Abstract
Melioidosis, caused by Burkholderia pseudomallei (B. pseudomallei), predominantly occurs in the tropical regions. Of various types of melioidosis, septicemic melioidosis is the most lethal one with a mortality rate of 40%. Early detection of the disease is paramount for the better chances of cure. In this study, we developed a novel approach for septicemic melioidosis detection, using a machine learning technique—support vector machine (SVM). Several SVM models were built, and 19 features characterized by the corresponding immune cell types were generated by Cell type Identification Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Using these features, we trained a binomial SVM model on the training set and evaluated it on the independent testing set. Our findings indicated that this model performed well with means of sensitivity and specificity up to 0.962 and 0.979, respectively. Meanwhile, the receiver operating characteristic (ROC) curve analysis gave area under curves (AUCs) ranging from 0.952 to 1.000. Furthermore, we found that a concise SVM model, built upon a combination of CD8+ T cells, resting CD4+ memory T cells, monocytes, M2 macrophages, and activated mast cells, worked perfectly on the detection of septicemic melioidosis. Our data showed that its mean of sensitivity was up to 0.976 while that of specificity up to 0.993. In addition, the ROC curve analysis gave AUC close to 1.000. Taken together, this SVM model is a robust classification tool and may serve as a complementary diagnostic technique to septicemic melioidosis.
Highlights
Melioidosis is a serious tropical infectious disease that frequently outbreaks in Southeast Asia and northern Australia
With a series of feature selections, we found that a novel support vector machine (SVM) model, trained by a combination of CD8+ T cells, resting CD4+ memory T cells, monocytes, M2 macrophages, and activated mast cells, is able to achieve accurate detection of septicemic melioidosis
Our findings indicated that five immune cell types (CD8 + T cells, resting CD4+ memory T cells, monocytes, M2 macrophages, and activated mast cells) were relevant to detection of septicemic melioidosis, suggesting that these cell types might be implicated in the onset and development of the disease
Summary
Melioidosis is a serious tropical infectious disease that frequently outbreaks in Southeast Asia and northern Australia. The disease is caused by the gram-negative bacillus Burkholderia pseudomallei (B. pseudomallei), which inhabits in soil and surface water [1]. Melioidosis is a main cause of lethal community-acquired septicemia and pneumonia in adults [2, 3], and mortality rates range from to 50% [4]. B. pseudomallei first invades epithelial cells of the mucosal surface or broken skin, is expanded intracellularly, and disseminates to a variety of cell types, resulting in bacteremia and sapremia [5]. The B. pseudomallei infection leads to two types of melioidosis, acute melioidosis and chronic melioidosis.
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