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With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all the parameters is prohibitively costly and eventually becomes practically infeasible. This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, which optimizes a small portion of the model parameters while keeping the rest fixed, drastically cutting down computation and storage costs. In general, it demonstrates that large-scale models could be effectively stimulated by the optimization of a few parameters. Despite the various designs, here we discuss and analyse the approaches under a more consistent and accessible term ‘delta-tuning’, where ‘delta’ a mathematical notation often used to denote changes, is borrowed to refer to the portion of parameters that are ‘changed’ during training. We formally describe the problem and propose a unified categorization criterion for existing delta-tuning methods to explore their correlations and differences. We also discuss the theoretical principles underlying the effectiveness of delta-tuning and interpret them from the perspectives of optimization and optimal control. Furthermore, we provide a holistic empirical study on over 100 natural language processing tasks and investigate various aspects of delta-tuning. With comprehensive study and analysis, our research demonstrates the theoretical and practical properties of delta-tuning in the adaptation of PLMs.

Abstract: Bioactive peptides are specific protein fragments that prove health-promoting potential for humans. The bioactivities include antimicrobial, antioxidant, anticancer, immunomodulatory activities, etc. Hence, bioactive peptides’ production technology and processes have attracted excessive attention, especially concerning peptides’ synthesis, separation, identification, and functionality. This review summarizes the relevant investigations from the above four aspects. Among the production technology of bioactive peptides, biosynthesis, chemosynthesis, technology for separation and purification, and the interactions responsible for peptide-based nanostructures are emphasized. Here, the biosynthesis of peptides includes enzymatic hydrolysis, microbial fermentation, and recombinant DNA technology, and chemosynthesis consists of solution-phase peptide synthesis and solid-phase peptide synthesis (SPPS). The commonly used enzymes in enzymatic hydrolysis are investigated, including pepsin, trypsin, and alcalase. The commonly used microorganisms, typical processes, protein sources, and advantages of microbial fermentation are analyzed. Membrane separation (including ultrafiltration and nanofiltration), chromatography technology (including ion-exchange chromatography, gel filtration chromatography, affinity chromatography, and reverse-phase high-performance liquid chromatography (RP-HPLC)), and electrophoresis technology are detailed for the purification technology. Mass spectrometry (MS), its combination with the high-performance separation method, and nuclear magnetic resonance (NMR) are elucidated for the identification technology. The non-covalent interactions responsible for peptide-based nanostructures involve electrostatic force, hydrogen bonds, π-π stacking, hydrophobic interaction, and van der Waals force. Afterward, we detail the peptides’ antihypertensive, antithrombotic, anticancer, antimicrobial, antioxidant, and immunomodulatory activities. The activity analysis mainly involves peptides’ sources, structural features, mechanisms of action, and influencing factors. Based on the production and functionality elucidation, potential challenges for peptide application in biomedicine are given. The challenge is analyzed from the aspects of purification and identification technologies and influencing factors of peptides’ bioactivities. Our work will elaborate on advances in the production technology of peptides and their bioactivities, which could promote and expand their industrial applications.

Aim: To develop and validate a radiomics-based combined model (ModelRC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, ModelRC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864–0.962), 0.882 (95% CI: 0.779–0.955) and 0.881 (95% CI: 0.815–0.939) for the training, internal validation and external validation sets, respectively. Conclusion: ModelRC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.

A diffusion SEIAR model with Beddington-DeAngelis type incidence is proposed to characterize the spread of COVID-19 with spatial transmission. First, the well-posedness of solution is studied. Second, the basic reproduction number \begin{document}$ \mathcal R_{0} $\end{document} is derived and served as a threshold value to determine whether COVID-19 will spread. Meanwhile, we consider the effect of diffusion on the spread of COVID-19 in spatial homogenous environment, by which we can obtain that if \begin{document}$ \mathcal R_{0} , then the infection-free steady state is globally asymptotically stable, while if \begin{document}$ \mathcal R_{0}>1 $\end{document} , then the endemic steady state is globally asymptotically stable. Furthermore, according to the official reporting data about COVID-19 in Wuhan, China, the actual value of \begin{document}$ \mathcal R_{0} $\end{document} is estimated, and comparing with other types of incidence, we find that the estimated peak with Beddington-DeAngelis type incidence is more close to the cases in reality. Finally, by numerical simulations, we can see that the diffusion behavior has evident impact on the spread of COVID-19 in spatial heterogeneity than homogeneity of environment.

Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various Natural Language Processing (NLP) and Information Retrieval (IR) downstream applications have benefited from ESE due to its ability to discover knowledge. Although existing corpus-based ESE methods have achieved great progress, they still rely on corpora with high-quality entity information annotated, because most of them need to obtain the context patterns through the position of the entity in a sentence. Therefore, the quality of the given corpora and their entity annotation has become the bottleneck that limits the performance of such methods. To overcome this dilemma and make the ESE models free from the dependence on entity annotation, our work aims to explore a new ESE paradigm, namely corpus-independent ESE. Specifically, we devise a context pattern generation module that utilizes autoregressive language models (e.g., GPT-2) to automatically generate high-quality context patterns for entities. In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities. Extensive experiments and detailed analyses on three widely used datasets demonstrate the effectiveness of our method. All the codes of our experiments are available at https://github.com/geekjuruo/GAPA., This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

The successful design strategy put reticular chemistry in uranyl phosphonate frameworks, which integrate multifunction to corresponding applications.

Soybean (Glycine max L.) seeds showing serious symptoms from rotted pods were collected from fields during the harvesting period (July to August 2020) in Taihu Farm, Jingzhou City, Hubei Province, China. Fusarium strains were frequently encountered during fungal isolation. According to the morphology and prepathogenicity tests, six strains showing variable effects on the seeds were selected for identification based on morphology and multilocus phylogenetic analysis of the internal transcribed spacer (ITS) region of the ribosomal DNA, translation elongation factor (EF-1α), calmodulin (CAM), β-tubulin (TUB), and partial RNA polymerase second largest subunit (RPB2), and to evaluate the pathogenic abilities on seed, root, and pod. The results indicated that the strains contained two species (Fusarium fujikuroi and F. proliferatum) in the Fusarium fujikuroi species complex (FFSC) and two species (F. luffae and F. sulawense) from the Fusarium incarnatum-equiseti species complex (FIESC). The two species of FFSC were more aggressive than those of FIESC on soybean seed, root, and pod. Among the strains, F. proliferatum YZU 201408 exhibited the most pathogenicity on all tests, with 72.2 to 90% disease severity.

Motivation: The prediction of drug-target interaction (DTI) from heterogeneous biological data is critical to predict drugs and therapeutic targets for known diseases such as tumor and bowel disease. The study of DTI based on drug representation learning can strengthen or integrate our knowledge of pharmacological and chemical phenomena. Therefore, there is a strong motivation to develop effective methods that can detect these potential drug-target interactions. Results: We have developed a novel Knowledge-Enhanced Multi-View framework (KEMV) to predict unknown DTIs from pharmacological data and chemical data on a large scale. The proposed method consists of two steps: (i) learning more comprehensive drug representations via the proposed multi-view attention mechanism, which bridges pharmacological and chemical information, and interactively summarizes the attention values depending on varying interactions between different pairs of drug features. (ii) predicting unknown drug-target interactions based on the drug and target representations. The method is tested on real-world dataset KEGG with three classes of important drug–target interactions involving enzymes, ion channels, and G-protein-coupled receptors. Our framework is proven to uncover potential DTIs with scientific evidences explaining the mechanism of the interactions through the processing of high-dimensional, heterogeneous, and sparse drug data. Availability: Our code is available at: https://github.com/YuanKQ/DTI-Prediction

