JCPP advances最新文献

{"title":"Commentary: Towards an integrated understanding of general psychopathology-A commentary on Devine et al. (2026).","authors":"Ling-Ling Wang, Yi Wang, Raymond C K Chan","doi":"10.1002/jcv2.70163","DOIUrl":"10.1002/jcv2.70163","url":null,"abstract":"","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70163"},"PeriodicalIF":4.1,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13498740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary: Understanding emerging psychopathology through symptom networks-A commentary on Häfeli et al. (2026).","authors":"Antonia N Kaczkurkin","doi":"10.1002/jcv2.70165","DOIUrl":"10.1002/jcv2.70165","url":null,"abstract":"","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70165"},"PeriodicalIF":4.1,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13498742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial perspective: Connecting transdiagnostic mechanisms targeted in cognitive behavioral therapies with empirically-based symptom dimensions.","authors":"Kasey Stanton, Jack D Burke, Helena Towne, Isabella Whitesides","doi":"10.1002/jcv2.70161","DOIUrl":"10.1002/jcv2.70161","url":null,"abstract":"<p><p>Various avenues of transdiagnostic research hold promise for improving diagnosis and treatment but remain fragmented in some ways. We provide recommendations for integrating dimensional assessment guided by the Hierarchical Taxonomy of Psychopathology (HiTOP) framework into clinical practice by more effectively connecting the HiTOP with social-cognitive vulnerability (SCV) mechanisms targeted in cognitive behavioral and third-wave therapies. Specifically, we present concrete ideas for (a) comprehensively understanding HiTOP symptom dimension-SCV associations, (b) integrating measure development efforts spanning the HiTOP and SCV literatures, and (c) sharpening terminology for improving construct description and clinical communication. We draw on studies from the 2026 Special Issue of <i>JCPP Advances on Transdiagnostic Approaches to Child and Adolescent Mental Health</i> that provide helpful examples illustrating these points, and we use these study examples to demonstrate how HiTOP-based assessment connects with personalized treatment planning.</p>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70161"},"PeriodicalIF":4.1,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Childhood adversity and psychotic-like experiences in adolescence: Investigating a pathway via disrupted pubertal and grey matter development.","authors":"Megan Thomas, Sarah Whittle, Elizabeth M Haris, Vanessa L Cropley","doi":"10.1002/jcv2.70159","DOIUrl":"https://doi.org/10.1002/jcv2.70159","url":null,"abstract":"<p><strong>Background: </strong>Childhood adversity increases psychosis risk, but the biological mechanisms responsible remain elusive. A possible but underexplored pathway involves altered pubertal and grey matter (GM) development.</p><p><strong>Methods: </strong>In youth from the Adolescent Brain Cognitive Development study (<i>N</i> = 3462), we investigated the effect of adversity at age 9-11 (indexing threat, socioeconomic deprivation and interpersonal deprivation) on psychotic-like experiences (PLEs) four years later, and the mediating role of pubertal timing and tempo. We then investigated whether pathways could be further explained by the influence of puberty on longitudinal GM change. Analyses for males and females were conducted separately.</p><p><strong>Results: </strong>Associations were found between all adversity types and greater PLEs, of which most were mediated by earlier pubertal timing and/or slower tempo. Mediation effects tended to be stronger in females, and for socioeconomic deprivation pathways. Results did not support the presence of serial mediation pathways from adversity to PLEs via puberty-related GM variation.</p><p><strong>Conclusion: </strong>Findings suggest that disruption to pubertal development may contribute to the link between adversity and psychosis risk, but not by altering GM development. Investigating puberty alongside alternative biological systems may help contextualise our findings.</p>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70159"},"PeriodicalIF":4.1,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13487410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifiable regional and neural network abnormalities in early childhood anxiety.","authors":"Khalil I Thompson, Yiwen Zhang, Denny Schaedig, Anna Bankston, Anoushka Parnerkar, Shrinidhi Jeyaram, Nadine Melhem, Susan B Perlman","doi":"10.1002/jcv2.70158","DOIUrl":"https://doi.org/10.1002/jcv2.70158","url":null,"abstract":"<p><strong>Background: </strong>Although the neural underpinnings of anxiety have been investigated, most research has been confined to adolescence and adulthood, with less of a focus on the early childhood period. Little is known about the role of anticipatory fear processing early childhood, which is a sensitive period for anxiety development. Free-viewing paradigms provide a useful method for investigating brain activation during fear anticipation because they present stimuli in a manner that mirrors the participant's natural environment.