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"Zhou, Qian"
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Enhanced gallbladder cancer detection via active and self-supervised learning integration: Innovating B-ultrasound image analysis
Gallbladder cancer, a common yet often under diagnosed malignancy, is typically characterized by late detection and a poor prognosis. The rise of deep learning has introduced new methods for its early identification through B-ultrasound imaging, but there are still challenges of inefficient data labeling and feature extraction. This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. Secondly, we introduce the MsHop module, which effectively captures the fine textures and patterns in ultrasound images through the integration of multi-scale and high-order information, thereby improving diagnostic accuracy. Additionally, we develop a dual-branch loss function that leverages data correlation and clustering features to enhance feature extraction and model stability. The experiments on a gallbladder ultrasound dataset have confirmed the effectiveness of our algorithm, achieving an accuracy of 0.884, a specificity of 0.932, and a sensitivity of 0.912—outperforming existing methods. The results exhibit lower variance, indicating improved model stability. Furthermore, the findings demonstrate that using active learning, one can achieve comparable results to those from the full dataset with only 35% of the data, reducing annotation costs and increasing model learning efficiency. Further research will concentrate on refining the algorithm for wider clinical use and identifying additional features that may further improve diagnostic accuracy.
Journal Article
Research Progress on the Pathogenesis of Knee Osteoarthritis
Knee osteoarthritis (KOA) is a chronic joint bone disease characterized by inflammatory destruction and hyperplasia of bone. Its main clinical symptoms are joint mobility difficulties and pain, severe cases can lead to limb paralysis, which poses major pressure to the quality of life and mental health of patients, but also brings serious economic burden to society. The occurrence and development of KOA is influenced by many factors, including systemic factors and local factors. The joint biomechanical changes caused by aging, trauma and obesity, abnormal bone metabolism caused by metabolic syndrome, the effects of cytokines and related enzymes, genetic and biochemical abnormalities caused by plasma adiponectin, etc. all directly or indirectly lead to the occurrence of KOA. However, there is little literature that systematically and comprehensively integrates macro‐ and microscopic KOA pathogenesis. Therefore, it is necessary to comprehensively and systematically summarize the pathogenesis of KOA in order to provide a better theoretical basis for clinical treatment. Macroscopic factors affecting the occurrence and development of knee osteoarthritis.
Journal Article
Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial Network
With the widespread adoption of Generative Adversarial Networks (GANs) in image generation and processing, enhancing their generation quality and visualization capabilities has become a prominent research focus. This study introduces a deep learning framework that integrates multi-scale information chart generation with visualization enhancement to improve the performance of GAN-based image generation models across various domains. Based on the Self-Attention Generative Adversarial Network (SAGAN), which leverages self-attention mechanisms to capture long-range dependencies in images, the proposed approach significantly enhances image quality and detail representation. The framework incorporates a multi-scale feature extraction method to optimize the feature maps at each layer of the generative network. Experimental results demonstrate that SAGAN outperforms traditional GAN models in terms of image clarity, detail preservation, and visual effects. The proposed model achieves notable improvements in diversity and generalization, with a mutual information content of 0.91, clustering uniformity of 0.89, and inter-cluster dissimilarity of 0.92 on the CelebA dataset. Furthermore, in terms of image quality, SAGAN attains a Structural Similarity Index Measure (SSIM) of 0.94 and a Peak Signal-to-Noise Ratio (PSNR) of 30.1, surpassing traditional GANs by a significant margin.Article HighlightsInnovatively combining the self-attention mechanism with multi-scale feature extraction, it significantly improves the quality and detail performance of image generation, and brings a breakthrough in the field of data visualization.On the CelebA dataset, the mutual information of the model reaches 0.91, clustering uniformity 0.89, and inter-cluster variability 0.92; in terms of image quality, the SSIM value is 0.94 and the PSNR value is 30.1, which significantly exceeds the traditional GAN model.Through strategies such as color adjustment, style optimization and line detail enhancement, the visual effect of the generated charts is further optimized, so that the charts not only accurately convey the data, but also have stronger readability and aesthetics.
