Chengdu Medical College

Hypervirulent Klebsiella pneumoniae (hvKP), a pathogen responsible for severe invasive infections, exhibits a hypermucoviscosity (HMV) phenotype that is closely associated with its virulence. While fumarate and nitrate reduction regulator (FNR), a global transcription regulator, is critical for bacterial adaptation to hypoxic conditions, its role in hvKP pathogenicity remains unexplored. This study demonstrates that FNR modulates the HMV phenotype and virulence of the hvKP strain NTUH-K2044 under anaerobic conditions. Through targeted deletion and complementation of the fnr gene, combined with phenotypic, molecular, cellular, and animal infection assays, we show that FNR positively regulates the HMV phenotype. Notably, this regulation is independent of several genes previously implicated in HMV formation, including rmpA, rmpA2, wzy-K1 (magA), rmpC, and rmpD. In the absence of fnr, the HMV phenotype was abolished, while the transcript levels of these genes increased significantly, suggesting a compensatory or indirect regulatory mechanism that warrants further investigation. Functionally, FNR-mediated HMV enhanced bacterial resistance to phagocytosis and serum killing while suppressing host colonization features such as fimbriae formation, biofilm production, and epithelial cell adhesion. In animal infection models, FNR also contributed positively to hvKP virulence. These findings highlight the role of FNR in regulating the HMV phenotype and virulence in hvKP, facilitating host adaptation and immune evasion. Targeting FNR may thus represent a promising strategy for the development of novel therapeutics.

To analyze the risk factors for falls in elderly maintenance hemodialysis patients, construct a nomogram prediction model and validate the application.

Elderly maintenance hemodialysis patients face a high risk of falls, and there are fewer and less effective fall-specific assessment tools.

A total of 871 elderly hemodialysis patients from 9 hospitals in Chengdu City from October 2023 to December 2024 were selected as the study objects. Baseline characteristics and fall outcomes of patients in the fall group and non-fall group were recorded and compared through 6-month follow-up. Multivariable logistic regression analysis was employed to identify independent risk factors, and construct the nomogram prediction model and complete the internal verification of the model. 218 elderly maintenance hemodialysis patients from three other hospitals in Chengdu City were selected for a 6-month follow-up of falls from January to February 2024 to complete the external validation of the model.

The incidence of falls in elderly maintenance hemodialysis patients was 31.96%, and logistic regression analysis showed that age, sex, visual impairment, intradialytic hypotension, cognitive impairment and depression were independent risk factors for falls. Both internal and external validation of the model demonstrated area under the curve greater than 0.80. Furthermore, calibration plots, the Hosmer-Lemeshow test, and clinical decision curves all demonstrated that the model had good calibration and clinical utility.

The nomogram constructed based on the above risk factors can provide scientific basis and practical tools for early clinical identification of high-risk groups of falls.

To explore the trajectory of cognitive function changes and influencing factors in maintenance hemodialysis (MHD) patients.

A convenience sampling method was used to select MHD patients from a tertiary hospital in Chengdu from August 2023 to April 2024. The general information questionnaire, Chinese version of the Montreal Cognitive Assessment (MoCA), Pittsburgh Sleep Quality Index (PSQI), Appetite Visual Analogue Scale (VAS), and Family Care Index (APGAR) were used for the investigation. Patients' cognitive function levels were assessed at baseline and at 3, 6, and 9 months after the initial survey. A latent growth model was used to identify potential categories of cognitive function trajectory, and univariate and binary logistic regression analyses were performed to analyze the influencing factors.

A total of 154 MHD patients completed the entire study. The trajectory of cognitive function changes was divided into two potential categories: low cognitive function-fast decline group and high cognitive function-slow decline group. Binary logistic regression results showed that educational level, hypertension, sleep quality, appetite, and family care were influencing factors for the trajectory of cognitive function changes in MHD patients.

Cognitive function in MHD patients showed an overall declining trend over time. The cognitive function change trajectory could be divided into two potential categories: fast decline group and high cognitive function-slow decline group. Healthcare professionals can develop targeted nursing intervention programs based on the characteristics of different patient types and their influencing factors to improve cognitive function and enhance quality of life.