Neuroblastoma is the most common solid tumour in childhood and prognosis remains poor for high-risk cases despite the use of multimodal treatment. Analysis of public drug sensitivity data showed neuroblastoma lines to be particularly sensitive to indisulam, a molecular glue that selectively targets RNA splicing factor RBM39 for proteosomal degradation via DCAF15-E3-ubiquitin ligase. In neuroblastoma models, indisulam induced rapid loss of RBM39, accumulation of splicing errors and growth inhibition in a DCAF15- dependent manner. Integrative analysis of RNAseq and proteomics data highlighted a distinct disruption to cell cycle and metabolism. Metabolic profiling demonstrated metabolome perturbations and mitochondrial dysfunction resulting from indisulam. Complete tumour without relapse was observed in both xenografts and the Th-MYCN transgenic model of neuroblastoma after indisulam treatment, with RBM39 loss confirmed in vivo. Our data imply that dual targeting of metabolism and RNA splicing with anti-cancer sulphonamides such as indisulam is a promising therapeutic approach for high-risk neuroblastoma.
At present, tumors especially malignancies, have become one of the most serious diseases that threaten human health. The use of chemotherapy, radiotherapy, surgery, biotherapy and integrated traditional Chinese and Western medicine is the most effective means of treating tumors. Among them, the application of new anti-tumor drugs, in improving the quality of life of cancer patients to extend the survival time, delay the development of the disease has played a huge role. In this paper, we reviewed the related research progress of tumor cells from the aspects of tumor features, related signal pathways, related genes, epigenetic modification, tumor stem cells and tumor microenvironment, so as to have a more comprehensive understanding of tumor and cell apoptosis.
Abstract Vision-language models, such as Contrastive Language-Image Pretraining (CLIP), have demonstrated powerful capabilities in image classification under zero-shot settings. However, current Zero-Shot Learning (ZSL) relies on manually tagged samples of known classes through supervised learning, resulting in a waste of labor costs and limitations on foreseeable classes in real-world applications. To address these challenges, we propose the Mixup Long-Tail Unsupervised (MLTU) approach for open-world ZSL problems. The proposed approach employed a novel long-tail mixup loss that integrated class-based re-weighting assignments with a given mixup factor for each mixed visual embedding. To mitigate the adverse impact over time, we adopted a noisy learning strategy to filter out samples that generated incorrect labels. We reproduced the unsupervised results of existing state-of-the-art long-tail and noisy learning approaches. Experimental results demonstrate that MLTU achieves significant improvements in classification compared to these proven existing approaches on public datasets. Moreover, it serves as a plug-and-play solution for amending previous assignments and enhancing unsupervised performance. MLTU enables the automatic classification and correction of incorrect predictions caused by the projection bias of CLIP.
Generalized zero-shot learning (GZSL) aims to classify seen classes and unseen classes that are disjoint simultaneously. Hybrid approaches based on pseudo-feature synthesis are currently the most popular among GZSL methods. However, they suffer from problems of negative transfer and low-quality class discriminability, causing poor classification accuracy. To address them, we propose a novel GZSL method of distinguishable pseudo-feature synthesis (DPFS). The DPFS model can provide high-quality distinguishable characteristics for both seen and unseen classes. Firstly, the model is pretrained by a distance prediction loss to avoid overfitting. Then, the model only selects attributes of similar seen classes and makes sparse representations based on attributes for unseen classes, thereby overcoming negative transfer. After the model synthesizes pseudo-features for unseen classes, it disposes of the pseudo-feature outliers to improve the class discriminability. The pseudo-features are fed into a classifier of the model together with features of seen classes for GZSL classification. Experimental results on four benchmark datasets verify that the proposed DPFS has GZSL classification performance better than that in existing methods.
This paper proposes a framework based on harmonic mean normalized Laplace-Beltrami spectral descriptor for non-rigid 3D shape retrieval. A series of experiments show harmonic mean normalization is suited to classification of stretched shapes, and is robust to isometric transformation, holes, local scaling, noise, shot noise and sampling. To better distinguish among shapes with fine or rough details, weighting method and fusion method are employed. We use the two methods to reduce the adverse impact of high frequency when the shapes with fine and rough details are distinguished. In the experiments, three 3D shape retrieval benchmarks are used, and our approach has better performance than other state-of-the-art methods on both retrieval accuracy and time performance for stretched non-rigid 3D shapes.
To construct gene co-expression networks, it is necessary to evaluate the correlation between different gene expression profiles. However, commonly used correlation metrics, including both linear (such as Pearson's correlation) and monotonic (such as Spearman's correlation) dependence metrics, are not enough to observe the nature of real biological systems. Hence, introducing a more informative correlation metric when constructing gene co-expression networks is still an interesting topic.In this paper, we test distance correlation, a correlation metric integrating both linear and non-linear dependence, with other three typical metrics (Pearson's correlation, Spearman's correlation, and maximal information coefficient) on four different arrays (macrophage and liver) and RNA-seq (cervical cancer and pancreatic cancer) datasets. Among all the metrics, distance correlation is distribution free and can provide better performance on complex relationships and anti-outlier. Furthermore, distance correlation is applied to Weighted Gene Co-expression Network Analysis (WGCNA) for constructing a gene co-expression network analysis method which we named Distance Correlation-based Weighted Gene Co-expression Network Analysis (DC-WGCNA). Compared with traditional WGCNA, DC-WGCNA can enhance the result of enrichment analysis and improve the module stability.Distance correlation is better at revealing complex biological relationships between gene profiles compared with other correlation metrics, which contribute to more meaningful modules when analyzing gene co-expression networks. However, due to the high time complexity of distance correlation, the implementation requires more computer memory.
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