A Pattern Recognition Approach to Myopathy.

Q1 Medicine
Margherita Milone
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引用次数: 0

Abstract

Objective: This article reviews the clinical elements, muscle involvement patterns, and diagnostic tools that can facilitate the diagnosis of muscle diseases, highlighting their heterogeneity and diagnostic challenges.

Latest developments: Increased access to genetic testing, the development of disease biomarkers, and research are expanding the spectrum of inherited and acquired muscle diseases. Muscle disease can exist in isolation or be part of a multisystem proteinopathy affecting other tissues outside skeletal muscle. Pathogenic variants in the same gene can lead to myopathy or peripheral neuropathy, in isolation or combination. This poses diagnostic challenges, especially when the myopathy manifests with distal weakness, mimicking a peripheral neuropathy. Therefore, diagnosis requires a rigorous characterization of the patient's phenotype and correlation with genetic findings. The ever-growing number of gene-specific and pathogenic variant-specific therapies, as well as drugs targeting pathogenic mechanisms of immune-mediated muscle diseases, underscore the importance of providing patients with a specific diagnosis to accelerate care and prevent potential complications. Artificial intelligence technologies in the neuromuscular field are further optimizing the diagnostic process and care of patients with muscle disease.

Essential points: Clinical assessment and the integration of clinical and laboratory findings remain key elements for the diagnosis and treatment of most muscle diseases. While genetic testing has replaced muscle biopsy in the diagnosis of many genetic myopathies, muscle biopsy remains crucial for the diagnosis of many acquired muscle diseases and is a tool to investigate the pathogenicity of genetic variants of uncertain significance and clarify disease mechanisms. All efforts should be made to provide patients with the diagnosis of their specific muscle disease as this has a huge impact on patient care and prognosis.

肌病的模式识别方法。
目的:本文综述了促进肌肉疾病诊断的临床因素、肌肉受累模式和诊断工具,强调了它们的异质性和诊断挑战。最新进展:基因检测的普及、疾病生物标志物的开发以及研究正在扩大遗传和获得性肌肉疾病的范围。肌肉疾病可以单独存在,也可以是影响骨骼肌以外其他组织的多系统蛋白病的一部分。同一基因的致病变异可单独或联合导致肌病或周围神经病变。这提出了诊断的挑战,特别是当肌病表现为远端无力,模仿周围神经病变。因此,诊断需要对患者的表型和与遗传发现的相关性进行严格的描述。越来越多的基因特异性和致病变异特异性疗法,以及针对免疫介导的肌肉疾病致病机制的药物,强调了为患者提供特定诊断以加速护理和预防潜在并发症的重要性。神经肌肉领域的人工智能技术正在进一步优化肌肉疾病患者的诊断过程和护理。要点:临床评估和临床和实验室结果的整合仍然是大多数肌肉疾病诊断和治疗的关键因素。虽然基因检测在许多遗传性肌病的诊断中已经取代了肌肉活检,但肌肉活检对于许多获得性肌肉疾病的诊断仍然至关重要,并且是研究不确定意义的遗传变异的致病性和阐明疾病机制的工具。应尽一切努力为患者提供其特定肌肉疾病的诊断,因为这对患者的护理和预后有巨大影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.80
自引率
0.00%
发文量
175
期刊介绍: Continue your professional development on your own schedule with Continuum: Lifelong Learning in Neurology®, the American Academy of Neurology" self-study continuing medical education publication. Six times a year you"ll learn from neurology"s experts in a convenient format for home or office. Each issue includes diagnostic and treatment outlines, clinical case studies, a topic-relevant ethics case, detailed patient management problem, and a multiple-choice self-assessment examination.
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