National Taiwan University Hospital, No. 1, Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan.
Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan.
Sci Data. 2024 Feb 14;11(1):203. doi: 10.1038/s41597-024-03052-2.
This study entailed a comprehensive GC‒MS analysis conducted on 121 patient samples to generate a clinical breathomics dataset. Breath molecules, indicative of diverse conditions such as psychological and pathological states and the microbiome, were of particular interest due to their non-invasive nature. The highlighted noninvasive approach for detecting these breath molecules significantly enhances diagnostic and monitoring capacities. This dataset cataloged volatile organic compounds (VOCs) from the breath of individuals with asthma, bronchiectasis, and chronic obstructive pulmonary disease. Uniform and consistent sample collection protocols were strictly adhered to during the accumulation of this extensive dataset, ensuring its reliability. It encapsulates extensive human clinical breath molecule data pertinent to three specific diseases. This consequential clinical breathomics dataset is a crucial resource for researchers and clinicians in identifying and exploring important compounds within the patient's breath, thereby augmenting future diagnostic and therapeutic initiatives.
本研究对 121 个患者样本进行了全面的 GC-MS 分析,以生成临床呼吸组学数据集。呼吸分子反映了多种情况,如心理和病理状态以及微生物组,由于其非侵入性而引起了特别关注。强调检测这些呼吸分子的非侵入性方法显著提高了诊断和监测能力。该数据集记录了哮喘、支气管扩张和慢性阻塞性肺疾病患者呼吸中的挥发性有机化合物 (VOC)。在积累这个广泛的数据集时,严格遵守统一和一致的样本收集协议,以确保其可靠性。它包含与三种特定疾病相关的广泛的人类临床呼吸分子数据。这个重要的临床呼吸组学数据集是研究人员和临床医生的重要资源,可用于识别和探索患者呼吸中的重要化合物,从而增强未来的诊断和治疗计划。