Objective:To identify key risk factors for bloodstream infection (BSI) within 30 days after liver transplantation and to develop an etiological model to predict BSI pathogens based on these factors.Methods:A retrospective study enrolled 122 patients who underwent blood culture after liver transplantation at Renji Hospital, School of Medicine, Shanghai Jiao Tong University from July 2021 to March 2025. Patients were classified into a blood culture positive group ( n=87), comprising non-fermenting Gram-negative bacilli (e.g., Pseudomonas, Acinetobacter, n=8), fungi ( n=8), Staphylococcus ( n=27), Enterobacteriaceae ( n=30), and Enterococcus ( n=14) and a blood culture negative group ( n=35). Baseline variables and laboratory parameters obtained within 24 h of admission and during the postoperative course were subjected to univariate and multivariate analyses to identify risk factors and infection characteristics.Machine learning models for etiological prediction were then developed using these variables. Results:Among the 87 positive cultures, Enterobacterales (30/87, 34.48%) and Staphylococcus (27/87, 31.03%) were the main pathogens. Whithin these two categories of pathogens, Staphylococcus epidermidis and Klebsiella pneumoniae were the primary species, respectively. Autoimmune liver disease was more prevalent in the blood-culture-positive group than in the negative group ( χ2=4.05, P=0.044). The distribution of pathogenic bacteria causing BSI after liver transplantation has certain clinical characteristics. Six-eighths of patients with non-fermenting Gram-negative BSI had underlying malignancies.Among Enterococcal and Enterobacterales BSI cases, viral hepatitis (5/14; 8/30, 26.7%) and autoimmune hepatitis (2/14; 6/30, 20.0%) were more common. The area under curve values of the LightGBM, support vector machine (SVM), and neural network models were all greater than 0.90, indicating that these three models all have high predictive value for the types of pathogens causing bloodstream infections after liver transplantation. Conclusion:The etiological distribution of BSI after liver transplantation exhibits distinct clinical characteristics, LightGBM, SVM and neural network models effectively integrate multi-dimensional data to predict pathogen types, thereby informing infection-prevention and control strategies. Limitations include single-center design and small sample size. Multicenter prospective studies are warranted to validate the model′s efficacy in future research.

Objective:To identify key risk factors for bloodstream infection (BSI) within 30 days after liver transplantation and to develop an etiological model to predict BSI pathogens based on these factors.Methods:A retrospective study enrolled 122 patients who underwent blood culture after liver transplantation at Renji Hospital, School of Medicine, Shanghai Jiao Tong University from July 2021 to March 2025. Patients were classified into a blood culture positive group ( n=87), comprising non-fermenting Gram-negative bacilli (e.g., Pseudomonas, Acinetobacter, n=8), fungi ( n=8), Staphylococcus ( n=27), Enterobacteriaceae ( n=30), and Enterococcus ( n=14) and a blood culture negative group ( n=35). Baseline variables and laboratory parameters obtained within 24 h of admission and during the postoperative course were subjected to univariate and multivariate analyses to identify risk factors and infection characteristics.Machine learning models for etiological prediction were then developed using these variables. Results:Among the 87 positive cultures, Enterobacterales (30/87, 34.48%) and Staphylococcus (27/87, 31.03%) were the main pathogens. Whithin these two categories of pathogens, Staphylococcus epidermidis and Klebsiella pneumoniae were the primary species, respectively. Autoimmune liver disease was more prevalent in the blood-culture-positive group than in the negative group ( χ2=4.05, P=0.044). The distribution of pathogenic bacteria causing BSI after liver transplantation has certain clinical characteristics. Six-eighths of patients with non-fermenting Gram-negative BSI had underlying malignancies.Among Enterococcal and Enterobacterales BSI cases, viral hepatitis (5/14; 8/30, 26.7%) and autoimmune hepatitis (2/14; 6/30, 20.0%) were more common. The area under curve values of the LightGBM, support vector machine (SVM), and neural network models were all greater than 0.90, indicating that these three models all have high predictive value for the types of pathogens causing bloodstream infections after liver transplantation. Conclusion:The etiological distribution of BSI after liver transplantation exhibits distinct clinical characteristics, LightGBM, SVM and neural network models effectively integrate multi-dimensional data to predict pathogen types, thereby informing infection-prevention and control strategies. Limitations include single-center design and small sample size. Multicenter prospective studies are warranted to validate the model′s efficacy in future research.

At present, traditional methods on statistics have limitations in controlling time- varying confounding. This paper introduces an analysis method, parametric g-formula, which would adjust time-varying confounding, and also exemplifies the steps of its implementation for purpose to provide a new reference for researchers to deal with long-term observational data.

In modern biology and biotechnology research, recombinant gene expression has been the most popular method to obtain the target protein. In recent years, many foreign genes have been efficiently expressed in Escherichia coli. However, proteins encoded by animal, plant or mesophilic microbial genes often lose activities or become denatured within a few hours at regular growth temperatures for E. coli; some other target proteins are toxic to host cells and therefore difficult to be over-expressed. The new T-vector, pEXC-T, was constructed by combining TA cloning and cold-shock induction to obtain high expression levels with low costs. This paper reports the construction of pEXC-T and optimization of induction techniques for gene expression. Two instable proteins were tested and successfully expressed in soluble form by using pEXC vector. The development of pEXC-T offers a convenient technique for the preparations of recombinant proteins to be used in structure/function studies, or as diagnostic markers and medicinal proteins.
Biotechnology ; Cold Temperature ; Escherichia coli ; genetics ; Gene Expression ; Genetic Vectors ; Plasmids ; genetics ; Recombinant Proteins