research protocols for the study were approved by the Ethics Committee of Jiaxing University (LS2019-105). Using the statistical program G*Power 3.1, we estimated that a minimum sample size of 23 was necessary for a paired–Eye tracking and eye fixation were recorded using an eye-mark recorder . This device was used to measure eye movement. When viewing a scene, eye movements reflect the patterns of visual exploration . In prevFor the assessment of their overall subjective feelings associated with the video (including both sound and vision), the subjects were asked to fill out a questionnaire using a semantic differential (SD) method . Based oWhen the participants arrived on the morning of the experiment, they were fully informed about the objectives, procedures, and how the instruments would be used. After receiving a description of the experiment, the subjects signed an agreement to participate. The experiment was performed from September 15 to 27, 2019. The time of the experiment was determined based on the weather forecast, avoiding sunny days so that participants would not feel the gap between the video content and the weather outside. Participants were instructed to turn off their mobile phones. In the experiment room, the participant sat on a comfortable chair, in the most comfortable position for them, 50 cm away from the monitor. The eye tracker device was placed in front of the participant to accurately record eye movement. The calibration for eye movement was carried out. The participant was instructed to relax and close their eyes and not move their head. While the participant rested with their eyes closed, the first video was replayed on the monitor. Simultaneously, the examiner asked the participant to open their eyes. The eye movements were recorded for 60 s. The participant was then asked to close their eyes again. After completing the psycho-physiological measurements, the participant was asked to fill out the SD questionnaire for the recent video. Then the whole process was repeated using the same video without sound. The order of the video was randomized across the participants (video with sound [VS] and video muted [VM]) in order to avoid habituation effect. The total procedure lasted approximately 15 min for each participant.t-test was used to verify the differences in psychological effect assessed by the SD scale. SD data were analyzed by comparing rating scores between the videos in the test. For the eye-movement data: to characterize the eye movements, two individual elements were identified—-Musa basjoo and Pond—-and the remaining part of the scene is background, completed the visual field, that is the two areas of interest (AOI) and the background in this experimental analysis  and 95% CI. All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) version 26.0 software .A paired analysis . We firsMusa basjoo is significantly longer compared to that of VM, Cohen’s d (based on differences) with Hedges correction = 0.57. Moreover, the number of fixations of VS on Musa basjoo is significantly greater compared to that of VM, Cohen’s d (based on differences) with Hedges correction = 0.45. Ratio of fixation duration on Musa basjoo was significantly greater when viewing the VS compared to VM, Cohen’s d (based on differences) with Hedges correction = 0.48. Ratio of fixations on Musa basjoo was significantly greater when viewing the VS compared to VM, Cohen’s d (based on differences) with Hedges correction = 0.35. A significant difference was not observed for the total fixation number, fixation duration, and average fixation duration between the whole viewing of both videos . The patients/participants provided their written informed consent to participate in this study.MS managed the project, designed the task, conducted the human experiments, wrote the main manuscript text, and gave advice about the manuscript. LD managed the project, statistically analyzed the data, wrote parts of the main manuscript text, and prepared the figures and tables. Both authors have read and agreed to the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pierre Robin syndrome (PRS) neonates are one of the most difficult cases to intubate even for an experienced paediatric anaesthesiologist. We describe a case of a PRS-related anatomical anomaly that hindered attempts to manage the airway and the final approach that made it possible to insert an endotracheal tube (ETT). We describe the novel use of a video ureteroscope (Olympus URF-V2) as an airway endoscope. A 7-day-old, 2-kg boy was referred to our tertiary