Objective To investigate the influencing factors of antibiotic-associated diarrhea(AAD)following congenital heart disease(CHD)surgery in pediatric patients,develop a nomogram-based predictive model,and validate its efficacy.Methods A retrospective analysis was conducted on the clinical data of pediatric patients who underwent CHD surgery in the Pediatric Intensive Care Unit(PICU)of a tertiary hospital in Guang-dong Province from July 2022 to July 2024.Patients were categorized into an AAD group and a non-AAD group.Univariate and multivariate logistic regression analyses were performed to identify risk factors for AAD occurrence following CHD surgery.A risk prediction model was developed,and a nomogram was constructed.The predictive performance of the model was evaluated using the Receiver Operating Characteristic(ROC)curve to calculate the area under the curve(AUC),the Hosmer-Lemeshow goodness-of-fit test,calibration curves,and clinical decision curve analysis.External validation of the model was conducted using data from patients in the Surgical Intensive Care Unit(SICU).Results The incidence of AAD following CHD surgery was 48.52%(229 out of 472 cases).Risk factors for AAD included the combined use of antibiotics,mechanical ventilation,elevated C-reactive protein levels,prolonged surgical duration,and extended antibiotic usage time(all with OR>1,P<0.05).Conversely,probiotic administration was identified as a protective factor(OR<1,P<0.05).The predictive model demon-strated excellent discrimination,as evidenced by the ROC curve areas:0.922(95%CI:0.894～0.951)in the modeling group,0.886(95%CI:0.838～0.915)in the internal validation group,and 0.862(95%CI:0.784～0.941)in the external validation group.Additionally,the model exhibited satisfactory calibration,as indicated by the Hosmer-Lemeshow test results:χ2=7.96,P=0.538 in the modeling group;χ2=4.24,P=0.895 in the inter-nal validation group;and χ2=9.923,P=0.270 in the external validation group.Furthermore,the model provided significant clinical utility.Conclusions Combined antibiotic use,duration of antibiotic therapy,mechanical ventilation,surgical duration,C-reactive protein(CRP)levels,and probiotic administration are key factors influ-encing the occurrence of AAD.The risk prediction model developed based on these variables demonstrates robust predictive performance and can serve as a valuable reference for the development and implementation of preventive and therapeutic strategies in clinical practice.

Objective To investigate the influencing factors of antibiotic-associated diarrhea(AAD)following congenital heart disease(CHD)surgery in pediatric patients,develop a nomogram-based predictive model,and validate its efficacy.Methods A retrospective analysis was conducted on the clinical data of pediatric patients who underwent CHD surgery in the Pediatric Intensive Care Unit(PICU)of a tertiary hospital in Guang-dong Province from July 2022 to July 2024.Patients were categorized into an AAD group and a non-AAD group.Univariate and multivariate logistic regression analyses were performed to identify risk factors for AAD occurrence following CHD surgery.A risk prediction model was developed,and a nomogram was constructed.The predictive performance of the model was evaluated using the Receiver Operating Characteristic(ROC)curve to calculate the area under the curve(AUC),the Hosmer-Lemeshow goodness-of-fit test,calibration curves,and clinical decision curve analysis.External validation of the model was conducted using data from patients in the Surgical Intensive Care Unit(SICU).Results The incidence of AAD following CHD surgery was 48.52%(229 out of 472 cases).Risk factors for AAD included the combined use of antibiotics,mechanical ventilation,elevated C-reactive protein levels,prolonged surgical duration,and extended antibiotic usage time(all with OR>1,P<0.05).Conversely,probiotic administration was identified as a protective factor(OR<1,P<0.05).The predictive model demon-strated excellent discrimination,as evidenced by the ROC curve areas:0.922(95%CI:0.894～0.951)in the modeling group,0.886(95%CI:0.838～0.915)in the internal validation group,and 0.862(95%CI:0.784～0.941)in the external validation group.Additionally,the model exhibited satisfactory calibration,as indicated by the Hosmer-Lemeshow test results:χ2=7.96,P=0.538 in the modeling group;χ2=4.24,P=0.895 in the inter-nal validation group;and χ2=9.923,P=0.270 in the external validation group.Furthermore,the model provided significant clinical utility.Conclusions Combined antibiotic use,duration of antibiotic therapy,mechanical ventilation,surgical duration,C-reactive protein(CRP)levels,and probiotic administration are key factors influ-encing the occurrence of AAD.The risk prediction model developed based on these variables demonstrates robust predictive performance and can serve as a valuable reference for the development and implementation of preventive and therapeutic strategies in clinical practice.

