Hepatitis B virus X protein promotes tumor glycolysis by downregulating lncRNA OIP5-AS1/HKDC1 in HCC.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide, with Hepatitis B virus (HBV) infection being the leading cause. This study aims to investigate the role of HBV in HCC pathogenesis involving glucose metabolism. Long non-coding RNA (lncRNA) OIP5-AS1 was significantly downregulated in HBV-positive HCC patients, and its low expression indicated a poor prognosis. This lncRNA was primarily localized in the cytoplasm, acting as a tumor suppressor. HBV protein X (HBx) repressed OIP5-AS1 expression by inhibiting a ligand-activated transcriptional factor peroxisome proliferator-activated receptor α (PPARα). Furthermore, mechanistic studies revealed that OIP5-AS1 inhibited tumor growth by suppressing Hexokinase domain component 1 (HKDC1)-mediated glycolysis. The expression of HKDC1 could be enhanced by transcriptional factor sterol regulatory element-binding protein 1 (SREBP1). OIP5-AS1 facilitated the ubiquitination and degradation of SREBP1 to suppress HKDC1 transcription, which inhibited glycolysis. The results suggest that lncRNA OIP5-AS1 plays an anti-oncogenic role in HBV-positive HCC via the HBx/OIP5-AS1/HKDC1 axis, providing a promising diagnostic marker and therapeutic target for HBV-positive HCC patients.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

ConspectusMolecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training.

The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep learning model for the prediction of protein-ligand atomic distance matrices, and a translation, rotation and torsion-guided geometry optimization procedure to reconstruct the matrices into a credible binding pose. The pre-training and multiple innovative architectural designs facilitate the dramatically improved docking accuracy of our approach over the baselines in terms of multiple docking scenarios, thereby contributing to its outstanding early recognition performance in several retrospective virtual screening campaigns. Further explorations demonstrate that CarsiDock can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.

Latent classes and associated factors of suicidal ideation among high school students / 中国学校卫生

Objective@#To understand current situation epidemiology and associated factors of suicidal ideation among high school students in Yixing, so as to provide basis for targeted intervention.@*Methods@#From March to May 2019, a questionnaire survey was conducted on 12 799 students from 3 junior high schools and 4 senior high schools in Yixing City, Jiangsu Province, using a stratified cluster random sampling method. Latent profile analysis was used to classify suicidal ideation among high school students, the chi square test was used to compare the differences in suicidal ideation among different characteristics of students, multiple Logistic regression was used to analyze influencing factors, a risk predictive nomogram model was constructed and then verified.@*Results@#Three latent classes of suicidal ideation among high school students was divided into three categories were observed: none or mild, moderate, and severe. Among them, 3 034 (23.7%) had moderate suicidal ideation and 753 (5.9%) had severe suicidal ideation. The Logistic regression results showed that gender was female, academic performance was lower midrange, smoking, drinking, popularity with classmates(less popular and unpopularity), family member relationships(general/occasional contradictions/contradictions), trust in others(more trusted/less trusted/less trusted at all), past or current relationships, physical bullying, relationship bullying, verbal bullying, and sexual bullying were the influencing factors for severe suicidal ideation among students ( OR =3.27； 2.18 ；1.63；1.72；2.66, 6.05；3.00,3.29, 6.38；1.71, 6.04, 12.48； 2.50； 1.59； 2.16； 1.45； 1.63, P <0.05). The nomogram prediction model had good discrimination.@*Conclusions@#Suicide ideation is influenced by multiple factors. Family and peer situations, as well as being bullied, are all related to the degree the severity of suicidal ideation. Efforts can be made to improve students family and interpersonal relationships, control bullying, then reduce their suicidal ideation which might help prevent suicide ideation among students.

Harnessing deep learning for enhanced ligand docking.

Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how deep learning (DL) could help to bridge this gap by examining recent advancements and projecting future trends.

A flexible data-free framework for structure-based de novo drug design with reinforcement learning.

Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.

ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions.

