BACKGROUND:Glucose metabolism plays a crucial role in maintaining the normal physiological function of the body.Glucose metabolism disorder can lead to a range of health problems.At present,the molecular mechanism of glucose metabolism and potential gene targets in osteoarthritis need to be further studied.OBJECTIVE:To analyze the genes related to glucose metabolism in osteoarthritis by bioinformatics methods,and to verify them by cell experiments in vitro,so as to provide new ideas for prevention and treatment of osteoarthritis from the perspective of glucose metabolism.METHODS:Differentially expressed genes and glucose metabolism related genes were screened out from GEO database and GeneCards database.The genes related to both osteoarthritis and glucose metabolism were obtained.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were used to screen the functions and pathways of these genes.To further investigate the interactions between these genes,a protein-protein interaction network was constructed and computational methods using Cytoscape software were utilized to identify key genes(Hub genes)for osteoarthritis glucose metabolism.In addition,CIBERSORT algorithm was used to analyze immune cell infiltration in GSE98918 data set.Finally,the expression of Hub gene was verified by cell experiment in vitro.RESULTS AND CONCLUSION:A total of 134 osteoarthritis glucose metabolism-related genes were obtained.GO enrichment analysis showed that GO was mainly involved in the reaction of toxic substances,the positive regulation of inflammatory reaction,the reaction of lipopolysaccharide and so on.KEGG enrichment analysis showed that it was closely related to PI3K-Akt signaling pathway,interleukin-17 signaling pathway,and AGE-RAGE signaling pathway in diabetic complications.Macrophages,monocytes,resting natural killer cells,regulatory T cells,and CD8+T cells were the main infiltrating cells obtained by immune infiltration analysis.In vitro cell experiments showed that the expression of Hub genes SERPINF1,TAC1,GLUL,APOE,and TMEM176A in the experimental group was significantly different from that in the control group.The mRNA expression of HLA-DRA was not statistically significant.The results show that SERPINF1,TAC1,Glul,APOE,and TMEM176A may be the key genes of glucose metabolism in osteoarthritis,and may be potential new targets for the prevention and treatment of osteoarthritis.

The biological activity of chitinase in degrading chitin has garnered extensive attention,particularly for its potential applications in biological control.This study utilized four spore-form-ing Bacillus strains isolated from Dermacentor nuttalli ticks collected in the Hulunbuir region.Traditional bacterial culture methods were employed for isolation and identification,followed by 16S rRNA sequencing and phylogenetic analysis of the purified cultures.chitin-hydrolyzing strains were screened using colloidal chitin plates,and specific chitinase genes were detected via PCR.Fer-mentation was conducted at 37.0 ℃ for 4 d,and the supernatants were subjected to enzyme activity analysis using the DNS method.Four Gram-positive Bacillus strains were successfully isolated from tick tissue samples,they were identified as B.proteolyticus,B.paramycoides,B.thuringien-sis,and B.cereus,and renamed IMH/B-1,IMH/P-1,IMH/T-1,and IMH/C-1,respectively.PCR a-nalysis detected chitinase genes in B.proteolyticus and B.thuringiensis,while B.cereus and B.pa-ramycoides lacked these genes.However,three strains B.proteolyticus,B.thuringiensis,and B.ce-reus demonstrated significant(P＜0.01)chitin degradation activity on colloidal chitin.Enzyme ac-tivity assays revealed that chitinase activity ranged from 1.292 to 2.032 U/mL,with B.proteolytic-us exhibiting the highest activity 2.032 U/mL,followed by B.cereus 1.496 U/mL and B.thuring-iensis 1.324 U/mL.This study provides a foundation for further research and application of chiti-nase-producing Bacillus strains.

