Background: We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). Methods: A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). Results: Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996–0.999) and 0.946 (95% CI, 0.906–0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. Conclusion: The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.

Endothelial progenitor cells (EPCs) improve neovascularization and endothelium regeneration. Transplantation with EPCs is a therapeutic strategy for the treatment of ischemic diseases. However, the transplanted EPCs are susceptible to adverse environments such as hypoxia, inflammation and oxidative stress. Oxidative stress-induced apoptosis of transplanted EPCs greatly reduces their therapeutic efficacy. Lipopolysaccharide (LPS) is a highly immunogenic antigen. Recent findings suggest that low dose of LPS pretreatment has protective effect against apoptosis. In this study, the role of LPS in apoptosis of EPCs was investigated. Pretreatment with 1µg/ml LPS prevented oxidative stress-induced EPCs apoptosis and ROS generation, which effects were abolished by TAK-242, a specific TLR4 antagonist. Further investigation of the mechanisms demonstrated that the activation was mediated by TLR4, and that PI3K/Akt/ NF-κB p65 signaling pathway may play a critical role in the process.

The low survival rate of endothelial progenitor cells (EPCs) in vivo which are susceptible to adverse microenvironments including inflammation and oxidative stress has become one primary challenge of EPCs transplantation for regenerative therapy. Recent studies reported functional expression of toll‐like receptor (TLR) 4 on EPCs and dose‐dependent effects of lipopolysaccharide (LPS) on cellular oxidative stress and angiogenic properties. However, the involved mechanism has not yet been elucidated well, and the influence of TLR4 signaling on EPCs survival and function in vivo is unknown. In the present study, we observed the effects of LPS and TLR4/SIRT3 on EPCs mitochondrial permeability and intracellular mitochondrial superoxide. We employed the monocrotaline‐induced pulmonary arteriolar injury model to observe the effects of TLR4/SIRT3 on the recruitment and survival of transplanted EPCs. We found the destructive effects of 10 μg/mL LPS on mitochondrial homeostasis, and cellular viability was mediated by TLR4/SIRT3 signals at least partially, and the TLR4 mediates the early‐stage recruitment of transplanted EPCs in pulmonary arteriolar inflammation injury; however, SIRT3 has more contribution to the survival of incorporated EPCs and ameliorated arteriolar remodeling in lung vascular tissue. The study provides insights for the critical role of TLR4/SIRT3 in LPS‐induced oxidative stress and mitochondrial disorder in EPCs in vitro and in vivo. The TLR4/SIRT3 signaling is important for EPCs resistance against inflammation and oxidative stress and may represent a new manipulating target for developing efficient cell therapy strategy.