Pulmonary lymphangioleiomyomatosis is a rare disease characterized by the abnormal proliferation of pulmonary lymphatic smooth muscle cells. It is common in women and often accompanied by recurrent pneumothorax, chylothorax and progressive dyspnea, imaging characterized by diffuse cystic lesions in both lungs. Pulmonary lymphangioleiomyomatosis progresses aggressively and has a very poor prognosis, with a lack of effective medical treatment options in the advanced stages. Lung transplantation is a safe and effective method for the treatment of advanced pulmonary lymphangioleiomyomatosis, which may significantly improve the survival rate and quality of life of patients. The median survival period after surgery can reach 12 years. This article reviews the pathogenesis, diagnosis, treatment of pulmonary lymphangioleiomyomatosis, and the current status and existing problems of lung transplantation in pulmonary lymphangioleiomyomatosis, aiming to provide a reference for the clinical treatment and subsequent research of pulmonary lymphangioleiomyomatosis.

OBJECTIVE To construct a nomogram diagnosis model for Epstein-Barr virus(EBV)co-infection in children with Mycoplasma pneumoniae pneumonia(MPP).METHODS Clinical data of 427 children with MPP ad-mitted to the Children's Hospital of Nanjing Medical University from Jul.2020 to Jul.2024 were retrospectively analyzed.The children were divided into a modeling group(n=299)and a validation group(n=128).The model-ing group was further categorized into an MPP group(n=235)and an MPP co-infection group(n=64)based on EBV infection status.Multivariate logistic regression was used to identify risk factors for EBV co-infection in chil-dren with MPP,and a nomogram diagnosis model was constructed.The diagnostic value and clinical application value of the model were evaluated by receiver operating characteristic(ROC)curves,calibration curves and deci-sion curve analysis(DCA).RESULTS The white blood cell count(WBC)in the MPP co-infection group was(12.37±2.32)× 109/L,significantly higher than that in the MPP group(P＜0.05).Platelet count(PLT)and hemoglobin(Hb)levels were(197.95±32.85)× 109/L and(102.58±13.74)g/L,respectively,lower than those in the MPP group(P＜0.05).Additionally,the MPP co-infection group exhibited higher proportions of fe-ver duration ≥10 days,dyspnea and pleural effusion compared to the MPP group(P＜0.05).Multivariate logistic regression analysis revealed that WBC(OR=1.514),PLT(OR=0.970),Hb(OR=0.959),fever duration(OR=4.790),dyspnea(OR=3.777)and pleural effusion(OR=4.795)were significantly associated with EBV infection in children with MPP(P＜0.05).The nomogram demonstrated that when the total model score reached 219 points,the probability of EBV infection in children with MMP was 0.9.The areas under the ROC curve for the modeling group and validation group were 0.882(95％CI:0.836-0.927)and 0.943(95％CI:0.902-0.984),respectively,with sensitivities of 76.56％and 91.30％,respectively,and specificities of 82.55％and 85.71％,respectively.The Hosmer-Lemeshow goodness-of-fit test showed x2=4.124,P=0.846 for the modeling group and x2=4.203,P=0.838 for the validation group.DCA curve indicated high clinical applica-tion value of the model.CONCLUSIONS WBC,PLT,Hb levels,fever duration,dyspnea and pleural effusion have diagnostic values for EBV co-infection in children with MPP.The nomogram model constructed based on these six factors demonstrates excellent diagnostic performance.

OBJECTIVE To construct a nomogram diagnosis model for Epstein-Barr virus(EBV)co-infection in children with Mycoplasma pneumoniae pneumonia(MPP).METHODS Clinical data of 427 children with MPP ad-mitted to the Children's Hospital of Nanjing Medical University from Jul.2020 to Jul.2024 were retrospectively analyzed.The children were divided into a modeling group(n=299)and a validation group(n=128).The model-ing group was further categorized into an MPP group(n=235)and an MPP co-infection group(n=64)based on EBV infection status.Multivariate logistic regression was used to identify risk factors for EBV co-infection in chil-dren with MPP,and a nomogram diagnosis model was constructed.The diagnostic value and clinical application value of the model were evaluated by receiver operating characteristic(ROC)curves,calibration curves and deci-sion curve analysis(DCA).RESULTS The white blood cell count(WBC)in the MPP co-infection group was(12.37±2.32)× 109/L,significantly higher than that in the MPP group(P＜0.05).Platelet count(PLT)and hemoglobin(Hb)levels were(197.95±32.85)× 109/L and(102.58±13.74)g/L,respectively,lower than those in the MPP group(P＜0.05).Additionally,the MPP co-infection group exhibited higher proportions of fe-ver duration ≥10 days,dyspnea and pleural effusion compared to the MPP group(P＜0.05).Multivariate logistic regression analysis revealed that WBC(OR=1.514),PLT(OR=0.970),Hb(OR=0.959),fever duration(OR=4.790),dyspnea(OR=3.777)and pleural effusion(OR=4.795)were significantly associated with EBV infection in children with MPP(P＜0.05).The nomogram demonstrated that when the total model score reached 219 points,the probability of EBV infection in children with MMP was 0.9.The areas under the ROC curve for the modeling group and validation group were 0.882(95％CI:0.836-0.927)and 0.943(95％CI:0.902-0.984),respectively,with sensitivities of 76.56％and 91.30％,respectively,and specificities of 82.55％and 85.71％,respectively.The Hosmer-Lemeshow goodness-of-fit test showed x2=4.124,P=0.846 for the modeling group and x2=4.203,P=0.838 for the validation group.DCA curve indicated high clinical applica-tion value of the model.CONCLUSIONS WBC,PLT,Hb levels,fever duration,dyspnea and pleural effusion have diagnostic values for EBV co-infection in children with MPP.The nomogram model constructed based on these six factors demonstrates excellent diagnostic performance.

