Lung allografts suffer from a high incidence of acute as well as chronic rejection. Due to exposure to the external milieu, lung allografts are also uniquely susceptible to damage from noxious stimuli. The diagnosis of allograft injury and differentiation from rejection requires transbronchial biopsy which is associated with severe complications, such as pneumothorax and bleeding, and is frequently inaccurate due to the heterogeneity observed in histopathology. The study by Halloran et al (1) attempts to operationalize machine learning based microarray analysis of pre‐validated rejection‐associated transcripts within mucosal biopsies, in lieu of transbronchial biopsies, to improve diagnostic accuracy and safety.

中文翻译：

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使用机器学习确定肺同种异体移植物功能障碍的可操作粘膜活检。

肺同种异体移植物遭受急性和慢性排斥反应的高发生率。由于暴露于外部环境，肺同种异体移植物也特别容易受到伤害性刺激的损害。同种异体移植物损伤的诊断和排斥反应的鉴别需要经支气管活检，这与严重的并发症有关，例如气胸和出血，并且由于在组织病理学中观察到的异质性而经常不准确。Halloran 等人 (1) 的研究试图对粘膜活检中预先验证的排斥反应相关转录本进行基于机器学习的微阵列分析，以代替经支气管活检，以提高诊断准确性和安全性。