BACKGROUND:Glucose metabolism plays a crucial role in maintaining the normal physiological function of the body.Glucose metabolism disorder can lead to a range of health problems.At present,the molecular mechanism of glucose metabolism and potential gene targets in osteoarthritis need to be further studied.OBJECTIVE:To analyze the genes related to glucose metabolism in osteoarthritis by bioinformatics methods,and to verify them by cell experiments in vitro,so as to provide new ideas for prevention and treatment of osteoarthritis from the perspective of glucose metabolism.METHODS:Differentially expressed genes and glucose metabolism related genes were screened out from GEO database and GeneCards database.The genes related to both osteoarthritis and glucose metabolism were obtained.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were used to screen the functions and pathways of these genes.To further investigate the interactions between these genes,a protein-protein interaction network was constructed and computational methods using Cytoscape software were utilized to identify key genes(Hub genes)for osteoarthritis glucose metabolism.In addition,CIBERSORT algorithm was used to analyze immune cell infiltration in GSE98918 data set.Finally,the expression of Hub gene was verified by cell experiment in vitro.RESULTS AND CONCLUSION:A total of 134 osteoarthritis glucose metabolism-related genes were obtained.GO enrichment analysis showed that GO was mainly involved in the reaction of toxic substances,the positive regulation of inflammatory reaction,the reaction of lipopolysaccharide and so on.KEGG enrichment analysis showed that it was closely related to PI3K-Akt signaling pathway,interleukin-17 signaling pathway,and AGE-RAGE signaling pathway in diabetic complications.Macrophages,monocytes,resting natural killer cells,regulatory T cells,and CD8+T cells were the main infiltrating cells obtained by immune infiltration analysis.In vitro cell experiments showed that the expression of Hub genes SERPINF1,TAC1,GLUL,APOE,and TMEM176A in the experimental group was significantly different from that in the control group.The mRNA expression of HLA-DRA was not statistically significant.The results show that SERPINF1,TAC1,Glul,APOE,and TMEM176A may be the key genes of glucose metabolism in osteoarthritis,and may be potential new targets for the prevention and treatment of osteoarthritis.

BACKGROUND:Endoplasmic reticulum stress is closely associated with the occurrence and progression of osteoarthritis,but the key genes and regulatory mechanisms remain unclear.OBJECTIVE:Utilizing bioinformatics to identify crucial endoplasmic reticulum stress-related genes in osteoarthritis,followed by experimental validation in cell models,aiming to offer new strategies for the prevention and treatment of osteoarthritis from the perspective of endoplasmic reticulum stress.METHODS:Osteoarthritis-related dataset GSE55235 was downloaded from the GEO database.Differential genes in synovial tissue of osteoarthritis were obtained through WGCNA machine learning algorithm and intersected with endoplasmic reticulum stress-related genes from the GeneCard database to acquire differential endoplasmic reticulum stress-related genes in osteoarthritis(ERSDEGs).These genes underwent GO and KEGG enrichment analysis,construction of a protein-protein interaction network,and validation of diagnostic efficiency in external datasets.Human primary synovioblast model of osteoarthritis was constructed.The control group was not treated,and the experimental group received 20 ng/mL lipopolysaccharide to simulate osteoarthritic synoviocyte modeling.Real-time fluorescence quantitative PCR was then performed to validate the expression level of each differential gene followed by immune infiltration analysis.RESULTS AND CONCLUSION:A total of 27 key endoplasmic reticulum stress-related genes in osteoarthritis were identified.GO enrichment analysis revealed that these genes were mainly enriched in collagen metabolism,chemokine,antigen binding,and immunoglobulin receptor binding processes.KEGG analysis indicated that they were mainly enriched in pathways such as rheumatoid arthritis and relaxin signaling pathways.The protein-protein interaction network was constructed,and the top five genes with the highest scores were identified using the Degree algorithm in Cytoscape software,including matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,collagen type 1 alpha 1,and chemokine C-X-C motif ligand 12.Immune infiltration analysis showed that immune cells were mainly distributed in M2 macrophages,chemokine C-X-C motif ligand 12 showed a significant positive correlation with resting mast cells(r=0.70,P＜0.001)and a significant negative correlation with resting memory CD4+T cells(r=-0.72,P＜0.001).Matrix metallopeptidase 9 showed a significant positive correlation with MO macrophages(r=0.94,P＜0.001).Collagen type 1 alpha 1 was significantly positively correlated with resting NK cells(r=0.77,P＜0.001)and MO macrophages(r=0.76,P＜0.001).Receiver operator characteristic curve analysis in external datasets GSE77298 and GSE1919 showed that the five key genes had good disease prediction value.In vitro cell experiments demonstrated significant differences in the expression levels of matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,and chemokine C-X-C motif ligand 12 in the osteoarthritic cell model compared to the control group.These results showed that the key genes related to endoplasmic reticulum stress in osteoarthritis,including matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,and chemokine C-X-C motif ligand 12,influence the occurrence and development of osteoarthritis through the links of collagen degradation and immune regulation,which are expected to provide new insights into the targeted treatment of osteoarthritis.