Cracking the entangling code of protein-ligand interaction (PLI) is of great importance to structure-based drug design and discovery. Different physical and biochemical representations can be used to describe PLI such as energy terms and interaction fingerprints, which can be analyzed by machine learning (ML) algorithms to create ML-based scoring functions (MLSFs). Here, we propose the ML-based PLI capturer (ML-PLIC), a web platform that automatically characterizes PLI and generates MLSFs to identify the potential binders of a specific protein target through virtual screening (VS). ML-PLIC comprises five modules, including Docking for ligand docking, Descriptors for PLI generation, Modeling for MLSF training, Screening for VS and Pipeline for the integration of the aforementioned functions. We validated the MLSFs constructed by ML-PLIC in three benchmark datasets (Directory of Useful Decoys-Enhanced, Active as Decoys and TocoDecoy), demonstrating accuracy outperforming traditional docking tools and competitive performance to the deep learning-based SF, and provided a case study of the Serine/threonine-protein kinase WEE1 in which MLSFs were developed by using the ML-based VS pipeline in ML-PLIC. Underpinning the latest version of ML-PLIC is a powerful platform that incorporates physical and biological knowledge about PLI, leveraging PLI characterization and MLSF generation into the design of structure-based VS pipeline. The ML-PLIC web platform is now freely available at http://cadd.zju.edu.cn/plic/.

Osteopontin May Improve Postinjury Muscle Repair Via Matrix Metalloproteinases And tgf-ß Activation in Regular Exercise.

Skeletal muscle injuries are commonly observed during sports and trauma. Regular exercise promotes muscle repair; however, the underlying mechanisms require further investigation. In addition to exercise, osteopontin (OPN) contributes to skeletal muscle regeneration and fibrosis following injury. However, whether and how OPN affects matrix proteins to promote post-injury muscle repair remains uncertain. We recruited regular exercise (RE) and sedentary control (SC) groups to determine plasma OPN levels. Additionally, we developed a murine model of muscle contusion injury and compared the extent of damage, inflammatory state, and regeneration-related proteins in OPN knockout (OPN KO) and wild-type (WT) mice. Our results show that regular exercise induced the increase of OPN, matrix metalloproteinases (MMPs), and transforming growth factor-ß (TGF-ß) expression in plasma. Injured muscle fibers were repaired more slowly in OPN-KO mice than in WT mice. The expression levels of genes and proteins related to muscle regeneration were lower in OPN-KO mice after injury. OPN also promotes fibroblast proliferation, differentiation, and migration. Additionally, OPN upregulates MMP expression by activating TGF-ß, which promotes muscle repair. OPN can improve post-injury muscle repair by activating MMPs and TGF-ß pathways. It is upregulated by regular exercise. Our study provides a potential target for the treatment of muscle injuries and explains why regular physical exercise is beneficial for muscle repair.

A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers.

Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions are only applicable to a specific task, e.g., binding affinity prediction, binding pose prediction or virtual screening, suggesting that the development of a scoring function with balanced performance in all critical tasks remains a grand challenge. To this end, we propose a novel parameterization strategy by introducing an adjustable binding affinity term that represents the correlation between the predicted outcomes and experimental data into the training of mixture density network. The resulting residue-atom distance likelihood potential not only retains the superior docking and screening power over all the other state-of-the-art approaches, but also achieves a remarkable improvement in scoring and ranking performance. We emphatically explore the impacts of several key elements on prediction accuracy as well as the task preference, and demonstrate that the performance of scoring/ranking and docking/screening tasks of a certain model could be well balanced through an appropriate manner. Overall, our study highlights the potential utility of our innovative parameterization strategy as well as the resulting scoring framework in future structure-based drug design.

M1 macrophage-derived exosomes promote autoimmune liver injury by transferring long noncoding RNA H19 to hepatocytes.

Exosomes mediate intercellular communication by transmitting active molecules. The function of long noncoding RNA (lncRNA) H19 in autoimmune liver injury is unclear. Concanavalin A (ConA)-induced liver injury is well-characterized immune-mediated hepatitis. Here, we showed that lncRNA H19 expression was increased in the liver after ConA treatment, accompanied by increased exosome secretion. Moreover, injection of AAV-H19 aggravated ConA-induced hepatitis, with an increase in hepatocyte apoptosis. However, GW4869, an exosome inhibitor, alleviated ConA-induced liver injury and inhibited the upregulation of lncRNA H19. Intriguingly, lncRNA H19 expression in the liver was significantly downregulated, after macrophage depletion. Importantly, the lncRNA H19 was primarily expressed in type I macrophage (M1) and encapsulated in M1-derived exosomes. Furthermore, H19 was transported from M1 to hepatocytes via exosomes, and exosomal H19 dramatically induced hepatocytes apoptosis both in vitro and vivo. Mechanistically, H19 upregulated the transcription of hypoxia-inducible factor-1 alpha (HIF-1α), which accumulated in the cytoplasm and mediated hepatocyte apoptosis by upregulating p53. M1-derived exosomal lncRNA H19 plays a pivotal role in ConA-induced hepatitis through the HIF-1α-p53 signaling pathway. These findings identify M1 macrophage-derived exosomal H19 as a novel target for the treatment of autoimmune liver diseases.

