ObjectiveTo construct and validate a predictive model for hepatic encephalopathy （HE） in patients with end-stage liver disease （ESLD） by combining key indicators such as cytokines and Model for End-Stage Liver Disease （MELD） score， and to provide evidence-based support for the early identification of high-risk populations and the optimization of intervention strategies in clinical practice. MethodsA retrospective analysis was performed for 2 167 patients with ESLD who were admitted to The Third People’s Hospital of Kunming from January 2022 to December 2024， the patients were divided into a training set with 1 517 patients and a validation set with 650 patients at a ratio of 7∶3 using the stratified random sampling method. A univariate analysis and a least absolute shrinkage and selection operator （LASSO） regression analysis were performed for the training set to identify potential influencing variables， and then a binary Logistic regression model was constructed to investigate the independent influencing factors for HE in ESLD patients. A nomogram prediction model was established based on this regression model， and the Hosmer-Lemeshow goodness-of-fit test was performed. The receiver operating characteristic （ROC） curve， calibration curves， decision curve analysis， and clinical impact curve were used to comprehensively evaluate the degree of fit， accuracy， consistency， and clinical practicability of the predictive model derived from the training set. The Mann-Whitney U test was used for comparison of non-normally distributed quantitative data between two groups. The chi-square test or Fisher exact test was used for comparison of categorical data between two groups. ResultsThe univariate analysis showed that there were significant differences between the study group and the control group in age， sex， blood ammonia， lymphocytes， hemoglobin， platelet count， prothrombin time， fibrinogen， international normalized ratio， total bilirubin， aspartate aminotransferase， total protein， albumin， prealbumin， alkaline phosphatase， cholinesterase， total bile acid， triglyceride， total cholesterol， high-density lipoprotein， low-density lipoprotein， blood glucose， CD3⁺ T cells， CD4⁺ T cells， CD8⁺ T cells， high-sensitivity C-reactive protein， carcinoembryonic antigen， triiodothyronine， thyroxine， free triiodothyronine， free thyroxine， interleukin-1β， interleukin-5， interleukin-6， interleukin-8， interleukin-12p70， interleukin-17， interferon-γ， and MELD score （all P<0.05）. The LASSO regression analysis identified nine variables of age， blood ammonia， albumin， prealbumin， cholinesterase， total cholesterol， thyroxine， interleukin-17， and MELD score， and the binary Logistic regression analysis showed that blood ammonia， interleukin-17， and MELD score were independent risk factors for HE in ESLD patients， while age and thyroxine were independent protective factors （all P<0.05）. A nomogram model was constructed based on these five variables. The Hosmer-Lemeshow goodness-of-fit test in the training set showed a good degree of fit （P=0.207）， with a McFadden’s pseudo-R² of 0.184， suggesting that the model had acceptable explanatory power； the Hosmer-Lemeshow goodness-of-fit test in the internal validation set further confirmed the calibration stability of the model （P=0.067）， suggesting that the model had a good degree of fit in independent data. The model had an area under the ROC curve of 0.784 in the validation set， with a sensitivity of 0.710 and a specificity of 0.752. The mean absolute error of the calibration curve was 0.067， and decision curve analysis and clinical impact curve showed that the nomogram model had positive net benefit and good clinical practicability. ConclusionThe nomogram model constructed based on age， blood ammonia， thyroxine， interleukin-17， and MELD score for predicting HE in patients with ESLD has a good degree of fit， high accuracy and consistency， and excellent clinical practicability.