</p><p><strong>Methods: </strong>One hundred and fifty-nine, high-risk children (<i>M</i> = 5.93 years; SD = 1.16) (54% female) watched an episode of a children's animated program that relied on anticipatory fear for the main plot line. Parents rated children on anxiety symptoms. Data was analyzed using both general linear modeling (GLM) and independent components analysis (ICA) to determine task-dependent regional and network response to positively and negatively-valenced time-series in the episode.</p><p><strong>Results: </strong>GLM analyses identified significant positive regional activation in the dorsomedial prefrontal cortex, middle temporal gyrus, and thalamus that fluctuated during the negative timecourse as a function of anxiety symptoms. Furthermore, ICA analyses revealed diminished network connectivity between (1) the anterior cingulate and raphe nuclei and (2) the right temporoparietal junction, left superior parietal lobule, and lingual gyri as a function of anxiety symptoms.</p><p><strong>Conclusion: </strong>Our findings point to fractured communication between emotion and sociocognitive processing regions as a function of increased anxiety in early childhood. The findings of this study can point to neural mechanisms present in early anxiety symptomatology before the traditionally defined onset.</p>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70158"},"PeriodicalIF":4.1,"publicationDate":"2026-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discrete components of stress impact trajectories of childhood irritability but not adolescent mental health outcomes.","authors":"Mariah DeSerisy, Huiyu Yang, Jacob W Cohen, David Pagliaccio, Julie B Herbstman, Bradley S Peterson, Frederica P Perera, Virginia Rauh, Amy E Margolis","doi":"10.1002/jcv2.70153","DOIUrl":"10.1002/jcv2.70153","url":null,"abstract":"<p><strong>Background: </strong>Exposure to stressors during early childhood has been linked to irritability and subsequent mental health problems; however, little work has examined the unique and combined effects of distinct stress exposures on trajectories of childhood irritability or adolescent mental health symptoms.</p><p><strong>Methods: </strong>Six components of family stress were assessed via maternal report at child age 5. Irritability was measured longitudinally at ages 7, 9, and 11. Adolescents self-reported on anxiety, depression, and externalizing symptoms at age 13-20. Latent growth mixture modeling examined classes of irritability. Bayesian kernel machine regression (BKMR) examined unique and shared effects of stress components on class assignment and to continuously measured irritability at individual age points (<i>N</i> = 472). Causal mediation tested if irritability mediated any stress-related mental health problems.</p><p><strong>Results: </strong>Latent growth mixture modeling identified high and low irritability classes. A positive joint mixture effect of stressors on irritability was detected, with additive contributions from intimate partner violence, material hardship, perceived stress, and maternal demoralization. Intimate partner violence independently predicted high irritability over time and irritability at age 7, and maternal perceived stress independently predicted irritability at age 11. Neither childhood irritability nor early stress predicted self-reported adolescent mental health symptoms.</p><p><strong>Conclusion: </strong>Early exposure to familial stressors are risk markers for childhood irritability. Interventions targeting intimate partner violence and co-occurring stressors could help mitigate persistent irritability, a well-known public health concern related to children's development.</p>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70153"},"PeriodicalIF":4.1,"publicationDate":"2026-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13454745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns of psychopathology and personal strengths among youth in psychiatric care.","authors":"Logan R Cummings, Gil Noam, Alessandro S De Nadai, Kathryn Parker, Patricia Allen, Julianne G Wilner, Jillian M Russo, Lauren O Delman, Aishwarya Surendran, Emily G Arnott, Dawn E Sugarman, Alisa Busch, Alexander Kos, Fairlee C Fabrett, Ralph Buonopane, Kristen L Batejan, Erika C Esposito, Tian Yu Cheng, Samuel J Marquis, Wilson Giles Stecher, Caroline S Abut, Fiona Ritchie, Jeffrey Zhang, Emily A Hutchinson, Isabella Kahhale, Hannah Becker, Jennifer T Sneider, Marisa M Silveri, Colin W Burke, Elan French, Esther S Tung, Abigail M Stark, Jennifer R Klix, Anna B Mazur, Denice E Cronin, Kayla M Turner, Melanie R Harkins, Peter Adams, Willow Clayton, Clare Foster, Dorothea R Volpp, Emily Gaddy, Florence R Stout, Alyssa L Faro, Maria G Fraire, Mary Gambill, Perihan Esra Guvenek-Cokol, Christopher Alpert, Cynthia Kaplan, Erin V Batarseh, Gillian Galen, Kesley A Hobbs, Lola M Rodriguez, Lukas A Turlick, Rhea Agrawal, Sophia E Nadeau, Allison P Falls, Eliot Fearey, Daniella H Levy, Ana L Ibarra, Bryan C Pridgen, Julia G Luppino, Kelly Molthrop, Louise J Weidner, Munya