Objective To examine the expression levels of inflammatory factors,including IL-6,IL-10,MMP-9,IL-17A,and LDH,in the cerebrospinal fluid(CSF)of patients with spontaneous intracerebral hemor-rhage(sICH),and to investigate their associations with disease severity and clinical outcomes.Methods A total of 168 patients with sICH admitted to Tangshan GongRen Hospital between January 2023 and January 2025 were prospectively enrolled as the study group,while 30 non-sICH patients who underwent lumbar puncture during the same period served as the control group.Levels of inflammatory factors in CSF were compared between the two groups.Spearman's rank correlation analysis was performed to assess the association between inflammatory factor levels and clinical severity in sICH patients.Binary logistic regression analysis was conducted to identify independent predictors of sICH prognosis.Receiver operating characteristic(ROC)curve analysis was employed to evaluate the prognostic value of these inflammatory factors in sICH.Results The levels of IL-6,IL-10,MMP-9,IL-17A,and LDH in the CSF of patients with sICH were significantly higher than those in non-sICH patients(all P<0.05).Furthermore,among sICH patients,these biomarker levels exhibited a graded increase according to disease severity:severe>moderate>mild(all P<0.05).Spearman correlation analysis revealed significant positive correlations between CSF levels of IL-6,IL-10,MMP-9,IL-17A,and LDH and the NIHSS scores,with correlation coefficients(r)of 0.686,0.553,0.685,0.593,and 0.695,respectively(all P<0.05).When comparing the prognoses of sICH patients,hematoma size,NIHSS score,and CSF levels of IL-6,IL-10,MMP-9,IL-17A,and LDH were significantly higher in the deceased group than in the survival group(P<0.05),whereas ApoA1 levels were lower in the deceased group(P<0.05).Logistic regression analysis revealed that hematoma size,NIHSS score,and elevated CSF levels of IL-6,IL-10,MMP-9,IL-17A,and LDH were independent risk factors for mortality in sICH patients(P<0.05).ROC curve analysis showed that the AUC values for CSF IL-6,IL-10,MMP-9,IL-17A,and LDH in predicting sICH prognosis were 0.794,0.754,0.670,0.717,and 0.683,respectively.Notably,the combination of CSF inflammatory markers with hematoma size and NIHSS score yielded an AUC of 0.993,demonstrating significantly greater predictive accuracy than CSF inflammatory markers alone(P<0.05).Conclusions The levels of inflammatory factors in the CSF,including IL-6,IL-10,MMP-9,IL-17A,and LDH,were elevated in patients with sICH and positively correlated with disease severity.Combining CSF inflammatory markers with the NIHSS score and hematoma size improved the predictive accuracy for sICH prognosis.

The biological activity of chitinase in degrading chitin has garnered extensive attention,particularly for its potential applications in biological control.This study utilized four spore-form-ing Bacillus strains isolated from Dermacentor nuttalli ticks collected in the Hulunbuir region.Traditional bacterial culture methods were employed for isolation and identification,followed by 16S rRNA sequencing and phylogenetic analysis of the purified cultures.chitin-hydrolyzing strains were screened using colloidal chitin plates,and specific chitinase genes were detected via PCR.Fer-mentation was conducted at 37.0 ℃ for 4 d,and the supernatants were subjected to enzyme activity analysis using the DNS method.Four Gram-positive Bacillus strains were successfully isolated from tick tissue samples,they were identified as B.proteolyticus,B.paramycoides,B.thuringien-sis,and B.cereus,and renamed IMH/B-1,IMH/P-1,IMH/T-1,and IMH/C-1,respectively.PCR a-nalysis detected chitinase genes in B.proteolyticus and B.thuringiensis,while B.cereus and B.pa-ramycoides lacked these genes.However,three strains B.proteolyticus,B.thuringiensis,and B.ce-reus demonstrated significant(P＜0.01)chitin degradation activity on colloidal chitin.Enzyme ac-tivity assays revealed that chitinase activity ranged from 1.292 to 2.032 U/mL,with B.proteolytic-us exhibiting the highest activity 2.032 U/mL,followed by B.cereus 1.496 U/mL and B.thuring-iensis 1.324 U/mL.This study provides a foundation for further research and application of chiti-nase-producing Bacillus strains.