BACKGROUND: Early identification of metabolic-associated fatty liver disease (MAFLD) is urgent. Atherogenic index of plasma (AIP) is a reference predictor of obesity-related diseases, but its predictive value for MAFLD remains unclear. No studies have reported whether its combination with waist circumference (WC) and body mass index (BMI) can improve the predictive performance for MAFLD. AIM: To systematically explore the relationship between AIP and MAFLD and evaluate its predictive value for MAFLD and to pioneer a novel noninvasive predictive model combining AIP, WC, and BMI while validating its predictive performance for MAFLD. METHODS: This cross-sectional study consecutively enrolled 864 participants. Multivariate logistic regression analysis and receiver operating characteristic curve were used to evaluate the relationship between AIP and MAFLD and its predictive power for MAFLD. The novel prediction model A-W-B combining AIP, WC, and BMI to predict MAFLD was established, and internal verification was completed by magnetic resonance imaging diagnosis. RESULTS: Subjects with higher AIP exhibited a significantly increased risk of MAFLD, with an odds ratio of 12.420 (6.008-25.675) for AIP after adjusting for various confounding factors. The area under receiver operating characteristic curve of the A-W-B model was 0.833 (0.807-0.858), which was significantly higher than that of AIP, WC, and BMI (all P < 0.05). Subgroup analysis illustrated that the A-W-B model had significantly higher area under receiver operating characteristic curves in female, young and nonobese subgroups (all P < 0.05). The best cutoff values for the A-W-B model to predict MAFLD in males and females were 0.5932 and 0.4105, respectively. Additionally, in the validation set, the area under receiver operating characteristic curve of the A-W-B model to predict MAFLD was 0.862 (0.791-0.916). The A-W-B level was strongly and positively associated with the liver proton density fat fraction (r = 0.630, P < 0.001) and significantly increased with the severity of MAFLD (P < 0.05). CONCLUSION: AIP was strongly and positively associated with the risk of MAFLD and can be a reference predictor for MAFLD. The novel prediction model A-W-B combining AIP, WC, and BMI can significantly improve the predictive ability of MAFLD and provide better services for clinical prediction and screening of MAFLD.

Este trabalho objetiva discutir a tradução de aspectos da cultura chinesa ao leitor brasileiro com base nas reflexões sobre a exotização e a domesticação. Para tanto, a partir da definição dos conceitos da cultura e de aspectos culturais, demonstramos que a dicotomia entre essas duas estratégias não contribuiria para a resolução da questão levantada. Em seguida, construiremos um percurso introdutivo sobre a teoria Zhongyong e apresentaremos um modelo teórico da tradução que se inspira nessa filosofia confuciana. Por fim, com a exemplificação de tal modelo na tradução de uma crônica chinesa, mostramos que, para lidar com os aspectos culturais na tradução entre o chinês o português, é possível o tradutor recorrer a um caminho do meio que harmoniza o uso de ambas a exotização e a domesticação.

Network traffic classification is important to intrusion detection and network management. Most of existing methods are based on machine learning techniques and rely on the features extracted manually from flows or packets. However, with the rapid growth of network applications, it is difficult for these approaches to handle new complex applications. In this paper, we design a novel neural network, the Extended Byte Segment Neural Network (EBSNN), to classify network traffic. EBSNN first divides a packet into header segments and payload segments, which are then fed into encoders composed of the recurrent neural networks with the attention mechanism. Based on the outputs, another encoder learns the high-level representation of the whole packet. In particular, side-channel features are learned from header segments to improve the performance. Finally, the label of the packet is obtained by the softmax function. Furthermore, EBSNN can classify network flows by examining the first few packets. Thorough experiments on the real-world datasets show that EBSNN achieves better performance than the state-of-the-art methods in both the application identification task and the website identification task.

Multiple studies have shown that mental disorders are common risk factors for cardiovascular diseases (CVD). However, the relationship between psychological factors and atrial fibrillation (AF) incidence remains unclear. Therefore, we performed a systematic review and meta-analysis to assess the risk of AF due to psychological factors.PubMed, Embase, Web of Science, and Cochrane Library were searched from inception to January 2022. Relevant and eligible cohort studies were included. Random or fixed effect model was used to estimate the pooled hazard ratios (HRs) and 95% confidence intervals (CIs). For non-randomized studies, the risk of bias was assessed using the Newcastle-Ottawa Scale (NOS).Thirteen publications, including 5,329,908 participants, met our inclusion criteria. The incidence of AF was increased by 10% (HR = 1.10, 95% CI 1.02 to 1.19, IAdverse psychological factors such as anxiety, anger, depression, and work stress may increase the risk of AF. Interventions to prevent mental disorders may reduce the growing global burden of AF and its associated healthcare costs. Given the current study's limitations, our findings need to be further confirmed by a larger prospective study.

