The Research on Operation of Obstructed Total Anomalous Pulmonary Venous Connection in Neonates

Objectives. Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart disease. This study aimed to evaluate the outcomes of TAPVC repair in neonates, controlling for anatomic subtypes and surgical techniques. Methods. Between 1997 and 2013, 88 patients (median age: 16 days) underwent repair for supracardiac (31), cardiac (18), infracardiac (36), or mixed (3) TAPVC. All the patients underwent emergency operation due to obstructed drainage. Supracardiac and infracardiac TAPVC repair included a side-to-side anastomosis between the pulmonary venous confluence and left atrium. Coronary sinus unroofing was preferred for cardiac TAPVC repair. Results. The early mortality rate was 2.3% (2/88 patients). The echocardiogram showed no obstruction in the pulmonary vein anastomosis, and flow rate was 1.1–1.42 m/s in the 3-year follow-up period. Conclusions. The accurate preoperative diagnosis, improved protection of heart function, use of pulmonary vein tissue to anastomose and avoid damage of the pulmonary vein, and delayed sternum closure can reduce the risk of mortality. The preoperative severity of pulmonary vein obstruction, the timing of the emergency operation, and infracardiac or mixed-type TAPVC can affect prognosis. Using our surgical technique, the TAPVC mortality among our patients was gradually reduced with remarkable results. However, careful monitoring of the patient with pulmonary vein restenosis and the timing and method of reoperation should also be given importance

ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting

Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence. In this paper, we propose a plug-and-play module named Action Prompt Module (APM) that effectively mines different kinds of action clues for 3D HPE. The highlight is that, the mining scheme of APM can be widely adapted to different frameworks and bring consistent benefits. Specifically, we first present a novel Action-related Text Prompt module (ATP) that directly embeds action labels and transfers the rich language information in the label to the pose sequence. Besides, we further introduce Action-specific Pose Prompt module (APP) to mine the position-aware pose pattern of each action, and exploit the correlation between the mined patterns and input pose sequence for further pose refinement. Experiments show that APM can improve the performance of most video-based 2D-to-3D HPE frameworks by a large margin.Comment: 6 pages, 4 figures, 2023ICM

Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation

There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies. However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topology in the self-attention mechanism. To tackle this issue, in this paper, we propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D HPE. Specifically, we first develop novel pose-oriented self-attention mechanism and distance-related position embedding for POT to explicitly exploit the human skeleton topology. The pose-oriented self-attention mechanism explicitly models the topological interactions between body joints, whereas the distance-related position embedding encodes the distance of joints to the root joint to distinguish groups of joints with different difficulties in regression. Furthermore, we present an Uncertainty-Guided Refinement Network (UGRN) to refine pose predictions from POT, especially for the difficult joints, by considering the estimated uncertainty of each joint with uncertainty-guided sampling strategy and self-attention mechanism. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods with reduced model parameters on 3D HPE benchmarks such as Human3.6M and MPI-INF-3DHPComment: accepted by AAAI202

The Neural Testbed: Evaluating Joint Predictions

Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open-source benchmark for controlled and principled evaluation of agents that generate such predictions. Crucially, the testbed assesses agents not only on the quality of their marginal predictions per input, but also on their joint predictions across many inputs. We evaluate a range of agents using a simple neural network data generating process. Our results indicate that some popular Bayesian deep learning agents do not fare well with joint predictions, even when they can produce accurate marginal predictions. We also show that the quality of joint predictions drives performance in downstream decision tasks. We find these results are robust across choice a wide range of generative models, and highlight the practical importance of joint predictions to the community

High expression of ubiquitin-conjugating enzyme 2C (UBE2C) correlates with nasopharyngeal carcinoma progression

BACKGROUND: Overexpression of ubiquitin-conjugating enzyme 2C (UBE2C) has been detected in many types of human cancers, and is correlated with tumor malignancy. However, the role of UBE2C in human nasopharyngeal carcinoma (NPC) is unclear. In this study, we investigated the role of aberrant UBE2C expression in the progression of human NPC. METHODS: Immunohistochemical analysis was performed to detect UBE2C protein in clinical samples of NPC and benign nasopharyngeal tissues, and the association of UBE2C expression with patient clinicopathological characteristics was analyzed. UBEC2 expression profiles were evaluated in cell lines representing varying differentiated stages of NPC and immortalized nasopharyngeal epithelia NP-69 cells using quantitative RT-PCR, western blotting and fluorescent staining. Furthermore, UBE2C was knocked down using RNA interference in these cell lines and proliferation and cell cycle distribution was investigated. RESULTS: Immunohistochemical analysis revealed that UBE2C protein expression levels were higher in NPC tissues than in benign nasopharyngeal tissues (P<0.001). Moreover, high UBE2C protein expression was positively correlated with tumor size (P=0.017), lymph node metastasis (P=0.016) and distant metastasis (P=0.015) in NPC patients. In vitro experiments demonstrated that UBE2C expression levels were inversely correlated with the degree of differentiation of NPC cell lines, whereas UBE2C displayed low level of expression in NP-69 cells. Knockdown of UBE2C led to significant arrest at the S and G2/M phases of the cell cycle, and decreased cell proliferation was observed in poorly-differentiated CNE2Z NPC cells and undifferentiated C666-1 cells, but not in well-differentiated CNE1 and immortalized NP-69 cells. CONCLUSIONS: Our findings suggest that high expression of UBE2C in human NPC is closely related to tumor malignancy, and may be a potential marker for NPC progression

Revealing the roles of glycosphingolipid metabolism pathway in the development of keloid: a conjoint analysis of single-cell and machine learning

Keloid is a pathological scar formed by abnormal wound healing, characterized by the persistence of local inflammation and excessive collagen deposition, where the intensity of inflammation is positively correlated with the size of the scar formation. The pathophysiological mechanisms underlying keloid formation are unclear, and keloid remains a therapeutic challenge in clinical practice. This study is the first to investigate the role of glycosphingolipid (GSL) metabolism pathway in the development of keloid. Single cell sequencing and microarray data were applied to systematically analyze and screen the glycosphingolipid metabolism related genes using differential gene analysis and machine learning algorithms (random forest and support vector machine), and a set of genes, including ARSA,GBA2,SUMF2,GLTP,GALC and HEXB, were finally identified, for which keloid diagnostic model was constructed and immune infiltration profiles were analyzed, demonstrating that this set of genes could serve as a new therapeutic target for keloid. Further unsupervised clustering was performed by using expression profiles of glycosphingolipid metabolism genes to discover keloid subgroups, immune cells, inflammatory factor differences and the main pathways of enrichment between different subgroups were calculated. The single-cell resolution transcriptome landscape concentrated on fibroblasts. By calculating the activity of the GSL metabolism pathway for each fibroblast, we investigated the activity changes of GSL metabolism pathway in fibroblasts using pseudotime trajectory analysis and found that the increased activity of the GSL metabolism pathway was associated with fibroblast differentiation. Subsequent analysis of the cellular communication network revealed the existence of a fibroblast-centered communication regulatory network in keloids and that the activity of the GSL metabolism pathway in fibroblasts has an impact on cellular communication. This contributes to the further understanding of the pathogenesis of keloids. Overall, we provide new insights into the pathophysiological mechanisms of keloids, and our results may provide new ideas for the diagnosis and treatment of keloids