[Comparison of Quality of Life of the Patients Three Months after Uniportal 
and Multiportal Thoracoscopic Lobectomy].

BACKGROUND: The relationship between quality of life at three months after lung cancer surgery and different surgical approaches is remains unclear. This study aimed to compare the quality of life of patients three months after uniportal and multiportal thoracoscopic lobectomy. METHODS: Data from patients who underwent lung surgery at the Department of Thoracic Surgery, Sichuan Cancer Hospital between April 2021 and October 2021 were collected. The European Organization for Research and Treatment of Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) and Quality of Life Questionnaire-Lung Cancer 29 (EORTC QLQ-LC29) were used to collect quality of life data of the patients. Potential confounding factors in the baseline data were included in a multivariate regression model for adjustment, and the quality of life of the two groups three months postoperatively was compared with traditional clinical outcomes. RESULTS: A total of 130 lung cancer patients were included, with 57 males (43.8%) and 73 females (56.2%), and an average age of (57.1±9.5) yr. In the baseline data of the two groups, there was a statistical difference in the number of chest drainage tubes placed (P<0.001). After adjustment with the regression model, at three months postoperatively, there were no significant differences in all symptoms and functional status scores between the two groups (all P>0.05). The multiportal group had longer surgery time (120.0 min vs 85.0 min, P=0.001), postoperative hospital stay (6.0 d vs 4.0 d, P=0.020), and a higher incidence of early ≥ grade 2 complications (39.0% vs 10.1%, P=0.011) compared to the uniportal group. CONCLUSIONS: Patients undergoing uniportal and multiportal thoracoscopic lobectomy have similar quality of life at three months postoperatively. The uniportal group may have certain advantages in terms of traditional clinical outcome indicators such as operation time, postoperative hospital stay, and early postoperative complications.

CircRNA ZKSCAN1 promotes lung adenocarcinoma progression by miR-185-5p/TAGLN2 axis.

BACKGROUND: Circ-ZKSCAN1 has been found to accelerate non-small cell lung cancer (NSCLC) progression; however, the role and mechanism of circ-ZKSCAN1 in lung adenocarcinoma (LUAD) cisplatin (DDP) resistance remain unclear. METHODS: Levels of genes and proteins were examined using qRT-PCR. Functional experiments were performed using CCK-8 assay, flow cytometry, transwell assay and xenograft model assay, respectively. Glucose metabolism was calculated by detecting glucose consumption, lactate production, ATP and HK-2 levels. The interaction between miR-185-5p and circ-ZKSCAN1 or transgelin 2 (TAGLN2) was validated by dual-luciferase reporter assay. RESULTS: Circ-ZKSCAN1 was highly expressed in DDP-resistant LUAD tissues and cell lines, and circ-ZKSCAN1 knockdown weakened DDP resistance and suppressed cell viability, migration, invasion, and glycolysis in LUAD. Circ-ZKSCAN1 acted as a sponge of miR-185-5p, and the regulatory effects of circ-ZKSCAN1 knockdown on LUAD were reversed by miR-185-5p downregulation. Meanwhile, miR-185-5p directly targeted TAGLN2, and performed anticancer effects by regulating TAGLN2. Importantly, silencing of circ-ZKSCAN1 hindered tumor growth and promoted DDP sensitivity in vivo via regulating miR-185-5p and TAGLN2. CONCLUSION: Circ-ZKSCAN1 promoted LUAD tumorigenesis and DDP resistance by regulating miR-185-5p/TAGLN2 axis.

D-BIN: A Generalized Disentangling Batch Instance Normalization for Domain Adaptation.

