Genomic insights into a clinical Salmonella Typhimurium isolate carrying plasmid-mediated bla.

Objective: Highly carbapenem-resistant Salmonella has emerged worldwide in recent years and is largely associated with the multiform transmission of resistance genes, which poses a huge challenge in clinical practice. Our study delves into the resistance mechanisms and epidemiology of bla-carrying plasmids.

Methods: Whole-genome sequencing was utilised to analyse the molecular characteristics and antimicrobial resistance mechanisms of Salmonella isolates recovered from the faeces of a paediatric patient at the Children's Hospital of Nanjing Medical University.

Molecular characterization of invasive clinical isolates from a tertiary children's hospital in eastern China.

is a common opportunistic pathogen that causes invasive pneumococcal disease (IPD), especially in children. This study aimed to determine the prevalence and molecular characteristics of isolated from children with IPD. A total of 78 .

MRL-Net: Multi-Scale Representation Learning Network for COVID-19 Lung CT Image Segmentation.

Accuracy segmentation of COVID-19 lesions in lung CT images can aid patient screening and diagnosis. However, the blurred, inconsistent shape and location of the lesion area poses a great challenge to this vision task. To tackle this issue, we propose a multi-scale representation learning network (MRL-Net) that integrates CNN with Transformer via two bridge unit: Dual Multi-interaction Attention (DMA) and Dual Boundary Attention (DBA).

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.

Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation.

COVID-19 CT image segmentation based on improved Res2Net.

Purpose: Corona virus disease 2019 (COVID-19) is threatening the health of the global people and bringing great losses to our economy and society. However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID-19-infected regions. Accurate segmentation infection area of COVID-19 can contribute screen confirmed cases.

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet.

Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet.