Suppr超能文献

全身性和局灶性癫痫中抗利尿激素、胰岛素样生长因子-1、肿瘤坏死因子-α、淀粉样前体蛋白、CD44、干扰素-β、干扰素Aβ-6、α-突触核蛋白、神经丝轻链蛋白和CLU基因表达模式的分析

Analysis of the expression patterns of AVP, IGF-1, and TNF-α, APP, CD44, IFN-β IFN A β-6, α-syn, and NFL and CLU genes in generalized and focal seizures.

作者信息

Razia Rabat, Majeed Fazeel, Amin Rehab, Ayub Mariam Nisar, Mukhtar Shahid, Mahmood Khalid, Shabbir Hamza R, Bashir Shahid, Noreen Baig Deeba

机构信息

KAM School of Life Sciences, Forman Christian College (A Chartered University), Lahore, 54600, Pakistan.

Rashid Latif Medical College, Lahore, Pakistan.

出版信息

Heliyon. 2024 Jul 19;10(14):e34912. doi: 10.1016/j.heliyon.2024.e34912. eCollection 2024 Jul 30.

Abstract

OBJECTIVE

The aim of our study was to investigate the relationship between clinical indicators and gene dysregulation in different types of epilepsy, while also seeking to identify a diagnostic model capable of distinguishing between focal and generalized seizures. This highlights the critical importance of understanding clinical indicators and gene dysregulation for targeted therapeutic interventions to effectively address the specific seizure types effectively.

MATERIALS AND METHODS

In this study, we conducted a comprehensive analysis of the peripheral blood of epilepsy patients (n = 100) and a control group (n = 51) to determine the differential gene expression. Our analysis involved a range of statistical approaches, including correlation analysis to establish the association between clinical indicators and gene dysregulation, and principal component analysis to highlight distinct disease group from control group. Furthermore, we developed diagnostic models using logistic regression to aid in the accurate diagnosis of epilepsy.

RESULTS

Among several selected genes in this study such as (AUC = 0.832, p < 0.0001), (AUC = 0.658, p = 0.0015), (AUC = 0.8970, p < 0.0001), (AUC = 0.742, p < 0.0001), (AUC = 0.614, p = 0.021) and (AUC = 0.937, p < 0.0001), and (AUC = 0.923, p < 0.0001) have shown the outstanding discrimination. In addition to this, when all genes were included in the model, the overall diagnostic power increased significantly (AUC = 0.9968). A differential diagnostic model for focal and generalized seizures was established which discloses AUC = 0.7027, (95 % CL, 0.5765 to 0.8289,  = 0.0019).

CONCLUSION

The conclusions drawn from these findings represented that this is the first study to highlight the distinctive gene patterns of both focal and generalized seizures, implying that peripheral blood can serve as a diagnostic source to distinguish between these seizures types, aiding in the accurate classification of epilepsy. The findings from this study indicate a promising direction for investigating more targeted pharmacological interventions directed to address the distinct needs of both focal and generalized epilepsy, which offers advancements in treatment strategies for distinctive seizure types.

摘要

目的

我们研究的目的是调查不同类型癫痫的临床指标与基因失调之间的关系,同时试图确定一种能够区分局灶性发作和全身性发作的诊断模型。这凸显了理解临床指标和基因失调对于有效针对特定发作类型进行靶向治疗干预的至关重要性。

材料与方法

在本研究中,我们对癫痫患者(n = 100)和对照组(n = 51)的外周血进行了全面分析,以确定差异基因表达。我们的分析涉及一系列统计方法,包括相关性分析以建立临床指标与基因失调之间的关联,以及主成分分析以突出疾病组与对照组的差异。此外,我们使用逻辑回归开发了诊断模型以辅助癫痫的准确诊断。

结果

在本研究中选择的几个基因中,如(AUC = 0.832,p < 0.0001)、(AUC = 0.658,p = 0.0015)、(AUC = 0.8970,p < 0.0001)、(AUC = 0.742,p < 0.0001)、(AUC = 0.614,p = 0.021)和(AUC = 0.937,p < 0.0001),以及(AUC = 0.923,p < 0.0001)表现出了出色的区分能力。除此之外,当所有基因纳入模型时,整体诊断能力显著提高(AUC = 0.9968)。建立了局灶性发作和全身性发作的鉴别诊断模型,其AUC = 0.7027,(95%置信区间,0.5765至0.8289,p = 0.0019)。

结论

这些发现得出的结论表明,这是第一项突出局灶性发作和全身性发作独特基因模式的研究,意味着外周血可作为区分这些发作类型的诊断来源,有助于癫痫的准确分类。本研究结果为研究针对局灶性和全身性癫痫不同需求的更具针对性的药物干预指明了一个有前景的方向,这为不同发作类型的治疗策略带来了进展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/832e/11325377/14981870f416/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验