Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.

Objective:To analyze the risk factors of anxiety and depression in elderly bronchiectasis patients and establish a nomogram model.Methods:A total of 90 elderly patients with bronchiectasis who were treated in Sichuan Santai County People′s Hospital from January 2020 to January 2023 were selected as the research subjects. The incidence of anxiety and depression in patients was observed, influencing factors were analyzed by univariate and multivariate Logistic regression analysis, and a predictive risk column chart model was established.Results:In 90 elderly patients, 40 patients experienced anxiety and depression, with an incidence rate of 44.44%. Univariate and multivariate Logistic regression analysis showed that hospitalization frequency, hemoptysis, educational level, arterial partial oxygen pressure (PaO 2), severity of the condition, copper green colonization, length of hospital stay, monthly family income, and family happiness index were risk factors for anxiety and depression in elderly patients with bronchiectasis ( P<0.05). A column line prediction model for anxiety and depression in elderly patients with bronchiectasis based on 9 independent risk factors was drawn. The calibration verification result show that the predicted values of the model had a high degree of fit with the measured values. The internal validation result of bootstrap showed that the C-index was 0.724 (95% CI 0.670 -0.779). Conclusions:The occurrence of anxiety and depression in elderly patients with bronchiectasis can be influenced by various factors. The model has high predictive performance.

Concepts are the cornerstone of the development of disciplines. The concepts of TCM present that a superior concept contains more subordinate concepts. The superordinate concepts are often used to refer to different subordinate concepts, which can refer to both superior concepts themselves and non-specific subordinate concepts, that is, the characteristics of subordinate coverage and indefinite reference, which cause confusion in concept meaning, concept relationships, reasoning logic, and other problems. Nowadays, the TCM scholars pay little attention to this characteristic. Therefore, this article analyzed this characteristic, discussed its impact on the inheritance and development of TCM, and proposed that starting from the anchoring of concepts and entities to clarify the connotation of concepts, looking forward to provide new ideas for the definition of the concepts of TCM and the development of the discipline.

Objective:To analyze the risk factors of anxiety and depression in elderly bronchiectasis patients and establish a nomogram model.Methods:A total of 90 elderly patients with bronchiectasis who were treated in Sichuan Santai County People′s Hospital from January 2020 to January 2023 were selected as the research subjects. The incidence of anxiety and depression in patients was observed, influencing factors were analyzed by univariate and multivariate Logistic regression analysis, and a predictive risk column chart model was established.Results:In 90 elderly patients, 40 patients experienced anxiety and depression, with an incidence rate of 44.44%. Univariate and multivariate Logistic regression analysis showed that hospitalization frequency, hemoptysis, educational level, arterial partial oxygen pressure (PaO 2), severity of the condition, copper green colonization, length of hospital stay, monthly family income, and family happiness index were risk factors for anxiety and depression in elderly patients with bronchiectasis ( P<0.05). A column line prediction model for anxiety and depression in elderly patients with bronchiectasis based on 9 independent risk factors was drawn. The calibration verification result show that the predicted values of the model had a high degree of fit with the measured values. The internal validation result of bootstrap showed that the C-index was 0.724 (95% CI 0.670 -0.779). Conclusions:The occurrence of anxiety and depression in elderly patients with bronchiectasis can be influenced by various factors. The model has high predictive performance.

Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.

Objective : To investigate the drug resistance and pathogenicity of six clinical isolates of Klebsiella pneu- moniae (Kp) ，and to provide a basis for prevention and treatment of Kp infection． Methods : The six strains from different hospitals were isolated ，cultured ，and identified by species-specific gene khe． Their whole genome se- quences (WGS) were obtained using next-generation sequencing technology (NGS) ．Based on the WGS，the cap- sular serotypes，sequence types (ST) and drug-resistance genes of six strains were identified．The capsular sero- type genes and virulence genes were validated or identified using PCR. Broth microdilution tests were conducted to validate their drug susceptibility，and mice were challenged with Kp aerosols by MicroSprayer aerosolizer to evaluate their pathogenicity. Results : The six strains were all serotype K2 but belonged to four ST types ( ST14 ，ST65， ST700，and ST86) ，and collectively carried six virulence genes and 23 drug-resistance genes．All the six strains were resistant to ampicillin，but only one strain was multidrug-resistant．Four strains exhibited high mucoid charac- teristics．Five strains could cause mortality in mice，which were preliminary identified as high virulence strains. Conclusion For the six Kp clinical isolates from different sources，only one strain named NY 13294 is both multi- drug-resistant and highly virulent，and other four highly virulent strains are resistant to one or two types of antibiot- ics.