The applications of nuclear technology in industries, medicine, and other fields have increased the risk of radiation injury. Although some small-molecule drugs for radiation injury treatment have been applied clinically or are in preclinical research, their therapeutic efficacy is significantly limited by short circulation time and rapid metabolism. Nanomaterials have attracted growing attention with their outstanding bioactivity, chemical stability, tissue compatibility, and targeted delivery capabilities, therefore having the promise of offering the potential solutions to the limitations of current small-molecule drugs. However, their biosafety and clinical efficacy require further investigation. This review summarizes the design strategies and classifications of nanomaterials for radiation injury treatment, analyzes current research progress in their therapeutic applications, and introduces nanomaterial-based approaches for enhancing the elimination of internal radionuclide contamination. Finally, the challenges and future prospects of nanomaterials in radiation injury treatment are discussed. This review aims to provide researchers with a comprehensive understanding of recent advances in nanomaterial-based radiation injury therapeutics, thereby promoting further development in this field.

Objective:To investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.

AIM: To identify the primary active components and underlying mechanisms of Yijing Decoction(YJD)in treating early diabetic retinopathy(DR)based on liquid chromatography-mass spectrometry and network pharmacology.METHODS: Active components of YJD were characterized through LC-MS. Components with optimal ADME(absorption, distribution, metabolism, excretion)properties were selected as key bioactive candidates. Network pharmacology approaches were employed to predict YJD-DR therapeutic targets. Protein-protein interaction(PPI)networks, gene ontology(GO)enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were subsequently conducted to predict core targets and networks. Critical targets and pathways were experimentally validated through Western blot.RESULTS: Ten core therapeutic targets were identified, including TNF, Alb, EGFR, STAT3, PTGS2, ESR1, PPAR, MMP9, TLR4, and MAPK. YJD was related to cancer-related signaling, fluid shear stress and atherosclerosis, and neurodegenerative diseases, encompassing key biological processes such as inflammatory response regulation, programmed cell death activation, and enhanced cell migration. Furthermore, Western blot analysis confirmed that YJD significantly inhibited high glucose-induced phosphorylation of STAT3(P-STAT3/STAT3)and ERK(P-ERK/ERK)in rat retinal microvascular endothelial cells.CONCLUSION: This study revealed YJD's pharmacodynamical basis and its multi-component, multi-target, and multi-paths pharmacology. YJD exerts therapeutic effects on DR by coordinately regulating critical signaling pathways and alleviating intraocular inflammation, thus preserving retinal vascular endothelial cells, maintaining blood-retinal barrier integrity, and facilitating retinal neurovascular repair.

Objective To establish an individualized nomogram model and evaluate its efficacy to provide a possible evaluation basis for the prognosis of lower third and abdominal part of oesophageal adenocarcinoma (EAC). Methods Lower third and abdominal part of EAC patients from 2010 to 2015 were chosen from the SEER Research Plus Database (17 Regs, 2022nov sub). The patients were randomly allocated to the training cohort and the internal validation cohort with a ratio of 7∶3 using bootstrap resampling. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) in EAC patients, which would be elected to construct the nomogram prediction model. C-index, calibration curve and receiver operating characteristic (ROC) curve were performed to evaluate its efficacy. Finally, the efficacy to evaluate the OS of EAC patients was compared between the nomogram prediction model and TNM staging system. Results In total, 3945 patients with lower third and abdominal part of EAC were enrolled, including 3475 males and 470 females with a median age of 65 (57-72) years. The 2761 patients were allocated to the training cohort and the remaining 1184 patients to the internal validation cohort. In the training and the internal validation cohorts, the C-index of the nomogram model was 0.705 and 0.713, respectively. Meanwhile, the calibration curve also suggested that the nomogram model had a strong capability of predicting 1-, 3-, and 5-year OS rates of EAC patients. The nomogram also had a higher efficacy than the TNM staging system in predicting 1-, 3-, and 5-year OS rates of EAC patients. Conclusion This nomogram prediction model has a high efficiency for predicting OS in the patients with lower third and abdominal part of EAC, which is higher than that of the current TNM staging system.