European Neuropsychopharmacology最新文献

{"title":"\"Compressed design, inflated conclusions?\" A cautionary note on vaporized DMT trials.","authors":"Zhihao Lei","doi":"10.1016/j.euroneuro.2025.07.008","DOIUrl":"10.1016/j.euroneuro.2025.07.008","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"13"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on the Article \"Differences in resting-state functional connectivity between depressedbipolar and major depressive disorder patients: A machine learning study\".","authors":"Wanlin Gao, Libin Zhan","doi":"10.1016/j.euroneuro.2025.07.009","DOIUrl":"10.1016/j.euroneuro.2025.07.009","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"14-15"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LARGE-SCALE ANALYSIS OF COPY NUMBER VARIANTS IN THE ETIOLOGY OF SUICIDE ATTEMPT AND SUICIDAL IDEATION","authors":"Lucas Ito , Omar Shanta , Sarah Colbert , Marcos Santoro , Suicide Working Group of the Psychiatric Genomics Consortium , CNV Working Group of the Psychiatric Genomics Consortium , Anna Docherty , Douglas Ruderfer , Jonathan Sebat , Niamh Mullins","doi":"10.1016/j.euroneuro.2025.08.531","DOIUrl":"10.1016/j.euroneuro.2025.08.531","url":null,"abstract":"<div><div>Suicide is a global public health issue, responsible for ∼700,000 preventable deaths annually. Suicide attempts (SA) and suicidal ideation (SI) are vastly more common and confer an increased risk of death by suicide. While psychiatric disorders are significant risk factors, the heritability of suicidality is well established (∼30–50%), and is partially distinct from the genetic etiology of psychiatric disorders. Recent genome-wide association studies (GWAS) for SA and SI have identified 12 and 4 genome-wide significant loci, respectively. However, there is an urgent need to explore rare and large-effect variants such as copy number variants (CNVs). CNVs are deletions or duplications typically >1 kb in size, often affecting multiple genes and have been implicated in many psychiatric disorders. Previous CNV studies of SA and SI have been underpowered and have yielded limited findings, and a large-scale analysis is essential given the potential impact of CNVs to elucidate the biological mechanisms underlying suicidality. By leveraging the largest datasets currently available, this study aims to identify CNVs associated with SA and SI, a critical step towards understanding their genetic and biological etiologies. Using data from Psychiatric Genomics Consortium (PGC) cohorts, we analysed CNV associations with SA (31 cohorts, 4,242 cases, 35,301 controls) and SI (34 cohorts, 10,951 cases, 28,164 controls). Phenotypes were defined using the PGC Suicide Working Group standardized Phenotyping Protocol. CNVs were called by the PGC CNV Working Group via at least two independent algorithms to create a consensus CNV set. Quality control excluded outliers based on CNV and sample-level metrics. CNV-GWAS of SA and SI was conducted separately for duplications and deletions based on CNV breakpoint counts via logistic regression adjusting for population structure. Results were meta-analyzed across cohorts using a sample size-weighted approach with multiple testing corrections for ∼500 independent tests. Based on current sample sizes, no CNV breakpoint reached genome-wide significance. The strongest associations for SA were a duplication at 19q13.42 (p=4.14e-3) and a deletion at 12q13.13 (p=4.50e-3). For SI, the most prominent associations were a duplication at 1p36.22 (p=7.53e-4) and a deletion at 16p13.11 (p=9.36e-4). The PGC CNV Working Group is currently expanding CNV calling to additional cohorts for which intensity files and SA/ SI phenotype data have been received. CNV analyses of SA and SI are also underway in external cohorts, including the UK Biobank and AllofUs, using PGC CNV pipelines. Final sample sizes will include at least 15,000 SA cases and 150,000 controls, and 15,000 SI cases and 40,000 controls. CNV burden analyses, based on total CNV length, will also be conducted to evaluate their impact in SA and SI. Newly identified CNVs will be cross-referenced with those previously associated with major psychiatric disorders. This is the first ","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 36-37"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MULTI-OMIC SIGNATURES FOR ANTIDEPRESSANT EXPOSURE","authors":"","doi":"10.1016/j.euroneuro.2025.08.505","DOIUrl":"10.1016/j.euroneuro.2025.08.505","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 24"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRECISION PHENOTYPING OF MAJOR DEPRESSIVE DISORDER: LEVERAGING DEEP LEARNING FOR ENHANCED GENETIC DISCOVERY","authors":"Yazheng Di , Vasileios Skaramagkas , Jonathan Flint , Na Cai","doi":"10.1016/j.euroneuro.2025.08.548","DOIUrl":"10.1016/j.euroneuro.2025.08.548","url":null,"abstract":"<div><h3>Background</h3><div>Major depressive disorder (MDD) is a leading cause of disability worldwide. Current genome-wide association studies (GWAS) often rely on shallow phenotyping—such as self-reports or electronic health records (EHR)—instead of structured clinical interviews (deep