Exploring brainstem auditory evoked potentials and mental development index as early indicators of autism spectrum disorders in high-risk infants / Xiaoyan WANG in Autism Research, 15-11 (November 2022)  

Public ISBD [article]  inAutism Research > 15-11 (November 2022) . - p.2012-2025 Titre : Exploring brainstem auditory evoked potentials and mental development index as early indicators of autism spectrum disorders in high-risk infants Type de document : texte imprimé Auteurs : Xiaoyan WANG, Auteur ; Xianming CARROLL, Auteur ; Ping ZHANG, Auteur ; Jean-Baptist DU PREL, Auteur ; Hong WANG, Auteur ; Haiqing XU, Auteur ; Sandra LEEPER-WOODFORD, Auteur Article en page(s) : p.2012-2025 Langues : Anglais (eng) Mots-clés : Infant  Humans  Evoked Potentials, Auditory, Brain Stem/physiology  Autism Spectrum Disorder/diagnosis  Mass Screening  Odds Ratio  China  Evoked Potentials, Auditory  absolute latencies  autism spectrum disorders  brainstem auditory evoked potential  infants  interpeak latencies  mental development index Index. décimale : PER Périodiques Résumé : This study of infants from Hubei Province, China examined brainstem auditory evoked potentials (BAEP) and mental development index (MDI) as possible early indicators associated with autism spectrum disorders (ASD). The 34 ASD cases and 102 controls who had recovered from perinatal conditions were matched for age, sex, gestational age, birth weight and maternal age. BAEP absolute latencies (AL) I, III, V and interpeak latencies (IPL) I-III, III-V, I-V were compared in ASD cases and controls at ages 1, 3 and 6 months. MDI scores were compared in these infants from 1 month to 2 years old. Multiple logistic regression analysis was performed to test associations among ASD, BAEP and MDI. Results showed BAEP AL I, V and IPL III-V prolonged in the ASD group (p < 0.001), and MDI scores in ASD cases sharply declining from 12 to 24 months (p < 0.001). Regression analysis revealed odds ratios (OR) indicating that ASD was likely associated with abnormal values of BAEP AL I at 1 and 3 months (OR(AL I) : 4.27; OR(AL I) : 4.13), and AL V at 6 months (OR(AL V) : 7.85). Lower MDI scores (MDI < 80) in infants at 1, 3, and 6 months were likely associated with ASD (OR(MDI) : 2.58; OR(MDI) : 3.83; OR(MDI) : 4.87). These data show that abnormal BAEP values and low MDI scores are independent factors associated with ASD, and that monitoring of BAEP and MDI during infancy might facilitate screening for ASD development. En ligne : http://dx.doi.org/10.1002/aur.2821 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=488 [article] Exploring brainstem auditory evoked potentials and mental development index as early indicators of autism spectrum disorders in high-risk infants [texte imprimé] / Xiaoyan WANG, Auteur ; Xianming CARROLL, Auteur ; Ping ZHANG, Auteur ; Jean-Baptist DU PREL, Auteur ; Hong WANG, Auteur ; Haiqing XU, Auteur ; Sandra LEEPER-WOODFORD, Auteur . - p.2012-2025. Langues : Anglais (eng) in Autism Research > 15-11 (November 2022) . - p.2012-2025 Mots-clés : Infant  Humans  Evoked Potentials, Auditory, Brain Stem/physiology  Autism Spectrum Disorder/diagnosis  Mass Screening  Odds Ratio  China  Evoked Potentials, Auditory  absolute latencies  autism spectrum disorders  brainstem auditory evoked potential  infants  interpeak latencies  mental development index Index. décimale : PER Périodiques Résumé : This study of infants from Hubei Province, China examined brainstem auditory evoked potentials (BAEP) and mental development index (MDI) as possible early indicators associated with autism spectrum disorders (ASD). The 34 ASD cases and 102 controls who had recovered from perinatal conditions were matched for age, sex, gestational age, birth weight and maternal age. BAEP absolute latencies (AL) I, III, V and interpeak latencies (IPL) I-III, III-V, I-V were compared in ASD cases and controls at ages 1, 3 and 6 months. MDI scores were compared in these infants from 1 month to 2 years old. Multiple logistic regression analysis was performed to test associations among ASD, BAEP and MDI. Results showed BAEP AL I, V and IPL III-V prolonged in the ASD group (p < 0.001), and MDI scores in ASD cases sharply declining from 12 to 24 months (p < 0.001). Regression analysis revealed odds ratios (OR) indicating that ASD was likely associated with abnormal values of BAEP AL I at 1 and 3 months (OR(AL I) : 4.27; OR(AL I) : 4.13), and AL V at 6 months (OR(AL V) : 7.85). Lower MDI scores (MDI < 80) in infants at 1, 3, and 6 months were likely associated with ASD (OR(MDI) : 2.58; OR(MDI) : 3.83; OR(MDI) : 4.87). These data show that abnormal BAEP values and low MDI scores are independent factors associated with ASD, and that monitoring of BAEP and MDI during infancy might facilitate screening for ASD development. En ligne : http://dx.doi.org/10.1002/aur.2821 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=488

