Background SIRT4, a protein localized in the mitochondria, is one of the least characteristic members of the sirtuin family. It is known that SIRT4 has deacetylase activity and plays a role in energy metabolism, but little is known about its possible role in carcinogenesis. Recently, several studies have suggested that SIRT4 may function as either a tumor oncogene or a tumor suppressor gene. However, its relationship with thyroid cancer remains unclear. Methods We stably overexpressed SIRT4 or silenced its expression in the human thyroid cancer cell line BCPAP by means of lentiviral vectors. We conducted a variety of tests, such as CCK-8, wound healing, migration, and invasion assays, to investigate the role of SIRT4 in the proliferation, migration, and invasion abilities of thyroid cancer cells. We also investigated the effects of SIRT4 overexpression on cell cycle progression and apoptosis of BCPAP cells and studied the role of glutamine metabolism in the effects of SIRT4 on BCPAP cell migration and invasion. Finally, we analyzed SIRT4 expression levels in thyroid cancer specimens by immunohistochemistry and investigated their association with clinicopathological features. Results Overexpression of SIRT4 inhibited the proliferation, migration, and invasion abilities of BCPAP thyroid cancer cells, blocked the cell cycle in the G0/G1 phase, and induced apoptosis. Mechanistically, SIRT4 inhibited BCPAP migration and invasion by inhibiting glutamine metabolism. Moreover, we found that SIRT4 protein levels in thyroid cancer tissues were markedly lower than in their non-neoplastic tissue counterparts ( P <0.001). Conclusion SIRT4 plays a pivotal role in the growth and metastasis of thyroid cancer cells and could be a potential therapeutic target in thyroid cancer.

Background: The aim of this study is to improve the preoperative diagnostic accuracy and treatment results by investigating the clinical features and prognosis of primary liver sarcoma (PLS). Methods: Clinical data, surgical treatments, adjuvant chemotherapy, and prognosis of 17 PLS patients whose diseases were pathologically confirmed were retrospectively analyzed. Results: The main clinical symptoms included epigastric pain in 9 patients, epigastric distention in 7, and loss of appetite in 4; these symptoms were detected during the postoperative follow-up for gastric carcinoma in 1. The resection rate was 64.7% (12/17), including R0 resection in 10 patients and R1 resection in 2, and laparotomy with biopsy in 5. Five patients accepted an adjuvant selective hepatic artery infusion chemotherapy (mitomycin C 16–20 mg+ 5-fluorouracil 5.0 g+ epirubicin 40–50 mg), and 4 accepted adjuvant systemic chemotherapy (vincristin, cisplatin, cyclophosphamide, and adriamycin). All 5 patients with simple laparotomy died within 1 year, and the overall 1-, 3-, and 5-year survival rates for all patients were 58.8% (10/17), 29.4% (5/17) and 11.7% (2/17), respectively, whereas those were 100.0% (10/10), 50.0% (5/10), and 20.0% (2/10) for R0 resected patients respectively. Conclusions: The diagnosis of PLS is difficult before operation due to its nonspecific manifestations, and the high survival rate can be achieved by radical resection with adjuvant chemotherapy.

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Purpose: Our study aimed to construct a visible model to evaluate the risk of infectious complications after gastrectomy. Methods: The clinical data of 856 patients who underwent gastrectomy were used to retrieve medical records. Univariate and multivariate analyses were performed to correlate early postoperative NLR and operative variables with postoperative complications, and the construction of the nomogram was based on logistic regression. The concordance index and receiver operating characteristic curves were used to evaluate the model performance. Results: The postoperative infectious and noninfectious complication rates after gastrectomy were 18.5% (158/856 cases) and 12.3% (105/856 cases) respectively. Postoperative NLR (within 24 h) independently predicted the development of postoperative infectious complication. Multivariate analysis revealed that age, diabetes, body mass index (BMI), intraoperative blood transfusion and postoperative NLR were independent risk factors. The nomogram model showed a good performance in terms of predicting infectious complications after gastrectomy (concordance index=0.718). Conclusion: Age, diabetes, BMI, intraoperative blood transfusion and postoperative NLR were independent risk factors of postoperative infectious complications after gastrectomy, and a novel nomogram based on these results can be used to predict postoperative infection and has the advantages of simple application and easy access.

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The point clouds scanned by lidar are generally sparse, which can result in fewer sampling points of objects. To perform precise and effective 3D object detection, it is necessary to improve the feature representation ability to extract more feature information of the object points. Therefore, we propose an adaptive feature enhanced 3D object detection network based on point clouds (AFE-RCNN). AFE-RCNN is a point-voxel integrated network. We first voxelize the raw point clouds and obtain the voxel features through the 3D voxel convolutional neural network. Then, the 3D feature vectors are projected to the 2D bird’s eye view (BEV), and the relationship between the features in both spatial dimension and channel dimension is learned by the proposed residual of dual attention proposal generation module. The high-quality 3D box proposals are generated based on the BEV features and anchor-based approach. Next, we sample key points from raw point clouds to summarize the information of the voxel features, and obtain the key point features by the multi-scale feature extraction module based on adaptive feature adjustment. The neighboring contextual information is integrated into each key point through this module, and the robustness of feature processing is also guaranteed. Lastly, we aggregate the features of the BEV, voxels, and point clouds as the key point features that are used for proposal refinement. In addition, to ensure the correlation among the vertices of the bounding box, we propose a refinement loss function module with vertex associativity. Our AFE-RCNN exhibits comparable performance on the KITTI dataset and Waymo open dataset to state-of-the-art methods. On the KITTI 3D detection benchmark, for the moderate difficulty level of the car and the cyclist classes, the 3D detection mean average precisions of AFE-RCNN can reach 81.53% and 67.50%, respectively.