ObjectiveTo investigate the therapeutic effect of sequential administration of Dihuang Baoyuan granules （DHBY， the prescription for consolidating body resistance） and Fuling Yunhua granules （FLYH， the prescription for treating symptoms） on spontaneous type 2 diabetes mellitus （T2DM） in mice. MethodsAccording to the fasting blood glucose （FBG） level， 12-week-old db/db mice were randomized into six groups： model， DHBY （18.02 g·kg^-1）， FLYH （14.80 g·kg^-1）， sequential administration 1 （SEQ-1， DHBY 18.02 g·kg^-1+FLYH 14.80 g·kg^-1）， sequential administration 2 （SEQ-2， FLYH 14.80 g·kg^-1+DHBY 18.02 g·kg^-1）， and dapagliflozin （Dapa， 1.3 mg·kg^-1）. The m/m mice in the same litter were selected as the normal group. The mice were administrated with corresponding drugs by gavage for 8 consecutive weeks. During the 8 weeks of drug administration and 2 weeks after withdrawal， the retinal thickness， FBG， hemoglobin A1c （HbA1c）， and insulin were determined， and histopathological changes of the pancreas， liver， kidney， and retina were observed by hematoxylin-eosin （HE） staining. ResultsCompared with the model group， SEQ-1 for 4 weeks lowered the FBG level （P<0.05）， raised the insulin level， decreased the triglyceride （TG） level （P<0.05）， increased the number of optic ganglion cells and diminished vacuolar degeneration of pancreatic islet and liver. SEQ-2 lowered FBG and HbA1c levels （P<0.05）， rose the insulin level， increased the retinal thickness and the number of optic ganglion cells （P<0.05）， and alleviated vacuolar degeneration of pancreatic islet and liver. Two weeks after drug withdrawal， Dapa tended to increase FBG and HbA1c compared with those at the time of drug withdrawal. However， the levels of FBG and HbA1c in the SEQ-2 group remained decreasing （P<0.05）. ConclusionSEQ-1 and SEQ-2 can lower the blood glucose level and ameliorate diabetic retinopathy， and SEQ-2 outperformed DHBY and FLYH in lowering the blood glucose level. Moreover， SEQ-2 can maintain the blood glucose-lowering effect after drug withdrawal.

ObjectiveTo investigate the therapeutic effect of sequential administration of Dihuang Baoyuan granules （DHBY， the prescription for consolidating body resistance） and Fuling Yunhua granules （FLYH， the prescription for treating symptoms） on spontaneous type 2 diabetes mellitus （T2DM） in mice. MethodsAccording to the fasting blood glucose （FBG） level， 12-week-old db/db mice were randomized into six groups： model， DHBY （18.02 g·kg^-1）， FLYH （14.80 g·kg^-1）， sequential administration 1 （SEQ-1， DHBY 18.02 g·kg^-1+FLYH 14.80 g·kg^-1）， sequential administration 2 （SEQ-2， FLYH 14.80 g·kg^-1+DHBY 18.02 g·kg^-1）， and dapagliflozin （Dapa， 1.3 mg·kg^-1）. The m/m mice in the same litter were selected as the normal group. The mice were administrated with corresponding drugs by gavage for 8 consecutive weeks. During the 8 weeks of drug administration and 2 weeks after withdrawal， the retinal thickness， FBG， hemoglobin A1c （HbA1c）， and insulin were determined， and histopathological changes of the pancreas， liver， kidney， and retina were observed by hematoxylin-eosin （HE） staining. ResultsCompared with the model group， SEQ-1 for 4 weeks lowered the FBG level （P<0.05）， raised the insulin level， decreased the triglyceride （TG） level （P<0.05）， increased the number of optic ganglion cells and diminished vacuolar degeneration of pancreatic islet and liver. SEQ-2 lowered FBG and HbA1c levels （P<0.05）， rose the insulin level， increased the retinal thickness and the number of optic ganglion cells （P<0.05）， and alleviated vacuolar degeneration of pancreatic islet and liver. Two weeks after drug withdrawal， Dapa tended to increase FBG and HbA1c compared with those at the time of drug withdrawal. However， the levels of FBG and HbA1c in the SEQ-2 group remained decreasing （P<0.05）. ConclusionSEQ-1 and SEQ-2 can lower the blood glucose level and ameliorate diabetic retinopathy， and SEQ-2 outperformed DHBY and FLYH in lowering the blood glucose level. Moreover， SEQ-2 can maintain the blood glucose-lowering effect after drug withdrawal.

