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Bladder cancer (BLCA) is a prevalent cancer type with an unmet need for new therapeutic strategies. Nucleoporin 93 (Nup93) is implicated in the pathophysiology of several cancers, but its relationship with bladder cancer remains unclear. Nup93 expression was analyzed in TCGA datasets and 88 BLCA patient samples. Survival analysis and Cox regression models evaluated the association between Nup93 levels and patient prognosis. BLCA cells were used to investigate the effects of Nup93 overexpression or knockdown on cell growth, invasion, stemness (sphere formation and ALDH2 + cancer stem cell marker), and Wnt/β-catenin signaling in vitro. The Wnt activator BML-284 was used to confirm the involvement of Wnt/β-catenin signaling pathway. A xenograft mouse model validated the in vitro findings. Nup93 was highly expressed in BLCA tissues and cell lines, and high Nup93 expression correlated with poor prognosis in BLCA patients. Nup93 silencing inhibited BLCA cell proliferation, Wnt/β-catenin activation, and cancer cell stemness. Conversely, Nup93 overexpression promoted these effects. BML-284 partially rescued the reduction in cell growth and stemness markers caused by Nup93 knockdown. Nup93 knockdown also suppressed the tumor formation of BLCA cells in vivo. Nup93 regulates BLCA cell growth and stemness via the Wnt/β-catenin pathway, suggesting its potential as a prognostic marker and therapeutic target in BLCA., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Ureteral urothelial carcinoma is a common urinary system tumor, accounting for 40% to 60% of all ureteral diseases. This study attempted to analyze the prognosis of patients with urothelial carcinoma, judging ureteral urothelial carcinoma by genes and biomarkers of inflammatory response. In this paper, co-expression network analysis and gene-based image fusion evaluation methods were proposed to obtain the prognosis results of patients with ureteral urothelial carcinoma. The experimental results showed that the levels of PLR and NLR increased, and the levels of HGB and HCT decreased; high PLR and high NLR levels, low HGB and low HCT levels were all risk factors affecting bladder urothelial carcinoma, and their ratios (OR) were 1.023, 1.611, 0.961, 0.859, 1.015, 1.072, 0.979, and 0.951, respectively. However, high PLR and high NLR levels were independent risk factors for bladder urothelial carcinoma, and their OR values were 1.497 and 1.071, respectively. Through biomarker diagnosis, the area under the curve, sensitivity, specificity and Youden index of hsa-mir-17, hsa-mir-93, hsa-mir-429 and hsa-mir-20a all exceeded 0.9, indicating that this is a potential diagnostic indicators. All in all, during the treatment of ureteral cancer, in order to reduce tumor recurrence, systemic therapy should be combined with ureteral cancer. In addition, this study also analyzed the prognosis of chemotherapy patients, and the results showed that immunotherapy may increase the risk of tumor cell reperfusion during chemotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chen, Liu, Li, Wang and Zhang.)

Saturation information in hazy images is conducive to effective haze removal, However, existing saturation-based dehazing methods just focus on the saturation value of each pixel itself, while the higher-level distribution characteristic between pixels regarding saturation remains to be harnessed. In this paper, we observe that the pixels, which share the same surface reflectance coefficient in the local patches of haze-free images, exhibit a linear relationship between their saturation component and the reciprocal of their brightness component in the corresponding hazy images normalized by atmospheric light. Furthermore, the intercept of the line described by this linear relationship on the saturation axis is exactly the saturation value of these pixels in the haze-free images. Using this characteristic of saturation, termed saturation line prior (SLP), the transmission estimation is translated into the construction of saturation lines. Accordingly, a new dehazing framework using SLP is proposed, which employs the intrinsic relevance between pixels to achieve a reliable saturation line construction for transmission estimation. This approach can recover the fine details and attain realistic colors from hazy scenes, resulting in a remarkable visibility improvement. Extensive experiments in real-world and synthetic hazy images show that the proposed method performs favorably against state-of-the-art dehazing methods. Code is available on https://github.com/LPengYang/Saturation-Line-Prior.

Recently, learning-based multi-exposure fusion (MEF) methods have made significant improvements. However, these methods mainly focus on static scenes and are prone to generate ghosting artifacts when tackling a more common scenario, i.e., the input images include motion, due to the lack of a benchmark dataset and solution for dynamic scenes. In this paper, we fill this gap by creating an MEF dataset of dynamic scenes, which contains multi-exposure image sequences and their corresponding high-quality reference images. To construct such a dataset, we propose a 'static-for-dynamic' strategy to obtain multi-exposure sequences with motions and their corresponding reference images. To the best of our knowledge, this is the first MEF dataset of dynamic scenes. Correspondingly, we propose a deep dynamic MEF (DDMEF) framework to reconstruct a ghost-free high-quality image from only two differently exposed images of a dynamic scene. DDMEF is achieved through two steps: pre-enhancement-based alignment and privilege-information-guided fusion. The former pre-enhances the input images before alignment, which helps to address the misalignments caused by the significant exposure difference. The latter introduces a privilege distillation scheme with an information attention transfer loss, which effectively improves the deghosting ability of the fusion network. Extensive qualitative and quantitative experimental results show that the proposed method outperforms state-of-the-art dynamic MEF methods. The source code and dataset are released at https://github.com/Tx000/Deep_dynamicMEF.

Most of the recent image segmentation methods have tried to achieve the utmost segmentation results using large-scale pixel-level annotated data sets. However, obtaining these pixel-level annotated training data is usually tedious and expensive. In this work, we address the task of semisupervised semantic segmentation, which reduces the need for large numbers of pixel-level annotated images. We propose a method for semisupervised semantic segmentation by improving the confidence of the predicted class probability map via two parts. First, we build an adversarial framework that regards the segmentation network as the generator and uses a fully convolutional network as the discriminator. The adversarial learning makes the prediction class probability closer to 1. Second, the information entropy of the predicted class probability map is computed to represent the unpredictability of the segmentation prediction. Then, we infer the label-error map of the segmentation prediction and minimize the uncertainty on misclassified regions for unlabeled images. In contrast to existing semisupervised and weakly supervised semantic segmentation methods, the proposed method results in more confident predictions by focusing on the misclassified regions, especially the boundary regions. Our experimental results on the PASCAL VOC 2012 and PASCAL-CONTEXT data sets show that the proposed method achieves competitive segmentation performance.

Pixel-by-pixel phase unwrapping (PPU) has been employed to rapidly achieve three-dimensional (3-D) shape measurement without additional projection patterns. However, the maximum measurement depth range that traditional PPU can handle is within 2π in phase domain; thus PPU fails to measure the dynamic object surface when the object moves in a large depth range. In this paper, we propose a novel adaptive pixel-by-pixel phase unwrapping (APPU), which extends PPU to an unlimited depth range. First, with PPU, temporary phase maps of objects are obtained referring to the absolute phase map of a background plane. Second, we quantify the difference between the image edges of the temporary phase maps and the practical depth edges of dynamic objects. Moreover, according to the degree of the edge difference, the temporary phase maps are categorized into two classes: failed phase maps and relative phase maps. Third, by combining a mobile reference phase map and the edge difference quantization technique, the failed phase maps are correspondently converted into relative phase maps. Finally, the relative phase maps are innovatively transformed into the absolute phase maps using a new shadow-informed depth estimation method (SDEM). The proposed approach is suitable for high-speed 3-D shape measurement without depth limitations or additional projection patterns.