The intervertebral disc (IVD) is a complex joint structure comprising three primary components—namely, nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP). The IVD retrieves oxygen from the surrounding vertebral body through CEP by diffusion and likely generates ATP via anaerobic glycolysis. IVD degeneration is characterized by a cascade of cellular, compositional, structural changes. With advanced age, pronounced changes occur in the composition of the disc extracellular matrix (ECM). NP and AF cells in the IVD possess poor regenerative capacity compared with that of other tissues. Hypoxia-inducible factor (HIF) is a master transcription factor that initiates a coordinated cellular cascade in response to a low oxygen tension environment, including the regulation of numerous enzymes in response to hypoxia. HIF-1α is essential for NP development and homeostasis and is involved in various processes of IVD degeneration process, promotes ECM in NP, maintains the metabolic activities of NP, and regulates dystrophic mineralization of NP, as well as angiogenesis, autophagy, and apoptosis during IVD degeneration. HIF-1α may, therefore, represent a diagnostic tool for early IVD degeneration and a therapeutic target for inhibiting IVD degeneration

Intervertebral discs (IVDs) have poor nutrient diffusion, because the nucleus pulposus (NP) lacks direct vascular supply and likely generates adenosine triphosphate by anaerobic glycolysis. Regulation of glycolysis is mediated by hypoxia-inducible factor-1α (HIF-1α), a transcription factor that responds to local oxygen tension. Constitutively active HIF-1α (CA HIF-1α) was created by point mutation and determined the protective role of HIF-1α in IVD degeneration. Under fluoroscopy, rat caudal IVD segments were stabbed by a needle puncture, and pcDNA3- HIF-1α wild-type (WT) or pcDNA3-CA HIF-1α was transfected into NP cell lines. The constitutive activity of CA HIF-1α was analyzed using a luciferase assay after cell lysis. Next, IVD tissue samples were retrieved from five patients with degenerative lumbar spinal stenosis at the time of surgery, and NP cells were cultured. NP cells were transfected with CA HIF-1α, and relevant gene expression was measured. HIF-1α protein levels in the nucleus were significantly higher, and transcriptional activity was 10.3-fold higher in NP cells with CA HIF-1α than in those with HIF-1α WT. Gene transfer of CA HIF-1α into NP cells enhanced the expression of Glut-1, Glut-3, aggrecan, type II collagen, and Sox9. Moreover, CA HIF-1α reduced the apoptosis of NP cells induced by the Fas ligand. The HIF-1α and collagen 2 expression levels were notably increased in the NP cells of the CA HIF-1α transfected segments in histology and immunohistochemistry study. Collectively, these results suggest that activation of HIF-1α signaling pathway may play a protective role against IVD degeneration and could be used as a future therapeutic agent.

Background: Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology.Methods: The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including “hip fractures” AND “artificial intelligence”.Results: A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%.Conclusions: We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.

Jang BW, et al. Clin Orthop Surg. 2023 Oct;15:e76. https://doi.org/10.4055/cios23223

Lee YK, et al. Clin Orthop Surg. 2018 Dec;10(4):393-397. https://doi.org/10.4055/cios.2018.10.4.393

Lee YK, et al. Clin Orthop Surg. 2018 Mar;10(1):9-13. https://doi.org/10.4055/cios.2018.10.1.9