研究和评论:医学和健康雷竞技苹果下载科学杂志》上
菜单ISSN: 2319 - 9865
ISSN: 2319 - 9865
马努西索迪亚*
印度阿格拉大学药学学院。
通讯作者:
马努西索迪亚
医药大学
印度阿格拉大学。
收到日期:02/02/2021;接受日期:19/02/2021;发表日期:26/02/2021
访问更多的相关文章rayapp3
代谢组学是生化事件涉及代谢产物的分析,无论是分子底物,反应中间体,产品或过程结束。的详细检查小亚原子代谢物中发现的细胞,组织,biofluids,细胞培养基的细胞过程或对环境压力的反应。代谢物的代谢产物的总数中包含一个有机样品在特定的遗传,营养,或环境条件。代谢组学的发展提供丰富的信息基本有机框架等领域科学研究和代谢证明,药物测试,营养,和毒理学。代谢组学是合成过程的系统研究,如代谢物,小原子基质,中间体,和消化的结果。尤其是代谢组学,是“有序的特殊化合物指纹分析独特的细胞类型放弃,”以及小粒子代谢物的研究资料。代谢物的代谢组是指整个安排形成的细胞类型在自然细胞,组织,器官或生物[1]。
代谢组学是生化事件涉及代谢产物的分析,无论是分子底物,反应中间体,产品或过程结束。的详细检查小亚原子代谢物中发现的细胞,组织,biofluids,细胞培养基的细胞过程或对环境压力的反应。代谢物的代谢产物的总数中包含一个有机样品在特定的遗传,营养,或环境条件。代谢组学的发展提供丰富的信息基本有机框架等领域科学研究和代谢证明,药物测试,营养,和毒理学。代谢组学是合成过程的系统研究,如代谢物,小原子基质,中间体,和消化的结果。尤其是代谢组学,是“有序的特殊化合物指纹分析独特的细胞类型放弃,”以及小粒子代谢物的研究资料。代谢物的代谢组是指整个安排形成的细胞类型在自然细胞,组织,器官或生物[1]。我们能够量化大量代谢物在血液样本,细胞提取物,和其他bio-fluids,以及活组织检查,使用复杂的代谢组学分析技术,导致特定的代谢指纹。这些代谢指纹有潜力成为诊断/预测资源可以帮助控制或治疗严重的疾病,如癌症和疾病涉及心脏功能监管。由于大量的代谢物在人类代谢组,综合代谢特征需要使用多个相互关联的技术和方法。 Nuclear resonance (NMR) and mass spectrometry (MS) techniques, when combined with gas chromatography (GC) and/or liquid chromatography (LC), are extremely sensitive and efficient in obtaining metabolic fingerprints of any biological sample. Food habits/diet, medication effects, sex-related changes/differences, and other comorbid illness or disorders, as well as exposures to chemicals and/or environmental irritants, are all reflected in these metabolomes [2]. As a result, clinical confounding factors may skew metabolomic findings, potentially leading to false conclusions. Nonetheless, applying metabolomics to a broad sample size (such as a population study during an epidemic) has allowed for robust statistical adjustments for possible confounders, resulting in externally reproducible results. Furthermore, metabolomics profiling has been used in small sample size clinical scenarios where serial sampling is needed before and after a controlled biological perturbation (e.g., drug doses, exercise testing, planned myocardial infarction, and so on). Further advances in investigational approaches, in addition to metabolomics, are being linked to other “omics” platforms in order to gain a better understanding of pathological interactions between biomolecules, metabolites, and disease states. Furthermore, advanced metabolomic methods provide us with a snapshot of individual patients' metabolic signatures, which can be used as diagnostic and/or prognostic instruments to detect disease-related physiological impairments as well as the timing of disease-specific therapies. As a result, metabolomics may be an important method for predicting, identifying, and recognising a wide variety of disease states, as well as tracking the efficacy of therapeutic treatments. As a result, metabolomics continues to contribute to our societal goal of personalising drug practice [3].