BackgroundPersistent cognitive impairment is prevalent among patients with stable schizophrenia. While serum total bile acid （TBA） level in acute-phase patients are known to be associated with cognitive dysfunction， the relationship between serum TBA and multi-dimensional cognitive functions in stable phase patients remains unclear. ObjectiveTo investigate the correlation between serum TBA level and cognitive function in patients with stable schizophrenia， and to evaluate its predictive value for cognitive impairment， thereby providing a serological biomarker for the timely identification and objective assessment of cognitive dysfunction. MethodsA cross-sectional study was conducted on 137 inpatients with stable schizophrenia at The Fourth People's Hospital of Yancheng from March to December 2024. All participants met the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5）. Cognitive function was evaluated using the Chinese Brief Cognitive Test （C-BCT）， patients were categorized into four groups： normal cognition （n=28）， mild impairment （n=28）， moderate impairment （n=47）， and severe impairment （n=34）. Fasting venous blood samples were collected， and serum TBA level was quantified using an enzymatic cycle assay. Spearman correlation analysis was ultilized to determine the relationship between serum TBA level， overall cognitive function， and specific cognitive domains. Binary Logistic regression model was used （adjusting for covariates such as age， gender， and disease duration） to analyze the impact of serum TBA level on overall and individual cognitive functions. The predictive value of serum TBA level for overall cognitive impairment was evaluated using receiver operating characteristic （ROC） curve. ResultsSerum TBA levels differed significantly among the four groups （H=18.677， P<0.01）. Specifically， serum TBA levels in both the moderate and severe cognitive impairment groups were significantly higher than those in the normal cognitive group （adjusted P<0.01）. Serum TBA level was positively correlated with the severity grading of overall cognitive impairment （r_s=0.354， P<0.05）， and negatively correlated with T-scores on the trail making test （r_s=-0.328， P<0.05）， continuous performance test （r_s=-0.247， P<0.05）， digit span （r_s=-0.265， P<0.05）， and symbol coding （r_s=-0.221， P<0.05）. Binary Logistic regression analysis identified serum TBA level as an independent risk factor for overall cognitive impairment （OR=1.322， 95% CI： 1.021 - 1.713， P=0.034）， with a particularly robust predictive ability for impaired information processing speed （OR=1.325， 95% CI： 1.057 - 1.661， P=0.015）. The area under ROC curve （AUC） for serum TBA level in predicting overall cognitive impairment was 0.738， with a sensitivity of 60.61% and a specificity of 78.64%. ConclusionIn patients with stable schizophrenia， elevated serum TBA levels are associated with worse overall cognitive function， as well as deficits in information processing speed， attention， working memory， and executive function. Serum TBA serves as an independent risk factor and exhibits moderate predictive value for overall cognitive impairmen，particularly in the domain of information processing speed. ［Funded by Yancheng Municipal Health Commission Medical Research Project （number， YK2024141）］

BACKGROUND:Rheumatoid arthritis is a chronic autoimmune disease.Early diagnosis is crucial for preventing disease progression and for effective treatment.Therefore,it is of significance to investigate the diagnostic characteristics and immune cell infiltration of rheumatoid arthritis. OBJECTIVE:Based on the Gene Expression Omnibus(GEO)database,to screen potential diagnostic markers of rheumatoid arthritis using machine learning algorithms and to investigate the relationship between the diagnostic characteristics of rheumatoid arthritis and immune cell infiltration in this pathology. METHODS:The gene expression datasets of synovial tissues related to rheumatoid arthritis were obtained from the GEO database.The data sets were merged using a batch effect removal method.Differential expression analysis and functional correlation analysis of genes were performed using R software.Bioinformatics analysis and three machine learning algorithms were used for the extraction of disease signature genes,and key genes related to rheumatoid arthritis were screened.Furthermore,we analyzed immune cell infiltration on all differentially expressed genes to examine the inflammatory state of rheumatoid arthritis and investigate the correlation between their diagnostic characteristics and infiltrating immune cells. RESULTS AND CONCLUSION:In both rheumatoid arthritis and normal synovial tissues,we identified 179 differentially expressed genes,with 124 genes up-regulated and 55 genes down-regulated.Enrichment analysis revealed a significant correlation between rheumatoid arthritis and immune response.Three machine learning algorithms identified LRRC15 and MICB as potential biomarkers of rheumatoid arthritis.LRRC15(area under the curve=0.964,95%confidence interval:0.924-0.992)and MICB(area under the curve=0.961,95%confidence interval:0.923-0.990)demonstrated strong diagnostic performance on the validation dataset.The infiltration of 13 types of immune cells was altered,with macrophages being the most affected.In rheumatoid arthritis,the majority of proinflammatory pathways in immune cell function were activated.Immunocorrelation analysis revealed that LRRC15 and MICB had the strongest correlation with M1 macrophages.To conclude,this study identified LRRC15 and MICB as potential diagnostic markers for rheumatoid arthritis,with strong diagnostic performance and significant correlation with immune cell infiltration.Machine learning and bioinformatics analysis deepened the understanding of immune infiltration in rheumatoid arthritis and provided new ideas for the diagnosis and treatment of rheumatoid arthritis.

