Neonatal diabetes mellitus （NDM） is a rare monogenic disorder primarily caused by insufficient insulin secretion resulting from mutations in the KCNJ11 and ABCC8 genes. Sulfonylureas， represented by glibenclamide， have become the standard therapy for this type of NDM by precisely closing the mutated ATP-sensitive potassium channels in pancreatic β cells， thereby restoring insulin secretion. Clinical studies confirm that sulfonylureas enable over 90% of patients to successfully transition from insulin to oral treatment， achieving long-term stable glycemic control and improving neurological outcomes to a certain extent. In terms of safety， severe hypoglycemia induced by sulfonylureas is relatively rare and gastrointestinal reactions are mild； moreover， sulfonylureas show good long-term tolerability， and have no adverse effects on child growth and development. In the future， by further refining the full-chain management pathway of “rapid genetic diagnosis-early intervention-specialized dosage forms-long-term follow-up”， the clinical application of sulfonylureas is expected to provide NDM patients with an optimized treatment regimen and maximize their health benefits.

OBJECTIVE To investigate the ameliorative effects of Imperatae Rhizoma （RI） extract against doxorubicin （DOX）-induced myocardial injury and its potential molecular mechanisms. METHODS Network pharmacology was employed to screen for target overlap between the predicted core targets of RI’s active components and targets associated with myocardial injury， followed by gene ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses. Based on the network pharmacology results， a DOX-induced myocardial injury mouse model was established using male C57BL/6J mice to observe the effects of RI extract [1 g/（kg·d）] on the survival rate， body weight， and cardiac damage. A DOX-injured HL-1 cardiomyocyte model was established to assess the effects of different mass concentrations of RI extract （50， 100， 200 μg/mL） on mitochondrial membrane potential， adenosine triphosphate （ATP） production， reactive oxygen species （ROS） generation， apoptosis， and expression of the relevant target proteins. The mechanism was validated by transfecting with small interfering RNA of estrogen receptor 1 （ESR1）. RESULTS A total of 58 overlapping targets were screened， which were enriched in biological processes such as hormone response and hypoxia response， as well as pathways including apoptosis， tricarboxylic acid cycle， and pyruvate metabolism. Key target proteins， such as ESR1， were also identified. Animal experiments confirmed that the survival rate of mice in the DOX+RI group was obviously higher than that in the DOX group， with a slower rate of weight loss and improved pathological changes， such as cardiac atrophy and inflammatory cell infiltration， compared to the DOX group. Cell experiments showed that， compared with the DOX group， the DOX+RI groups exhibited significantly increased or upregulated mitochondrial membrane potential， relative ATP levels， and mRNA expressions of isocitrate dehydrogenase 3A （IDH3A）， succinate dehydrogenase A （SDHA） and peroxisome proliferators-activated receptor γ coactivator-1α （PGC-1α）， as well as mRNA and protein expressions of ESR1； conversely， relative ROS levels and apoptosis rates were significantly reduced （ P ＜0.05）. Following ESR1 knockdown， the anti-apoptotic effect of the RI extract on cardiomyocytes was significantly attenuated （ P ＜0.05）. CONCLUSIONS The RI extract may alleviate DOX-induced myocardial injury by activating the ESR1 signaling pathway， improving mitochondrial function， and inhibiting excessive ROS production and cardiomyocyte apoptosis.

