ObjectiveTo investigate the risk factors for rebleeding after endoscopic therapy for esophageal and gastric varices in liver cirrhosis patients， to construct a clinical predictive model， and to provide a reference for predicting rebleeding in the early stage， reducing the incidence rate of rebleeding， and improving the clinical outcome of patients. MethodsA retrospective analysis was performed for the clinical data of 194 liver cirrhosis patients with gastroesophageal variceal bleeding who received initial endoscopic therapy at Department of Gastroenterology， The Second People’s Hospital of Lanzhou， from January 1， 2021 to May 31， 2025. According to whether rebleeding occurred within 1 year after endoscopic therapy， the patients were divided into rebleeding group and non-rebleeding group. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The patients enrolled were randomly divided into a training set and a validation set at a ratio of 7∶3. In the training set， the Lasso regression analysis was used to obtain optimal predictive variables， and the factors that might affect prognosis were included in the univariate and multivariate Logistic regression analyses to identify independent predictive factors for rebleeding after endoscopic therapy for esophageal and gastric varices in liver cirrhosis patients， which were used to construct a nomogram model. In both the training set and the validation set， the receiver operating characteristic （ROC） curve and the calibration curve were used to assess the discriminatory ability and calibration of the model， and decision curve analysis and the clinical impact curve were used to assess the clinical practicability of the model. ResultsAmong the 194 liver cirrhosis patients with esophageal and gastric varices， 116 （59.79%） experienced rebleeding within 1 year after endoscopic therapy， with 76 patients in the training set and 40 patients in the validation set. In the training set， the Lasso regression analysis and the univariate and multivariate Logistic regression analyses showed that etiology of liver cirrhosis （odds ratio ［OR］=3.540， 95% confidence interval ［CI］： 1.520 — 7.150， P<0.001）， Child-Pugh class （OR=3.560， 95%CI： 1.380 — 9.500， P=0.019）， severity of esophageal and gastric varices （OR=8.190， 95%CI： 3.568 — 17.850， P=0.026）， and main portal vein diameter （OR=2.954， 95%CI： 1.349 — 15.030， P=0.044） were independent predictive factors for rebleeding of esophageal and gastric varices in liver cirrhosis patients. A nomogram model was constructed based on the above independent predictive factors. The ROC curve analysis showed that this model had an area under the ROC curve of 0.845 （95%CI： 0.778 — 0.912） in the training set and 0.801 （95%CI： 0.798 — 0.868） in the validation set. The model had an index of concordance of 0.832 in the training set and 0.820 in the validation set， suggesting that the model had a good discriminatory ability. The Hosmer-Lemeshow test showed P values of 0.320 and 0.550 in the training set and validation set， respectively， the calibration curve indicated that the predicted probabilities of the nomogram model were in good concordance with the actual observed probabilities， suggesting that the model had good calibration. The decision curve analysis and the clinical impact curve showed that the model had good clinical utility. ConclusionThe nomogram model based on etiology of liver cirrhosis， Child-Pugh class， severity of esophageal and gastric varices， and main portal vein diameter has a certain clinical value in predicting the risk of rebleeding from esophageal and gastric varices in liver cirrhosis.

To characterize the genomes of different emm-type group A Streptococcus (GAS), their virulence genes and drug resistance profiles in Tianjin City from 2011 to 2024. After PCR, a total of 42 strains with different years and emm types were selected for whole genome sequencing and multi-locus sequence typing (MLST), and the core genomes were used to generate a phylogenetic tree, after which the virulence genes and resistance genes were identified and analyzed, followed by the drug susceptibility test. In this study, the GAS strains were dominated by emm1 (50.0%) and emm12 (40.4%), and the MLST phenotypes were categorized into six types: ST36 (40.4%), ST1274 (26.1%), ST28 (23.8%), ST921 (4.7%), ST46 (2.3%), and ST403 (2.3%). There was a high consistency between their emm-types and ST types. A total of 68 virulence genes were detected in the genomes of 42 GAS strains, involving functional genes encoding exotoxin, bacterial adhesion, extracellular enzymes, etc. The virulence genes they carried were significantly different between emm1-type and emm12-type strains, such as speA. At the same time, the carrying rates of some virulence genes in the same emm-type strains changed with time, such as hyl. The resistance genes were basically the same among different emm-type strains except for the vanSE gene detected in all emm12 strains. The results of drug sensitivity showed that the GAS strains isolated in Tianjin City from 2011 to 2024 were sensitive to penicillin, cefazolin, chloramphenicol, vancomycin, and levofloxacin, while the resistance rates to erythromycin, azithromycin, clarithromycin, and clindamycin ranged from 88.5% to 100.0%, and there was a certain degree of consistency between the resistance phenotypes and the detected resistance genes. Overall, the main emm types and evolutionary features of GAS in Tianjin City from 2011 to 2024 were consistent with the dominant types in China, and the carrying rate of virulence genes and drug resistance genes differed significantly among different emm-type strains, and there were continuous evolution and variation in the prevalence of virulence genes in GAS.

