OBJECTIVES: To investigate the therapeutic mechanism of Thesium chinense Turcz. (TCT) for antibiotic-associated diarrhea (AAD). METHODS: Network pharmacology, KEGG pathway enrichment analysis and molecular docking were used to identify the shared targets and genes of TCT and AAD, the key signaling pathways and the binding between the active components in TCT and the core protein targets. In a Kunming mouse model of AAD established by intragastric administration of lincomycin hydrochloride, the effects of daily gavage of 1% carboxymethyl cellulose sodium or TCT gel solutions at 1.5 g/kg and 3 g/kg (n=10) on body weight and diarrhea were observed. HE staining, ELISA, 16S rRNA sequencing, and Western blotting were used to examine pathologies, expression levels of IL-6 and TNF-α, changes in gut microbiota, and protein expressions of EGFR, p-EGFR, PI3K, p-PI3K, Akt, and p-Akt in the colon tissues of the mice. RESULTS: We identified a total of 66 active components of TCT and 68 core targets including EGFR, STAT3 and PIK3CA. KEGG pathway enrichment analysis suggested that the therapeutic effects of TCT was mediated primarily through the PI3K/Akt signaling pathway. Molecular docking showed that EGFR had the highest binding affinity with coniferin, and the EGFR-coniferin complex maintained a stable conformation at 10 ns, whose stability was also confirmed by Gibbs free energy analysis. In the mouse models of AAD, treatment with TCT significantly improved colonic tissue morphology, decreased colonic levels of TNF-α and IL-6, increased gut microbiota diversity, and modulated the relative abundances of the key genera including Lactobacillus and Bacteroides. TCT treatment also markedly reduced protein expressions of p-EGFR, p-PI3K and p-Akt in the colon tissues of the mice. CONCLUSIONS TCT can alleviate AAD in mice by modulating gut microbiota composition, regulating the EGFR/PI3K/Akt signaling pathway, and reducing TNF‑α and IL-6 expressions.
Animals ; Gastrointestinal Microbiome/drug effects* ; Signal Transduction/drug effects* ; Mice ; ErbB Receptors/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Diarrhea/drug therapy* ; Phosphatidylinositol 3-Kinases/metabolism* ; Anti-Bacterial Agents/adverse effects* ; Drugs, Chinese Herbal/therapeutic use* ; Molecular Docking Simulation

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.

In recent years,organs-on-chips(OOCs)technology has gradually matured and been recognized by regulatory agencies,playing a key role in the construction of pathophysiological models,pharmacodynamic screening,toxicological evaluation and other fields,providing guidance for the research and development of innovative drugs of Chinese materia medica and the clinical application of classic prescriptions.This article reviewed the application progress of OOCs technology in the research of Chinese materia medica from four dimensions:pharmacodynamic and toxicological evaluation,quality control,active component analysis and innovative drug research and development,in order to provide a reference for the precise application of this technology in the field of Chinese materia medica.

Objective To investigate the therapeutic mechanism of Thesium chinense Turcz.(TCT)for antibiotic-associated diarrhea(AAD).Methods Network pharmacology,KEGG pathway enrichment analysis and molecular docking were used to identify the shared targets and genes of TCT and AAD,the key signaling pathways and the binding between the active components in TCT and the core protein targets.In a Kunming mouse model of AAD established by intragastric administration of lincomycin hydrochloride,the effects of daily gavage of 1%carboxymethyl cellulose sodium or TCT gel solutions at 1.5 g/kg and 3 g/kg(n=10)on body weight and diarrhea were observed.HE staining,ELISA,16S rRNA sequencing,and Western blotting were used to examine pathologies,expression levels of IL-6 and TNF-α,changes in gut microbiota,and protein expressions of EGFR,p-EGFR,PI3K,p-PI3K,Akt,and p-Akt in the colon tissues of the mice.Results We identified a total of 66 active components of TCT and 68 core targets including EGFR,STAT3 and PIK3CA.KEGG pathway enrichment analysis suggested that the therapeutic effects of TCT was mediated primarily through the PI3K/Akt signaling pathway.Molecular docking showed that EGFR had the highest binding affinity with coniferin,and the EGFR-coniferin complex maintained a stable conformation at 10 ns,whose stability was also confirmed by Gibbs free energy analysis.In the mouse models of AAD,treatment with TCT significantly improved colonic tissue morphology,decreased colonic levels of TNF-α and IL-6,increased gut microbiota diversity,and modulated the relative abundances of the key genera including Lactobacillus and Bacteroides.TCT treatment also markedly reduced protein expressions of p-EGFR,p-PI3K and p-Akt in the colon tissues of the mice.Conclusion TCT can alleviate AAD in mice by modulating gut microbiota composition,regulating the EGFR/PI3K/Akt signaling pathway,and reducing TNF-α and IL-6 expressions.

