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Models of mild cognitive deficits in risk assessment in early psychosis

Published online by Cambridge University Press:  04 March 2024

TianHong Zhang Open the ORCID record for TianHong Zhang [Opens in a new window] ,
HuiRu Cui ,
XiaoChen Tang ,
LiHua Xu ,
YanYan Wei ,
YeGang Hu ,
YingYing Tang ,
ZiXuan Wang ,
HaiChun Liu  and
Tao Chen
...Show all authors
TianHong Zhang*
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
HuiRu Cui
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
XiaoChen Tang
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
LiHua Xu
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
YanYan Wei
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
YeGang Hu
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
YingYing Tang
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
ZiXuan Wang
Affiliation:
Shanghai Xinlianxin Psychological Counseling Co., Ltd, Shanghai, China
HaiChun Liu
Affiliation:
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Tao Chen
Affiliation:
Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada Labor and Worklife Program, Harvard University, Cambridge, MA, USA
ChunBo Li
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
JiJun Wang*
Affiliation:
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, People's Republic of China Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, People's Republic of China
*
Corresponding author: TianHong Zhang; Email: zhang_tianhong@126.com JiJun Wang; Email: jijunwang27@163.com
Corresponding author: TianHong Zhang; Email: zhang_tianhong@126.com JiJun Wang; Email: jijunwang27@163.com
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Abstract

Background

Mild cognitive deficits (MCD) emerge before the first episode of psychosis (FEP) and persist in the clinical high-risk (CHR) stage. This study aims to refine risk prediction by developing MCD models optimized for specific early psychosis stages and target populations.

Methods

A comprehensive neuropsychological battery assessed 1059 individuals with FEP, 794 CHR, and 774 matched healthy controls (HCs). CHR subjects, followed up for 2 years, were categorized into converters (CHR-C) and non-converters (CHR-NC). The MATRICS Consensus Cognitive Battery standardized neurocognitive tests were employed.

Results

Both the CHR and FEP groups exhibited significantly poorer performance compared to the HC group across all neurocognitive tests (all p < 0.001). The CHR-C group demonstrated poorer performance compared to the CHR-NC group on three sub-tests: visuospatial memory (p < 0.001), mazes (p = 0.005), and symbol coding (p = 0.023) tests. Upon adjusting for sex and age, the performance of the MCD model was excellent in differentiating FEP from HC, as evidenced by an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.895 (p < 0.001). However, when applied in the CHR group for predicting CHR-C (AUC = 0.581, p = 0.008), the performance was not satisfactory. To optimize the efficiency of psychotic risk assessment, three distinct MCD models were developed to distinguish FEP from HC, predict CHR-C from CHR-NC, and identify CHR from HC, achieving accuracies of 89.3%, 65.6%, and 80.2%, respectively.

Conclusions

The MCD exhibits variations in domains, patterns, and weights across different stages of early psychosis and diverse target populations. Emphasizing precise risk assessment, our findings highlight the importance of tailored MCD models for different stages and risk levels.

Keywords

cognitionpredictionschizophreniatransitionultra-high risk
Type
Original Article
Information
Psychological Medicine , First View , pp. 1 - 12
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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References