ObjectiveTo investigate the independent risk factors for concurrent myocardial injury before intervention in patients with primary hepatic carcinoma （PHC）， to construct and validate a nomogram prediction model for the early identification of such patients， and to provide a basis for early clinical prevention and management. MethodsA retrospective analysis was performed for the clinical data of 1 822 patients with PHC in two medical institutions from January 2019 to June 2025， among whom the 1 518 patients from The Third People’s Hospital of Kunming were used as the training set to construct the nomogram prediction model， and the 304 patients from The Second People’s Hospital of Baoshan were used as the validation set. According to the result of high-sensitivity cardiac troponin T measurement before intervention， the patients with PHC were divided into myocardial injury group and non-myocardial injury group. The univariate analysis， the LASSO regression analysis， and the multivariate Logistic regression analysis were used to identify the risk factors of concurrent myocardial injury in PHC patients before intervention， and then a nomogram prediction model was constructed based on these findings. The independent data of the 304 patients in the validation set were used for external validation， and the area under the receiver operating characteristic curve （AUC）， the Hosmer-Lemeshow test， the calibration curve， decision curve analysis， and clinical impact curve were used to assess model performance. ResultsIn the training set， there were 346 patients in the myocardial injury group and 1 172 patients in the non-myocardial injury group， and the prevalence rate of concurrent myocardial injury was 22.79%. The mean age was 56.44±9.45 years， and the male patients accounted for 81.49%. The patients with drinking， smoking， chronic hepatitis B， chronic hepatitis C， hypertension， diabetes， Child-Pugh class A liver disease， BCLC stage B liver disease， or abnormal electrocardiographic findings accounted for 49.34%， 57.44%， 61.73%， 32.48%， 22.66%， 17.19%， 50.53%， 33.86%， and 62.12%， respectively. The univariate analysis， the LASSO regression analysis， and the multivariate Logistic regression analysis showed that age （odds ratio ［OR］=1.051， 95% confidence interval ［CI］： 1.036 — 1.067， P<0.001）， history of diabetes （OR=1.862， 95%CI： 1.346 — 2.574， P<0.001）， electrocardiographic findings （OR=1.446， 95%CI： 1.077 — 1.940， P=0.014）， hemoglobin （HGB） （OR=0.993， 95%CI： 0.988 — 0.998， P=0.003）， albumin （Alb） （OR=0.951， 95%CI： 0.930 — 0.972， P<0.001）， and creatinine （Cr） （OR=1.021， 95%CI： 1.016 — 1.026， P<0.001） were independent influencing factors for myocardial injury in PHC patients. A regression equation was established as Logit（Y）=-3.406+0.050×age+0.621×history of diabetes+0.369×electrocardiogram－0.007×HGB－0.051×Alb+0.021×Cr， and a nomogram prediction model was constructed based on these factors. The Bootstrap internal validation of the model yielded an AUC of 0.743 （95%CI： 0.707 — 0.778）， and the model had an AUC of 0.766 （95%CI： 0.738 — 0.794） in the training set and 0.730 （95%CI： 0.688 — 0.793） in the validation set. The Hosmer-Lemeshow goodness-of-fit test yielded a P value of 0.716 for the training set and 0.408 for the validation set， suggesting that the model had a good degree of fitting. Calibration curves， decision curve analysis， and clinical impact curves in both the training set and the validation set showed that the model had a good clinical application value. ConclusionAge， history of diabetes， electrocardiographic findings， HGB， Alb， and Cr are independent influencing factors for myocardial injury in patients with PHC， and the nomogram prediction model constructed based on these factors has good discriminatory ability， accuracy， and clinical practicability.

To study the molecular mechanism of salt stress response of peanut small GTP binding protein gene AhRabG3f, a 1 914 bp promoter fragment upstream of the start codon of AhRabG3f gene (3f-P) from peanut was cloned. Subsequently, five truncated fragments (3f-P1-3f-P5) with lengths of 1 729, 1 379, 666, 510 and 179 bp were obtained through deletion at the 5' end, respectively. Plant expression vectors where these six promoter fragments were fused with the gus gene were constructed and transformed into tobacco by Agrobacterium-mediated method, respectively. GUS expression in transgenic tobacco and activity analysis were conducted. The gus gene expression can be detected in the transgenic tobacco harboring each promoter segment, among which the driving activity of the full-length promoter 3f-P was the weakest, while the driving activity of the promoter segment 3f-P3 was the strongest. Upon exposure of the transgenic tobacco to salt stress, the GUS activity driven by 3f-P, 3f-P1, 3f-P2 and 3f-P3 was 3.3, 1.2, 1.9 and 1.2 times compared to that of the transgenic plants without salt treatment. This suggests that the AhRabG3f promoter was salt-inducible and there might be positive regulatory elements between 3f-P and 3f-P3 in response to salt stress. The results of GUS activity driven by promoter fragments after salt treatment showed that elements included MYB and GT1 between 1 930 bp and 1 745 bp. Moreover, a TC-rich repeat between 682 bp and 526 bp might be positive cis-elements responsible for salt stress, and an MYC element between 1 395 bp and 682 bp might be a negative cis-element responsible for salt stress. This study may facilitate using the induced promoter to regulate the salt resistance of peanut.