phenotyping) to increase sample size. While efficient, this approach blurs MDD-specific genetic signals with those from comorbid psychiatric or somatic conditions, limiting biological interpretability. Deep learning (DL) offers a promising solution. Models trained on deeply phenotyped MDD cases can impute risk scores for individuals with only shallow proxies (e.g., family history, personality traits), enhancing power while maintaining genetic specificity. This strategy may also refine noisy EHR-based diagnoses. However, key questions remain: How can deep phenotypes be predicted from shallow inputs? What biological signals support such predictions? And how can we design more cost-effective sample collection strategies by identifying the minimal number of labeled samples and input features required for optimal phenotype refinement?</div></div><div><h3>Method</h3><div>We aim to build a DL model that is minimalist in terms of (1) input features and (2) labeled sample size, using genomic and healthcare data from the UK Biobank. We assess how labeled sample ratio and input dimensionality affect phenotype refinement using AutoComplete, an established DL model, and evaluate performance with genetic metrics. To improve feature efficiency, we apply Shapley additive explanations (SHAP), phenotype correlation, and genetic correlation to rank and prune features. To enhance sample efficiency, we implement active learning to identify a core set of diverse and uncertain samples. After model training, we analyze feature representations and cluster structures derived from the minimalist model to explore MDD heterogeneity and boundary cases between subtypes and pathology vs. health.</div></div><div><h3>Results</h3><div>Using just the top 20 features (5.6% of the original 351) ranked by SHAP, we achieve phenotype refinement comparable to the full set (Pearson’s r with deep-phenotyped Lifetime MDD = 0.70). Key features include age, gender, neuroticism items, and health-related help-seeking behaviour. Additionally, by using uncertainty-based active learning, we only needed 2% of samples with deeply phenotyped labels to refine phenotype for over 330,000 individuals—a 90% reduction in labeled data compared to models trained without active learning. We are currently testing a hybrid strategy combining sample diversity and uncertainty to further boost efficiency.</div></div><div><h3>Discussion</h3><div>By improving feature and sample efficiency, our approach enables more cost-effective sample collection without compromising genetic specificity, supporting scalable and biologically meaningful MDD research. Importantly, the final minimalist model reveals a compact, biologically informative","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 45-46"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TRANSCRIPTOMIC AND CHROMATIN DYNAMICS OF THE HUMAN PTSD BRAIN AT SINGLE CELL RESOLUTION","authors":"Matthew Girgenti ,&nbsp;Ahyeon Hwang ,&nbsp;Mario Skarica ,&nbsp;Jing Zhang ,&nbsp;Siwei Xu ,&nbsp;Jensine Coudriet ,&nbsp;Alexa-Nicole Sliby ,&nbsp;Jiawei Wang ,&nbsp;Dianne Cruz ,&nbsp;Douglas Williamson ,&nbsp;Alicia Che ,&nbsp;Hongyu Zhao ,&nbsp;Keith Young ,&nbsp;John Krystal","doi":"10.1016/j.euroneuro.2025.08.508","DOIUrl":"10.1016/j.euroneuro.2025.08.508","url":null,"abstract":"<div><div>Post-traumatic stress disorder is a multigenic disorder occurring in the aftermath of severe trauma exposure. We isolated &gt;2M nuclei from human postmortem dorsolateral prefrontal cortex from cases and controls for single nucleus(sn) RNA sequencing across three diagnostic cohorts: PTSD, major depression (Psychiatric control), and neurotypical controls to identify neuronal and non-neuronal cell type clusters and cell type-specific gene expression changes (DEGs). We then performed paired sn-sequencing of the same samples for ATAC-sequencing, to measure chromatin accessibility. In addition, we validated our transcriptomic findings by performing single cell, spatial transcriptomics on a subset of donors using the Xenium platform. We identified 14 distinct cell type clusters including neuronal and non-neuronal cell types. We identified over 1142 FDR significant DEGs across many cell types and confirmed expression changes of several genes implicated in PTSD pathophysiology by spatial transcriptomics. We found PTSD specific cis-regulatory elements for several genes including ELFN1, FKBP5, and KCNIP4. We also identified disease-specific receptor-ligand communication pattern disruption between cell types of the DLPFC. We constructed the gene expression regulatory landscape of PTSD by integrating RNA and ATAC modalities to define cis-regulatory elements (CREs) and transcription factor regulatory networks and linked these to DEGs. This enabled us to fine-map all of the PTSD GWAS risk genes from the Million Veteran Program into specific cell types. We discovered selective changes in the glucocorticoid system (long implicated in PTSD pathology) that were surprisingly most pronounced in endothelial cells and to a lesser extent other non-neuronal cells. In addition, we identify vulnerability of somatostatin (SST) interneurons in PTSD and global shifts in the transcriptome reflecting decreases in SST signaling and neurotransmission. These changes are accompanied by decreased output of microglia signaling suggesting suppression of neuroimmune mechanisms in the PTSD PFC. Overall, this work enabled characterization of gene pathways and their dynamics in diverse cortical cell types and prediction of cis-regulatory logic and associated factors underpinning the molecular etiology of PTSD.