Objective To observe the value of multi-sequence magnetic resonance imaging(MRI)radiomics in predicting the efficacy of concurrent chemoradiotherapy(CCRT)in locally advanced cervical squamous cell carcinoma(CSCC)patients.Methods Clinical data of 100 CSCC patients underwent CCRT treatment were selected.In order to better validate the performance of the model,patients were randomly divided into the training set(70 cases)and the validation set(30 cases)in a 7∶3 ratio.According to the efficacy criteria for solid tumors,patients were divided into the complete response(CR)group(n=16)and the partial response(PR)group(n=14).Examination images of cross-sectional DWI,T2WI and enhanced T1WI were collected from all patients before treatment.ITK-SNAP software package combined with three sequences were used to outline ROI,and the open source software PyRadiomics was used to extract image omics features.For MRI omics features,the minimum redundancy maximum correlation(mRMR)algorithm was used to analyze and screen out the first 30 main features,and then the minimum absolute contraction and selection method(Lasso)based on 10-fold cross-validation was used to reduce dimensionality to screen the non-zero coefficient features.According to the weighting coefficient of Lasso-Logistic regression model in the training set,patient omics labels were calculated.Logistic regression analysis was used to construct a prediction model based on DWI,T2WI and T1WI sequence prediction models and multiple sequenomics labels.Receiver operating characteristic(ROC)curves evaluated the predictive value of each omics model for CCRT treatment in patients with locally advanced CSCC.Results There were 38 cases in the CR group and 32 cases in the PR group in the training set.There were 16 cases in the CR group and 14 cases in the PR group in the validation set.There were no significant differences in patient age,FIGO stage,differentiation degree,maximum lesion diameter and menstrual status between the CR group and the PR group in the training and validation sets.A total of 851 imaging features were extracted from the ROI target area.After the first 30 features were retained by mRMR algorithm,3 CR-related features were selected from the 851 imaging omics features of each individual sequence by Lasso algorithm and 10-fold cross-validation.Eight CR related features were selected from 2 553 features after the combination of the three sequences.ROC curve results showed that in the training set and validation set,the AUC of multiple sequences combined to predict the therapeutic effect of CCRT in patients with locally advanced CSCC was 0.971 and 0.946,respectively,which was higher than that of T1WI,T2WI and DWI single sequence prediction(training set Z=2.683,2.046,2.817,P＜0.05;verification set Z=2.075,2.117,2.005,P＜0.05).Conclusion The multi sequence MRI radiomics model has high predictive value for the efficacy of CCRT treatment in locally advanced CSCC patients.

Objective To identify the key biomarkers for diagnosing chronic obstructive pulmonary disease(COPD)complicated with pulmonary arterial hypertension(PAH)using bioinformatics,and validate their clinical significance.Methods High-throughput sequencing data analysis was employed to identify differentially expressed genes(DEGs)in COPD-PAH.Functional enrichment analysis was then conducted to explore the biological functions of these DEGs.Machine learning methods,including least absolute shrinkage and selection operator(LASSO),random forest(RF),and support vector machine-recursive feature elimination(SVM-RFE),were utilized to screen 5 potential biomarkers.Single-cell analysis was performed to reveal the expression patterns of these key genes in macrophages.The clinical significance of these biomarkers was further validated using peripheral blood mononuclear cells(PBMC)data.A mouse model of COPD-PAH was established using hypoxia exposure.Sixteen mice(either sexes,8 weeks old,weighing 20～22 g)were randomly divided into a hypoxia group[O2(10.0±0.5)%,COPD-PAH,n=8]and a normoxia group(COPD,n=8).Immunofluorescence assay was used to label the key biomarkers,and their expression levels were quantified.Results A total of 28 DEGs(|Log2FC|≥2,P<0.05)were identified in COPD-PAH patients.Functional enrichment analysis indicated that DEGs in COPD were primarily associated with major histocompatibility complex(MHC)Ⅱ and cell division,and involved in lysosomes,oxidative phosphorylation,and cell cycle pathways(P<0.05).Machine learning identified 5 potential biomarkers(GRN,KLF4,SHTN1,LRP1,and GPNMB),and subsequent single-cell analysis revealed that these markers exhibited reverse expression patterns during disease progression.A nomogram model constructed based on PBMC data yielded an area under the curve(AUC)of 0.907 in diagnosing COPD-PAH.GRN,KLF4,SHTN1,LRP1 and GPNMB were significantly upregulated in the COPD-PAH group(P<0.05).Conclusion GRN,KLF4,SHTN1,LRP1 and GPNMB are identified as key biomarkers for the prediction and diagnosis of COPD-PAH,which providing new insights for the clinical and treatment of the condition.

