Na Zhao | Search PubMed

PubMed2026

Population structure and antibiotic resistance of Salmonella isolates from diseased poultry in Jiangxi Province, China.

Salmonella poses a significant threat to human and animal health. However, the relationship among population diversity, antibiotic resistance, and infection risk remains largely unexplored. In this study, 69 Salmonella strains were isolated from diseased poultry in Jiangxi Province from 2021 to 2024. Using whole-genome sequencing, serotype prediction, MLST, virulence and resistance gene analysis, antibiotic susceptibility testing, and mobile genetic element annotation, we characterized the diversity, resistance profiles, and transmission mechanisms of these strains. The results showed high diversity, with Salmonella enterica subsp. enterica serovar Typhimurium (>60%) and ST19 (62.31%) as the dominant serovar and sequence type, respectively. Several avian isolates were genomically similar to human isolates, indicating potential zoonotic risk. All strains harbored conserved core virulence modules, whereas accessory modules (e.g., cdtB, astA, pefA) varied and may affect pathogenicity. The multidrug resistance rate was 97.1%, with 100% resistance to erythromycin, tilmicosin and tiamulin, and resistance rates of 91.3%, 84.1%, and 71.0% to sulfonamides, enrofloxacin, and ceftiofur, respectively. Sixty-eight resistance genes were identified. Highly conserved antimicrobial resistance gene (ARG) modules (e.g., sul2-aph(3″)-Ib-aph(6')-Id-tet(A)) were shared between chromosomes and plasmids and were flanked by mobile elements such as Tn3 and IS3. Genomic islands (GIs) and plasmids in some strains carried resistance gene clusters highly homologous to those in pathogens from humans, pigs, and chickens, suggesting active horizontal transfer of resistance genes across hosts. This study revealed high diversity, prevalent multidrug resistance, and active horizontal transfer of resistance genes in avian-derived Salmonella from Jiangxi Province, emphasizing the need for cross-host resistance monitoring and antibiotic management within the 'One Health' framework.

Horizontal gene transfer↗

PubMed2026

SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.

Bladder cancer survival outcomes exhibit significant heterogeneity, influenced by multifaceted factors. While digital pathology-based survival models leveraging artificial intelligence show promise, they often overlook complementary data sources. Conversely, imaging lacks cellular detail, and genomics/proteomics entail complexity and cost. To integrate multidimensional data for enhanced survival prediction, we propose SurvGRN, a multi-feature fusion framework. SurvGRN synergistically combines clinical variables, transcriptomics, and digital pathology slides using a gated residual network architecture. Pathological features are extracted via multiple instance learning, while clinical and transcriptomic data are processed as static inputs. These features are dynamically fused using a long short-term memory (LSTM) network for comprehensive survival risk assessment. Evaluated on 400 bladder cancer patients, SurvGRN significantly outperformed existing methods: improving the C-index by 12.6% over DeepMISL; 20.6% and 7.1% over graph-based models (DeepGraphConv and Patch-GCN); and 5.4% and 4.0% over attention-based approaches (Surformer and HVTSurv). Ablation studies confirmed the contributions of pathology features (extracted via ResNet-50 pre-trained on bladder tissue), clinical/transcriptomic data, and the LSTM fusion. SurvGRN also enabled significant stratification of patients into distinct risk cohorts. This work demonstrates that holistic integration of multi-source data through tailored fusion architectures substantially improves bladder cancer survival prediction.

bladder cancer↗