Hayek, Kayla J Saucedo, Magdalena S Batlle, Payton B Maclean, Rajvi Broker-Sen, Wendy P Bamatter, Charles Moore, Rachel L Wallace, Rae-Anna Muise, Tessa N Hamilton, Jacqueline B Sperling, Paulina Loo, R Meredith Elkins, Michelle C Silverman, Daniel P Dickstein","doi":"10.1002/jcv2.70156","DOIUrl":"10.1002/jcv2.70156","url":null,"abstract":"<p><strong>Background: </strong>The American Academy of Pediatrics and the American Academy of Child and Adolescent Psychiatry have declared a National State of Emergency in Children's Mental Health, calling for improved approaches to address youth mental health. We sought to leverage the first child psychiatry division-wide measurement-based care initiative to advance understanding of how both psychopathology and social-emotional skills are related among youth patients, as these associations are rarely examined together. In doing so, our objective was to provide an empirical basis for integrative approaches that target psychopathology, while also leveraging social-emotional strengths.</p><p><strong>Methods: </strong>Youth patients receiving a continuum of psychiatric services, from intensive outpatient to short-term residential programs (<i>N</i> = 926; ages 11-24; 58% Cis-Female; 84.6% White), completed self-report measures of psychopathology and social-emotional skills. Latent profile analyses were conducted to identify distinct profiles within the sample.</p><p><strong>Results: </strong>Seven profiles were identified. Profiles with overlapping syndromes were differentiated by highly distinct patterns of socio-emotional strengths and challenges.</p><p><strong>Conclusion: </strong>Social-emotional skills assessment provides unique information beyond syndrome severity and can be used to create personalized subgroups of clinical patients. We observed many youth with similar syndromes report markedly different patterns of social-emotional skills. We emphasize the clinical utility of integrating strengths- and resilience-based frameworks into psychiatric assessment and treatment planning-supporting the shift beyond treatment models focused solely on reducing symptoms and skills deficits, toward more personalized approaches that also leverage youths' existing capacities for growth and recovery.</p>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70156"},"PeriodicalIF":4.1,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13447985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing an integrated algorithm to support autism diagnostic decisions: Model performance and statistical fairness.","authors":"Aaron J Kaat, Ashlynn Campagna, Hannah Feiner, Jeffrey Michael Grauzer, Amanda Nicole Hobson, Maranda Jones, Jordan Paige Lee, Laura Jean Sudec, Megan York Roberts","doi":"10.1002/jcv2.70157","DOIUrl":"10.1002/jcv2.70157","url":null,"abstract":"<p><strong>Background: </strong>Best practices in diagnosing autism spectrum disorder require an expert diagnostician to integrate multiple sources of information, including direct observations, interviews, and rating scales completed by knowledgeable informants. Machine learning methods are well-positioned to integrate disparate information sources to improve classification. This study sought to evaluate whether a machine learning algorithm could support appropriately weighting multiple sources of diagnostically relevant information.</p><p><strong>Methods: </strong>Telehealth diagnostic assessments were conducted for 639 toddlers (age mean = 30.4, SD = 4.3 months; 29.7% female) already receiving early intervention (EI). The Toddler Autism Symptom Interview (TASI) and the TELE-ASD-PEDS (TAP) were completed during separate visits. Each child's caregivers and usual EI providers asynchronously completed rating scales of autistic characteristics developed for this study. Calibration and validation data subsets were created using a 2:1 split. The optimal algorithm was developed using elastic net regularized regression in the calibration dataset, which was then evaluated in the validation dataset. Statistical fairness criteria evaluated whether the proposed algorithm functioned similarly across multiple protected group statuses.</p><p><strong>Results: </strong>The prevalence of autism in the sample was 80.4%. The algorithm developed in the calibration dataset included both the caregiver- and EI provider-completed rating scales, four items from the TASI, and six items from the TAP. Model performance was high in both the calibration and validation samples (sensitivity >0.90; specificity >0.75; kappa >0.60), but statistical fairness criteria varied. False positives were relatively rare, but were more common in advantaged groups, which may suggest systematic under-classification by the algorithm in minoritized groups.</p><p><strong>Conclusion: </strong>Diagnostic practices for autism require integrating multiple sources of information. Machine learning can support diagnosticians in this effort, as demonstrated here utilizing multiple rating scales, the TASI, and the TAP. However, there is a risk for bias when using machine learning and as such, no algorithm should replace expert clinical judgment.