OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

OBJECTIVES: Osteoarthritis (OA) is one of the most common chronic degenerative diseases, with chondrocyte apoptosis and extracellular matrix (ECM) degradation as the major pathological changes. The mechanical stimulation can attenuate chondrocyte apoptosis and promote ECM synthesis, but the underlying molecular mechanisms remain unclear. This study aims to investigate the role of primary cilia (PC) in mediating the effects of mechanical stimulation on OA progression. METHODS: In vivo, conditional knockout mice lacking intraflagellar transport 88 (IFT88^flox/flox IFT88 knockout; i.e., primary cilia-deficient mice) were generated, with wild-type mice as controls. OA models were established via anterior cruciate ligament transection combined with destabilization of the medial meniscus, followed by treadmill exercise intervention. OA progression was evaluated by hematoxylin-eosin staining, safranin O-fast green staining, and immunohistochemistry; apoptosis was assessed by TUNEL staining; and limb function by rotarod testing. In vitro, primary articular chondrocytes were isolated from mice and transfected with lentiviral vectors to suppress IFT88 expression, thereby constructing a primary cilia-deficient cell model. Interleukin-1β (IL-1β) was used to induce an inflammatory environment, while cyclic tensile strain (CTS) was applied via a cell stretcher to mimic mechanical loading on chondrocytes. Immunofluorescence and Western blotting were used to detect the protein expression levels of type II collagen α1 chain (COL2A1), primary cilia, IFT88, and caspase-12; reverse transcription polymerase chain reaction was performed to assess COL2A1 mRNA levels; and flow cytometry was used to evaluate apoptosis. RESULTS: In vivo, treadmill exercise significantly reduced Osteoarthritis Research Society International (OARSI) scores and apoptotic cell rates, and improved balance ability in wild-type OA mice, whereas IFT88-deficient OA mice showed no significant improvement. In vitro, CTS inhibited IL-1β-induced ECM degradation and apoptosis in primary chondrocytes; however, this protective effect was abolished in cells with suppressed primary cilia expression. CONCLUSIONS Mechanical stimulation delays OA progression by mediating signal transduction through primary cilia, thereby inhibiting cartilage degeneration and chondrocyte apoptosis.
Animals ; Chondrocytes/cytology* ; Apoptosis/physiology* ; Mice ; Cilia/metabolism* ; Osteoarthritis/pathology* ; Extracellular Matrix/metabolism* ; Mice, Knockout ; Disease Progression ; Interleukin-1beta ; Male ; Cells, Cultured

Objective To investigate the expression levels of serum tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein β(14-3-3β)and clara's cell secretory protein 16(CC16)in patients with chronic obstructive pulmonary disease(COPD)complicated by respiratory failure,and their relationship with prognosis.Methods A total of 232 patients with COPD complicated with respiratory failure admitted to Yantian Hospital of Southern University of Science and Technology from April 2020 to October 2023 were enrolled in the COPD complicated with respiratory failure group.According to the severity of the disease,they were divided into mild group(n=67),moderate group(n=73)and severe group(n=92).According to the 28-day prognosis,they were divided into death group(n=73)and survival group(n=159).In addition,80 patients with simple COPD(COPD group)and 80 healthy subjects(control group)were selected at the same time.Enzyme-linked immunosorbent assay(ELISA)was used to detect the expression of serum 14-3-3β and CC16.Multivariate Logistic regression analysis was used to analyze the factors of death in patients with COPD complicated with respiratory failure.The receiver operating characteristic(ROC)curve was used to analyze the predictive value of serum 14-3-3β and CC16 expression on the death of patients with COPD complicated with respiratory failure.Results The expression of serum 14-3-3β in COPD complicated with respiratory failure group was higher than that in COPD group and control group(U=3.894,11.417),the expression of CC16 was lower than that in COPD group and control group(t=5.845,14.306),and the differences were statistically significant(all P<0.05),respectively.The expression of serum 14-3-3β in severe group was higher than that in moderate group and mild group(U=5.179,8.234),the expression of CC16 was lower than that of moderate group and mild group(t=4.090,9.281),and the differences were statistically significant(all P<0.05),respectively.The 28-day mortality rate of 232 COPD patients with respiratory failure was 31.47%(73/232).The expression of serum 14-3-3β in the death group was higher than that in the survival group,and the expression of CC16 was lower than that in the survival group,the differences were statistically significant(U/t=6.790,8.265,all P<0.05).The age of the death group was older than that of the survival group,the degree of airflow limitation and the number of acute exacerbations within 1 year were higher than those of the survival group,and the differences were statistically significant(t/χ2/U=3.895,7.202,3.360,all P<0.05).Age,severe airflow limitation,extremely severe airflow limitation,and the number of acute exacerbations within 1 year,elevated 14-3-3β were independent risk factors for death in patients with COPD complicated with respiratory failure(Wald χ2=3.914～22.668,all P<0.05),and elevated CC16 was an independent protective factor(Wald χ2=23.675,P<0.05).The area under the curve(AUC)of serum 14-3-3β combined and CC16 expression in predicting the death of patients with COPD complicated with respiratory failure which was greater than that of serum 14-3-3β and CC16 expression alone,the differences were statistically significant(Z=3.995,3.813,all P<0.01).Conclusion The increase of serum 14-3-3β expression and the decrease of CC16 expression in patients with COPD complicated by respiratory failure are closely related to the aggravation of the disease and poor prognosis.The combination of serum 14-3-3β and CC16 expression is of high value in predicting the death of patients with COPD complicated with respiratory failure.

