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AbstractThe aim of the study was to investigate the biomechanical behavior of three-dimensionally (3D)-printed surgical plates used for mandibular defect reconstruction, compare them with conventional surgical plates, and provide experimental evidence for their clinical application. Three-dimensional models were created for the normal mandible and for mandibular body defects reconstructed using free fibula and deep circumflex iliac artery flaps. Three-dimensional finite element models of reconstructed mandibles fixed using 3D-printed and conventional surgical plates were established. Vertical occlusal forces were applied to the remaining teeth and the displacement and Von Mises stress distributions were studied using finite element analysis. The normal and reconstructed mandibles had similar biomechanical behaviors. The displacement distributions for the surgical plates were similar, and the maximum total deformation occurred at the screw hole of the anterior segment of the surgical plates. However, there were differences in the Von Mises stress distributions for the surgical plates. In reconstructed mandibles fixed using 3D-printed surgical plates, the maximum equivalent Von Mises stress occurred at the screw hole of the posterior segment, while in those fixed using conventional surgical plates, the maximum equivalent Von Mises stress was at the screw hole of the anterior segment. In the mandible models reconstructed with the same free flap but fixed with different surgical plates, the plates had similar biomechanical behaviors. The biomechanical behavior of 3D-printed surgical plates was similar to conventional surgical plates, suggesting that 3D-printed surgical plates used to reconstruct mandibular body defects with vascularized autogenous bone grafts could lead to secure and stable fixation.

Abstract Background Ensuring high accuracy in multimodal image fusion for oral and maxillofacial tumors is crucial before further application. The aim of this study was to explore the factors influencing the accuracy of multimodal image fusion for oral and maxillofacial tumors. Methods Pairs of single-modality images were obtained from oral and maxillofacial tumor patients, and were fused using a proprietary navigation system by using three algorithms (automatic fusion, manual fusion, and registration point-based fusion). Fusion accuracy was evaluated including two aspects—overall fusion accuracy and tumor volume fusion accuracy—and were indicated by mean deviation and fusion index, respectively. Image modality, fusion algorithm, and other characteristics of multimodal images that may have potential influence on fusion accuracy were recorded. Univariate and multivariate analysis were used to identify relevant affecting factors. Results Ninety-three multimodal images were generated by fusing 31 pairs of single-modality images. The interaction effect of image modality and fusion algorithm (P = 0.02, P = 0.003) and thinner slice thickness (P = 0.006) were shown to significantly influence the overall fusion accuracy. The tumor volume (P

ObjectiveTo evaluate the feasibility and accuracy of mixed reality combined with surgical navigation in oral and maxillofacial tumor surgery.MethodsRetrospective analysis of data of seven patients with oral and maxillofacial tumors who underwent surgery between January 2019 and January 2021 using a combination of mixed reality and surgical navigation. Virtual surgical planning and navigation plan were based on preoperative CT datasets. Through IGT-Link port, mixed reality workstation was synchronized with surgical navigation, and surgical planning data were transferred to the mixed reality workstation. Osteotomy lines were marked with the aid of both surgical navigation and mixed reality images visualized through HoloLens. Frozen section examination was used to ensure negative surgical margins. Postoperative CT datasets were obtained 1 week after the surgery, and chromatographic analysis of virtual osteotomies and actual osteotomies was carried out. Patients received standard oncological postoperative follow-up.ResultsOf the seven patients, four had maxillary tumors and three had mandibular tumors. There were total of 13 osteotomy planes. Mean deviation between the planned osteotomy plane and the actual osteotomy plane was 1.68 ± 0.92 mm; the maximum deviation was 3.46 mm. Chromatographic analysis showed error of ≤3 mm for 80.16% of the points. Mean deviations of maxillary and mandibular osteotomy lines were approximate (1.60 ± 0.93 mm vs. 1.86 ± 0.93 mm). While five patients had benign tumors, two had malignant tumors. Mean deviations of osteotomy lines was comparable between patients with benign and malignant tumors (1.48 ± 0.74 mm vs. 2.18 ± 0.77 mm). Intraoperative frozen pathology confirmed negative resection margins in all cases. No tumor recurrence or complications occurred during mean follow-up of 15.7 months (range, 6-26 months).ConclusionThe combination of mixed reality technology and surgical navigation appears to be feasible, safe, and effective for tumor resection in the oral and maxillofacial region.

Ginsenoside Rg3 is a steroidal saponin isolated from Panax ginseng. Previous studies have shown that Rg3 treatment downregulates the activity of rapamycin complex 1 (mTORC1) activity and inhibits the growth of cancer cells. However, the inhibitory effect of Rg3 on cancer cells is associated with high concentrations of Rg3 that are difficult to achieve in vivo. The human cervix adenocarcinoma HeLa cells were treated with Rg3. The protein levels of AMP-activated protein kinase alpha (AMPKα), protein kinase B(Akt), ribosomal S6 protein(S6), and Erk were determined by immunoblotting analyses. We used a fluorescent probe to detect reactive oxygen species (ROS) production in living cells. The oxygen consumption rate (OCR) was examined by the Seahorse Extracellular Flux Analyzer. The content of adenosine triphosphate (ATP) was measured by ATPlite kit and Mitotracker was applied to detect the mitochondria. We showed that at lower concentrations, Rg3 activates mTORC1 independent of AKT and AMP-activated protein kinase (AMPK). Rg3 promotes mitochondrial biogenesis and function, increases the oxygen consumption of mitochondria and the content of ATP. This effect is in contrast to that of high concentrations of Rg3, which inhibits cell growth. These findings demonstrate a pro-growth activity of Rg3 that acts through mTORC1 and mitochondrial biogenesis and suggest a dose-dependent effect of Rg3 on tumor cell growth.

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA-miRNA-mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.