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Li J, Yan WJ, Wu Y, Tian XX, Zhang YW. Synaptosomal-Associated Protein 25 Gene Polymorphisms Affect Treatment Efficiency of Methylphenidate in Children With Attention-Deficit Hyperactivity Disorder: An fNIRS Study. Front Behav Neurosci 2022; 15:793643. [PMID: 35069142 PMCID: PMC8766417 DOI: 10.3389/fnbeh.2021.793643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Methylphenidate (MPH) is the first-line drug for the treatment of children with attention-deficit hyperactivity disorder (ADHD); however, individual curative effects of MPH vary. Many studies have demonstrated that synaptosomal-associated protein 25 (SNAP-25) gene MnlI polymorphisms may be related to the efficacy of MPH. However, the association between SNAP-25MnlI polymorphisms and changes in brain hemodynamic responses after MPH treatment is still unclear. This study used functional near-infrared spectroscopy (fNIRS) to preliminarily investigate the interaction of MPH treatment-related prefrontal inhibitory functional changes with the genotype status of the SNAP-25 gene in children with ADHD. In total, 38 children with ADHD aged 6.76–12.08 years were enrolled in this study and divided into the following two groups based on SNAP-25 gene MnlI polymorphisms: T/T genotype group (wild-type group, 27 children) and G allele carrier group (mutation group, 11 children). The averaged oxygenated hemoglobin concentration changes [Δavg oxy-Hb] and deoxyhemoglobin concentration changes [Δavg deoxy-Hb] in the frontal cortex before MPH treatment and after 1.5 h (post-MPH1.5h) and 4 weeks (post-MPH4w) of MPH treatments were monitored using fNIRS during the go/no-go task. SNAP-IV scores were evaluated both pre-MPH and post-MPH4w treatments. In the T/T genotype group, [Δavg oxy-Hb] in the dorsolateral prefrontal cortex was significantly higher after 4 weeks of MPH (post-MPH4W) treatment than pre-treatment; however, in the G allele group, no significant differences in [Δavg oxy-Hb] were observed between pre- and post-treatments. In the go/no-go task, the accuracy was significantly increased post-MPH4w treatment in the T/T genotype group, while no significant differences were observed in response time and accuracy of the “go” sand no-go task in the G allele group for pre-MPH, post-MPH1.5h, and post-MPH4w treatments. The T/T genotype group exhibited a significant decrease in SNAP-IV scores after MPH treatment, while the G allele group showed no significant difference. In conclusion, fNIRS data combined with SNAP-25 MnlI polymorphism analysis may be a useful biomarker for evaluating the effects of MPH in children with ADHD.
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Affiliation(s)
- Jie Li
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Pediatrics, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - Wen-Jie Yan
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Wu
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xin-Xin Tian
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yi-Wen Zhang
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Yi-Wen Zhang
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Deng F, Mu J, Qu C, Yang F, Liu X, Zeng X, Peng X. A Novel Prognostic Model of Endometrial Carcinoma Based on Clinical Variables and Oncogenomic Gene Signature. Front Mol Biosci 2021; 7:587822. [PMID: 33490103 PMCID: PMC7817972 DOI: 10.3389/fmolb.2020.587822] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
Due to the difficulty in predicting the prognosis of endometrial carcinoma (EC) patients by clinical variables alone, this study aims to build a new EC prognosis model integrating clinical and molecular information, so as to improve the accuracy of predicting the prognosis of EC. The clinical and gene expression data of 496 EC patients in the TCGA database were used to establish and validate this model. General Cox regression was applied to analyze clinical variables and RNAs. Elastic net-penalized Cox proportional hazard regression was employed to select the best EC prognosis-related RNAs, and ridge regression was used to construct the EC prognostic model. The predictive ability of the prognostic model was evaluated by the Kaplan-Meier curve and the area under the receiver operating characteristic curve (AUC-ROC). A clinical-RNA prognostic model integrating two clinical variables and 28 RNAs was established. The 5-year AUC of the clinical-RNA prognostic model was 0.932, which is higher than that of the clinical-alone (0.897) or RNA-alone prognostic model (0.836). This clinical-RNA prognostic model can better classify the prognosis risk of EC patients. In the training group (396 patients), the overall survival of EC patients was lower in the high-risk group than in the low-risk group [HR = 32.263, (95% CI, 7.707-135.058), P = 8e-14]. The same comparison result was also observed for the validation group. A novel EC prognosis model integrating clinical variables and RNAs was established, which can better predict the prognosis and help to improve the clinical management of EC patients.
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Affiliation(s)
- Fang Deng
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Jing Mu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Chiwen Qu
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
| | - Fang Yang
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Xing Liu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Xiaomin Zeng
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Xiaoning Peng
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China.,Department of Pathology and Pathophysiology, Hunan Normal University School of Medicine, Changsha, China.,Department of Pathophysiology, Jishou University School of Medicine, Jishou, China
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