In the diagnosis and treatment of orthopedic diseases, medical imaging plays a crucial role. However, as traditional imaging relies heavily on the personal experience and subjective judgment of clinicians, it may lead to diagnostic bias. In recent years, artificial intelligence (AI) technology has developed rapidly in the field of medical imaging, offering new opportunities for orthopedic surgeons to interpret imaging results. Deep learning, the essence of AI technology, can automatically extract characteristic images from vast amounts of X-ray films, CT scans, MRIs, and other imaging data, for intelligent analysis. This enables early diagnosis and accurate identification of diseases such as occult fractures, bone tumors, and degenerative lesions, significantly improving the rate and accuracy of disease detection. This article reviews the application of AI technology in X-ray film, CT plain scan and 3D reconstruction, MRI, and ultrasound 3D reconstruction in imaging examination based on the latest research progress. In addition, the potential values of AI technology in orthopedic imaging diagnosis and prediction are explored, and the "bottleneck" problems in AI technology are also analyzed in the diagnosis of orthopedic diseases.

Objective To include patients with clinical data matched by propensity scores and to explore the value of 18F-fluorode-oxyglucose(18F-FDG)PET/CT metabolic parameters and radiomics in differentiating benign and malignant solitary pulmonary nod-ule(SPN).Methods A total of 54 patients with SPN(27 benign and 27 malignant)were retrospectively selected,all of them under-went 18F-FDG PET/CT scans.Then the metabolic parameters were analyzed,and the metabolic parameters model was established.After delineating the lesion,imaging features were selected through variance and correlation analysis.The logistic regression was used to build the model,and balance accuracy(bACC)was used to compare the performance of the models.The correlation between meta-bolic parameters and radiomics features was analyzed.Results The maximum standardized uptake value(SUVmax),total lesion uptake(TLU),and coefficient of variation(COV)of malignant were higher than those of benign(P＜0.05).SUVmax and COV had positive predictive value for malignant lesions[odds ratio(OR)＞1,P＜0.05].There was no statistical difference between the performance of the metabolic parameters model and the radiomics model(P＞0.05).There was a strong correlation between radiomics features and metabolic parameters.Conclusion After propensity score matching,metabolic parameters and radiomics show no statistical difference in differentiating benign from malignant SPN.

