Intermediary roles of prospective memory and retrospective memory in the comorbidity of depression and pain

Background Patients who suffer comorbidity of major depressive disorder(MDD)and chronic pain(CP)maintain a complex interplay between maladaptive prospective memory(PM)and retrospective memory(RM)with physical pain,and their complications are still unknown.Aims We aimed to focus on the full cognitive performance and memory complaints in patients with MDD and CP,patients with depression without CP,and control subjects,considering the possible influence of depressed affect and chronic pain severity.Methods According to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders and the criteria given by the International Association of Pain,a total of 124 participants were included in this cross-sectional cohort study.Among them,82 depressed inpatients and outpatients from Anhui Mental Health centre were divided into two groups:a comorbidity group(patients with MDD and CP)(n=40)and a depression group(patients with depression without CP)(n=42).Meanwhile,42 healthy controls were screened from the hospital’s physical examination centre from January 2019 to January 2022.The Hamilton Depression Rating Scale-24(HAMD-24)and Beck Depression Inventory-II(BDI-II)were used to evaluate the severity of depression.The Pain Intensity Numerical Rating Scale(PI-NRS),Short-Form McGill Pain Questionnaire-2 Chinese version(SF-MPQ-2-CN),Montreal Cognitive Assessment-Basic Section(MoCA-BC),and Prospective and Retrospective Memory Questionnaire(PRMQ)were used to assess pain-related features and the global cognitive functioning of study participants.Results The impairments in PM and RM differed remarkably among the three groups(F=7.221,p<0.001;F=7.408,p<0.001)and were severe in the comorbidity group.Spearman correlation analysis revealed the PM and RM were positively correlated with continuous pain and neuropathic pain(r=0.431,p<0.001;r=0.253,p=0.022 and r=0.415,p<0.001;r=0.247,p=0.025),respectively.Regression analysis indicated a significant positive relationship between affective descriptors and total BDI-II score(β=0.594,t=6.600,p<0.001).Examining the mediator pathways revealed the indirect role of PM and RM in patients with comorbid MDD and CP.Conclusions Patients with comorbid MDD and CP presented more PM and RM impairments than patients with MDD without CP.PM and RM are possibly mediating factors that affect the aetiology of comorbid MDD and CP.

Aripiprazole in the treatment of acute episode of schizophrenia: a real-world study in China

To the Editor:Schizophrenia is a progressive,debilitating disease.[1]Safe and effective treatment performed during the acute phase is the first step to improve prognosis,and it is critically important.Aripiprazole is known as a dopamine system stabilizer,and it exerts partial agonism with high affinity at dopamine D2 and serotonin-1A(5-HT1A)receptors as well as antagonism at serotonin-2A(5-HT2A)receptors.[2]It has been shown to be efficacious in amelioration of both positive and negative symptoms of acute schizophrenia,and it shows better tolerance than most antipsychotics.[3]We conducted a real-world study to further confirm the efficacy and safety of aripiprazole in patients with acute episodes of schizophrenia in China and tried to explore the clinical variables,including various drug regimens,which may be correlated with the efficacy of aripiprazole,and provide a basis for the optimization of aripiprazole treatment for patients with acute schizophrenia.

MHGCN:Multiview Highway Graph Convolutional Network for Cross-Lingual Entity Alignment

Knowledge graphs(KGs)provide a wealth of prior knowledge for the research on social networks.Crosslingual entity alignment aims at integrating complementary KGs from different languages and thus benefits various knowledge-driven social network studies.Recent entity alignment methods often take an embedding-based approach to model the entity and relation embedding of KGs.However,these studies mostly focus on the information of the entity itself and its structural features but ignore the influence of multiple types of data in KGs.In this paper,we propose a new embedding-based framework named multiview highway graph convolutional network(MHGCN),which considers the entity alignment from the views of entity semantic,relation semantic,and entity attribute.To learn the structural features of an entity,the MHGCN employs a highway graph convolutional network(GCN)for entity embedding in each view.In addition,the MHGCN weights and fuses the multiple views according to the importance of the embedding from each view to obtain a better entity embedding.The alignment entities are identified based on the similarity of entity embeddings.The experimental results show that the MHGCN consistently outperforms the state-of-the-art alignment methods.The research also will benefit knowledge fusion through cross-lingual KG entity alignment.

Graph Neural Architecture Search:A Survey

In academia and industries,graph neural networks(GNNs)have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks.GNN models are usually handcrafted.However,building handcrafted GNN models is difficult and requires expert experience because GNN model components are complex and sensitive to variations.The complexity of GNN model components has brought significant challenges to the existing efficiencies of GNNs.Hence,many studies have focused on building automated machine learning frameworks to search for the best GNN models for targeted tasks.In this work,we provide a comprehensive review of automatic GNN model building frameworks to summarize the status of the field to facilitate future progress.We categorize the components of automatic GNN model building frameworks into three dimensions according to the challenges of building them.After reviewing the representative works for each dimension,we discuss promising future research directions in this rapidly growing field.