Qiang Zheng

Osteomyelitis, a severe skeletal infection, involves complex pathogenic mechanisms. Mitophagy (mitophagy) is crucial for cellular homeostasis and has been linked to various diseases, including osteomyelitis. This study explores the genetic basis of mitophagy in osteomyelitis, identifying differentially expressed genes related to mitophagy and their potential as biomarkers and therapeutic targets. Using the GSE30119 dataset from GEO, mRNA expression profiles from 49 osteomyelitis patients and 44 healthy individuals were analyzed. Differential gene expression analysis identified 2876 differentially expressed genes with a threshold of |log FC| > 0.5 and P < .05. Mitophagy-related genes were sourced from GeneCards, with 25 overlapping genes identified. Functional enrichment analyses, including gene ontology and Kyoto encyclopedia of genes and genomes, were conducted. Four machine learning models (generalized linear model, random forest, support vector machine, and extreme gradient boosting) identified 9 key genes for a diagnostic model. Gene set enrichment analysis and immune cell infiltration analysis using CIBERSORT were performed. Furthermore, RT-qPCR was performed to experimentally validate the expression patterns of the identified key genes in an inflammatory cellular model. Twenty-five intersecting genes were identified, with gene ontology and Kyoto encyclopedia of genes and genomes analyses highlighting their roles in autophagy and mitochondrial functions. The random forest model identified 9 key genes: BMP2KL, ALPK1, PGAM5, PINK1, TP53, GPC1, ITPKC, PEX3, and P2RX5, validated with an area under the receiver operating characteristic curve of 0.905. Gene set enrichment analysis indicated their involvement in metabolic and signaling pathways. Immune infiltration analysis revealed significant differences between patients and healthy individuals. RT-qPCR validation confirmed significant expression changes of the 9 key genes under inflammatory conditions, consistent with bioinformatics predictions. This study identifies key mitophagy genes and targets in osteomyelitis, providing a basis for future research and therapies.

Purpose To evaluate the predictive ability of AI HIP in determining the size and position of prostheses during complex total hip arthroplasty (THA). Additionally, it investigates the factors influencing the accuracy of preoperative planning predictions. Methods From April 2021 to December 2023, patients with complex hip joint diseases were divided into the AI preoperative planning group (n = 29) and the X-ray preoperative planning group (n = 27). Postoperative X-rays were used to measure acetabular anteversion angle, abduction angle, tip-to-sternum distance, intraoperative duration, blood loss, planning time, postoperative Harris Hip Scores (at 2 weeks, 3 months, and 6 months), and visual analogue scale (VAS) pain scores (at 2 weeks and at final follow-up) to analyze clinical outcomes. Results On the acetabular side, the accuracy of AI preoperative planning was higher compared to X-ray preoperative planning (75.9% vs. 44.4%, P = 0.016). On the femoral side, AI preoperative planning also showed higher accuracy compared to X-ray preoperative planning (85.2% vs. 59.3%, P = 0.033). The AI preoperative planning group showed superior outcomes in terms of reducing bilateral leg length discrepancy (LLD), decreasing operative time and intraoperative blood loss, early postoperative recovery, and pain control compared to the X-ray preoperative planning group (P < 0.05). No significant differences were observed between the groups regarding bilateral femoral offset (FO) differences, bilateral combined offset (CO) differences, abduction angle, anteversion angle, or tip-to-sternum distance. Factors such as gender, age, affected side, comorbidities, body mass index (BMI) classification, bone mineral density did not affect the prediction accuracy of AI HIP preoperative planning. Conclusion Artificial intelligence-based 3D planning can be effectively utilized for preoperative planning in complex THA. Compared to X-ray templating, AI demonstrates superior accuracy in prosthesis measurement and provides significant clinical benefits, particularly in early postoperative recovery.