Lung cancer is the malignant tumor with the highest morbidity and mortality. The tumor-node-metastasis (TNM) staging has gradually shown its limitations in the accurate prediction of lung cancer, so it is urgent to construct a new clinical predictive model to guide the prevention and treatment of lung cancer. In recent years, as a comprehensive evaluation system based on peripheral immune related parameters, the value of peripheral immunoscore in the construction of predictive model has gradually become prominent. By quantifying the quantity and proportion of immune components in peripheral blood, the score can dynamically reflect the overall immune function and tumor microenvironment characteristics of the body. This paper systematically summarizes the latest research progress of peripheral immunoscore in early diagnosis, drug efficacy prediction, early warning of adverse reactions and prognosis evaluation of lung cancer, aiming to tap its potential clinical application value and provide some ideas and directions for developing new lung cancer-related predictive models.
.
Humans ; Lung Neoplasms/drug therapy* ; Tumor Microenvironment ; Prognosis

Objective To evaluate the diagnostic value of adenosine load-resting gated myocardial perfusion imaging for three-vessel disease in coronary artery disease(CAD)patients using coronary angiography as the gold standard.Methods A retrospective study was conducted,including 318 patients diagnosed with CAD who underwent coronary angiography at Yanan Hospital Affiliated to Kunming Medical University from January 2021 to December 2022.Based on the results of coronary angiography,the 318 CAD patients were divided into a three-vessel disease group(n=166)and a non-three-vessel disease group(single and double vessel disease group,n=152).All the subjects underwent adenosine stress-resting GMPI within two weeks.Adenosine stress-resting GMPI myocardial perfusion parameters(SSS,SRS,SDS),cardiac function parameters(LVEF,LVEDV,LVSV)and left ventricular mechanical contraction synchronization parameters(PSD,PHB)were collected.The diagnostic value of adenosine stress-resting gated myocardial perfusion imaging for three-vessel disease in CAD was explored.Results Among the perfusion parameters,SSS had the highest AUC of 0.781,while sLVEF had the highest AUC of 0.748 among cardiac function parameters,and sPHB had the highest AUC of 0.724 among synchrony parameters.The AUCs of combined parameters were all higher than those of perfusion parameters,cardiac function parameters,and synchrony parameters(P＜0.05).The changes in Δ LVESV and Δ LVEF between the three-vessel disease group and the non-three vessel disease group showed statistical significance(P＜0.05).Conclusion The perfusion,cardiac function and synchronization parameters of adenosine stress-resting gated myocardial perfusion imaging have high diagnostic value for three-vessel coronary heart disease,and the combined detection of the three parameters provides even greater diagnostic value for three-vessel coronary heart disease.

In order to study whether there is a direct interaction between the key variant gene(AP-PV-YN-Mut)protein of atypical porcine pestivirus(APPV)Yunnan strain and the host protein signal sequence receptor subunit 4(SSR4),the physical and chemical properties,spatial structure and subcellular localization of SSR4 protein were analyzed and predicted using online software such as ProtParam,PredictProtein and TMHMM.The recombinant vectors pCMV-Tag4A-SSR4 and pET-GST-Mut were constructed for GST pull-down test in vitro.The recombinant vectors pBiFC-VN173-SSR4 and pBiFC-VC155-Mut were constructed and the bimolecular fluorescence comple-mentary test(BiFC)was performed in cells to verify whether there was direct interaction between APPV-YN-Mut and host protein SSR4 in vitro and in cells.The results showed that SSR4 protein was a hydrophobic stable protein with no transmembrane structure and signal peptide.The second-ary structure was mainly irregular curl,and the tertiary structure was stable,mainly located in the endoplasmic reticulum.GST pull-down and BiFC experiments showed that APPV-YN-Mut interac-ted directly with host protein SSR4 in vitro and in cells.