Abel, K. M., Drake, R., & Goldstein, J. M. (2010). Sex differences in schizophrenia. International Review of Psychiatry, 22(5), 417–428. doi: 10.3109/09540261.2010.515205CrossRefGoogle ScholarPubMed
Amoretti, S., Rabelo-da-Ponte, F. D., Rosa, A. R., Mezquida, G., Sanchez-Torres, A. M., Fraguas, D., … Group, P. E. (2021). Cognitive clusters in first-episode psychosis. Schizophrenia Research, 237, 31–39. doi: 10.1016/j.schres.2021.08.021CrossRefGoogle ScholarPubMed
Bang, M., Kim, K. R., Song, Y. Y., Baek, S., Lee, E., & An, S. K. (2015). Neurocognitive impairments in individuals at ultra-high risk for psychosis: Who will really convert? The Australian and New Zealand Journal of Psychiatry, 49(5), 462–470. doi: 10.1177/0004867414561527CrossRefGoogle ScholarPubMed
Bolt, L. K., Amminger, G. P., Farhall, J., McGorry, P. D., Nelson, B., Markulev, C., … Allott, K. A. (2019). Neurocognition as a predictor of transition to psychotic disorder and functional outcomes in ultra-high risk participants: Findings from the NEURAPRO randomized clinical trial. Schizophrenia Research, 206, 67–74. doi: 10.1016/j.schres.2018.12.013CrossRefGoogle ScholarPubMed
Bora, E., Lin, A., Wood, S. J., Yung, A. R., McGorry, P. D., & Pantelis, C. (2014). Cognitive deficits in youth with familial and clinical high risk to psychosis: A systematic review and meta-analysis. Acta Psychiatrica Scandinavica, 130(1), 1–15. doi: 10.1111/acps.12261CrossRefGoogle ScholarPubMed
Bora, E., & Murray, R. M. (2014). Meta-analysis of cognitive deficits in ultra-high risk to psychosis and first-episode psychosis: Do the cognitive deficits progress over, or after, the onset of psychosis? Schizophrenia Bulletin, 40(4), 744–755. doi: 10.1093/schbul/sbt085CrossRefGoogle ScholarPubMed
Cannon, T. D., Yu, C., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., … Kattan, M. W. (2016). An individualized risk calculator for research in prodromal psychosis. The American Journal of Psychiatry, 173(10), 980–988. doi: 10.1176/appi.ajp.2016.15070890CrossRefGoogle ScholarPubMed
Cowman, M., Holleran, L., Lonergan, E., O'Connor, K., Birchwood, M., & Donohoe, G. (2021). Cognitive predictors of social and occupational functioning in early psychosis: A systematic review and meta-analysis of cross-sectional and longitudinal data. Schizophrenia Bulletin, 47(5), 1243–1253. doi: 10.1093/schbul/sbab033CrossRefGoogle ScholarPubMed
Cuesta, M. J., Sanchez-Torres, A. M., Moreno-Izco, L., Garcia de Jalon, E., Gil-Berrozpe, G. J., Zarzuela, A., … Zandio, M. (2022). Neurocognitive correlates of the varied domains of outcomes at 20 year follow-up of first-episode psychosis. Psychiatry Research, 318, 114933. doi: 10.1016/j.psychres.2022.114933CrossRefGoogle ScholarPubMed
Cui, H., Giuliano, A. J., Zhang, T., Xu, L., Wei, Y., Tang, Y., … Stone, W. S. (2020). Cognitive dysfunction in a psychotropic medication-naive, clinical high-risk sample from the ShangHai-At-Risk-for-Psychosis (SHARP) study: Associations with clinical outcomes. Schizophrenia Research, 226, 138–146. doi: 10.1016/j.schres.2020.06.018CrossRefGoogle Scholar
Fagerlund, B., Pantelis, C., Jepsen, J. R. M., Raghava, J. M., Rostrup, E., Thomas, M. B., … Glenthoj, B. Y. (2021). Differential effects of age at illness onset on verbal memory functions in antipsychotic-naive schizophrenia patients aged 12–43 years. Psychological Medicine, 51(9), 1570–1580. doi: 10.1017/S0033291720000409CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Deste, G., Smieskova, R., Barlati, S., Yung, A. R., Howes, O., … Borgwardt, S. (2012). Cognitive functioning in prodromal psychosis: A meta-analysis. Archives of General Psychiatry, 69(6), 562–571. doi: 10.1001/archgenpsychiatry.2011.1592CrossRefGoogle ScholarPubMed