care hospital with diagnosed PRS. He was planned for correction of the mandible with mandibular distraction osteogenesis under general anaesthesia. Fibreoptic scope  revealed the epiglottis lying on the posterior pharynx, which could not be manoeuvred. Due to repeated attempts, the patient developed laryngospasm, and his pulse arterial oxygen saturation (SpO2) was reduced to 70%. Following jaw thrust and slight pulling of the tongue with Magill’s Forceps, a 150-cm long and 0.035-inch diameter atraumatic, Roadrunner® hydrophilic polyurethane-coated guidewire was introduced through the working channel of the video ureteroscope into the trachea under the vision (and a 3.5-mm ID ETT was railroaded over it and a definitive airway was established). A flexible fibreoptic ureteroscope may be useful in the management of a difficult airway and may become an important tool in the armoury of an anaesthesiologist. At our institute, which is a tertiary care centre, we are now training and utilising video-ureteroscope as an airway endoscope. To our knowledge, there is no documentary evidence of the use of a video ureteroscope for difficult airway management of a neonate. Micrognathia, glossoptosis and upper airway obstruction are the three characteristics of the Pierre Robin Syndrome (PRS), a congenital defect that usually coexists with cleft palate . Because2) of 77.8 mmHg. He had a 0.2-cm mouth opening and hence laryngoscopy was not possible. His saturation was improved to 95% by oxygen with a facial mask. The same night he deteriorated to an SpO2 of 80%. Next, we tried blind nasal intubation with an ETT loaded with stylet, which failed. Then, we tried Size 1 laryngeal mask airway  which failed as well. His respiratory distress was improved after we placed an NPA and saturation returned to 100%.A 7-day-old, 2-kg boy was referred to our tertiary care hospital with diagnosed PRS. He had agnathia, glossoptosis and cleft palate. The patient was a preterm child born by normal delivery at 34 weeks of gestation to a middle-class family of south Asian ethnicity. Upon admission, he developed cyanosis with an arterial saturation measured by pulse oximetry (SpO2) of 90% and a venous carbon dioxide pressure (PvCOHe was planned for correction of the mandible with mandibular distraction osteogenesis (MDO) under general anaesthesia. After giving oxymetazoline nasal drops to dry his secretion, we slowed dialled sevoflurane to 6% and then back to 3% to maintain his spontaneous breathing. Fibreoptic scope  revealed the epiglottis lying on the posterior pharynx, which could not be manoeuvred. Due to repeated attempts, the patient developed laryngospasm, and his (SpO2) was reduced to 70%. Anaesthetic depth was increased with sevoflurane and SpO2 improved to 94%. Once laryngospasm settled, a 2.8-mm outer diameter video ureteroscope was introduced through the nasal cavity. Glottis was visualized but the ETT could not be negotiated into the trachea. Following jaw thrust and slight pulling of the tongue with Magill’s Forceps, a 150-cm long and 0.035-inch diameter atraumatic, Roadrunner® hydrophilic polyurethane-coated (PC) guidewire was introduced through the working channel of the video ureteroscope into the trachea under the vision  and a 3.Infants with PRS may exhibit stridor, chest retraction, sleep apnoea, cyanosis and respiratory insufficiency along with different degrees of upper airway blockage , 4. FurtWe describe the novel use of a video ureteroscope (Olympus URF-V2) as an airway endoscope. In urology, kidney stones are typically removed with a video ureteroscope. It has a wider field of vision and is longer than a FOB. Currently, sizes with an outside diameter of only 1.8 mm are accessible. In contrast to a FOB, which only offers an angulation of 160° up and 90°, it offers 275° of up- and downangulation [We conclude that a flexible fibreoptic ureteroscope may be useful in the management of a difficult airway and may become an important tool for difficult airway management.A.B. was responsible for the conception and design of the work; R.G. did the analysis of review of literature; S.T. drafted the work and compared our analysis with previous literature on the case. M.K. was responsible for substantively revising the case report. The manuscript has been read and approved by the authors, the requirements for authorship as stated earlier in this document have been met and the author believes that the manuscript represents honest work if that information is not provided in another form. The certificate to this effect will be attached.


