Shamabadi Ahmad, Hasanzadeh Alireza, Akhondzadeh Shahin
School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Psychiatric Research Center, Roozbeh Psychiatric Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Avicenna J Med Biotechnol. 2021 Oct-Dec;13(4):172-175.
Besides concerns about the increasing prevalence of psychiatric disorders and the significant burdens and costs, there are concerns about its validity. The dilemma of validity went so far that studies described the diagnoses in psychiatry as scientifically worthless. We suggest integrating psychiatry and medical biotechnology and using biotechnological products in psychiatric aspects help psychiatry become more precise, strengthen its position among other sciences, and increase its scientific credibility by giving examples. For this matter, we need different inputs to choose between the vast outputs. The most common inputs are clinical symptoms, cognitive function, individual and environmental risk factors, molecular markers, genetic markers, neuroimaging signs, and big data. Some molecular markers have been shown to have a relationship with psychiatric disorders such as Interleukin-6 (IL-6) and Tumor Necrosis Factor-α (TNF-α). Genetic studies might evolve the most accurate part of precision psychiatry. Currently, and through the developments in technology, genome-wide association studies have become available. In neuroimaging signs, psychiatric disorders are associated with generalized rather than focal brain network dysfunction, and functional magnetic resonance imaging could be performed to study them. It would exhibit different aberrancies in various psychiatric disorders. In big data, the constitution of predictive models and movement toward precision psychiatry can be led by using artificial intelligence and machine learning.
除了对精神疾病患病率上升以及巨大负担和成本的担忧之外,人们还对其有效性表示担忧。有效性的困境甚至发展到研究将精神病学诊断描述为毫无科学价值。我们建议将精神病学与医学生物技术相结合,并在精神科使用生物技术产品,通过举例说明,这有助于精神病学变得更加精确,加强其在其他学科中的地位,并提高其科学可信度。对于此事,我们需要不同的输入来在大量的输出中进行选择。最常见的输入包括临床症状、认知功能、个体和环境风险因素、分子标记、基因标记、神经影像体征和大数据。一些分子标记已被证明与精神疾病有关,如白细胞介素 -6(IL-6)和肿瘤坏死因子 -α(TNF-α)。基因研究可能会发展成为精准精神病学中最精确的部分。目前,随着技术的发展,全基因组关联研究已经可行。在神经影像体征方面,精神疾病与广泛的而非局灶性的脑网络功能障碍有关,可以通过功能磁共振成像来研究它们。它会在各种精神疾病中表现出不同的异常。在大数据方面,可以通过使用人工智能和机器学习来构建预测模型并朝着精准精神病学发展。