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 25-26"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TRANSLATING GENETIC FINDINGS INTO MOLECULAR AND CELLULAR RISK MECHANISMS FOR PSYCHIATRIC DISORDERS","authors":"Jareth Wolfe Chair ,&nbsp;Hyejung Won Co-chair ,&nbsp;Mary-Ellen Lynall Discussant","doi":"10.1016/j.euroneuro.2025.08.491","DOIUrl":"10.1016/j.euroneuro.2025.08.491","url":null,"abstract":"<div><div>Psychiatric disorders such as schizophrenia, bipolar disorder, and major depressive disorder are highly heritable yet genetically complex, involving a multitude of common and rare genetic variants. With the advent of high-throughput sequencing and genome-wide association studies (GWAS), hundreds of risk loci have been identified. However, translating these associations into mechanistic insights remains a significant challenge due to the polygenic nature of these disorders and the fact that most associated variants lie in non-coding regions. Many of these variants are implicated across multiple disorders, meaning disorder-specific and pleiotropic variants must also be disentangled. Further complicating the translation from genetic findings to risk mechanisms is that common and rare variants may produce indistinguishable phenotypes, despite significant variation in affected molecular processes.</div><div>This series of presentations is from members of the PGC working group for Functional Genomics and showcases how different methodologies can improve our understanding of the etiology of psychiatric disorders. The first talk describes an experimental approach that disentangles disease-specific and pleiotropic genetic variants and describes the molecular differences observed between the two. The second talk reviews the findings from a recent large-scale methylation-wide association study (MWAS) of psychiatric disorders and discusses their mechanistic implications. The third talk describes recent work mapping cell-specific expression QTLs in the developing brain and discusses how these findings relate to GWAS results from a range of psychiatric disorders. The fourth talk discusses how the 3q29 deletion, a rare variant in schizophrenia, contributes to observed molecular and cellular changes which occur across mouse and human samples. All four speakers have extensive experience in using functional genomics to translate genetic variation into risk mechanisms. Each of our speakers will be showcasing a different methodology, will be presenting their latest findings, and will discuss the benefits and limitations of their own methodological approaches.</div><div><strong>Disclosure:</strong> Nothing to disclose. (Wolfe, Won, Lynall)</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 19"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MODELING MATERNAL INFLAMMATION AND NEURODEVELOPMENT-RELEVANT ENDPOINTS IN BRAIN ORGANOIDS","authors":"Lena Smirnova,&nbsp;Alexandra Rittenhouse","doi":"10.1016/j.euroneuro.2025.08.470","DOIUrl":"10.1016/j.euroneuro.2025.08.470","url":null,"abstract":"<div><h3>Background</h3><div>Autism spectrum disorder (ASD) is increasingly understood as the product of complex gene–environment interactions. Traditional animal models, including maternal immune activation (MIA) paradigms in rodents, provide insights into the developmental impact of inflammation but lack the human-specific context needed to model genetic risk factors such as the 16p11.2 deletion and the intricacies of human brain development.</div></div><div><h3>Methods</h3><div>To overcome these limitations, we established a human in vitro model using immune-competent neural organoid containing microglia, derived from induced pluripotent stem cells (iPSCs) harboring a 16p11.2 deletion. Neurotypical controls were included for comparison. These organoids were subjected to an inflammatory challenge mimicking maternal immune activation, modeling an in utero environment during critical windows of neurodevelopment.</div></div><div><h3>Results</h3><div>At baseline, deletion-carrying organoids exhibited reduced expression of inflammatory mediators. However, upon immune stimulation, they responded with exaggerated secretion of pro-inflammatory cytokines, significantly exceeding responses observed in control organoids. This hyperinflammatory profile was accompanied by changes in microglia, evidenced by changes in expression levels of CD68 and TREM2, indicating a heightened vulnerability of the 16p11.2-deleted brain to immune stressors. Importantly, organoids derived from ASD-affected individuals displayed stronger inflammatory and microglial responses than non-ASD donors with the same genetic deletion, underscoring the role of individual variability and gene–environment interactions in ASD phenotypic expression.