TB-IECS: an accurate machine learning-based scoring function for virtual screening.

Machine learning-based scoring functions (MLSFs) have shown potential for improving virtual screening capabilities over classical scoring functions (SFs). Due to the high computational cost in the process of feature generation, the numbers of descriptors used in MLSFs and the characterization of protein-ligand interactions are always limited, which may affect the overall accuracy and efficiency. Here, we propose a new SF called TB-IECS (theory-based interaction energy component score), which combines energy terms from Smina and NNScore version 2, and utilizes the eXtreme Gradient Boosting (XGBoost) algorithm for model training. In this study, the energy terms decomposed from 15 traditional SFs were firstly categorized based on their formulas and physicochemical principles, and 324 feature combinations were generated accordingly. Five best feature combinations were selected for further evaluation of the model performance in regard to the selection of feature vectors with various length, interaction types and ML algorithms. The virtual screening power of TB-IECS was assessed on the datasets of DUD-E and LIT-PCBA, as well as seven target-specific datasets from the ChemDiv database. The results showed that TB-IECS outperformed classical SFs including Glide SP and Dock, and effectively balanced the efficiency and accuracy for practical virtual screening.

Topology-Based and Conformation-Based Decoys Database: An Unbiased Online Database for Training and Benchmarking Machine-Learning Scoring Functions.

Machine-learning-based scoring functions (MLSFs) have gained attention for their potential to improve accuracy in binding affinity prediction and structure-based virtual screening (SBVS) compared to classical SFs. Developing accurate MLSFs for SBVS requires a large and unbiased dataset that includes structurally diverse actives and decoys. Unfortunately, most datasets suffer from hidden biases and data insufficiency. Here, we developed topology-based and conformation-based decoys database (ToCoDDB). The biological targets and active ligands in ToCoDDB were collected from scientific literature and established datasets. The decoys were generated and debiased by using conditional recurrent neural networks and molecular docking. ToCoDDB is presently the largest unbiased database with 2.4 million decoys encompassing 155 targets. The detailed information and performance benchmark for each target are provided, which are beneficial for training and evaluating MLSFs. Moreover, the online decoys generation function of ToCoDDB further expands its application range to any target. ToCoDDB is freely available at http://cadd.zju.edu.cn/tocodecoy/.

Molecular Generation with Reduced Labeling through Constraint Architecture.

In the past few years, a number of machine learning (ML)-based molecular generative models have been proposed for generating molecules with desirable properties, but they all require a large amount of label data of pharmacological and physicochemical properties. However, experimental determination of these labels, especially bioactivity labels, is very expensive. In this study, we analyze the dependence of various multi-property molecule generation models on biological activity label data and propose Frag-G/M, a fragment-based multi-constraint molecular generation framework based on conditional transformer, recurrent neural networks (RNNs), and reinforcement learning (RL). The experimental results illustrate that, using the same number of labels, Frag-G/M can generate more desired molecules than the baselines (several times more than the baselines). Moreover, compared with the known active compounds, the molecules generated by Frag-G/M exhibit higher scaffold diversity than those generated by the baselines, thus making it more promising to be used in real-world drug discovery scenarios.

Important oncogenic and immunogenic roles of SPP1 and CSF1 in hepatocellular carcinoma.