Arachis/genetics* ; Fabaceae/genetics* ; GTP-Binding Proteins/metabolism* ; Gene Expression Regulation, Plant ; Glucuronidase/metabolism* ; Plant Proteins/metabolism* ; Plants, Genetically Modified/genetics* ; Salt Stress ; Stress, Physiological/genetics* ; Tobacco/genetics*

Objective: To evaluate the interobserver agreement, diagnostic value, and associated clinical factors of automated breastultrasound (ABUS) coronal features in differentiating breast lesions. Materials and Methods: This study enrolled 457 pathologically confirmed lesions in 387 female (age, 46.4 ± 10.3 years),including 377 masses and 80 non-mass lesions (NMLs). The unique coronal features, including retraction phenomenon,hyper- or hypoechoic rim (continuous or discontinuous), skipping sign, and white wall sign, were defined and recorded. Theinterobserver agreement on image type and coronal features was evaluated. Furthermore, clinical factors, including the lesionsize, distance to the nipple or skin, palpability, and the histological grade were analyzed. Results: Among the 457 lesions, 296 were malignant and 161 were benign. The overall interobserver agreement for imagetype and all coronal features was moderate to good. For masses, the retraction phenomenon was significantly associated withmalignancies (p < 0.001) and more frequently presented in small and superficial invasive carcinomas with a low histologicalgrade (p = 0.027, 0.002, and < 0.001, respectively). Furthermore, continuous hyper- or hypoechoic rims were predictive ofbenign masses (p < 0.001), whereas discontinuous rims were predictive of malignancies (p < 0.001). A hyperechoic rim wasmore commonly detected in masses more distant from the nipple (p = 0.027), and a hypoechoic rim was more frequently foundin large superficial masses (p < 0.001 for both). For NMLs, the skipping sign was a predictor of malignancies (p = 0.040). Conclusion The coronal plane of ABUS may provide useful diagnostic value for breast lesions.

Objective To explore children infected immune functional state and change .Methods using nephelometry in 58 ca‐ses of EBV‐IgM positive children serum immunoglobulin ,T lymphocyte subsets CD3+ ,CD4+ ,CD8+ ,CD19+ blood flow cytometry peripheral detection at the same period ,50 cases of healthy children as control group in the same index .Results EBNA‐IgG nega‐tive group of children IgG ,IgA was EBNA‐IgG positive group and control group decreased significantly ,the difference was statisti‐cally significant(P< 0 .05) ,The EBNA‐IgG negative group ,EBNA‐IgG positive group serum total IgM and control group com‐pared ,the difference was not statistically significant (P> 0 .05) .The EBNA‐IgG negative group of CD3+ ,CD4+ ,CD4+ /CD8+ , CD19+ in children with EBNA‐IgG positive group and control group decreased significantly ,the difference was statistically signifi‐cant(P<0 .05) ,while CD8+ was the EBNA‐IgG positive group and the control group increased significantly ,the difference was sta‐tistically significant(P<0 .05) .Conclusion EBNA‐IgG positive immune function of children with close to healthy group is better than that of EBNA‐IgG negative group ,the emergence of EBNA‐IgG status on immune function of children do preliminary assess‐ment ,through the detection of immunoglobulin and T cell subsets ,B lymphocyte ratio can understand the immune state of children′s infection ,further evaluation of infection phase .