Objective To understand the influencing factors of chronic dyslipidemia in T2DM patients who signed a contract for diabetes point system management in Qingpu District, and to provide a basis for comprehensive intervention and prevention and control of dyslipidemia in T2DM patients and to optimize the management strategy of Qingpu District diabetes point system. Methods Among the T2DM patients who signed the diabetes point system from 2017 to 2023, patients with chronic dyslipidemia and normal blood lipids were selected and included in the case group and the control group, respectively. A case-control study was conducted with 1:1 matching by age and gender to analyze the factors influencing dyslipidemia. Results Multifactorial paired logistic regression analysis showed that overweight/obesity and central obesity and smoking in T2DM patients increased the risk of dyslipidemia by 1.93, 2.27, and 2.16 times, respectively. Long-term use of lipid-lowering drugs, duration of diabetes for 5 years or more, regular physical exercise, knowledge of blood lipid status, and married status could reduce the risk of dyslipidemia in T2DM patients (OR values were 0.547, 0.452, 0.685, 0.386 and 0.354, respectively). Current complications (history of stroke, coronary heart disease, and renal insufficiency) were also associated with dyslipidemia (OR=1.802, 95% CI:1.125-2.888). Conclusion The management of diabetes point system in Qingpu District should strengthen the feedback and interpretation of blood lipid monitoring results, improve patients’ health awareness of blood lipid management, and actively take comprehensive management of lifestyle intervention and drug treatment to effectively control blood lipid and reduce the occurrence of related complications.

Objective:To investigate the comorbidity rate of chronic diseases and prevalence of depressive symptoms among the middle-aged and elderly population,and explore the association between comorbidity with-chronic disease and depressive symptoms in middle-aged and older people.Methods:A cross-sectional study was conducted in Shandong Province.A total of 9 535 middle-aged and older people aged 45 years and over were recrui-ted.General information questionnaire,the chronic disease information questionnaire and Patient Health Question-naire were used for face-to-face interview to screen symptoms and collect information.Univariate analysis and mult-ivariate logistic regression analysis were employed to explore the association between comorbidity with chronic dis-ease and depressive symptoms.Results:The prevalence of depressive symptoms among middle-aged and older peo-ple was 8.7％,while among those with comorbid chronic diseases,the prevalence of depressive symptoms was 29.6％.Respectively,demonstrating an increasing trend with the number of chronic diseases(P＜0.001).Multiva-riate analysis revealed that comorbid chronic disease(OR=1.47,95％CI:1.22-1.78)were risk factors of depres-sive symptoms.Conclusion:Middle-aged and older people with multiple chronic conditions are more likely to be di-agnosed with depressive symptoms,and the detection rate of these symptoms increases with the number of comorbid chronic conditions.Therefore,it is recommended to strengthen mental health interventions for those with multiple chronic conditions.

Moyamoya disease (MMD) is a relatively rare chronic occlusive cerebrovascular disorder. In recent years, with advanced imaging technology, MMD detection rate has been increasing annually. Multimodal MRI has played an important role in the screening, diagnosis and treatment evaluation of MMD. This article reviews the research progress of multimodal MRI in MMD from aspects of vascular morphology, hemodynamics, microstructural damage of brain tissues and brain functional networks, aiming to provide references for MMD diagnosis and treatment.

Objective:To identify competency evaluation indicators for psychological hotline professionals,provi-ding scientific reference for their training and assessment.Methods:Through literature review,initial competency evalua-tion indicators were selected,followed by Delphi consultation with sixteen experts across China.The analytic hierarchy process(AHP)was used to assign weights to each indicators.Results:The response rate of expert consultation question-naire was 100％,and the expert authority coefficients for the two rounds of consultation were 0.92 and 0.90 respective-ly.After two rounds of expert consultation,a competency evaluation index system for professional counselors of psycho-logical aids hotline was constructed,including four first-level indexes,nine second-level indexes,and 34 third-level inde-xes.The weight of each index was determined using the AHP.The Kendall coefficient of concordance of all level's inde-xes in the two rounds of consultation were 0.16-0.18 and 0.16-0.25,respectively.Conclusion:The expert consensus developed in this study serves as an reference for evaluating the competency of psychological hotline professionals,sup-porting their training and assessment.

Objective:To identify competency evaluation indicators for psychological hotline professionals,provi-ding scientific reference for their training and assessment.Methods:Through literature review,initial competency evalua-tion indicators were selected,followed by Delphi consultation with sixteen experts across China.The analytic hierarchy process(AHP)was used to assign weights to each indicators.Results:The response rate of expert consultation question-naire was 100％,and the expert authority coefficients for the two rounds of consultation were 0.92 and 0.90 respective-ly.After two rounds of expert consultation,a competency evaluation index system for professional counselors of psycho-logical aids hotline was constructed,including four first-level indexes,nine second-level indexes,and 34 third-level inde-xes.The weight of each index was determined using the AHP.The Kendall coefficient of concordance of all level's inde-xes in the two rounds of consultation were 0.16-0.18 and 0.16-0.25,respectively.Conclusion:The expert consensus developed in this study serves as an reference for evaluating the competency of psychological hotline professionals,sup-porting their training and assessment.

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.