Analysis of unsuccessful tests and the effect of prolonged clinical sample preprocessing in the GeneXpert MTB/RIF assay.

BACKGROUND: The GeneXpert MTB/RIF (Xpert) assay is a widely used technology for detecting Mycobacterium tuberculosis (MTB) in clinical samples. However, the study on the failure of the Xpert assay during routine implementation and its potential solutions is limited. METHODS: We retrospectively analyzed the records of unsuccessful tests in the Xpert and the GeneXpert MTB/RIF Ultra (Ultra) assays between April 2017 and April 2021 at the Shanghai Public Health Clinical Center. To further investigate the effect of prolonged preprocessing on clinical sputum, an additional 120 sputum samples were collected for Xpert testing after 15 min, 3 h, and 6 h preprocessing. The analysis was performed by SPSS version 19.0 software. RESULTS: A total of 11,314 test records were analyzed, of which 268 (2.37%) had unsuccessful test results. Among these, 221 (1.95%) were reported as "Error", 43 (0.38%) as "Invalid", and 4 (0.04%) as "No result". The most common clinical specimen for Xpert tests was sputum, accounting for 114 (2.17%) unsuccessful tests. The failure rate of urine specimens was lower than that of sputum (OR = 0.12, 95% CI: 0.02-0.88, χ2 = 6.22, p = 0.021). In contrast, the failure rate of stool specimens was approximately twice as high as that of sputum (OR = 1.93, 95% CI: 1.09-3.40, χ2 = 5.35, p = 0.014). In the prolonged preprocessing experiment, 102 cases (85%) yielded consistent results in Xpert tests. Furthermore, 7 cases (5.83%) detected an increase in MTB load, 8 cases (6.67%) detected a decrease in MTB load, and 3 cases (2.5%) yielded incongruent results in MTB and rifampicin resistance detection. CONCLUSIONS: The primary cause of unsuccessful tests in the Xpert assay was reported as "Error". Despite varying failure rates depending on the samples, the Xpert assay can be applied to extrapulmonary samples. For paucibacillary specimens, retesting the remaining preprocessed mixture should be carefully considered.

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly different from colon cancer in terms of treatment, prognosis, and metastasis. Recently, the differential mRNA expression of colon cancer and rectal cancer has received a great deal of attention. The current study aimed to identify significant differences between colon cancer and rectal cancer based on RNA sequencing (RNA-seq) data via support vector machines (SVM). Here, 393 CRC samples from the The Cancer Genome Atlas (TCGA) database were investigated, including 298 patients with colon cancer and 95 with rectal cancer. Following the random forest (RF) analysis of the mRNA expression data, 96 genes such as HOXB13, PRAC, and BCLAF1 were identified and utilized to build the SVM classification model with the Leave-One-Out Cross-validation (LOOCV) algorithm. In the training (n=196) and the validation cohorts (n=197), the accuracy (82.1 % and 82.2 %, respectively) and the AUC (0.87 and 0.91, respectively) indicated that the established optimal SVM classification model distinguished colon cancer from rectal cancer reasonably. However, additional experiments are required to validate the predicted gene expression levels and functions.