Video summarization (VS) is a widely used technique for facilitating the effective reading, fast comprehension, and effective retrieval of video content. Certain properties of the new video data, such as a lack of prominent emphasis and a fuzzy theme development border, disturb the original thinking mode based on video feature information. Moreover, it introduces new challenges to the extraction of video depth and breadth features. In addition, the diversity of user requirements creates additional complications for more accurate keyframe screening issues. To overcome these challenges, this paper proposes a hierarchical spatial–temporal cross-attention scheme for video summarization based on comparative learning. Graph attention networks (GAT) and the multi-head convolutional attention cell are used to extract local and depth features, while the GAT-adjusted bidirection ConvLSTM (DB-ConvLSTM) is used to extract global and breadth features. Furthermore, a spatial–temporal cross-attention-based ConvLSTM is developed for merging hierarchical characteristics and achieving more accurate screening in similar keyframes clusters. Verification experiments and comparative analysis demonstrate that our method outperforms state-of-the-art methods. With the rapid development of multimedia information technology and intelligent terminal equipment, video data have emerged as a critical medium of information transmission due to its lack of reading threshold and high data-carrying capacity. However, the openness and informality of video production result in the accelerated growth of video data and several undesirable phenomena, such as widespread data redundancy , unclearRecent methods for video summarization rely heavily on the superior performance of deep learning, particularly in feature extraction. In addition, feature extraction is a fundamental component of video summarization algorithms that extract time series ,6 or spaTo be more precise, existing algorithms can meet a portion of the user-centered requirements and capture good summarization performance. However, the following challenges remain: contradiction between breadth extraction of salient video characteristics and multi-source of user diversified requirements; the contradiction between depth extraction of salient video characteristics and unbounded new video productions; the contradiction between similarity frames and more accurate keyframe screening.A hierarchical spatial–temporal video feature extraction approach is developed. The purpose is to ensure as much characteristic information as possible for generating video summarization;A cross-attention cell that combines the local and global features information based on DB-ConvLSTM is proposed. It seeks to emphasize the difference between related frames and achieve more accurate screening in similar keyframes clusters for video summary generation;Verification experiments and comparative analysis are performed on two benchmark datasets (TVSum and SumMe) for this paper’s algorithm. The results demonstrate that the proposed algorithm is extremely rational, effective, and usable.To address the issues mentioned above, this paper proposes a hierarchical spatial–temporal cross-attention scheme based on contrastive learning, as shown in In this section, we briefly overview some state-of-the-art video summarization approaches and correlation techniques pertaining to our hierarchical spatial–temporal cross-attention scheme.Generally speaking, pre-processing, feature extraction, post-processing, and VS creation comprise the video summary generating procedures. The post-processing can be left out. In particular, feature extraction is the central stage of the algorithm. The initial algorithm is based on time series techniques such as vsLSTM/dppLSTM . The iniAdditionally, for the video summarization algorithm, both video feature information and video frame relational are crucial . ContinuRefs. ,20,21,22Veličković et al.  give a nFollowing the algorithm phases, this part elaborates the DB-ConvLSTM and CB-ConvLSTM, contrastive adjustment learning, and spatial–temporal cross-attention for the keyframes screening module. The contrastive adjustment learning is adjustment learning based on contrastive learning. Finally, we will introduce the loss function used in our framework.n frames in a video, the whole video can be written as Both DB-ConvLSTM and CB-ConvLSTM are founded on the technology of ConvLSTM. ConvLSTM is not only designed for extracting spatial–temporal information features but also for inferring saliency information concurrently. Then, suppose there are te. From , we can W are the training parameters, denoting the learnable weights, H is the hidden state, In the video information processing methods, the DB-ConvLSTM  network CB-ConvLSTM is capable of extracting not only the characteristics of a single video frame but also the spatial–temporal relationships between different frames . From 77, we can⊕ is the operation of fusing two vectors, Contrastive learning  introducng. DDPG  is used  same as .S is the function for measuring the