Center for Disease Control

BackgroundEscherichia albertii (E. albertii) is an emerging foodborne pathogen of growing clinical significance and increasing multidrug resistance (MDR). This study characterized the multidrug-resistant clinical strain E. albertii ESA311 to uncover the genetic basis of its resistance and the transmission potential of its mobile genetic elements.MethodsWe performed whole-genome sequencing on strain ESA311 to identify plasmids, resistance genes, and virulence factors. Conjugation experiments were conducted to evaluate plasmid transferability. Phylogenetic analysis of the MDR plasmids elucidated their evolutionary history and geographical distribution. The prevalence of intI1 and its correlation with MDR were analyzed across 160 clinical E. albertii isolates.ResultsWhole-genome sequencing identified five plasmids coexisting in ESA311, with pESA311_1 and pESA311_2 harboring diverse antimicrobial resistance genes (ARGs) conferring resistance to seven antibiotic classes, facilitated by mobile genetic elements including insertion sequences (ISs) and a class 1 integron (intI1). Conjugation assays revealed a stable co-transfer consortium of pESA311_1, pESA311_2, and pESA311_5, driving concurrent dissemination of multidrug resistance and virulence (sporadically co-mobilize of pESA311_4) and posing a co-selection risk. Further phylogenetic analysis identified homologous plasmids in other species, such as Salmonella enterica and Escherichia coli. Whereas the pESA311_1 lineage is largely restricted to China, pESA311_2 homologs have dispersed more broadly across different regions. In a broader surveillance of 160 clinical E. albertii isolates, intI1 prevalence was 19.8% and strongly correlated with MDR.ConclusionOur findings establish plasmids and intI1 as pivotal drivers of MDR in E. albertii, and highlight the associated risks of resistance-virulence co-selection and interspecies plasmid dissemination.

Background Work-related musculoskeletal disorders (WMSD) are highly prevalent among coal miners and pose a significant threat to occupational health. Understanding the underlying risk factors and developing a predictive model for WMSD risk can help to mitigate WMSD. Objective To identify key determinants of WMSD among coal miners in Jinang, China, and construct a predictive model to assess risk. Methods One thousand four hundred nine coal miners from two coal mining companies were surveyed using the modified Chinese Muscle Questionnaire (CMQ). Prevalence rates and risk factors were assessed using logistic regression. Machine learning algorithms were applied to construct the predictive model. Results The 12-month overall prevalence of WMSD was 82%, with the neck (59.5%), shoulders (53.4%), and lower back (46.5%) being the most affected. Eight variables, including smoking behaviors, perceived health status, and uncomfortable working posture, were significantly associated with WMSD ( p < 0.05). The neural network model achieved the highest performance (area under the curve: 0.886 on training and 0.704 on test). The fused model outperformed individual models in the final stacking integration learning. Conclusion Work-related musculoskeletal disorders are highly prevalent among Chinese coal miners and are influenced by personal and work-related factors. Machine learning models, particularly ensemble approaches, offer promise for risk prediction and targeted prevention.

Abstract Background HIV-1 genetic diversity and the spread of pretreatment drug resistance (PDR) complicate epidemic control. Southwestern China has a heterogeneous HIV epidemic, but molecular transmission dynamics in many prefecture-level settings remain poorly described. Methods We analyzed HIV-1 pol sequences from 208 newly diagnosed individuals in Zunyi, Guizhou Province. Genotypes were assigned by reference-based phylogenetic analysis; PDR was interpreted using the Stanford HIV Drug Resistance Database. Molecular transmission networks were inferred from pairwise genetic distances calculated with the TN93 model, and factors associated with PDR and clustering were evaluated using logistic regression. Results CRF07_BC (56.3%) and CRF01_AE (29.3%) were the predominant genotypes. PDR was detected in 21.2% of participants and varied significantly across genotypes, with higher prevalence observed in CRF08_BC and other recombinant forms. Molecular network analysis identified 31 transmission clusters involving 36.1% of sequences, with cluster sizes ranging from 2 to 6 nodes. Most clusters were small, and both densely connected subgraphs and simple linear chains were observed, indicating heterogeneous transmission patterns. Clustered cases were largely heterosexual and included a high proportion of individuals aged ≥ 50 years. CRF07_BC formed more tightly connected clusters, whereas CRF01_AE exhibited greater structural diversity. Multivariate analysis showed that divorced or widowed individuals were less likely to be included in transmission clusters. Conclusions The HIV epidemic in Zunyi is characterized by high genetic diversity, fragmented molecular transmission networks, and genotype-specific clustering patterns. Integrating molecular network analysis with routine drug resistance surveillance can improve understanding of local transmission dynamics and support precision HIV prevention strategies in southwestern China.