Objective To address the challenges of poor performance and lack of interpretability in existing models,the SHAP algorithm is used to develop an interpretable machine learning model that offers a novel approach to wound age estimation,Methods Based on the previous discovery of the expression of 35 wound age healing-related genes in contused skeletal muscle,the woun age estimaton model was constructed using four algorithms,namly,Multilayer Perceptron(MLP),Random Forest(RF),LightGBM(LGBM),and Support Vector Machine(SVM).The SHAP(Shapley Additive Explanation)algorithm was used to rank the importance of genetic features,eliminate redundant attributes,and optimize the model for accurate wound age estimation.the genetic features of the optimal model were analyzed using SHAP's local interpretation capabilities.Results The best results were obtained using model of MLP(area under the curve(AUC)=0.99)The wound ages were classified into four categories:4～12 h,16～24 h,28～36 h,and 40～48 h,using only 15 gene features.According to SHAP analysis,Fam210a was identified as the most relevant gene.Local analysis revealed that high expression of Fam210a contributed to an increase in the predicted probability of 4 h～12 h,while high expression of Rae1 contributed to an increase in the predicted probability of 16 h～24 h.Additionally,low expression of Tbx18 contributed to an increase in the predicted probability of 28 h～36 h,whereas high expression of Tbx18 contributed to an increase in the predicted probability of 40 h～48 h.Conclusions The combined MLP and SHAP model can be used to predict wound age.Using the SHAP interpreter can better understand the degree of contribution of feature genes to the model prediction,and lay the foundation for further in-depth study of wound age estimation.

Objective To construct and validate a risk prediction model for delayed healing of venous leg ulcer(VLU),so as to provide a reference basis for early identification of people at high risk of delayed healing.Methods Using a convenience sampling method,331 VLU patients attending vascular surgery departments in 2 tertiary A hospitals in Sichuan Province from January 2018 to December 2022 were selected as a modeling group and an internal validation group,and 112 patients admitted to another tertiary A hospital were selected as an external validation group.Risk factors for delayed healing in VLU patients were screened using univariate analysis,LASSO regression,and multivariate logistic regression analysis,and a risk prediction model was constructed using R software,and the predictive effects of the models were examined using the area under the receiver operating characteristic curve,the Hosmer-Lemeshow test,decision curve,and the bootstrap resampling for internal validation and spatial external validation were performed,respectively.Results The predictors that ultimately entered the prediction model were diabetes(OR=4.752),deep vein thrombosis(OR=4.104),lipodermatosclerosis(OR=5.405),ulcer recurrence(OR=3.239),and ankle mobility(OR=5.520).The model had good discrimination(AUC:0.819 for internal validation and 0.858 for external validation),calibration(Hosmer-Lemeshow test:χ2=13.517,P=0.095 for internal validation and χ2=3.375,P=0.909 for external validation)and clinical validity.Conclusion The model constructed in this study has good differentiation and calibration,and it can effectively predict people at high risk of delayed healing of VLU,which facilitates targeted clinical interventions to improve ulcer outcomes and reduce the risk of delayed ulcer healing.

The purpose of this study is to construct a muscle-specific synthetic promoter library, screen out muscle-specific promoters with high activity, analyze the relationship between element composition and activity of highly active promoters, and provide a theoretical basis for artificial synthesis of promoters. In this study, 19 promoter fragments derived from muscle-specific elements, conserved elements, and viral regulatory sequences were selected and randomLy connected to construct a muscle-specific synthetic promoter library. The luciferase plasmids pCMV-Luc and pSPs-Luc were constructed and transfected into the myoblast cell line C2C12. The activities of the synthesized promoters were evaluated by the luciferase activity assay. Two non-muscle-derived cell lines HeLa and 3T3 were used to verify the muscle specificity of the highly active promoters. The sequences of promoters with high activity, good muscle specificity, and correct sequences were analyzed to explore the relationship between the element composition and activity of promoters. We successfully constructed a muscle-specific promoter library and screened out 321 effective synthetic promoter plasmids. Among them, the activity of SP-301 promoter was 5.63 times that of CMV. The 15 promoters with high activity were muscle-specific. In the promoters with high activity and correct sequences, there was a relationship between their element composition and activity. Muscle-specific elements accounted for a high proportion in the promoters, while they had weak correlations with the promoter activity, being tissue-specific determinants. Viral elements accounted for no less than 20% in highly active promoters, which may be the key elements for the promoter activity. The content of conserved elements was proportional to the promoter activity. This study lays a theoretical foundation for the synthesis of tissue-specific efficient promoters and provides a new idea for the construction and application of in-situ gene delivery systems.
Promoter Regions, Genetic ; Humans ; Animals ; Mice ; Gene Library ; Cell Line ; Transfection ; HeLa Cells ; Luciferases/metabolism* ; Muscle, Skeletal/metabolism* ; Plasmids/genetics* ; Myoblasts/cytology*