Abstract：
Aspergillosis of the newborn remains a rare but severe disease. We report four cases of primary cutaneous Aspergillus flavus infections in premature newborns linked to incubators contamination by putative clonal strains. Our objective was to evaluate the ability of matrix-assisted laser desorption/ionisation time of flight (MALDI-TOF) coupled to convolutional neural network (CNN) for clone recognition in a context where only a very small number of strains are available for machine learning. Clinical and environmental A. flavus isolates (n = 64) were studied, 15 were epidemiologically related to the four cases. All strains were typed using microsatellite length polymorphism. We found a common genotype for 9/15 related strains. The isolates of this common genotype were selected to obtain a training dataset (6 clonal isolates/25 non-clonal) and a test dataset (3 clonal isolates/31 non-clonal), and spectra were analysed with a simple CNN model. On the test dataset using CNN model, all 31 non-clonal isolates were correctly classified, 2/3 clonal isolates were unambiguously correctly classified, whereas the third strain was undetermined (i.e., the CNN model was unable to discriminate between GT8 and non-GT8). Clonal strains of A. flavus have persisted in the neonatal intensive care unit for several years. Indeed, two strains of A. flavus isolated from incubators in September 2007 are identical to the strain responsible for the second case that occurred 3 years later. MALDI-TOF is a promising tool for detecting clonal isolates of A. flavus using CNN even with a limited training set for limited cost and handling time.
Cutaneous aspergillosis is a rare but potentially fatal disease of the prematurely born infant. We described here several cases due to Aspergillus flavus and have linked them to environnemental strains using MLP genotyping and MALDI-TOF mass spectrometry coupled with artificial intelligence.
Cutaneous aspergillosis is a rare but potentially fatal disease of the prematurely born infant. We described here several cases due to Aspergillus flavus and have linked them to environnemental strains using MLP genotyping and MALDI-TOF mass spectrometry coupled with artificial intelligence.
摘要：
新生儿曲霉病仍然是一种罕见但严重的疾病。我们报告了4例早产新生儿的原发性皮肤黄曲霉感染，这些感染与推定的克隆菌株对孵化器的污染有关。我们的目标是评估MALDI-TOF与卷积神经网络（CNN）耦合的克隆识别能力，其中只有极少数的菌株可用于机器学习。研究了临床和环境黄曲霉分离株(n=64)，15个病例在流行病学上与4个病例有关。所有菌株均使用微卫星长度多态性进行分型。我们发现了9/15相关菌株的常见基因型。选择这种常见基因型的分离株，以获得训练数据集（6个克隆分离株/25个非克隆）和测试数据集（3个克隆分离株/31个非克隆），用简单的CNN模型分析光谱。在使用CNN模型的测试数据集上，所有31个非克隆分离株都被正确分类，2/3的克隆分离株被明确地正确分类，而第三个菌株则未确定（i。eCNN模型无法区分GT8和非GT8)。黄曲霉的克隆菌株已在新生儿重症监护病房中持续存在数年。的确,2007年9月从孵化器中分离出的两株黄曲霉，与3年后发生的第二例病例的菌株相同。MALDI-TOF是一种有前途的工具，用于使用CNN检测黄曲霉的克隆分离株，即使训练集有限，成本和处理时间也有限。
皮肤曲霉病是一种罕见但潜在致命的早产儿疾病。我们在这里描述了几种由于黄曲霉引起的病例，并使用MLP基因分型和MALDI-TOF质谱结合人工智能将它们与环境元素菌株联系起来。
皮肤曲霉病是一种罕见但潜在致命的早产儿疾病。我们在这里描述了几种由于黄曲霉引起的病例，并使用MLP基因分型和MALDI-TOF质谱结合人工智能将它们与环境元素菌株联系起来。