Giuliano, A. J., Li, H., Mesholam-Gately, R. I., Sorenson, S. M., Woodberry, K. A., & Seidman, L. J. (2012). Neurocognition in the psychosis risk syndrome: A quantitative and qualitative review. Current Pharmaceutical Design, 18(4), 399–415. doi: 10.2174/138161212799316019CrossRefGoogle ScholarPubMed
Hafner, H., an der Heiden, W., Behrens, S., Gattaz, W. F., Hambrecht, M., Loffler, W., … Stein, A. (1998). Causes and consequences of the gender difference in age at onset of schizophrenia. Schizophrenia Bulletin, 24(1), 99–113. doi: 10.1093/oxfordjournals.schbul.a033317CrossRefGoogle ScholarPubMed
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13(2), 261–276.CrossRefGoogle ScholarPubMed
Kern, R. S., Gold, J. M., Dickinson, D., Green, M. F., Nuechterlein, K. H., Baade, L. E., … Marder, S. R. (2011). The MCCB impairment profile for schizophrenia outpatients: Results from the MATRICS psychometric and standardization study. Schizophrenia Research, 126(1–3), 124–131. doi: S0920-9964(10)01637-3[pii]10.1016/j.schres.2010.11.008CrossRefGoogle ScholarPubMed
Kern, R. S., Nuechterlein, K. H., Green, M. F., Baade, L. E., Fenton, W. S., Gold, J. M., … Marder, S. R. (2008). The MATRICS consensus cognitive battery, part 2: Co-norming and standardization. The American Journal of Psychiatry, 165(2), 214–220. doi: 10.1176/appi.ajp.2007.07010043CrossRefGoogle ScholarPubMed
Kim, S. J., Jung, D. U., Moon, J. J., Jeon, D. W., Seo, Y. S., Jung, S. S., … Kim, Y. S. (2021). Relationship between disability self-awareness and cognitive and daily living function in schizophrenia. Schizophrenia Research Cognition, 23, 100192. doi: 10.1016/j.scog.2020.100192CrossRefGoogle ScholarPubMed
Li, A. W. Y., Hui, C. L. M., Lee, E. H. M., Chang, W. C., Chan, S. K. W., & Chen, E. Y. H. (2019). Gender differences in correlates of cognition in first-episode psychosis. Psychiatry Research, 271, 412–420. doi: 10.1016/j.psychres.2018.12.011CrossRefGoogle ScholarPubMed
Li, H., Zhang, T., Xu, L., Tang, Y., Cui, H., Wei, Y., … Wang, J. (2018). A comparison of conversion rates, clinical profiles and predictors of outcomes in two independent samples of individuals at clinical high risk for psychosis in China. Schizophrenia Research, 197, 509–515. doi: 10.1016/j.schres.2017.11.029CrossRefGoogle ScholarPubMed
Luo, X., Zhang, L., Zhang, J., Chen, H., Hong, H., Luo, R., … Jiang, Z. (2021). Changes in the cognitive function of Chinese college students with a clinical high risk of psychosis. Psychiatry Research, 305, 114242. doi: 10.1016/j.psychres.2021.114242CrossRefGoogle ScholarPubMed
Mam-Lam-Fook, C., Danset-Alexandre, C., Pedron, L., Amado, I., Gaillard, R., & Krebs, M. O. (2017). Neuropsychology of subjects with ultra-high risk (UHR) of psychosis: A critical analysis of the literature. L'Encephale, 43(3), 241–253. doi: 10.1016/j.encep.2017.02.001CrossRefGoogle ScholarPubMed
McGlashan, T., Walsh, B., & Woods, S. (2010). The psychosis-risk syndrome: Handbook for diagnosis and follow-up. New York: Oxford University Press.Google Scholar
Miller, T. J., McGlashan, T. H., Rosen, J. L., Cadenhead, K., Cannon, T., Ventura, J., … Woods, S. W. (2003). Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: Predictive validity, interrater reliability, and training to reliability. Schizophrenia Bulletin, 29(4), 703–715. doi: 10.1093/oxfordjournals.schbul.a007040CrossRefGoogle ScholarPubMed
Mollon, J., & Reichenberg, A. (2018). Cognitive development prior to onset of psychosis. Psychological Medicine, 48(3), 392–403. doi: 10.1017/S0033291717001970CrossRefGoogle ScholarPubMed
Nuechterlein, K. H., Green, M. F., Kern, R. S., Baade, L. E., Barch, D. M., Cohen, J. D., … Marder, S. R. (2008). The MATRICS consensus cognitive battery, part 1: Test selection, reliability, and validity. The American Journal of Psychiatry, 165(2), 203–213. doi: 10.1176/appi.ajp.2007.07010042CrossRefGoogle ScholarPubMed