Objective To investigate the expression levels of serum tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein β(14-3-3β)and clara's cell secretory protein 16(CC16)in patients with chronic obstructive pulmonary disease(COPD)complicated by respiratory failure,and their relationship with prognosis.Methods A total of 232 patients with COPD complicated with respiratory failure admitted to Yantian Hospital of Southern University of Science and Technology from April 2020 to October 2023 were enrolled in the COPD complicated with respiratory failure group.According to the severity of the disease,they were divided into mild group(n=67),moderate group(n=73)and severe group(n=92).According to the 28-day prognosis,they were divided into death group(n=73)and survival group(n=159).In addition,80 patients with simple COPD(COPD group)and 80 healthy subjects(control group)were selected at the same time.Enzyme-linked immunosorbent assay(ELISA)was used to detect the expression of serum 14-3-3β and CC16.Multivariate Logistic regression analysis was used to analyze the factors of death in patients with COPD complicated with respiratory failure.The receiver operating characteristic(ROC)curve was used to analyze the predictive value of serum 14-3-3β and CC16 expression on the death of patients with COPD complicated with respiratory failure.Results The expression of serum 14-3-3β in COPD complicated with respiratory failure group was higher than that in COPD group and control group(U=3.894,11.417),the expression of CC16 was lower than that in COPD group and control group(t=5.845,14.306),and the differences were statistically significant(all P<0.05),respectively.The expression of serum 14-3-3β in severe group was higher than that in moderate group and mild group(U=5.179,8.234),the expression of CC16 was lower than that of moderate group and mild group(t=4.090,9.281),and the differences were statistically significant(all P<0.05),respectively.The 28-day mortality rate of 232 COPD patients with respiratory failure was 31.47%(73/232).The expression of serum 14-3-3β in the death group was higher than that in the survival group,and the expression of CC16 was lower than that in the survival group,the differences were statistically significant(U/t=6.790,8.265,all P<0.05).The age of the death group was older than that of the survival group,the degree of airflow limitation and the number of acute exacerbations within 1 year were higher than those of the survival group,and the differences were statistically significant(t/χ2/U=3.895,7.202,3.360,all P<0.05).Age,severe airflow limitation,extremely severe airflow limitation,and the number of acute exacerbations within 1 year,elevated 14-3-3β were independent risk factors for death in patients with COPD complicated with respiratory failure(Wald χ2=3.914～22.668,all P<0.05),and elevated CC16 was an independent protective factor(Wald χ2=23.675,P<0.05).The area under the curve(AUC)of serum 14-3-3β combined and CC16 expression in predicting the death of patients with COPD complicated with respiratory failure which was greater than that of serum 14-3-3β and CC16 expression alone,the differences were statistically significant(Z=3.995,3.813,all P<0.01).Conclusion The increase of serum 14-3-3β expression and the decrease of CC16 expression in patients with COPD complicated by respiratory failure are closely related to the aggravation of the disease and poor prognosis.The combination of serum 14-3-3β and CC16 expression is of high value in predicting the death of patients with COPD complicated with respiratory failure.

Objective To investigate the effect of sigma-1 receptor(sig-1R)inhibition on apoptosis in the dorsal root ganglion(DRG)of rats with sciatic nerve chronic constriction injury(CCI)-mediated neuropathic pain.Methods Rats undergoing intrathecal intubation were randomly divided into three groups with 12 in each:sham group,model group(CCI modeling one week after intrathecal intubation)and sig-1R inhibitor group(BD1047 group)that was injected intrathecally on the fourth,fifth,and sixth days after the CCI operation.The mechanical withdrawal threshold(MWT)of rats was detected on the day before surgery and then first,third,fifth and sev-enth days after surgery.The expression of sig-1R,Bcl-2 and Bax was detected by Western blot and immunofluo-rescence in DRG cell;TUNEL staining microscopy was used to observe the apoptosis of DRG cells.The changes of endoplasmic reticulum and mitochondria were observed by transmission electron microscopy of DRG cells.Results Compared with the sham group,the model group showed a decreased MWT at all time points after surgery,up-regulation of DRG cell apoptosis,up-regulation of sig-1R and Bax,down-regulation of Bcl-2,swelling of en-doplasmic reticulum,disruption of mitochondrial membrane and reduction of mitochondrial cristae in the DRG cell endoplasmic reticulum after surgery(P＜0.05).BD1047 group showed elevated MWT at the fifth and seventh postoperative days,down-regulation of DRG cell apoptosis,down-regulated expression of sig-1R and Box,up-regulated expression of Bcl-2 and slightly damaged endoplasmic reticulum as well as mitochondria of DRG cells compared with model group(P＜0.05).Conclusions Inhibition of sig-1R up-regulates mechanical withdrawal threshold and reduces DRG cell apoptosis in CCI rats.