The treatment and prognosis of liver cancer remain the focus of medical research. Studies have shown that SPP1 and CSF1 play important roles in cell proliferation, invasion, and metastasis. Therefore, this study analyzed the oncogenic and immunologic roles of SPP1 and CSF1 in hepatocellular carcinoma (HCC). We found that the expression levels of SPP1 and CSF1 in HCC were markedly increased and positively correlated. High SPP1 expression was significantly associated with poor OS, DSS, PFS, and RFS. It was not affected by gender, alcohol use, HBV, or race, whereas CSF1 was affected by these factors. Higher expression levels of SPP1 and CSF1 indicated higher levels of immune cell infiltration and a higher immune score with the R software package ESTIMATE. Further analysis revealed that many genes work co-expressed between SPP1 and CSF1 with the LinkedOmics database, which were mainly involved in signal transduction, the integral components of the membrane, protein binding, and osteoclast differentiation. In addition, we screened ten hub genes using cytoHubba, among which the expression of four genes was significantly associated with the prognosis of HCC patients. Finally, we demonstrated the oncogenic and immunologic roles of SPP1 and CSF1 using the vitro experiments. Reducing the expression of either SPP1 or CSF1 could significantly reduce the proliferation of HCC cells and the expression of CSF1, SPP1, and the other four hub genes. This study suggested that SPP1 and CSF1 interact with each other and have the potential to be therapeutic and prognostic targets for HCC.

MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions.

Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.

A High-Salt Diet Exacerbates Liver Fibrosis through Enterococcus-Dependent Macrophage Activation.

People consume more salt than the recommended levels due to poor dietary practices. The effects of long-term consumption of high-salt diets (HSD) on liver fibrosis are unclear. This study aimed to explore the impact of HSD on liver fibrosis. In this study, a carbon tetrachloride (CCL4)-induced liver fibrosis mouse model was used to evaluate fibrotic changes in the livers of mice fed a normal diet (ND) and an HSD. The HSD exacerbated liver injury and fibrosis. Moreover, the protein expression levels of transforming growth factor ß (TGF-ß), tumor necrosis factor alpha (TNF-α), and monocyte chemoattractant protein 1 (MCP-1) were significantly higher in the HSD group than in the normal group. The proportion of macrophages and activation significantly increased in the livers of HSD-fed mice. Meanwhile, the number of macrophages significantly increased in the small intestinal lamina propria of HSD-fed mice. The levels of profibrotic factors also increased in the small intestine of HSD-fed mice. Additionally, HSD increased the profibrotic chemokines and monocyte chemoattractant levels in the portal vein blood. Further characterization suggested that the HSD decreased the expression of tight junction proteins (ZO-1 and CLDN1), enhancing the translocation of bacteria. Enterococcus promoted liver injury and inflammation. In vitro experiments demonstrated that Enterococcus induced macrophage activation through the NF-κB pathway, thus promoting the expression of fibrosis-related genes, leading to liver fibrogenesis. Similarly, Enterococcus disrupted the gut microbiome in vivo and significantly increased the fibrotic markers, TGF-ß, and alpha smooth muscle actin (α-SMA) expression in the liver. IMPORTANCE This study further confirms that Enterococcus induce liver fibrosis in mice. These results indicate that an HSD can exacerbate liver fibrosis by altering the gut microbiota composition, thus impairing intestinal barrier function. Therefore, this may serve as a new target for liver fibrosis therapy and gut microbiota management.

School Bullying Victimization and Associated Factors Among School-Aged Adolescents in China.

There has been a significant amount of research on correlates of bullying victimization, but most prior studies are descriptive and do not distinguish between different types of bullying. The current study used a case-control study design to explore factors related to different types of bullying victimization, including physical, relational, verbal, sexual, property, and poly-bullying victimization. This study was conducted in a southern city in China, including 3054 cases who self-reported being victims of school bullying and 3054 controls who reported not being involved in any school bullying in the past 12 months. Each victim case was matched with a control on gender, school, and grade level. Univariate logistic analyses and multivariate conditional logistic regression analyses were used to identify factors associated with being a victim of school bullying. Results suggest physical bullying victimization was only associated with a family-level characteristic (parenting style) while the other four types of bullying victimization (relational, verbal, sexual, and property bullying) and poly-bullying victimization were associated with multiple social domain variables at individual, family, and school levels. Findings from this study provide evidence of factors for different types of bullying victimization and have implications for potential measures to prevent bullying. Measures from multiple social domains, including individual, family and school (e.g., developing healthy behaviors, improving social skills, positive parent-child interactions, building trust between teachers and peers, and forming strong friendships), should be considered in order to effectively prevent adolescent victimization from bullying.