Placenta accreta spectrum disorder (PASD) is a life-threatening obstetric complication. China still lacks high-quality data on the epidemiology of PASD. We intend to examine the major risk factors for PASD, explore the association of PASD with severe adverse perinatal outcomes and describe the geographical variations in China.We used data from the China Labor and Delivery Survey, which included a total of 75,132 births from 96 hospitals in 24 provinces in China in 2015 and 2016. Each participating hospital randomly selected 6-10 weeks within a 12-month period. In the selected weeks, all births with gestational age ≥24 weeks or birth weight ≥500 g were included. We analyzed the demographic characteristics and prevalence of PASD in pregnant women. Multivariable logistic regression was used to examine the association of previous caesarean section (CS) and repeated surgical abortion with PASD. We explored the association of PASD with severe adverse perinatal outcomes, which indicated by Weighted Adverse Outcome Score (WAOS) ≥ 20. Multivariable logistic regression was used to examine the association of PASD with WAOS. We also calculated and compared the prevalence of PASD in different regions of China.The weighted prevalence of PASD was 2.20% (95% CI 0.76 to 4.95) in our population，and there was a substantial difference in the prevalence of PASD by geographic regions in China. Two or more previous CS (adjusted OR 2.34, 95%CI 1.41 to 3.88) and repeated surgical abortion (twice: 2.16, 1.20 to 3.92; 3 times: 4.31, 1.70 to 10.96; ≥4 times: 4.76, 3.12 to 7.26) were significant risk factors for PASD. PASD (adjusted OR 3.77, 95% CI 2.80 to 5.06) was a significant risk factor for severe adverse perinatal outcomes indicated by WAOS score.The prevalence of PASD in China appeared higher than that in other countries, and varied substantially by geographic regions. Two or more previous CS and repeated surgical abortion were major risk factors for PASD. Pregnant women with PASD had more severe adverse pregnancy outcomes. Reducing primary cesarean section and repeated surgical abortion are the key to decreasing PASD.

To improve the reliability of power supply, reclosing schemes are required after transient faults, which commonly occur in overhead line based high voltage DC (HVDC) systems. However, in the event of permanent faults, the auto-reclosing scheme may cause a severe strike. To avoid the severe impacts caused by permanent faults, the fault type should be discriminated before activating the reclosing scheme. Therefore, an adaptive reclosing scheme based on phase characteristics is proposed in this paper. Firstly, the modulation of a periodic voltage by actively controlling the hybrid DC circuit breaker (DCCB) is introduced. Then, a cascaded π equivalent model and its decoupling algorithm are presented to analyze the frequency-domain characteristics of the measured impedance of the coupled overhead lines. From the frequency-domain characteristics, the frequency of the periodic detecting voltage is determined to analyze the phase features of the measured impedance at primary frequency. The permanent or transient faults can thus be accurately identified by using these different phase characteristics, with negligible influence on the healthy lines. In addition, the proposed scheme is robust to various fault resistances, leading to improved reliability. The effectiveness of the proposed scheme is verified in PSCAD/EMTDC.

Background. Ferroptosis is a nonapoptotic form of programmed cell death, which may be related to the occurrence and development of sepsis-induced acute respiratory distress syndrome (ARDS)/acute lung injury (ALI). Mucin 1 (MUC1) is a kind of macromolecule transmembrane glycoprotein. Previous studies have shown that MUC1 could relieve ALI in sepsis and predict whether sepsis patients would develop into ARDS. However, the role of MUC1 in the ferroptosis of sepsis-induced ALI/ARDS remains unclear. Materials and Methods. Sera samples from 50 patients with sepsis/septic shock were used to detect iron metabolism-related markers. Western blot and qRT-PCR were conducted to detect the expression levels of ferroptosis-related genes. Enzyme-linked immunosorbent assay (ELISA) was performed to evaluate inflammatory factors. Transmission electron microscopy (TEM) was used to assess morphological changes of cells. Results. The results showed that the iron metabolism-related indicators in sepsis-induced ARDS patients changed significantly, suggesting the iron metabolism disorder. The expression levels of ferroptosis-related genes in lung tissues of sepsis had marked changes, and the lipid peroxidation levels increased, while Ferrostatin-1 (Fer-1) could reverse the above results, which confirmed the occurrence of ferroptosis. In terms of mechanism studies, inhibition of MUC1 dimerization could increase the expression level of Keap1, reduce the phosphorylation level of GSK3β, inhibit the entry of Nrf2 into the nucleus, further inhibit the expression level of GPX4, enhance the lipid peroxidation level of lung tissues, trigger ferroptosis, and aggravate lung injury. Besides, inhibiting MUC1 reversed the alleviating effect of vitamin E on ALI caused by sepsis, increased the aggregation of inflammatory cells in lung tissues, and aggravated alveolar injury and edema. Conclusions. Our study was the first to explore the changes of iron metabolism indicators in ALI/ARDS of sepsis, clarify the important role of ferroptosis in ALI/ARDS induced by sepsis, and reveal the effects and specific mechanisms of MUC1 in regulating ferroptosis, as well as the sensitization on vitamin E.