Objective:To explore the construction of a medical Artificial Intelligence(AI)technology assessment system suitable for the national conditions in China.Methods:Summarize the domestic and international traditional health technology assessment system,and analyze the distinctive characteristics and novel risk factors of medical AI technology.Results:The existing evaluation indexes of technical characteristics are most likely to assess AI technology based on the sub-evaluation indexes of reliability,but the evaluation focus of reliability cannot be assessed for algorithms,big data,arithmetic power and the existence of risk factors,and the existing system of health technology assessment needs to be further improved.Conclusion:Traditional health technology assessment system is challenging to apply to the evaluation of medical AI technology.It is recommended to use the traditional health technology assessment framework as a foundational structure,incorporate evaluation indicators related to the characteristics of medical AI technology,and refer to evaluation indicators and methods from relevant fields to adjust and refine the medical AI technology assessment system.

Objective:To explore the construction of a medical Artificial Intelligence(AI)technology assessment system suitable for the national conditions in China.Methods:Summarize the domestic and international traditional health technology assessment system,and analyze the distinctive characteristics and novel risk factors of medical AI technology.Results:The existing evaluation indexes of technical characteristics are most likely to assess AI technology based on the sub-evaluation indexes of reliability,but the evaluation focus of reliability cannot be assessed for algorithms,big data,arithmetic power and the existence of risk factors,and the existing system of health technology assessment needs to be further improved.Conclusion:Traditional health technology assessment system is challenging to apply to the evaluation of medical AI technology.It is recommended to use the traditional health technology assessment framework as a foundational structure,incorporate evaluation indicators related to the characteristics of medical AI technology,and refer to evaluation indicators and methods from relevant fields to adjust and refine the medical AI technology assessment system.

Objective:To explore the construction of a medical Artificial Intelligence(AI)technology assessment system suitable for the national conditions in China.Methods:Summarize the domestic and international traditional health technology assessment system,and analyze the distinctive characteristics and novel risk factors of medical AI technology.Results:The existing evaluation indexes of technical characteristics are most likely to assess AI technology based on the sub-evaluation indexes of reliability,but the evaluation focus of reliability cannot be assessed for algorithms,big data,arithmetic power and the existence of risk factors,and the existing system of health technology assessment needs to be further improved.Conclusion:Traditional health technology assessment system is challenging to apply to the evaluation of medical AI technology.It is recommended to use the traditional health technology assessment framework as a foundational structure,incorporate evaluation indicators related to the characteristics of medical AI technology,and refer to evaluation indicators and methods from relevant fields to adjust and refine the medical AI technology assessment system.

Objective:To explore the construction of a medical Artificial Intelligence(AI)technology assessment system suitable for the national conditions in China.Methods:Summarize the domestic and international traditional health technology assessment system,and analyze the distinctive characteristics and novel risk factors of medical AI technology.Results:The existing evaluation indexes of technical characteristics are most likely to assess AI technology based on the sub-evaluation indexes of reliability,but the evaluation focus of reliability cannot be assessed for algorithms,big data,arithmetic power and the existence of risk factors,and the existing system of health technology assessment needs to be further improved.Conclusion:Traditional health technology assessment system is challenging to apply to the evaluation of medical AI technology.It is recommended to use the traditional health technology assessment framework as a foundational structure,incorporate evaluation indicators related to the characteristics of medical AI technology,and refer to evaluation indicators and methods from relevant fields to adjust and refine the medical AI technology assessment system.

Objective:To explore the construction of a medical Artificial Intelligence(AI)technology assessment system suitable for the national conditions in China.Methods:Summarize the domestic and international traditional health technology assessment system,and analyze the distinctive characteristics and novel risk factors of medical AI technology.Results:The existing evaluation indexes of technical characteristics are most likely to assess AI technology based on the sub-evaluation indexes of reliability,but the evaluation focus of reliability cannot be assessed for algorithms,big data,arithmetic power and the existence of risk factors,and the existing system of health technology assessment needs to be further improved.Conclusion:Traditional health technology assessment system is challenging to apply to the evaluation of medical AI technology.It is recommended to use the traditional health technology assessment framework as a foundational structure,incorporate evaluation indicators related to the characteristics of medical AI technology,and refer to evaluation indicators and methods from relevant fields to adjust and refine the medical AI technology assessment system.