RESUMEN
BACKGROUND: Carbapenem-resistant E. coli (CREco) pose a significant public health threat due to their multidrug resistance. Colistin is often a last-resort treatment against CREco; however, the emergence of colistin resistance gene mcr-1 complicates treatment options. METHODS: Two E. coli strains (ECO20 and ECO21), recovered from hospitalized patients in distinct wards, exhibited resistance to carbapenems and colistin. Whole-genome sequencing and phenotypic characterization were employed to study resistance patterns, plasmid profiles, transferability of resistance and virulence genes, and siderophore production capabilities. Comparative genome analysis was used to investigate the genetic environment of mcr-1, blaNDM-7, and virulence clusters. RESULTS: Both E. coli strains exhibited thr presence of both mcr-1 and blaNDM-7 genes, showing high resistance to multiple antibiotics. Genomic analysis revealed the clonal transmission of these strains, possessing identical plasmid profiles (pMCR, pNDM, and pVir) associated with colistin resistance, carbapenem resistance, and virulence factors. Conjugation experiments confirmed the transferability of these plasmids, indicating their potential to disseminate resistance and virulence traits to other strains. Comparative genomic analyses unveiled the distribution of mcr-1 (IncX4-type) and blaNDM (IncX3-type) plasmids across diverse bacterial species, emphasizing their adaptability and threat. The novelty of pVir indicates its potential role in driving the evolution of highly adaptable and pathogenic strains. CONCLUSIONS: Our findings underscore the co-occurrence of mcr-1, blaNDM-7, and siderophore-producing plasmids in E. coli, which poses a significant concern for global health. This research is crucial to unravel the complex mechanisms governing plasmid transfer and recombination and to devise robust strategies to control their spread in healthcare settings.
Asunto(s)
Antibacterianos , Farmacorresistencia Bacteriana Múltiple , Infecciones por Escherichia coli , Proteínas de Escherichia coli , Escherichia coli , Plásmidos , Sideróforos , Plásmidos/genética , Escherichia coli/genética , Escherichia coli/efectos de los fármacos , Proteínas de Escherichia coli/genética , Humanos , Infecciones por Escherichia coli/microbiología , Antibacterianos/farmacología , China , Farmacorresistencia Bacteriana Múltiple/genética , Secuenciación Completa del Genoma , Colistina/farmacología , Pruebas de Sensibilidad Microbiana , beta-Lactamasas/genética , Hospitales , Carbapenémicos/farmacología , Factores de Virulencia/genéticaRESUMEN
Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.
Asunto(s)
Adenoma , Inteligencia Artificial , Neoplasias Colorrectales , Humanos , Neoplasias Colorrectales/diagnóstico , Neoplasias Colorrectales/clasificación , Adenoma/diagnóstico , Adenoma/clasificación , Colonoscopía/métodos , Detección Precoz del Cáncer/métodos , Pólipos del Colon/diagnóstico , Pólipos del Colon/clasificación , Pólipos del Colon/patología , AlgoritmosRESUMEN
DNA damage is a prevalent phenomenon in the context of cancer progression. Evidence suggests that DNA damage responses (DDR) are pivotal in overcoming tumor immune evasion. Alternatively, traditional radiotherapy and chemotherapy operate by inducing DNA damage, consequently stimulating the immune system to target tumors. The intricate interplay between signaling pathways involved in DDR and immune activation underscores the significance of considering both factors in developing improved immunotherapies. By delving deeper into the mechanisms underlying immune activation brought on by DNA damage, it becomes possible to identify novel treatment approaches that boost the anticancer immune response while minimizing undesirable side effects. This review explores the mechanisms behind DNA damage-induced antitumor immune responses, the importance of DNA damage in antitumor immunity, and potential therapeutic approaches for cancer immunotherapy targeting DDR. Additionally, we discuss the challenges of combination therapy and strategies for integrating DNA damage-targeting therapies with current cancer immunotherapy. In summary, this review highlights the critical role of DNA damage in tumor immunology, underscoring the potential of DDR inhibitors as promising therapeutic modalities for cancer treatment.