The 1,2-diamine motif is prevalent in natural products, small-molecule pharmaceuticals, and catalysts for asymmetric synthesis. Transition metal catalyzed alkene diazidation has evolved to be an attractive strategy to access vicinal primary diamines but remains challenging, especially for practical applications, due to the restriction to a certain type of olefins, the frequent use of chemical oxidants, and the requirement for high loadings of metal catalysts (1 mol % or above). Herein we report a scalable Cu-electrocatalytic alkene diazidation reaction with 0.02 mol % (200 ppm) of copper(II) acetylacetonate as the precatalyst without exogenous ligands. In addition to its use of low catalyst loading, the electrocatalytic method is scalable, compatible with a broad range of functional groups, and applicable to the diazidation of α,β-unsaturated carbonyl compounds and mono-, di-, tri-, and tetrasubstituted unactivated alkenes.

AimAdverse (poor or excessive) fetal growth “programs” an elevated risk of type 2 diabetes. Fatty acid binding protein 4 (FABP4) has been implicated in regulating insulin sensitivity and lipid metabolism relevant to fetal growth. We sought to determine whether FABP4 is associated with poor or excessive fetal growth and fetal lipids.MethodsIn a nested case-control study in the Shanghai Birth Cohort including 60 trios of small-for-gestational-age (SGA, an indicator of poor fetal growth), large-for-gestational-age (LGA, an indicator of excessive fetal growth) and optimal-for-gestational-age (OGA, control) infants, we measured cord blood concentrations of FABP4 and lipids [high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterols, triglycerides (TG)].ResultsAdjusting for maternal and neonatal characteristics, higher cord blood FABP4 concentrations were associated with a lower odds of SGA [OR = 0.29 (0.11–0.77) per log unit increment in FABP4, P = 0.01], but were not associated with LGA (P = 0.46). Cord blood FABP4 was positively correlated with both LDL (r = 0.29, P = 0.025) and HDL (r = 0.33, P = 0.01) in LGA infants only.ConclusionFABP4 was inversely associated with the risk of SGA. The study is the first to demonstrate LGA-specific positive correlations of cord blood FABP4 with HDL and LDL cholesterols, suggesting a role of FABP4 in fetal lipid metabolism in subjects with excessive fetal growth.

Developing chiral receptors with an endo-functionalized cavity for chiral recognition is of great sig-nificance in the field of molecular recognition. This study presents two pairs of chiral naphthotubes con-taining a bis-thiourea endo-functionalized cavity. Each chiral naphthotube has two homochiral centers which were fixed adjacent to the thiourea groups, causing the skeleton and thiourea groups to twist en-antiomerically through chiral transfer. These chiral naphthotubes are highly effective at enantiomerical-ly recognizing various neutral chiral molecules, particularly bioactive cyclic dipeptides, with an enanti-omeric selectivity up to 17.0. Furthermore, the mechanism of the chiral recognition has been revealed and attributed to slight differences of multiple noncovalent interactions, as well as the orientation of binding and enthalpy-entropy compensation also affect the enantioselectivity.