Hepatic alveolar echinococcosis (HAE), a life-threatening zoonosis, poses formidable surgical challenges when involving critical vasculature. Herein, we reported the periprocedural management dilemmas in radical resection for advanced HAE. A 58-year-old female visited the outpatient department presented with HAE. Imaging examination revealed extensive invasion of the hilum, bile duct, and several hepatic vessels, as well as left adrenal metastasis. The patient underwent right trisegmentectomy with left hepatic vein reconstruction, auto-transplantation, and adrenalectomy, with intraoperative Doppler demonstrating patent portal flow before abdominal closure. However, emergency thrombectomy and transcatheter thrombolysis were performed due to the abrupt occurrence of portal vein thrombosis 3 h after surgery. Despite intervention, the residual liver volume remained insufficient (approximately 28% of the standard liver volume), leading to progressive liver failure. The patient expired from multiorgan failure 9 days after operation. This case underscores not only the critical balance between radical resection and preservation of residual liver function in the surgical management of complex HAE, but also the imperative need to establish a comprehensive postoperative thromboprophylaxis.

Although numerous studies have investigated the link between tropical cyclones and dengue transmission, this body of research is predominantly ecological. How individual-level factors modify vulnerability to this risk remains understudied. We investigated how socioeconomic status, knowledge, attitudes, practices (KAP), and risk perception modulate post-cyclone vulnerability to inform targeted interventions. We conducted a 1:1 matched case-control study in Guangzhou, Zhongshan, and Foshan cities between September 14 and October 1, 2024, following the Super Typhoon Yagi on September 7, 2024. Cases were confirmed using the local surveillance system and matched to controls recruited via community sampling by age and sex. Structural equation modeling (SEM) was used to identify the mediating pathways between SES and vulnerability, while multivariable conditional logistic regression was used to identify independent direct predictors of dengue infection. Structural equation modeling identified a significant pathway in which a higher socioeconomic status predicted greater knowledge (β=0.34, p<0.01), which in turn was associated with stronger preventive practices. In the final multivariable logistic regression, higher scores for practice (aOR=0.62, 95% CI: 0.43, 0.88), environment risk perception (aOR=0.73, 95% CI: 0.59, 0.91), and knowledge (aOR=0.57, 95% CI: 0.42, 0.77) were significant protective factors against dengue. Conversely, frequent mosquito exposure emerged as the strongest risk factor. Compared to individuals bitten weekly or less often, those experiencing daily bites had more than double the odds of infection (aOR=2.38, 95% CI: 1.43, 3.97). Individual-level determinants, particularly adaptive practices and environmental risk perception, are critical mediators of post-cyclone dengue risk. Public health interventions should be tailored to bolster these protective behaviors, mitigating outbreak threats in vulnerable populations.

Abstract Tick distribution in China has significantly expanded with urbanization and climate change. Chongqing faces a significant risk of severe fever with thrombocytopenia syndrome (SFTS), but targeted prevention efforts are challenging due to unclear exposure pathways. This study combined generalized linear model (GLM), Bayesian networks, and Bayesian multivariate GLM to assess environmental, agricultural, and socioeconomic drivers of tick exposure in southwestern China. Direct tick exposure risks primarily arose from proximity to shrublands, forests, and cattle farming. Pet ownership also increased risk, while proximity to croplands reduced exposure, likely due to the pesticide/herbicide use and tillage. Bayesian networks revealed that socioeconomic factors indirectly mediated risk. Higher education levels reduced cattle farming likelihood and increased income tier, lowering exposure by altering land-use proximity and agricultural activities. Key factors showing no significant association included demographics (age/gender), grassland proximity, and crop cultivation. Bayesian methods resolved collinearity and mediation effects in GLM, clarifying township-level tick exposure mechanisms in Southwest China and mapping driver networks. Findings demonstrate that tick exposure stems from complex interactions among environmental, agricultural, and socioeconomic factors. Prevention in mountainous southwest China should prioritize the shrubland/forest–cattle farming ecological interface. Future studies should integrate geospatial data for enhanced risk mapping.