[Objective]To explore the effects of astragalin(AST)on autophagy and apoptosis of astrocytes in the L4-5 dorsal horn of the spinal cord in mice with inflammatory pain induced by complete Freund's adjuvant(CFA).[Methods]Twenty-four male C57BL/6 mice,aged six months,were randomly assigned to four groups:control group,saline group,CFA model group,and CFA+AST group,six mice in each group.The inflammatory pain model was established by injection of 10 μL CFA into the right lateral malleolus fossa.The saline group were injected with an equal amount of normal saline at the same site.The inflammatory pain mice in CFA+AST group were further treated with AST(60 mg/kg)intraperitoneally once a day for 21 consecutive days.Multiplex immunofluorescence staining was used to detect the coexpression of autophagy-related factors including ATG 12 and Beclin-1,apoptosis-related factors including Cleaved-Caspase3 and Caspase9,and the astrocyte marker such as GFAP in the L4-5 spinal dorsal horn of the mice in each group.Western blot was used to examine the protein expression levels of autophagy-related proteins(ATG12,Beclin-1)and apoptosis-related proteins(Caspase 3,Caspase 9)in the L4-5 spinal dorsal horn of mice.[Results]Immunofluorescent staining showed that in the L4-5 dorsal horn of the spinal cord,the fluorescence intensity of ATG12(P<0.000 1)and Beclin-1(P<0.000 1)was significantly increased,while that of Cleaved-Caspase 3(P<0.001)and Caspase 9(P<0.000 1)was decreased in the CFA+AST group when compared to the CFA model group.Furthermore,AST could inhibit the activation of astrocytes.Western blot further confirmed that AST significantly upregulated the expression of ATG12(P<0.000 1)and Beclin-1(P<0.000 1)in the L4-5 spinal cord of CFA mice,and downregulated the expression of Caspase 3(P<0.01)and Caspase 9(P<0.001).[Conclusions]AST promotes autophagy of astrocytes and inhibits their apoptosis in the L4-5 spinal dorsal horn of CFA mice.

Objective To include patients with clinical data matched by propensity scores and to explore the value of 18F-fluorode-oxyglucose(18F-FDG)PET/CT metabolic parameters and radiomics in differentiating benign and malignant solitary pulmonary nod-ule(SPN).Methods A total of 54 patients with SPN(27 benign and 27 malignant)were retrospectively selected,all of them under-went 18F-FDG PET/CT scans.Then the metabolic parameters were analyzed,and the metabolic parameters model was established.After delineating the lesion,imaging features were selected through variance and correlation analysis.The logistic regression was used to build the model,and balance accuracy(bACC)was used to compare the performance of the models.The correlation between meta-bolic parameters and radiomics features was analyzed.Results The maximum standardized uptake value(SUVmax),total lesion uptake(TLU),and coefficient of variation(COV)of malignant were higher than those of benign(P＜0.05).SUVmax and COV had positive predictive value for malignant lesions[odds ratio(OR)＞1,P＜0.05].There was no statistical difference between the performance of the metabolic parameters model and the radiomics model(P＞0.05).There was a strong correlation between radiomics features and metabolic parameters.Conclusion After propensity score matching,metabolic parameters and radiomics show no statistical difference in differentiating benign from malignant SPN.

In the diagnosis and treatment of orthopedic diseases, medical imaging plays a crucial role. However, as traditional imaging relies heavily on the personal experience and subjective judgment of clinicians, it may lead to diagnostic bias. In recent years, artificial intelligence (AI) technology has developed rapidly in the field of medical imaging, offering new opportunities for orthopedic surgeons to interpret imaging results. Deep learning, the essence of AI technology, can automatically extract characteristic images from vast amounts of X-ray films, CT scans, MRIs, and other imaging data, for intelligent analysis. This enables early diagnosis and accurate identification of diseases such as occult fractures, bone tumors, and degenerative lesions, significantly improving the rate and accuracy of disease detection. This article reviews the application of AI technology in X-ray film, CT plain scan and 3D reconstruction, MRI, and ultrasound 3D reconstruction in imaging examination based on the latest research progress. In addition, the potential values of AI technology in orthopedic imaging diagnosis and prediction are explored, and the "bottleneck" problems in AI technology are also analyzed in the diagnosis of orthopedic diseases.