Background Polymorphisms in microRNAs (miRNAs) play an important role in acute coronary syndromes (ACS). The purpose of this study was to assess the association of miR-146a rs2910164 and miR-34b rs4938723 polymorphisms with the development and prognosis of ACS and to explore the underlying mechanisms. Methods A case–control study of 1171 subjects was included to determine the association of miR-146a rs2910164 and miR-34b rs4938723 polymorphisms with ACS risk. An additional 612 patients with different miR-146a rs2910164 genotypes, who underwent percutaneous coronary intervention (PCI) were included in the validation cohort and followed for 14 to 60 months. The endpoint was major adverse cardiovascular events (MACE). A luciferase reporter gene assay was used to validate the interaction of oxi-miR-146a(G) with the IKBA 3'UTR. Potential mechanisms were validated using immunoblotting and immunostaining. Results The miR-146a rs2910164 polymorphism was significantly associated with the risk of ACS (Dominant model: CG + GG vs. CC, OR = 1.270, 95% CI (1.000–1.613), P = 0.049; Recessive model: GG vs. CC + CG, OR = 1.402, 95% CI (1.017–1.934), P = 0.039). Serum inflammatory factor levels were higher in patients with the miR-146a rs2910164 G allele than in those with the C allele. MiR-146a rs2910164 polymorphism in dominant model was associated with the incidence of MACE in post-PCI patients (CG + GG vs. CC, HR = 1.405, 95% CI (1.018–1.939), P = 0.038). However, the miR-34b rs4938723 polymorphism was not associated with the prevalence and prognosis of ACS. The G allele of miR-146a rs2910164 tends to be oxidized in ACS patients. The miRNA fractions purified from monocytes isolated from ACS patients were recognized by the 8OHG antibody. Mispairing of Oxi-miR-146a(G) with the 3'UTR of IKBA results in decreased IκBα protein expression and activation of the NF-κB inflammatory pathway. P65 expression was higher in atherosclerotic plaques from patients carrying the miR-146a rs2910164 G allele. Conclusion The variant of miR-146a rs2910164 is closely associated with the risk of ACS in Chinese Han population. Patients carrying miR-146a rs2910164 G allele may have worse pathological change and poorer post-PCI prognosis, partly due to the oxidatively modified miR-146a mispairing with 3′UTR of IKBA and activating NF-κB inflammatory pathways.

Background The hypereosinophilic syndrome (HES) is a group of rare blood disorders characterized by persistent eosinophilia and damage to multiple organs. HES can be either primary, secondary or idiopathic. Secondary HES are commonly caused by parasitic infections, allergic reactions or cancer. We described a pediatric case of HES associated with liver damage and multiple thrombi. Case summary A 12-year-old boy with eosinophilia was complicated with severe thrombocytopenia, liver damage, portal vein, splenic vein, and superior mesenteric vein thromboses. The thrombi recanalized after treatment with methylprednisolone succinate and low molecular weight heparin. No side effects appeared after 1-month. Conclusions Corticosteroids should be used at an early stage of HES to prevent further damage to vital organs. Anticoagulants should be recommended only in cases with thrombosis which should be actively screened as a part of evaluation of end organ damage.

BackgroundAnxiety and depression are the most common psychiatric comorbidities in migraine, but their impact on the risk of developing migraine and their gender and age differences are unclear, and research on their associations with migraine-related burdens are limited.ObjectiveTo systematically explore the association between anxiety and depression with migraine and migraine-related burdens, including the risk of developing migraine, as well as migraine frequency, severity, disability, headache impact, quality of life and sleep quality.MethodsA total of 170 migraineurs and 85 sex-and age-matched healthy control subjects were recruited consecutively for this study. Anxiety and depression were assessed using Zung’s Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS), respectively. Logistic regression and linear regression analyses were used to explore the associations between anxiety and depression with migraine and its burdens. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of SAS score and SDS score on migraine and its severe burdens.ResultsAfter adjusting for confounders, anxiety and depression remained significantly associated with an increased risk of developing migraine, with odds ratios of 5.186 (95% CI:1.755–15.322) and 3.147 (95% CI:1.387–7.141), respectively. Meanwhile, there were significant additive interactions between the association of anxiety and depression with the risk of developing migraine in gender and age (P for interaction P trend p ConclusionAnxiety and depression were significantly independently associated with the increased risk of migraine and migraine-related burdens. Enhanced assessment of SAS score and SDS score is of great clinical value for the early prevention and treatment of migraine and its burdens.