Junjie Li

Because of its remarkable ability to acquire antibiotic resistance and to survive in nosocomial environments, Acinetobacter baumannii has become a significant nosocomial infectious agent worldwide. Tigecycline is one of the few therapeutic options for treating infections caused by A. baumannii isolates. However, tigecycline resistance has increasingly been reported. Our aim was to assess the prevalence and characteristics of efflux-based tigecycline resistance in clinical isolates of A. baumannii collected from a hospital in China. A total of 74 A. baumannii isolates, including 64 tigecycline-nonsusceptible A. baumannii (TNAB) and 10 tigecycline-susceptible A. baumannii (TSAB) isolates, were analyzed. The majority of them were determined to be positive for adeABC, adeRS, adeIJK, and abeM, while the adeE gene was found in only one TSAB isolate. Compared with the levels in TSAB isolates, the mean expression levels of adeB, adeJ, adeG, and abeM in TNAB isolates were observed to increase 29-, 3-, 0.7-, and 1-fold, respectively. The efflux pump inhibitors (EPIs) phenyl-arginine-β-naphthylamide (PAβN) and carbonyl cyanide 3-chlorophenylhydrazone (CCCP) could partially reverse the resistance pattern of tigecycline. Moreover, the tetX1 gene was detected in 12 (18.8%) TNAB isolates. To our knowledge, this is the first report of the tetX1 gene being detected in A. baumannii isolates. ST208 and ST191, which both clustered into clonal complex 92 (CC92), were the predominant sequence types (STs). This study showed that the active efflux pump AdeABC appeared to play important roles in the tigecycline resistance of A. baumannii. The dissemination of TNAB isolates in our hospital is attributable mainly to the spread of CC92.

INTRODUCTION: Breast cancer, one of the most common health threats to females worldwide, has always been a crucial topic in the medical field. With the rapid development of digital pathology, many scholars have used AI-based systems to classify breast cancer pathological images. However, most existing studies only stayed on the binary classification of breast lesions (normal vs tumor or benign vs malignant), far from meeting the clinical demand. Therefore, we established a multi-class classification system of breast digital pathology images based on AI, which is more clinically practical than the binary classification system. METHODS: In this paper, we adopted a two-stage architecture based on deep learning method and machine learning method for the multi-class classification (normal tissue, benign lesion, ductal carcinoma in situ, and invasive carcinoma) of breast digital pathological images. RESULTS: The proposed approach achieved an overall accuracy of 86.67% at patch-level. At WSI-level, the overall accuracies of our classification system were 88.16% on validation data and 90.43% on test data. Additionally, we used two public datasets, the BreakHis and BACH, for independent verification. The accuracies our model obtained on these two datasets were comparable to related publications. Furthermore, our model could achieve accuracies of 85.19% on multi-classification and 96.30% on binary classification (non-malignant vs malignant) using pathology images of frozen sections, which was proven to have good generalizability. Then, we used t-SNE for visualization of patch classification efficiency. Finally, we analyzed morphological characteristics of patches learned by the model. CONCLUSION: The proposed two-stage model could be effectively applied to the multi-class classification task of breast pathology images and could be a very useful tool for assisting pathologists in diagnosing breast cancer.