BACKGROUND:Our previous research found that Panax notoginseng can repair the morphological structure of bone cells,which has a good application prospect in the treatment of osteoarthritis,but the specific mechanism of Panax notoginseng is still unclear. OBJECTIVE:To identify the main components of Panax notoginseng using ultra-high performance liquid chromatography-Q exactive-mass spectrometry(UHPLC-QE-MS),and to explore the mechanism of Panax notoginseng in the treatment of osteoarthritis by combining network pharmacology,molecular docking and molecular dynamics simulation. METHODS:After identifying the main components of Panax notoginseng by UHPLC-QE-MS technology,the active components were screened by TCMSP database,and the targets of active components were found by TCMSP and Uniprot database.Osteoarthritis targets were screened out through disease databases.After the intersection of drug targets and disease targets,the protein-protein interaction network was constructed by importing STRING database and Cytoscape software,and the"active ingredient-action target"network was constructed to screen key active ingredients.Then the key targets were enriched and analyzed,and the key active components and key targets were verified by molecular docking.Finally,the results with the lowest binding energy were selected for molecular dynamics simulation. RESULTS AND CONCLUSION:A total of 57 active components were identified in the solution of Panax Notoginseng,including 50 intersection targets of components and disease targets,5 key active components(quercetin,ursodeoxycholic acid,kaempferol,naringenin and erythrocyanine),and 5 key targets(interleukin 6,matrix metalloproteinase 9,interleukin 1β,albumin and recombinant chemokine c-motif ligand 2).Gene ontology enriched 642 entries,among which 620 entries represent biological processes,21 entries represent molecular functions,and 1 entry represents cellular components.Kyoto encyclopedia of genes and genomes analysis indicated 63 pathways,mainly including estrogen signaling pathway,interleukin 17 signaling pathway and hyperglycosylation end product-hyperglycosylation end product receptor signaling pathway.Molecular docking showed good binding activity of key active components and key targets.Molecular dynamics simulation indicated that the stable interaction between quercetin and matrix metalloproteinase 9.The composition of Panax notoginseng was comprehensively studied,and the material basis of its efficacy was preliminarily clarified.It was predicted that Panax notoginseng could play an anti-inflammatory,cartilage-protective,and immunomodulatory role in treating osteoarthritis through multiple components,targets,approaches and pathways.

“Chinese Pharmacopoeia” is the legal basis for drug development, production, operation, use and management in China, and the Chinese Pharmacopoeia 2025 Edition is going to be issued and implemented. This article introduces the revision and amendment situations, analyzes the characteristics of the new edition of the Pharmacopoeia and the future development direction of national standards for better understanding and implementation of the latest edition of pharmacopoeia.

The lacrimal gland organoids are innovative in vitro cultured tissue model that mimics the lacrimal gland, retaining its original histological and molecular biological properties. This model can more accurately reproduce the physiological environment of the lacrimal gland, including its ductal system and tear film protein secretion. It offers a new platform for studying the physiopathological basis of the lacrimal gland, establishing disease models, conducting regenerative medicine applications, and performing drug screening. Currently, organoids technology is continuously evolving, with ongoing updates to the methods for in vitro culturing of the lacrimal gland. These advancements gradually address challenges related to cultivation complexity, cost, and time, demonstrating a wide range of application potential. In this paper, we summarize the latest progress in lacrimal gland organoids research both domestically and internationally, exploring the development of lacrimal gland organoids, 3D construction technologies, and their potential for clinical applications, in order to provide new insights for clinical research on lacrimal gland-related diseases and to promote broader application of lacrimal gland organoids in drug development and personalized diagnosis and treatment.

The lacrimal gland organoids are innovative in vitro cultured tissue model that mimics the lacrimal gland, retaining its original histological and molecular biological properties. This model can more accurately reproduce the physiological environment of the lacrimal gland, including its ductal system and tear film protein secretion. It offers a new platform for studying the physiopathological basis of the lacrimal gland, establishing disease models, conducting regenerative medicine applications, and performing drug screening. Currently, organoids technology is continuously evolving, with ongoing updates to the methods for in vitro culturing of the lacrimal gland. These advancements gradually address challenges related to cultivation complexity, cost, and time, demonstrating a wide range of application potential. In this paper, we summarize the latest progress in lacrimal gland organoids research both domestically and internationally, exploring the development of lacrimal gland organoids, 3D construction technologies, and their potential for clinical applications, in order to provide new insights for clinical research on lacrimal gland-related diseases and to promote broader application of lacrimal gland organoids in drug development and personalized diagnosis and treatment.

Glycoprotein non-metastatic melanoma protein B (GPNMB) is a transmembrane glycoprotein that plays an important role in various physiological and pathological processes. In recent years, its role in lung diseases has gradually attracted attention. Studies have found that GPNMB is abnormally expressed in lung diseases and is involved in regulating pathological processes such as inflammatory responses, fibrosis, and tumorigenesis. This article systematically reviews the research progress of GPNMB in common lung diseases such as chronic obstructive pulmonary disease (COPD), pulmonary fibrosis, and lung cancer, and explores its potential as a therapeutic target, providing new insights for the diagnosis and treatment of lung diseases in the future.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.