</p>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70157"},"PeriodicalIF":4.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Body weight in autism spectrum disorder: A systematic review and meta-analysis.","authors":"Victoria Nieuwenhuys-Ruiz, Leyre Gambra, Samuele Cortese, Pablo Lizoain, Diana Rodriguez-Romero, Ursula Paiva, Carmen Gándara, Sara Magallón, Gonzalo Arrondo","doi":"10.1002/jcv2.70151","DOIUrl":"10.1002/jcv2.70151","url":null,"abstract":"<p><strong>Background: </strong>Evidence on the relationship of autism spectrum disorder (ASD) to overweight/obesity and underweight is mixed. Moreover, it is unclear how this relationship changes with age.</p><p><strong>Methods: </strong>We carried out a systematic review (SR) with meta-analysis on the association between ASD and obesity, overweight and underweight across the life span. The study was based on a pre-registered protocol (PROSPERO: CRD42024524084). Web of Science and PubMed were searched until the 8th of April 2025. We assessed the quality of the included studies with the Newcastle-Ottawa scales (NOS).</p><p><strong>Results: </strong>A total of 9075 records were screened, of which 64 studies reporting data on obesity, overweight and/or underweight on ASD and controls general participants were included in the SR and 62 meta-analyzed. We found a significant association between ASD and obesity (OR = 1.93; CI = 1.70-2.19; <i>i</i> ² = 99.02%), ASD and overweight (OR = 1.73; CI = 1.43-2.09; <i>i</i> ² = 95.44%) but, at the group level, not ASD and underweight (OR = 1.22; CI = 0.91-1.64; <i>i</i> ² = 78.37%). There was a statistically significant association between ASD and underweight in adolescents (OR = 1.32; CI = 1.12-1.56) and adults (OR = 2.14; CI = 1.5-3.04). The score on the Newcastle-Ottawa Scale showed that, overall, studies had a medium risk of bias (median = 6, range 1-10).</p><p><strong>Conclusion: </strong>Individuals with ASD have an elevated risk of being obese or overweight, and, in adolescents and adults, of underweight. Our study may contribute to the development and implementation of targeted prevention and intervention strategies, aimed at enhancing the physical well-being of individuals with ASD and their caregivers, ultimately improving their quality of life.</p>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70151"},"PeriodicalIF":4.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13438009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sleep patterns during infancy and neurodevelopmental and behavioral outcomes in early childhood: A prospective cohort study.","authors":"Wei Wei, Jun Zhang, Hui Wang","doi":"10.1002/jcv2.70155","DOIUrl":"10.1002/jcv2.70155","url":null,"abstract":"<p><strong>Background: </strong>Infant sleep is multidimensional, with sleep habits and sleep problems often co-occurring and changing rapidly during early life. Evidence links infant sleep with later neurodevelopmental and behavioral outcomes, yet few studies have examined overall sleep patterns, their changes over time, and outcomes across multiple preschool domains. We examined associations of sleep patterns at 6 and 12 months and their transitions with preschool neurodevelopmental and behavioral outcomes.</p><p><strong>Methods: </strong>This prospective cohort study included 1707 children from the Shanghai Birth Cohort. Infant sleep at 6 and 12 months was assessed using the Brief Infant Sleep Questionnaire. Sleep patterns were identified at each time point using latent class analysis based on multiple sleep indicators, and children with sleep data at both ages were classified into transition groups. At age 4, outcomes were assessed using the Strengths and Difficulties Questionnaire (SDQ), Social Responsiveness Scale-Short Form (SRS-SF), and Behavior Rating Inventory of Executive Function-Preschool Version (BRIEF-P). Multivariable linear regression estimated associations.</p><p><strong>Results: </strong>At 6 and 12 months, 66.02% and 69.18% of infants were classified as having a good sleep pattern. Among children with sleep data at both time points, 51.57% had consistently good sleep, 16.68% improved, 14.44% deteriorated, and 17.31% had consistently poor sleep. Poor sleep at 6 months was associated with higher SDQ Total Difficulties scores. Poor sleep at 12 months was associated with higher SDQ Total Difficulties, SRS-SF Total, and BRIEF-P Global Executive Composite scores. Consistently poor sleep from 6 to 12 months was associated with less favorable outcomes than consistently good sleep.</p><p><strong>Conclusion: </strong>Poorer infant sleep patterns, especially at 12 months and when persistent from 6 to 12 months, were associated with less favorable preschool neurodevelopmental and behavioral outcomes. These findings support infant sleep patterns as an early-life factor relevant to developmental research and monitoring.</p>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":" ","pages":"e70155"},"PeriodicalIF":4.1,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13428066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148655083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}