Analysis of subtypes and risk characteristics of depression and anxiety among primary and secondary school students / 中国学校卫生

Objective@#To understand different types of depression and anxiety among primary and secondary school students, as well as their influencing factors, so as to provide a basis for the development of future intervention programs for adolescent mental health.@*Methods@#From December 2022 to February 2023, a self administered questionnaire survey was administered among grades 4 to 6 students and junior school, senior school students in Yixing City using Depression Anxiety and Stress Scale-21 (DASS-21), Insomnia Severity Index (ISI), Family Environment Scale (FES), and modified Yale Food Addiction Scale 2.0 (mYFAS 2.0), Ottawa Self injury Inventory (OSI). A total of 4 180 students were included in the analysis. Different types of depression and anxiety in students and their influencing factors were analyzed by the Chi squaretest and multivariate Logistic regression.@*Results@#The overall prevalence of depression and anxiety among primary and secondary school students were 16.6% and 22.4%, respectively, with 14.0% of depression and anxiety comorbidities. The Logistic regression results showed that, compared to students with low depression-low anxiety, students of depression and anxiety who had parents in conflicts ( OR =3.06), smoked ( OR =3.16), exhibited moderate food addiction ( OR =3.56), and had non suicidal self injury (NSSI) ( OR =2.26) were more likely to be classified as a depression predominant-depression anxiety type. Students of depression and anxiety who consumed alcohol ( OR =2.00), had serious food addiction ( OR =5.44), moderate to severe insomnia ( OR =3.25), and mild insomnia ( OR =1.88) were more likely to be classified as anxiety predominant-depression anxiety type. Students of depression and anxiety with low mood ( OR =10.87), mild food addiction ( OR =2.00), moderate food addiction ( OR =4.32), and severe food addiction ( OR =7.35), mild ( OR =2.96) or moderate to severe ( OR =16.52) insomnia, and NSSI ( OR =4.24) were more likely to be classified as the severe depression anxiety type( P < 0.05 ).@*Conclusions@#There are significant differences between different depression-anxiety types among primary and secondary school students with respect to food addiction, insomnia, NSSI, smoking, and alcohol use. Relevant departments should engage with schools and families to adopt targeted interventions for students to reduce the occurrence of mental health problems.

Association between negative emotion, family intimacy and Internet addiction among junior and senior school students / 中国学校卫生

Objective@#To explore the association between negative emotion (depression, anxiety and stress), family intimacy and Internet addiction, so as to provide a basis for the intervention of Internet addiction among junior and senior high school students.@*Methods@#Students were selected by stratified random cluster sampling method from junior high schools and senior high schools from December 2022 to February 2023 in Yixing City, Jiangsu Provicne. A total of 3 026 students completed the questionnaire survey, including the demographic characteristics, Depression, Anxiety and Stress Scale-21 (DASS-21), Family Environment Scale-Chinese Version (FES-CV), and Chinese Internet Addiction Scale Revised (CIAS-R). Bivariate correlation was used to analyzed the association of family intimacy, depression, anxiety, stress, and Internet addiction. Mediating effect model was used to analyzed the mediating effect of negative emotion between family intimacy and Internet addiction.@*Results@#The average score of Internet addiction among junior and senior high school students was (46.26±15.58), and there were statistical differences in the average scores of Internet addiction across different grades ( F=87.15, P <0.01). Depression ( r =0.57), anxiety ( r =0.56), stress ( r = 0.57) were positively correlated with Internet addiction, and family intimacy ( r =-0.34) was negatively correlated with Internet diction ( P <0.01). In the mediating effect model, family intimacy negatively predicted negative emotion ( β =-0.48) and Internet addiction ( β =-0.10), and negative emotion positively predicted Internet addiction ( β =0.45) ( P <0.01). Negative emotion played a partial mediating role between family intimacy and Internet addiction (the mediation value:-1.71, 95% CI =-1.96--1.49, mediation ratio:67.9%, P <0.05).@*Conclusions@#There are associations between negative emotion, family intimacy and Internet addiction among junior and senior school students. Family intimacy indirectly affects Internet addiction mainly through negative emotion. It suggests that family education is in need of attention to reduce the prevalence rate of Internet addiction among junior and senior high school students, especially family intimacy.