samples’ similarity, and similar to [milar to , the rulQ. Additionally, it employs ConvLSTM and average pooling to produce two sets of projections of key K and value V, enhancing the K and V dimensions of the attention mechanism while also boosting the performance and consistency of feature information extraction. Finally, the attention is calculated as:The temporal, spatial, and multi-element video properties are all important parts of our approach. As a consequence, a new network cell is constructed using ConvLSTM and multi-head attention. It uses convolution to improve the attention mechanism’s ability to get as much video information as possible. In our multi-conv-attention cell, we first adopt a set of projections to obtain query The Cross-Attention module is shown in Finally, calculate the cross-attention using Equation .p is the probability distribution of background data, and q is the predicted probability distribution, and the cross-entropy i of background data, The total loss is primarily made up of three components, and all the loss functions of these parts are based on cross-entropy. In items of supervised learning, the selection of keyframes is ultimately intended to decrease the discrepancy between predicted and background data. The cross-entropy is used to approximate the distribution of the learnt model to the background data. The lower the value is, the more similar the probability distributions of the anticipated and background data. ction is:L=−1m∑i=1ction is:L=−1m∑i=1Each database has its focus, so before the experiment, the two databases TVsum  and SumMS is the video summarization generated by the algorithm, G denotes the ground user-marked ground truth, and the following definitions apply to Precision, Recall, and F-score:To facilitate a comparison study of the experimental influence on current research findings, the Precision, Recall, and F-score are used as measurement standards, similar to the literature . S is thshown in , randomlThe deep learning platform for operating our approach is Pytorch. The hidden states are with dimensionality of 256 for ConvLSTM, and other parameters settings are as follows: similar to other algorithms, we use the pool5 layer of GoogleNet to extract the visual features for each video frame. The number of ConvLSTMs hidden layers is 256, the learning rate initialized is This section verifies the feasibility and effectiveness of the proposed strategy through two ways: one is validation of the algorithm itself, and the other one is comparative analysis with state-of-the-art video summarization approaches.Before comparing the scheme to other state-of-the-art algorithms, it is vital to validate the scheme’s performance itself. (1)Comparison With “Bi-LSTM+” MethodsThe primary components of our algorithm consist of the attention mechanism, ConvLSTM, and GATs. In this section, we compared our approach with some state-of-the-art video summarization methods on SumMe and TvSum. Comparison methods can be classified into three categories: based on “LSTM+”, based on “Attention+”, and based on GATs methods.Due to the few research results on the summary algorithm based on ConvLSTM, this section compares our scheme to the Bi-LSTM based algorithms. Some classic algorithms are compared, as shown in (2)Comparison With “Attention+” MethodsH-RNN  and HAS-(3)Comparison With “Graph Attention+” MethodsSince the scheme in this paper involves not only the combination of ConvLSTM and attention but also the graph neural network, we will analyze it separately. The results of comparison with “Attention+” methods are shown in The extraction of spatial–temporal characteristics and frame–relationship features is facilitated by a graph neural network. Following the comparison tests outlined above, it can be seen that the proposed method has certain advantages over existing approaches, most notably in the SumMe database set. Specifically, the hierarchical spatial–temporal cross-attention scheme in this research enhances the algorithm’s stability, scalability, and other performance characteristics.This paper proposes a hierarchical spatial–temporal cross-attention scheme for video summarization using contrastive learning. The scheme solves the contradictions of diversification user requirements, depth and breadth of features extraction and new creation videos. The hierarchical architecture is divided primarily into depth and breadth feature extraction and spatial–temporal cross-attention feature merging. This paper extracts local and depth features using a graph attention network and multi-head attention mechanism, and it extracts global and breadth features using a GAT adjusted DB-ConvLSTM. Furthermore, merging hierarchical characteristics via spatial–temporal cross-attention cells is used for more precise keyframe screening. Finally, video summarization is generated by CB-ConvLSTM. In practice, results from the TVSum and SumMe datasets indicate that the proposed algorithm is highly rational, effective, and usable. Nevertheless, the analysis of similarity keyframe screening is still insufficiently detailed.