Objective To explore the risk factors for healthcare-associated infection(HAI)in lung transplant re-cipients(LTRs),and construct a predictive nomogram model.Methods Clinical data of patients who underwent lung transplant in Wuxi People's Hospital from January 2019 to December 2023 were analyzed retrospectively.The patients were divided into a training set(n=506)and a validation set(n=218).Independent risk factors were screened through LASSO regression,and multivariate logistic regression was included to construct a nomogram pre-diction model.The discrimination,calibration,and clinical applicability of the model were evaluated using receiver operating characteristic(ROC)curves,Hosmer-Lemeshow goodness-of-fit,and decision curves.Results Among the 506 LTRs,201 developed HAIs,with an incidence of 39.72％.The major infection site was lower respiratory tract,and the major pathogen were Gram-negative bacilli(Acinetobacter baumannii).Older age,use of extracorpo-real membrane oxygenation(ECMO),double-lung transplant,surgery duration＞3 hours,long duration of contin-uous fever,frequent abnormal blood routine examination,and long duration of combined use of antimicrobial agents were identified as independent risk factors for HAI after lung transplant.The ROC curve analysis results showed that the areas under the curve(AUCs)of the training set and the validation set were 0.74(95％CI:0.70-0.78)and 0.71(95％CI:0.64-0.78),respectively.The Hosmer-Lemeshow test results showed that there was no sta-tistically significant difference between the predictive and actual probability of HAI(P＞0.05).The clinical decision curve results indicated that the model had clinical benefits at a threshold probability value of 7％-71％.Conclusion The nomogram prediction model constructed in this study can effectively evaluate the risk of postoperative infection in LTRs.The model is stable and has high clinical application value,providing scientific reference for postoperative infection prevention and control.

Objective To explore the risk factors for healthcare-associated infection(HAI)in lung transplant re-cipients(LTRs),and construct a predictive nomogram model.Methods Clinical data of patients who underwent lung transplant in Wuxi People's Hospital from January 2019 to December 2023 were analyzed retrospectively.The patients were divided into a training set(n=506)and a validation set(n=218).Independent risk factors were screened through LASSO regression,and multivariate logistic regression was included to construct a nomogram pre-diction model.The discrimination,calibration,and clinical applicability of the model were evaluated using receiver operating characteristic(ROC)curves,Hosmer-Lemeshow goodness-of-fit,and decision curves.Results Among the 506 LTRs,201 developed HAIs,with an incidence of 39.72％.The major infection site was lower respiratory tract,and the major pathogen were Gram-negative bacilli(Acinetobacter baumannii).Older age,use of extracorpo-real membrane oxygenation(ECMO),double-lung transplant,surgery duration＞3 hours,long duration of contin-uous fever,frequent abnormal blood routine examination,and long duration of combined use of antimicrobial agents were identified as independent risk factors for HAI after lung transplant.The ROC curve analysis results showed that the areas under the curve(AUCs)of the training set and the validation set were 0.74(95％CI:0.70-0.78)and 0.71(95％CI:0.64-0.78),respectively.The Hosmer-Lemeshow test results showed that there was no sta-tistically significant difference between the predictive and actual probability of HAI(P＞0.05).The clinical decision curve results indicated that the model had clinical benefits at a threshold probability value of 7％-71％.Conclusion The nomogram prediction model constructed in this study can effectively evaluate the risk of postoperative infection in LTRs.The model is stable and has high clinical application value,providing scientific reference for postoperative infection prevention and control.

AIM To investigate the actions of basic fibroblast growth factor (bFGF) on the proliferation and differentiation of the non-adherent stromal precursor (NASP) cells. METHODS The osteogenic potential of the bone marrow stromal cells (BMSC) isolated from Wistar rats were cultured in the absence or presence of bFGF. After ALP cytochemistry, the colonies were counted by image analysis. The cells cultured in petri dishes were stained by the avidin-biotin-peroxidase complex (ABC) immunoperoxidase technique for collagen type Ⅰ, proliferating cell nuclear antigen ( PCNA ) and double staining for calcium and osteocalcin. RESULTS Many stromal precursor cells are present in bone marrow in a non-adherent form. bFGF not only stimulated the proliferation of NASP cells, but also enhanced the differentiation of NASP cells into osteoblasts. CONCLUSIONS NASP cells are possibly the main targets of the anabolic action of bFGF which may play an important role in fracture healing and bone formation.