Objective To create a mouse model of colorectal polyps with Apc-Kras-Cre gene mutations using the tamoxifen induction method.Methods Mice with Apc-Kras-Cre mutations were divided into four groups and injected intraperitoneally with different concentrations and dosages of tamoxifen for different durations,with group 1 injected with low dosage tamoxifen(5 mg/kg)for 1 day,group 2 injected with low dosage tamoxifen(5 mg/kg)for 3 days,group 3 injected with high dosage tamoxifen(50 mg/kg)for 1 day,group 4 injected with high dosage tamoxifen(50 mg/kg)for 3 days.C57BL/6J mice were used as a healthy control group and survival and changes in body weight were observed.All mice were euthanized 4 weeks post-tamoxifen induction and the colon length and number and size of intestinal polyps were observed.Histological changes in the intestinal tissue and polyps were detected by hematoxylin and eosin staining.Results The survival rate of male mice was higher(P＜0.001)and the morbidity rate of male mice was lower compared with female mice(P＜0.05).The survival rate differed significantly among the four groups(P＜0.01).All groups showed significant changes in body weight compared with the healthy control group(P＜0.001).There were also significant differences in weight changes between tamoxifen-induced groups 1 and 2,between groups 2 and 3,and between groups 1 and 4(P＜0.001,P＜0.01,P＜0.05,respectively).There were no significant differences in colon length between any treated group and the healthy control group(P＞0.05),but colon length did differ between tamoxifen-induced groups 1 and 3(P＜0.05).Polyp size varied in each group of tamoxifen-treated mice,with most polyps occuring at the distal end of the colon,while mice in groups 3 and 4 had more and larger polyps.Histopathological examination showed intestinal polyps with uneven and misaligned glandular and epithelial arrangements,a loosely-packed intestinal mucosal barrier,and irregularly-distributed crypts in tamoxifen-induced mice compared with the healthy control group,while mice in tamoxifen-induced groups 3 and 4 showed signs of inflammation and mice in group 4 showed necrosis of cells in some regions.Conclusions Tamoxifen-induced Apc-Kras-Cre model mice were successfully established,with the group 3 induction method being the most suitable.

Objective:To evaluate the safety and effectiveness for the treatment of totally robotic fundoplication.Methods:A retrospective analysis was conducted on the clinical data of 21 patients with gastroesophageal reflux disease (GERD) who underwent unassisted totally robotic fundoplication at the Second Department of General Surgery, Yan'an Hospital Affiliated to Kunming Medical University from Aug 2023 to Jan 2024. The postoperative outcomes were evaluated using SF-36, GERD-Q, and NRS scoring indicators.Results:All 21 patients successfully underwent the surgery. The robotic surgery time was (99±41) minutes, with precise intraoperative anatomy and insignificant blood loss of (1.7±1.4) ml. There were no intraoperative or postoperative complications, and no conversions to open surgery . Postoperative recovery of bowel function was rapid (11.71±3.33) hours, with minimal postoperative pain (NRS score of 1.67±0.48).The postoperative hospital stay was short (3.86±2.90) days, and patient satisfaction was high, SF-36 score of (80.90±1.14);The symptoms of reflux after surgery was significantly reduced.Postoperative GERD-Q score of (4.38±1.69) significantly lower than the preoperative score of (13.90±2.07).Conclusion:Totally robotic fundoplication provides clear view of intraoperative anatomical structures, rapid postoperative recovery, minimal pain, and effective anti-reflux outcomes.

Geobacillus thermoglucosidasius is a kind of Gram-positive facultative anaerobic bacteria. The fast growth rate under high temperature and less susceptibility to microbial contamination enable G. thermoglucosidasius to be a desirable producer of biofuels and high-value-added chemicals for the next-generation industrial biotechnology. However, compared with the classical model strain Escherichia coli, the applications of G. thermoglucosidasius are hampered by its low transformation efficiency. This study aimed at obtaining competent cells with high transformation efficiency through inactivating restriction enzymes, adding cell membrane inhibitors and cell wall weakening agents. The results showed that the electro-transformation efficiency achieved 1.2×10⁴ CFU/(μg DNA) by knocking out four genes encoding restriction enzymes. Adding a certain amount of tween 80, dl-threonine and glycine further increased the competent efficiency about 22.5, 44, and 334 times, respectively. The electro-transformation efficiency was enhanced to 4.6×10⁶ CFU/(μg DNA) under the optimized conditions, laying a foundation for genetic manipulation and metabolic engineering of G. thermoglucosidasius.
Electroporation ; Electroporation Therapies ; Bacillaceae ; Cell Membrane ; Escherichia coli/genetics*