Department of Physical and Analytical Chemistry, Voronezh State University of Engineering Technologies, Voronezh 394000, Russia.
Propaedeutics of Childhood Diseases and Polyclinic Pediatrics, Voronezh State Medical University Named after N. N. Burdenko, Voronezh 394000, Russia.
Sensors (Basel). 2022 Nov 4;22(21):8496. doi: 10.3390/s22218496.
Currently, antibiotics are often prescribed to children without reason due to the inability to quickly establish the presence of a bacterial etiology of the disease. One way to obtain additional diagnostic information quickly is to study the volatile metabolome of biosamples using arrays of sensors. The goal of this work was to assess the possibility of using an array of chemical sensors with various sensitive coatings to determine the presence of a bacterial infection in children by analyzing the equilibrium gas phase (EGP) of urine samples. The EGP of 90 urine samples from children with and without a bacterial infection (urinary tract infection, soft tissue infection) was studied on the "MAG-8" device with seven piezoelectric sensors in a hospital. General urine analysis with sediment microscopy was performed using a Uriscan Pro analyzer and using an Olympus CX31 microscope. After surgical removal of the source of inflammation, the microbiological studies of the biomaterial were performed to determine the presence and type of the pathogen. The most informative output data of an array of sensors have been established for diagnosing bacterial pathology. Regression models were built to predict the presence of a bacterial infection in children with an error of no more than 15%. An indicator of infection is proposed to predict the presence of a bacterial infection in children with a high sensitivity of 96%.
目前，由于无法快速确定疾病的细菌病因，经常毫无理由地给儿童开抗生素。一种快速获得额外诊断信息的方法是使用传感器阵列研究生物样本的挥发性代谢组。这项工作的目的是评估使用具有各种敏感涂层的化学传感器阵列通过分析尿液样本的平衡气相 (EGP) 来确定儿童是否存在细菌感染的可能性。在医院的“MAG-8”设备上，使用 7 个压电传感器研究了 90 个来自患有和不患有细菌感染（尿路感染、软组织感染）的儿童的尿液样本的 EGP。使用 Uriscan Pro 分析仪和 Olympus CX31 显微镜对一般尿液分析进行了沉淀物显微镜检查。在炎症源切除后，对生物材料进行了微生物学研究，以确定病原体的存在和类型。已经为诊断细菌病理学建立了最具信息量的传感器阵列输出数据。建立了回归模型来预测儿童是否存在细菌感染，误差不超过 15%。提出了一个感染指标来预测儿童是否存在细菌感染，具有 96%的高灵敏度。