</div></div><div><h3>Conclusions</h3><div>This human-specific brain organoid model can capture the synergistic effects of genetic susceptibility and maternal inflammation in ASD. This approach may advance mechanistic understanding of ASD pathogenesis and supports the development of more predictive tools for assessing neurodevelopmental risk in vulnerable populations.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 9"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WHAT DO DNA METHYLATION DIFFERENCES TELL US ABOUT PSYCHIATRIC DISORDERS? INSIGHTS FROM MWAS","authors":"Stephanie Le Hellard ,&nbsp;Kira Höffler ,&nbsp;Anne-Kristin Stavrum ,&nbsp;Markos Tesfaye ,&nbsp;Leticia Spindola","doi":"10.1016/j.euroneuro.2025.08.494","DOIUrl":"10.1016/j.euroneuro.2025.08.494","url":null,"abstract":"<div><div>DNA methylation is an epigenetic modification that can be influenced by a range of factors, including genetic background, environmental exposures, biological variables (such as age and sex), pharmacological treatment, or the physiological effects of a disorder (such as stress). Unlike many other epigenetic modifications, DNA methylation can be measured using DNA extracted from clinical samples, making it particularly suitable for large-scale case-control studies. In recent years, methylation-wide association studies (MWAS) have been conducted across several psychiatric disorders, including schizophrenia, bipolar disorder, post-traumatic stress disorder (PTSD), major depressive disorder, and obsessive-compulsive disorder (OCD).</div><div>While significant progress has been made in identifying methylation differences associated with disease status, key questions remain. Specifically, it is still unclear to what extent these methylation changes reflect underlying genetic influences, represent independent environmental or disease-related effects, or could complement genetic studies to shed light on disease mechanisms. Importantly, there is growing interest in whether methylation signatures could serve as biomarkers to improve disease prediction, diagnosis, or treatment response.</div><div>In this presentation, I will review recent findings from large-scale MWAS in psychiatric disorders, discuss their implications for understanding biological mechanisms, explore their potential to translate genetic findings into functional insights, and consider their promise as biomarkers for clinical application.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 20"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STEPS AND SYMPTOMS: TRACKING THE INTERPLAY OF MOVEMENT AND MOOD OVER TIME","authors":"Julia Sealock","doi":"10.1016/j.euroneuro.2025.08.488","DOIUrl":"10.1016/j.euroneuro.2025.08.488","url":null,"abstract":"<div><div>Changes in mood are a normal part of life; however, prolonged episodes of depressed mood are associated with significant health and behavior changes. Depressed mood is associated with decreased physical activity, such as daily steps. Previous studies point towards a bidirectional relationship between mood and activity, creating a cycle of decreased mood and decreased physical activity. Digital phenotyping is a powerful approach for gathering extensive longitudinal data on individuals, using both passive data collection (e.g., steps, sleep) and active surveys (e.g., mood). The All of Us (AoU) Research Program contains digital phenotyping data through Fitbit activity and Patient Health Questionnaire-9 (PHQ-9) mood surveys. Additionally, AoU data is linked to longitudinal electronic health record information, providing a powerful opportunity to study the temporal dynamics of mood and activity in the context of health outcomes. In this talk, Julia Sealock, a research fellow at the Broad Institute’s Stanley Center for Psychiatric Research in Cambridge, USA, describes using longitudinal modelling in 4,582 samples in AoU to replicate the bidirectional association between activity (steps) and mood (PHQ-9 scores). Julia further refined the temporal association using time-lagged models with average steps one week, two weeks, and one month surrounding mood assessments. Mood was best predicted by steps averaged over the preceding month (p = 3.49e-43, beta = -0.16), while same-day mood best predicted activity levels (p = 2.38e-31, beta = -0.15), suggesting activity has a longer acting effect on mood than the reverse. The effect of steps on mood corresponded to a decrease of 0.0001 PHQ-9 points for each step increase, while the effect of mood on steps corresponded to 155 decreased steps for each 1-point increase in PHQ-9. Finally, Julia will integrate genetic analysis to determine if increased average daily steps can help overcome the effects of increased depression polygenic score on risk of future depression diagnosis. This work highlights the potential of EHR-integrated digital phenotyping to advance precision psychiatry by informing behavior-based prevention and treatment strategies.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 18"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}