Wastewater-based epidemiology(WBE)has emerged in recent years as a crucial complementary tool for tracking the community transmission and infection prevalence of SARS-CoV-2 and other viral pathogens.This short review comprehensively de-scribes the diversity of viruses in wastewater,with a focus on current advancements in aspects including sampling methods,concentra-tion and enrichment strategies,detection approaches,and setting up quality controls for wastewater-based viral pathogen surveillance.The systematic summary of global status,technical progress,and challenges highlights the feasibility of WBE viral surveillance within a One Health framework,thus facilitating use of WBE as a tool for early warning of viral disease outbreaks and predicting epidemic trends in the future.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

To characterize the genomes of different emm-type group A Streptococcus (GAS), their virulence genes and drug resistance profiles in Tianjin City from 2011 to 2024. After PCR, a total of 42 strains with different years and emm types were selected for whole genome sequencing and multi-locus sequence typing (MLST), and the core genomes were used to generate a phylogenetic tree, after which the virulence genes and resistance genes were identified and analyzed, followed by the drug susceptibility test. In this study, the GAS strains were dominated by emm1 (50.0%) and emm12 (40.4%), and the MLST phenotypes were categorized into six types: ST36 (40.4%), ST1274 (26.1%), ST28 (23.8%), ST921 (4.7%), ST46 (2.3%), and ST403 (2.3%). There was a high consistency between their emm-types and ST types. A total of 68 virulence genes were detected in the genomes of 42 GAS strains, involving functional genes encoding exotoxin, bacterial adhesion, extracellular enzymes, etc. The virulence genes they carried were significantly different between emm1-type and emm12-type strains, such as speA. At the same time, the carrying rates of some virulence genes in the same emm-type strains changed with time, such as hyl. The resistance genes were basically the same among different emm-type strains except for the vanSE gene detected in all emm12 strains. The results of drug sensitivity showed that the GAS strains isolated in Tianjin City from 2011 to 2024 were sensitive to penicillin, cefazolin, chloramphenicol, vancomycin, and levofloxacin, while the resistance rates to erythromycin, azithromycin, clarithromycin, and clindamycin ranged from 88.5% to 100.0%, and there was a certain degree of consistency between the resistance phenotypes and the detected resistance genes. Overall, the main emm types and evolutionary features of GAS in Tianjin City from 2011 to 2024 were consistent with the dominant types in China, and the carrying rate of virulence genes and drug resistance genes differed significantly among different emm-type strains, and there were continuous evolution and variation in the prevalence of virulence genes in GAS.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Hot melt extrusion(HME)technology employs thermodynamic and kinetic principles to mix pharmaceutical polymers with crystalline drugs at high temperatures and extrude them,embedding drug molecules within the polymer matrix to form solid dispersions.Due to its solvent-free nature,capability for one-step processing,and support for continuous operation,HME has garnered significant attention in the pharmaceutical industry in recent years.This article introduced the basic principles and development history of HME technology and its marketed drugs.It reviewed the research progress of HME technology in improving drug solubility,masking taste,controlled release,targeted release,oral dispersible films,implant formulations,semi-solid formulations,and 3D printed formulations.Additionally,the article summarized the advantages and limitations of HME technology and provided an outlook on its future development.

