Staphylococcus aureus is a major foodborne pathogen that causes food poisoning due to the ingestion of heat-stable staphylococcal enterotoxins (Balaban & Rasooly, 2000;Le Loir, Baron, & Gautier, 2003). S. aureus can spread from food handlers, hand contact surfaces, and food contact surfaces during processing and packaging (Sospedra, Manes, & Soriano, 2012). Consequently, S. aureus has been repeatedly detected in a variety of foods (Vazquez-Sanchez, Habimana, & Holck, 2013).Biofilms are considered a part of the normal life cycle of S. aureus in the environment (Otto, 2008), where planktonic cells attach themselves to solid surfaces and subsequently proliferate and accumulate in multilayer cell clusters embedded in special three-dimensional structures as mushrooms or towers separated by fluid-filled channels (Azara,

Staphylococcal food poisoning is an illness caused by the consumption of food that contains sufficient amounts of one or more enterotoxins. In the present study, a total of 37 S. aureus isolates were recovered from leftover food, swabs from a kitchen environment, and patient feces associated with four foodborne outbreaks that occurred in Hangzhou, southeast China, and were characterized by multilocus sequence typing (MLST), spa typing, pulse-field gel electrophoresis (PFGE), and antimicrobial susceptibility. Classical enterotoxin and enterotoxin-like genes were profiled by PCR analysis. ST6-t304 was the most common clone (40.54%), followed by ST2315-t11687 (32.43%). Six clusters (A to F) were divided based on PFGE patterns, and Clusters A and C were the most common types, constituting 86.49% of all isolates. Moreover, sea was the most frequently identified enterotoxin gene (81.08%), followed by the combination of seg–sei–selm–seln–sleo–selu and sec–sell (each 48.65%). Five isolates also harbored the exotoxin cluster sed–selj–ser. In addition, resistance to penicillin (97.30%), erythromycin (37.85), tetracycline (32.43%), clindamycin, gentamicin, and sulfamethoxazole (each 10.81%) was observed. Our research demonstrated the link between leftover foods and patients by molecular typing and detecting the profiles of enterotoxin or enterotoxin-like genes in human and food isolates. S. aureus maintains an extensive repertoire of enterotoxins and drug resistance genes that could cause potential health threats to consumers.

Purpose Clinical indicators of histological information are important for breast cancer treatment and operational decision making, but these histological data suffer from frequent missing values due to various experimental/clinical reasons. The limited amount of histological information from breast cancer samples impedes the accuracy of data imputation. The purpose of this study was to impute missing histological data, including Ki‐67 expression level, luminal A subtype, and histological grade, by integrating tumor radiomics. Methods To this end, a deep matrix completion (DMC) method was proposed for imputing missing histological data using nonmissing features composed of histological and tumor radiomics (termed radiohistological features). DMC finds a latent nonlinear association between radiohistological features across all samples and samples for all the features. Radiomic features of morphologic, statistical, and texture were extracted from dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) inside the tumor. Experiments on missing histological data imputation were performed with a variable number of features and missing data rates. The performance of the DMC method was compared with those of the nonnegative matrix factorization (NMF) and collaborative filtering (MCF)‐based data imputation methods. The area under the curve (AUC) was used to assess the performance of missing histological data imputation. Results By integrating radiomics from DCE‐MRI, the DMC method showed significantly better performance in terms of AUC than that using only histological data. Additionally, DMC using 120 radiomic features showed an optimal prediction performance (AUC = 0.793), which was better than the NMF (AUC = 0.756) and MCF methods (AUC = 0.706; corrected p = 0.001). The DMC method consistently performed better than the NMF and MCF methods with a variable number of radiomic features and missing data rates. Conclusions DMC improves imputation performance by integrating tumor histological and radiomics data. This study transforms latent imaging‐scale patterns for interactions with molecular‐scale histological information and is promising in the tumor characterization and management of patients.