Integrating machine learning and WGCNA for nomogram diagnostic model unveiling previously unrecognized phase separation-related molecular markers and immuno-cytotoxic pathways in Williams-Beuren syndrome / Mingyi WANG in Research in Autism, 128 (October 2025)  

Public ISBD [article]  inResearch in Autism > 128 (October 2025) . - p.202691 Titre : Integrating machine learning and WGCNA for nomogram diagnostic model unveiling previously unrecognized phase separation-related molecular markers and immuno-cytotoxic pathways in Williams-Beuren syndrome Type de document : texte imprimé Auteurs : Mingyi WANG, Auteur ; Hong WANG, Auteur ; Xiao ZHU, Auteur ; Yongmei HUANG, Auteur Article en page(s) : p.202691 Langues : Anglais (eng) Mots-clés : Williams-Beuren syndrome  Biomarker  Phase separation  Machine learning  Nomogram diagnostic model  WGCNA Index. décimale : PER Périodiques Résumé : Background Understanding the role of phase separation-related genes in Williams-Beuren Syndrome (WBS), a neurodevelopmental disorder, may provide valuable insights into its pathophysiology. This study aimed to identify potential biomarkers and functional pathways associated with WBS through integrated analyses. Methods Differentially expressed genes (DEGs) in WBS patients were identified using GEO datasets. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to explore co-expression modules, and machine learning techniques were applied to select potential biomarkers. Gene Set Enrichment Analysis (GSEA) was used to investigate functional pathways, while single-sample GSEA (ssGSEA) assessed marker gene activity. Results We identified 3519 differentially expressed genes (DEGs) in WBS samples, including 19 core phase separation-related genes. WGCNA revealed six co-expression modules, with the yellow module exhibiting the strongest correlation. Functional analysis indicated enrichment in glycolipid binding and cytoskeletal structural components. Disease ontology analysis implicated developmental and ocular disorders. ssGSEA highlighted associations with immune-related pathways. MAG and ZNF385A emerged as potential diagnostic biomarkers. Conclusion Our integrated approach, combining machine learning and WGCNA, highlights the potential of phase separation-related biomarkers and immune/cytotoxic pathways in the diagnosis of WBS. This study provides valuable insights into the development of diagnostic models for WBS and underscores the importance of investigating protein phase separation in neurodevelopmental disorders. En ligne : https://doi.org/10.1016/j.reia.2025.202691 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=570 [article] Integrating machine learning and WGCNA for nomogram diagnostic model unveiling previously unrecognized phase separation-related molecular markers and immuno-cytotoxic pathways in Williams-Beuren syndrome [texte imprimé] / Mingyi WANG, Auteur ; Hong WANG, Auteur ; Xiao ZHU, Auteur ; Yongmei HUANG, Auteur . - p.202691. Langues : Anglais (eng) in Research in Autism > 128 (October 2025) . - p.202691 Mots-clés : Williams-Beuren syndrome  Biomarker  Phase separation  Machine learning  Nomogram diagnostic model  WGCNA Index. décimale : PER Périodiques Résumé : Background Understanding the role of phase separation-related genes in Williams-Beuren Syndrome (WBS), a neurodevelopmental disorder, may provide valuable insights into its pathophysiology. This study aimed to identify potential biomarkers and functional pathways associated with WBS through integrated analyses. Methods Differentially expressed genes (DEGs) in WBS patients were identified using GEO datasets. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to explore co-expression modules, and machine learning techniques were applied to select potential biomarkers. Gene Set Enrichment Analysis (GSEA) was used to investigate functional pathways, while single-sample GSEA (ssGSEA) assessed marker gene activity. Results We identified 3519 differentially expressed genes (DEGs) in WBS samples, including 19 core phase separation-related genes. WGCNA revealed six co-expression modules, with the yellow module exhibiting the strongest correlation. Functional analysis indicated enrichment in glycolipid binding and cytoskeletal structural components. Disease ontology analysis implicated developmental and ocular disorders. ssGSEA highlighted associations with immune-related pathways. MAG and ZNF385A emerged as potential diagnostic biomarkers. Conclusion Our integrated approach, combining machine learning and WGCNA, highlights the potential of phase separation-related biomarkers and immune/cytotoxic pathways in the diagnosis of WBS. This study provides valuable insights into the development of diagnostic models for WBS and underscores the importance of investigating protein phase separation in neurodevelopmental disorders. En ligne : https://doi.org/10.1016/j.reia.2025.202691 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=570

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