Objective:To predict and analyze the T/B combined epitope of EM10 protein in Echinococcus multilocularis, and identify the expressed products of the biosynthetic EM10 multi epitopes. Methods:The gene-related information of EM10 protein was obtained through NCBI GenBank public database. Bioinformatics technique was used to predict and analyze the T/B binding epitopes of EM10 protein. The prokaryotic expession recombinant plasmid pET30a-EM10 (epitope) was synthesized, and transformed into host bacteria Ecoli. BL21 (DE3). The expression of EM10 recombinant multi-epitope protein was identified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Western blotting after induced expression by isopropyl thiogalactopyranoside (IPTG). Results:The total length of EM10 gene was 1 759 bp (GenBank registration number: U05573), and its protein amino acid sequence (GenBank registration number: AAA50580.1) was 559 amino acids. By using Phyre software for homology modeling, the tertiary structure of EM10 protein was obtained, and the T/B combined epitope of EM10 protein was successfully predicted, the dominant epitope was located at 46 - 61, 133 - 183, 239 - 255 and 442 - 475 amino acid sites. The (GGGGS)n linker sequence was used to connect the epitopes to form an EM10 recombinant multi-epitope protein with a total of 206 amino acid. The size of the DNA fragment was 618 bp and the relative molecular weight of the protein was 22.66 × 10 3. The prokaryotic expession recombinant plasmid was validated by enzyme digestion, the results showed that the plasmid size was between 5 000 and 6 000 bp, which was consistent with the length of the constructed plasmid (5 854 bp). SDS-PAGE showed that the target protein was expressed in the supernatant induced by IPTG at 37 ℃ and the effect was the best. The relative molecular weight of the protein was 22.66 × 10 3 by Western blotting, which was consistent with the constructed plasmid. Conclusions:The combined epitope of EM10 T/B is successfully designed and predicted using bioinformatics technology. A prokaryotic expression recombinant plasmid is constructed, the expression of EM10 recombinant multi-epitope protein is verified through experiments, providing an experimental basis for the construction of an EM10 dominant epitope diagnostic kit.

Diabetes mellitus (DM) is a major metabolic disease endangering global health, with diabetic nephropathy (DN) as a primary complication lacking curative therapy. Sporoderm-broken spores of Ganoderma lucidum (GLP), an herbal medicine, has been used for the treatment of metabolic disorders. In this study, DN was induced in Sprague-Dawley rats using streptozotocin (STZ) and a high-fat diet (HFD), and the protective mechanisms of GLP were investigated through transcriptomic, metabolomic, and network pharmacology (NP) analyses. Our results demonstrated that GLP intervention ameliorated renal damage and inflammation levels in DN rats. Integrative metabolomic and transcriptomic analysis revealed that GLP treatment modulated glucose and cellular energy metabolisms by regulating relevant genes. GLP significantly suppressed the inflammations by impacting glucose and energy metabolism-related gene expression (Igfbp1 and Angptl4) and enhanced metabolic biomarkers of 4-Aminocatechol. In addition, NP analysis further indicated that GLP may efficiently alleviate DN via immune-related pathways. In conclusion, this study provides supportive evidence of the anti-inflammatory effects of GLP supplements, highlighting their potential for promising clinical applications in treating DN.

Urate oxidase (Uox) plays a pivotal role in uric acid (UA) degradation, and it has been applied in controlling serum UA level in clinical treatment of hyperuricemia (HUA). However, because Uox is a heterogenous protein to the human body, the immune rejections typically occur after intravenous administration, which greatly hampers the application of Uox-based agents. In this study, we used Lactococcus lactis NZ9000, a food-grade bacterium, as a host to express exogenous Uox genes, to generate the Uox-expressing engineered strains to treat HUA. Aspergillus flavus-derived Uox (aUox) and the "resurrected" human-derived Uox (hUox) were cloned into vector and expressed in NZ9000, to generate engineered strains, respectively. The engineered NZ9000 strains were confirmed to express Uox and showed UA-lowering activity in a time-dependent manner in vitro. Next, in an HUA mice model established by oral gavage of yeast paste, the UA levels were increased by 85.4% and 106.2% at day 7 and day 14. By contrast, in mice fed with NZ9000-aUox, the UA levels were increased by 39.5% and 48.3% while in mice fed with NZ9000-hUox were increased by 57.0% and 82.9%, suggesting a UA-lowering activity of both engineered strains. Furthermore, compared with allopurinol, the first-line agent for HUA treatment, mice fed with NZ9000-aUox exhibited comparable liver safety but better kidney safety than allopurinol, indicating that the use of engineered NZ9000 strains not only alleviated kidney injury caused by HUA, but could also avoided the risk of kidney injury elicited by using allopurinol. Collectively, our study offers an effective and safe therapeutic approach for HUA long-term treatment and controlling.
Animals ; Lactococcus lactis/metabolism* ; Urate Oxidase/genetics* ; Mice ; Uric Acid/blood* ; Hyperuricemia ; Humans ; Administration, Oral ; Aspergillus flavus/genetics* ; Male

Diabetes mellitus(DM)is a major metabolic disease endangering global health,with diabetic ne-phropathy(DN)as a primary complication lacking curative therapy.Sporoderm-broken spores of Ganoderma lucidum(GLP),an herbal medicine,has been used for the treatment of metabolic disorders.In this study,DN was induced in Sprague-Dawley rats using streptozotocin(STZ)and a high-fat diet(HFD),and the protective mechanisms of GLP were investigated through transcriptomic,metabolomic,and network pharmacology(NP)analyses.0ur results demonstrated that GLP intervention ameliorated renal damage and inflammation levels in DN rats.Integrative metabolomic and transcriptomic analysis revealed that GLP treatment modulated glucose and cellular energy metabolisms by regulating relevant genes.GLP significantly suppressed the inflammations by impacting glucose and energy metabolism-related gene expression(Igfbp1 and Angptl4)and enhanced metabolic biomarkers of 4-Aminocatechol.In addition,NP analysis further indicated that GLP may efficiently alleviate DN via immune-related pathways.In conclusion,this study provides supportive evidence of the anti-inflammatory effects of GLP supplements,highlighting their potential for promising clinical applications in treating DN.