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Simple Summary: Salmonella is an important pathogen, which causes a variety of animal salmonellosis and foodborne diseases in humans. In recent years, due to the abuse of antimicrobials, the rapid emergence of resistant Salmonella, especially extended spectrum β-lactamase-positive strains (ESBL-producers), has brought great challenges to animal industries and public health safety. The pathogenicity of Salmonella depends on a variety of virulence factors coded by genes in chromosomal pathogenicity islands (SPI) and plasmids. The antibiotic resistance and pathogenic potential of Salmonella have been studied extensively. However, there are gaps that still exist in our understanding of the relationship between resistance and virulence. In this study, we collected 117 Salmonella isolates from diseased chickens in Sichuan Province of China from 2019 to 2021, and investigated antimicrobial resistance (AMR) patterns, including extended spectrum β-lactamase (ESBL) resistance, the prevalence and con-translation of ESBL and virulence genes, and sequence type (ST) among these isolates. The Salmonella isolates showed different frequencies of resistance to antimicrobials, 41.03% of which were ESBL-producers. ESBL genes, such as blaCTX-M-55 (63.29%), blaOXA-31 (26.58%), blaCTX-M-65 (20.25%), and blaTEM-1 (18.99%), were detected. Moreover, the 117 isolates had 11 virulence genes, with frequencies ranging from 29.06% to 100%. There were associations between resistance to cephalosporins and the ESBL genes. The blaCTX-M-55 showed the largest effect on the resistance and there was also a significant association between the presence of most virulence genes and ESBL genes. All isolates were divided into 11 types. The co-transfer of ESBL and virulence genes was observed in the plasmid conjugation test. From this study, we can conclude that Salmonella isolates from chicken were common carriers of ESBLs and multiple virulence genes, and the horizontal transfer played a key role in the dissemination of antimicrobial resistance and pathogenesis. Importantly, the interaction of resistance and virulence factors reinforces the marked pathogenic potential of Salmonella. The aim of this study was to explore the association between antimicrobial resistance, ESBL genes, and virulence genes of Salmonella isolates. From 2019 to 2021, a total of 117 Salmonella isolates were obtained from symptomatic chickens in Sichuan Province, China. The strains were tested for antimicrobial resistance and the presence of ESBL according to the Clinical and Laboratory Standards Institute (CLSI) instructions. The presence of ESBL genes and genes for virulence was determined using Polymerase Chain Reaction (PCR). In addition, Multilocus Sequence Typing (MLST) was applied to confirm the molecular genotyping. Moreover, the mechanism of ESBL and virulence gene transfer and the relationships between the resistance phenotype, ESBL genes, and virulence genes were explored. The isolates exhibited different frequencies of resistance to antibiotics (resistance rates ranged from 21.37% to 97.44%), whereas 68.38% and 41.03% of isolates were multi-drug resistance (MDR) and ESBL-producers, respectively. In the PCR analysis, blaCTX-M was the most prevalent ESBL genotype (73.42%, 58/79), and blaCTX-M-55 showed the most significant effect on the resistance to cephalosporins as tested by logistic regression analysis. Isolates showed a high carriage rate of invA, avrA, sopB, sopE, ssaQ, spvR, spvB, spvC, stn, and bcfC (ranged from 51.28% to 100%). MLST analysis revealed that the 117 isolates were divided into 11 types, mainly ST92, ST11, and ST3717. Of 48 ESBL-producers, 21 transconjugants were successfully obtained by conjugation. Furthermore, ESBL and spv virulence genes were obtained simultaneously in 15 transconjugants. These results highlighted that Salmonella isolates were common carriers of ESBLs and multiple virulence genes. Horizontal transfer played a key role in disseminating antimicrobial resistance and pathogenesis. Therefore, it is necessary to continuously monitor the use of antimicrobials and the prevalence of AMR and virulence in Salmonella from food animals and to improve the antibiotic stewardship for salmonellosis. [ABSTRACT FROM AUTHOR]

Simple Summary: Perineural invasion (PNI) is present in 17–75% of prostate cancer patients and is an important mechanism for cancer progression, leading to poor prognoses. An optimized preoperative technique is needed to detect PNI in prostate cancer patients and administer the best treatment. The aim of our retrospective study was to develop a model based on high-throughput radiomic features of bi-parametric MRI combined with clinical factors that can predict PNI status in high-grade prostate cancers. In total, 183 high-grade PCa patients were included in this retrospective study, and the radiomics model based on 13 selected features of bi-parametric MRI showed better discrimination than did the conventional model in the test cohort (area under the curve (AUC): 0.908). Discrimination efficiency improved when the radiomics and clinical models were combined (AUC: 0.947). This improved model may help predict PNI in prostate cancer patients and allow more personalized clinical decision-making. Purpose: To explore the role of bi-parametric MRI radiomics features in identifying PNI in high-grade PCa and to further develop a combined nomogram with clinical information. Methods: 183 high-grade PCa patients were included in this retrospective study. Tumor regions of interest (ROIs) were manually delineated on T2WI and DWI images. Radiomics features were extracted from lesion area segmented images obtained. Univariate logistic regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for feature selection. A clinical model, a radiomics model, and a combined model were developed to predict PNI positive. Predictive performance was estimated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: The differential diagnostic efficiency of the clinical model had no statistical difference compared with the radiomics model (area under the curve (AUC) values were 0.766 and 0.823 in the train and test group, respectively). The radiomics model showed better discrimination in both the train cohort and test cohort (train AUC: 0.879 and test AUC: 0.908) than each subcategory image (T2WI train AUC: 0.813 and test AUC: 0.827; DWI train AUC: 0.749 and test AUC: 0.734). The discrimination efficiency improved when combining the radiomics and clinical models (train AUC: 0.906 and test AUC: 0.947). Conclusion: The model including radiomics signatures and clinical factors can accurately predict PNI positive in high-grade PCa patients. [ABSTRACT FROM AUTHOR]