Penn, D. L., Mueser, K. T., & Spaulding, W. (1996). Information processing, social skill, and gender in schizophrenia. Psychiatry Research, 59(3), 213–220. doi: 10.1016/0165-1781(95)02768-8CrossRefGoogle ScholarPubMed
Rajji, T. K., Ismail, Z., & Mulsant, B. H. (2009). Age at onset and cognition in schizophrenia: Meta-analysis. The British Journal of Psychiatry, 195(4), 286–293. doi: 10.1192/bjp.bp.108.060723CrossRefGoogle ScholarPubMed
Ramyead, A., Kometer, M., Studerus, E., Baumeler, D., von Rotz, R., & Riecher-Rossler, A. (2017). Alpha oscillations underlie working memory abnormalities in the psychosis high-risk state. Biological Psychology, 126, 12–18. doi: 10.1016/j.biopsycho.2017.04.002CrossRefGoogle ScholarPubMed
Reichenberg, A., Caspi, A., Harrington, H., Houts, R., Keefe, R. S., Murray, R. M., … Moffitt, T. E. (2010). Static and dynamic cognitive deficits in childhood preceding adult schizophrenia: A 30-year study. The American Journal of Psychiatry, 167(2), 160–169. doi: 10.1176/appi.ajp.2009.09040574CrossRefGoogle ScholarPubMed
Rodriguez-Sanchez, J. M., Setien-Suero, E., Suarez-Pinilla, P., Mayoral Van Son, J., Vazquez-Bourgon, J., Gil Lopez, P., … group, P. (2022). Ten-year course of cognition in first-episode non-affective psychosis patients: PAFIP cohort. Psychological Medicine, 52(4), 770–779. doi: 10.1017/S0033291720002408CrossRefGoogle Scholar
Sawada, K., Kanehara, A., Sakakibara, E., Eguchi, S., Tada, M., Satomura, Y., … Kasai, K. (2017). Identifying neurocognitive markers for outcome prediction of global functioning in individuals with first-episode and ultra-high-risk for psychosis. Psychiatry and Clinical Neurosciences, 71(5), 318–327. doi: 10.1111/pcn.12522CrossRefGoogle ScholarPubMed
Seidman, L. J., Shapiro, D. I., Stone, W. S., Woodberry, K. A., Ronzio, A., Cornblatt, B. A., … Woods, S. W. (2016). Association of neurocognition with transition to psychosis: Baseline functioning in the second phase of the North American prodrome longitudinal study. JAMA Psychiatry, 73(12), 1239–1248. doi: 10.1001/jamapsychiatry.2016.2479CrossRefGoogle ScholarPubMed
Shi, C., He, Y., Cheung, E. F., Yu, X., & Chan, R. C. (2013). An ecologically valid performance-based social functioning assessment battery for schizophrenia. Psychiatry Research, 210(3), 787–793. doi: S0165-1781(13)00613-6[pii]10.1016/j.psychres.2013.09.023CrossRefGoogle ScholarPubMed
Smelror, R. E., Jorgensen, K. N., Lonning, V., Kelleher, I., Cannon, M., DeRosse, P., … Agartz, I. (2019). Healthy adolescent performance with standardized scoring tables for the MATRICS consensus cognitive battery: A multisite study. Schizophrenia Bulletin, 45(4), 773–783. doi: 10.1093/schbul/sby131CrossRefGoogle ScholarPubMed
Stouten, L. H., Veling, W., Laan, W., van der Helm, M., & van der Gaag, M. (2014). Psychotic symptoms, cognition and affect as predictors of psychosocial problems and functional change in first-episode psychosis. Schizophrenia Research, 158(1-3), 113–119. doi: 10.1016/j.schres.2014.06.023CrossRefGoogle ScholarPubMed
Tang, Y., Xu, L., Zhu, T., Cui, H., Qian, Z., Kong, G., … Wang, J. (2023). Visuospatial learning selectively enhanced by personalized transcranial magnetic stimulation over Parieto-hippocampal network among patients at clinical high-risk for psychosis. Schizophrenia Bulletin, 49(4), 923–932. doi: 10.1093/schbul/sbad015CrossRefGoogle ScholarPubMed
Vazquez-Barquero, J. L., Cuesta Nunez, M. J., de la Varga, M., Herrera Castanedo, S., Gaite, L., & Arenal, A. (1995). The Cantabria first episode schizophrenia study: A summary of general findings. Acta Psychiatrica Scandinavica, 91(3), 156–162. doi: 10.1111/j.1600-0447.1995.tb09759.xCrossRefGoogle ScholarPubMed
Watson, A. J., Harrison, L., Preti, A., Wykes, T., & Cella, M. (2022). Cognitive trajectories following onset of psychosis: A meta-analysis. The British Journal of Psychiatry, 221(6), 714–721. doi: 10.1192/bjp.2022.131CrossRefGoogle ScholarPubMed