Pattern recognition is significantly challenging in real-world scenarios by the variability of visual statistics. Therefore, most existing algorithms relying on the independent identically distributed assumption of training and test data suffer from the poor generalization capability of inference on unseen testing datasets. Although numerous studies, including domain discriminator or domain-invariant feature learning, are proposed to alleviate this problem, the data-driven property and lack of interpretation of their principle throw researchers and developers off. Consequently, this dilemma incurs us to rethink the essence of networks' generalization. An observation that visual patterns cannot be discriminative after style transfer inspires us to take careful consideration of the importance of style features and content features. Does the style information related to the domain bias? How to effectively disentangle content and style features across domains? In this article, we first investigate the effect of feature normalization on domain adaptation. Based on it, we propose a novel normalization module to adaptively leverage the propagated information through each channel and batch of features called disentangling batch instance normalization (D-BIN). In this module, we explicitly explore domain-specific and domaininvariant feature disentanglement. We maneuver contrastive learning to encourage images with the same semantics from different domains to have similar content representations while having dissimilar style representations. Furthermore, we construct both self-form and dual-form regularizers for preserving the mutual information (MI) between feature representations of the normalization layer in order to compensate for the loss of discriminative information and effectively match the distributions across domains. D-BIN and the constrained term can be simply plugged into state-of-the-art (SOTA) networks to improve their performance. In the end, experiments, including domain adaptation and generalization, conducted on different datasets have proven their effectiveness.

Fraxetin down-regulates polo-like kinase 4 (PLK4) to inhibit proliferation, migration and invasion of prostate cancer cells through the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway.

Fraxetin, a natural product isolated from herb Cortex Fraxini, has been demonstrated to exhibit anti-cancer effects on various cancers. The aim of this work is to investigate the anti-tumor effect of Fraxetin in prostate cancer and the potential mechanisms. In this study, the prostatic epithelial cell RWPE-1 and prostate cancer cell DU145 were exposed to Fraxetin (10, 20, 40, and 80 µM) to detect the changes in cell viability using cell counting kit-8 (CCK-8) assay. Fraxetin (10, 20, and 40 µM) was utilized to treat DU145 cell, then the changes in cell proliferation, apoptosis, migration, and invasion were assessed. Western blot assay was employed to detect the expression of proteins that participate in the above cellular processes as well as Polo-like kinase 4 (PLK4), phosphatidylinositol 3-kinase (PI3K). In addition to 40 µM Fraxetin treatment, DU145 cells were overexpressed with PLK4, and then the above experiments were repeated. Results revealed that Fraxetin markedly decreased DU145 cell viability, but didn't affect the cell viability of RWPE-1. Fraxetin suppressed cell proliferation, migration, invasion, and induced apoptosis of DU145 cells in a concentration-dependent manner. Furthermore, the expression of PLK4 and phosphorylated PI3K and protein kinase B (Akt) were reduced upon Fraxetin treatment. Finally, PLK4 overexpression significantly reversed all the effects of Fraxetin on DU145 cells. Collectively, Fraxetin acted as a cancer suppressor in prostate cancer through inhibiting PLK4 expression thereby inactivating PI3K/Akt signaling.

Deep Stereo Matching With Hysteresis Attention and Supervised Cost Volume Construction.

Stereo matching disparity prediction for rectified image pairs is of great importance to many vision tasks such as depth sensing and autonomous driving. Previous work on the end-to-end unary trained networks follows the pipeline of feature extraction, cost volume construction, matching cost aggregation, and disparity regression. In this paper, we propose a deep neural network architecture for stereo matching aiming at improving the first and second stages of the matching pipeline. Specifically, we show a network design inspired by hysteresis comparator in the circuit as our attention mechanism. Our attention module is multiple-block and generates an attentive feature directly from the input. The cost volume is constructed in a supervised way. We try to use data-driven to find a good balance between informativeness and compactness of extracted feature maps. The proposed approach is evaluated on several benchmark datasets. Experimental results demonstrate that our method outperforms previous methods on SceneFlow, KITTI 2012, and KITTI 2015 datasets.

A Local Metric for Defocus Blur Detection Based on CNN Feature Learning.

Defocus blur detection is an important and challenging task in computer vision and digital imaging fields. Previous work on defocus blur detection has put a lot of effort into designing local sharpness metric maps. This paper presents a simple yet effective method to automatically obtain the local metric map for defocus blur detection, which based on the feature learning of multiple convolutional neural networks (ConvNets). The ConvNets automatically learn the most locally relevant features at the super-pixel level of the image in a supervised manner. By extracting convolution kernels from the trained neural network structures and processing it with principal component analysis, we can automatically obtain the local sharpness metric by reshaping the principal component vector. Meanwhile, an effective iterative updating mechanism is proposed to refine the defocus blur detection result from coarse to fine by exploiting the intrinsic peculiarity of the hyperbolic tangent function. The experimental results demonstrate that our proposed method consistently performed better than the previous state-of-the-art methods.