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 learn the status and influencing factors of development among infants at ages of 0 to 36 months in Xiaoshan District, Hangzhou City, so as to provide the reference for promoting healthy development of infants. Methods: Infants at ages of 0-36 months who underwent physical examination in Child Health Clinic of Xiaoshan District Community Health Service Center were selected in 2022. General data of infants and their mothers were collected through questionnaires, and the development status of infants was screened by Age and Stages Questionnaire (third edition). Factors affecting the development status were identified using a multivariable logistic regression model. Results: A total of 2 519 infants were investigated, including 1 339 males (53.16%) and 1 180 females (46.84%). There were 608 infants with abnormal development of at least one functional area of communication (CM), gross motor (GM), fine motor (FM), problems solving (CG) and personal-social (PS). The abnormal rate was 24.14%, and the abnormal rates of the above functional areas were 9.77%, 6.59%, 7.98%, 6.39% and 9.33%, respectively. Multivariable logistic regression analysis showed that gender (male, OR=1.563, 95%CI: 1.191-2.052), mother's childbearing age (≥35 years, OR=1.411, 95%CI: 1.001-1.988), mother's educational level (lower than junior college, OR=1.460, 95%CI: 1.116-1.912) were factors affecting abnormal development of CM; preterm birth (OR=2.323, 95%CI: 1.315-4.103) was factors affecting abnormal development of GM; gender (male, OR=1.654, 95%CI: 1.225-2.232) was factors affecting abnormal development of FM; gender (male, OR=1.511, 95%CI: 1.086-2.102) and mode of delivery (cesarean section, OR=1.460, 95%CI: 1.060-2.010) were factors affecting abnormal development of CG; gender (male, OR=1.340, 95%CI: 1.019-1.763) and birth weight (low birth weight, OR=1.985, 95%CI: 1.149-3.432) were factors affecting abnormal development of PS. Conclusions The rate of abnormal development among infants at ages of 0 to 36 months in Xiaoshan District is 24.14%. Gender, preterm birth, mode of delivery, birth weight, mother's childbearing age and mother's educational level could affect the development status of infants.

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.

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.