Xu, L., Cui, H., Wei, Y., Qian, Z., Tang, X., Hu, Y., … Wang, J. (2022a). Relationships between self-reflectiveness and clinical symptoms in individuals during pre-morbid and early clinical stages of psychosis. General Psychiatry, 35(3), e100696. doi: 10.1136/gpsych-2021-100696CrossRefGoogle ScholarPubMed
Xu, L., Hao, D., Wei, Y., Cui, H., Qian, Z., Wang, Y., … Wang, J. (2022b). Effect of cognitive insight on clinical insight from pre-morbid to early psychosis stages. Psychiatry Research, 313, 114613. doi: 10.1016/j.psychres.2022.114613CrossRefGoogle ScholarPubMed
Yung, A. R., McGorry, P. D., McFarlane, C. A., Jackson, H. J., Patton, G. C., & Rakkar, A. (1996). Monitoring and care of young people at incipient risk of psychosis. Schizophrenia Bulletin, 22(2), 283–303. doi: 10.1093/schbul/22.2.283CrossRefGoogle ScholarPubMed
Zhang, T., Cui, H., Tang, Y., Xu, L., Li, H., Wei, Y., … Wang, J. (2016). Correlation of social cognition and neurocognition on psychotic outcome: A naturalistic follow-up study of subjects with attenuated psychosis syndrome. Scientific Reports, 6, 35017. doi: 10.1038/srep35017CrossRefGoogle ScholarPubMed
Zhang, T., Cui, H., Wei, Y., Tang, X., Xu, L., Hu, Y., … Wang, J. (2022). Neurocognitive assessments are more important among adolescents than adults for predicting psychosis in clinical high risk. Biological Psychiatry Cognitive Neuroscience and Neuroimaging, 7(1), 56–65. doi: 10.1016/j.bpsc.2021.06.015CrossRefGoogle ScholarPubMed
Zhang, T., Li, H., Stone, W. S., Woodberry, K. A., Seidman, L. J., Tang, Y., … Wang, J. (2015a). Neuropsychological impairment in prodromal, first-episode, and chronic psychosis: Assessing RBANS performance. PLoS ONE, 10(5), e0125784. doi: 10.1371/journal.pone.0125784CrossRefGoogle ScholarPubMed
Zhang, T., Li, H., Tang, Y., Niznikiewicz, M. A., Shenton, M. E., Keshavan, M. S., … Wang, J. (2018). Validating the predictive accuracy of the NAPLS-2 psychosis risk calculator in a clinical high-risk sample from the SHARP (Shanghai at risk for psychosis) program. The American Journal of Psychiatry, 175(9), 906–908. doi: 10.1176/appi.ajp.2018.18010036CrossRefGoogle Scholar
Zhang, T., Li, H., Woodberry, K. A., Seidman, L. J., Chow, A., Xiao, Z., & Wang, J. (2015b). Interaction of social role functioning and coping in people with recent-onset attenuated psychotic symptoms: A case study of three Chinese women at clinical high risk for psychosis. Neuropsychiatric Disease and Treatment, 11, 1647–1654. doi: 10.2147/NDT.S85654CrossRefGoogle ScholarPubMed
Zhang, T., Li, H., Woodberry, K. A., Seidman, L. J., Zheng, L., Li, H., … Wang, J. (2014). Prodromal psychosis detection in a counseling center population in China: An epidemiological and clinical study. Schizophrenia Research, 152(2-3), 391–399. doi: 10.1016/j.schres.2013.11.039CrossRefGoogle Scholar
Zhang, T., Xu, L., Li, H., Woodberry, K. A., Kline, E. R., Jiang, J., … Wang, J. (2021). Calculating individualized risk components using a mobile app-based risk calculator for clinical high risk of psychosis: Findings from ShangHai At Risk for Psychosis (SHARP) program. Psychological Medicine, 51(4), 653–660. doi: 10.1017/S003329171900360XCrossRefGoogle ScholarPubMed
Zhang, T. H., Li, H. J., Woodberry, K. A., Xu, L. H., Tang, Y. Y., Guo, Q., … Wang, J. J. (2017). Two-year follow-up of a Chinese sample at clinical high risk for psychosis: Timeline of symptoms, help-seeking and conversion. Epidemiology and Psychiatric Sciences, 26(3), 287–298. doi: 10.1017/S2045796016000184CrossRefGoogle ScholarPubMed
Zheng, L., Wang, J., Zhang, T., Li, H., Li, C., & Jiang, K. (2012). The Chinese version of the SIPS/SOPS: A pilot study of reliability and validity. Chinese Mental Health Journal, 26(8), 571–576.Google Scholar
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