In order to study whether there is a direct interaction between the key variant gene(AP-PV-YN-Mut)protein of atypical porcine pestivirus(APPV)Yunnan strain and the host protein signal sequence receptor subunit 4(SSR4),the physical and chemical properties,spatial structure and subcellular localization of SSR4 protein were analyzed and predicted using online software such as ProtParam,PredictProtein and TMHMM.The recombinant vectors pCMV-Tag4A-SSR4 and pET-GST-Mut were constructed for GST pull-down test in vitro.The recombinant vectors pBiFC-VN173-SSR4 and pBiFC-VC155-Mut were constructed and the bimolecular fluorescence comple-mentary test(BiFC)was performed in cells to verify whether there was direct interaction between APPV-YN-Mut and host protein SSR4 in vitro and in cells.The results showed that SSR4 protein was a hydrophobic stable protein with no transmembrane structure and signal peptide.The second-ary structure was mainly irregular curl,and the tertiary structure was stable,mainly located in the endoplasmic reticulum.GST pull-down and BiFC experiments showed that APPV-YN-Mut interac-ted directly with host protein SSR4 in vitro and in cells.

Objective To observe the value of multi-sequence magnetic resonance imaging(MRI)radiomics in predicting the efficacy of concurrent chemoradiotherapy(CCRT)in locally advanced cervical squamous cell carcinoma(CSCC)patients.Methods Clinical data of 100 CSCC patients underwent CCRT treatment were selected.In order to better validate the performance of the model,patients were randomly divided into the training set(70 cases)and the validation set(30 cases)in a 7∶3 ratio.According to the efficacy criteria for solid tumors,patients were divided into the complete response(CR)group(n=16)and the partial response(PR)group(n=14).Examination images of cross-sectional DWI,T2WI and enhanced T1WI were collected from all patients before treatment.ITK-SNAP software package combined with three sequences were used to outline ROI,and the open source software PyRadiomics was used to extract image omics features.For MRI omics features,the minimum redundancy maximum correlation(mRMR)algorithm was used to analyze and screen out the first 30 main features,and then the minimum absolute contraction and selection method(Lasso)based on 10-fold cross-validation was used to reduce dimensionality to screen the non-zero coefficient features.According to the weighting coefficient of Lasso-Logistic regression model in the training set,patient omics labels were calculated.Logistic regression analysis was used to construct a prediction model based on DWI,T2WI and T1WI sequence prediction models and multiple sequenomics labels.Receiver operating characteristic(ROC)curves evaluated the predictive value of each omics model for CCRT treatment in patients with locally advanced CSCC.Results There were 38 cases in the CR group and 32 cases in the PR group in the training set.There were 16 cases in the CR group and 14 cases in the PR group in the validation set.There were no significant differences in patient age,FIGO stage,differentiation degree,maximum lesion diameter and menstrual status between the CR group and the PR group in the training and validation sets.A total of 851 imaging features were extracted from the ROI target area.After the first 30 features were retained by mRMR algorithm,3 CR-related features were selected from the 851 imaging omics features of each individual sequence by Lasso algorithm and 10-fold cross-validation.Eight CR related features were selected from 2 553 features after the combination of the three sequences.ROC curve results showed that in the training set and validation set,the AUC of multiple sequences combined to predict the therapeutic effect of CCRT in patients with locally advanced CSCC was 0.971 and 0.946,respectively,which was higher than that of T1WI,T2WI and DWI single sequence prediction(training set Z=2.683,2.046,2.817,P＜0.05;verification set Z=2.075,2.117,2.005,P＜0.05).Conclusion The multi sequence MRI radiomics model has high predictive value for the efficacy of CCRT treatment in locally advanced CSCC patients.