Poloxamer， as a non-ionic surfactant， exhibits a unique triblock [polyethylene oxide-poly （propylene oxide）-polyethylene oxide] structure， which endows it with broad application potential in various fields， including solid dispersion technology， nanotechnology， gel technology， biologics， gene engineering and 3D printing. As a carrier， it enhances the solubility and bioavailability of poorly soluble drugs. In the field of nanotechnology， it serves as a stabilizer etc.， enriching preparation methods. In gel technology， its self-assembly behavior and thermosensitive properties facilitate controlled drug release. In biologics， it improves targeting efficiency and reduces side effects. In gene engineering， it enhances delivery efficiency and expression levels. In 3D printing， it provides novel strategies for precise drug release control and the production of high-quality biological products. As a versatile material， poloxamer holds promising prospects in the pharmaceutical field.

Objective To investigate the association between lifestyle and risk of heart disease and diabetes in the elderly population in Xi'an City. Methods From January 2021 to January 2024, a staged cluster sampling method was used to investigate the lifestyle and the occurrence of heart disease and diabetes in elderly population aged 60 years and above in the communities of Xi'an. Multivariate logistic regression was used to analyze the relationship between lifestyle and the risk of heart disease and diabetes. Results A total of 413 elderly people were investigated, of which 31.96% had heart disease, 27.12% had diabetes, and 10.90% had diabetes with heart disease. Multivariate logistic regression analysis revealed that age, BMI, family history, sweet food preference, smoking, and sitting and lying for a long time were risk factors for diabetes in the elderly population (P<0.05). Age, BMI, family history, history of diabetes, preference for salted products, smoking, drinking, and sitting and lying for a long time were risk factors for heart disease in the elderly population (P<0.05). Conclusion The incidence rates of heart disease and diabetes are high in the elderly population in Xi'an City. The risk of diabetes is related to unhealthy lifestyles such as sweet food preference, smoking, and sitting and lying for a long time, while heart disease is related to unhealthy lifestyles such as preference for salted products, smoking, drinking, and sitting and lying for a long time.

Objective: To evaluate the efficacy and safety of plasma exchange (PE) in thymoma-associated myasthenia gravis (MG), thereby to provide theoretical support for its application in the treatment of thymoma-associated MG. Methods: A total of 133 patients with thymoma-associated MG admitted from January 2018 to September 2024 were retrospectively analyzed. Patients were matched using propensity score to reduce selection bias, yielding 22 matched pairs for both PE group (n=22) and non-PE group (n=22). Patient characteristics including gender, age of disease onset, course of disease, history of thymoma resection, clinical absolute scores [clinical absolute scores (CAS) and clinical relative scores (CRS)], and synchronized immunotherapy regimen of the two groups were analyzed. The CAS scores before and after treatment were compared between the two groups, and the CRS was used to assess the treatment efficiency. Safety of the two treatment regimens were also compared. Continuous variables were compared using the t-test or ANOVA, while categorical data were compared by the chi-square test. Results: A total of 133 patients were included and divided into two groups according to whether they underwent plasma exchange treatment: the PE group (n=22) and the non-PE group (n=111). To exclude bias caused by large difference in the number of cases between the two groups, we performed propensity score matching. After matching, the number of cases in both groups was 22. There was no significant difference in baseline clinical characteristics between the two groups (P>0.05), including gender, age of onset, duration of disease course, history of thymectomy and baseline CAS score before treatment. Compared to the non-PE group, patients in the PE group showed more significant improvement in CAS score (5.09±1.95 vs 3.59±1.50, P<0.05) and a higher CRS score (75.00% vs 50.00%, P<0.001). Compared to the non-PE group, PE group had significantly longer ICU stay, longer hospital stay and higher hospitalization cost (P<0.05). There was no statistically significant difference in adverse events between the two groups during treatment (P>0.05). During long-term follow-up, both the PE and non-PE groups showed relatively low 1-, 3-, and 5-year recurrence rate, with no significant difference between the two groups (P>0.05). Conclusion: This study indicates that plasma exchange has clear value in the treatment of patients with thymoma-associated myasthenia gravis. It can not only significantly improve patients' muscle strength to alleviate motor dysfunction and enhance quality of life, but also does not significantly increase the incidence of adverse reactions. Therefore, it can be regarded as one of the preferred treatment options that achieve a "balance between efficacy and safety" for such patients, and provides an important basis for optimizing treatment strategies, improving prognosis, and promoting the application of subsequent treatment regimens.