Journal Article
RETRACTED: Resveratrol Inhibits the Migration and Metastasis of MDA-MB-231 Human Breast Cancer by Reversing TGF-β1-Induced Epithelial-Mesenchymal Transition
Metastasis is a major cause of death in patients with breast cancer. In the process of cancer development, epithelial-mesenchymal transition (EMT) is crucial to promoting the invasion and migration of tumor cells. In a previous study, the role of resveratrol in migration and metastasis was investigated in MDA-MB-231 (MDA231) human breast cancer cells and a xenograft-bearing mouse model. Additionally, the related mechanism was explored. In the present study, in vitro Transwell assays showed that resveratrol can inhibit the migration of transforming growth factor (TGF)-β1-induced MDA231 cells in a concentration-dependent manner. An enzyme-linked immunosorbent assay (ELISA) showed that resveratrol can reduce the secretion of matrix metalloproteinase (MMP)-2 and MMP-9. Immunofluorescence was performed to confirm the expression of EMT-related markers. Immunofluorescence assays confirmed that resveratrol changed the expression of the EMT-related markers E-cadherin and vimentin. Western blot analysis demonstrated that resveratrol decreased the expression levels of MMP-2, MMP-9, Fibronectin, α-SMA, P-PI3K, P-AKT, Smad2, Smad3, P-Smad2, P-Smad3, vimentin, Snail1, and Slug, as well as increased the expression levels of E-cadherin in MDA231 cells. In vivo, resveratrol inhibited lung metastasis in a mouse model bearing MDA231 human breast cancer xenografts without marked changes in body weight or liver and kidney function. These results indicate that resveratrol inhibits the migration of MDA231 cells by reversing TGF-β1-induced EMT and inhibits the lung metastasis of MDA231 human breast cancer in a xenograft-bearing mouse model.
Journal Article
Peptide Biomarkers Discovery for Seven Species of Deer Antler Using LC-MS/MS and Label-Free Approach
Deer antler is a globally widely used precious natural medicine and the material of deer horn gelatin. However, identification of deer antler species based on traditional approaches are problematic because of their similarity in appearance and physical-chemical properties. In this study, we performed a comprehensive antler peptidome analysis using a label-free approach: nano LC-Orbitrap MS was applied to discover peptide biomarkers in deer adult beta-globin (HBBA), and HPLC-Triple Quadrupole MS was used to verify their specificity. Nineteen peptide biomarkers were found, on which foundation a strategy for antlers and a strategy for antler mixtures such as flakes or powder are provided to identify seven species of deer antler including Eurasian elk (Alces alces), reindeer (Rangifer tarandus), white-tailed deer (Odocoileus viginianus), white-lipped deer (Przewalskium albirostris), fallow deer (Dama dama), sika deer (Cervus nippon), and red deer (Cervus elaphus) simultaneously. It is worth noting that our search found that the HBBA gene of sika deer, red deer, and North American wapiti (Cervus canadensis) in China may have undergone severe genetic drifts.
Journal Article
Resveratrol Enhances Inhibition Effects of Cisplatin on Cell Migration and Invasion and Tumor Growth in Breast Cancer MDA-MB-231 Cell Models In Vivo and In Vitro
Triple-negative breast cancer (TNBC) is a refractory type of breast cancer that does not yet have clinically effective drugs. The aim of this study is to investigate the synergistic effects and mechanisms of resveratrol combined with cisplatin on human breast cancer MDA-MB-231 (MDA231) cell viability, migration, and invasion in vivo and in vitro. In vitro, MTS assays showed that resveratrol combined with cisplatin inhibits cell viability as a concentration-dependent manner, and produced synergistic effects (CI < 1). Transwell assay showed that the combined treatment inhibits TGF-β1-induced cell migration and invasion. Immunofluorescence assays confirmed that resveratrol upregulated E-cadherin expression and downregulated vimentin expression. Western blot assay demonstrated that resveratrol combined with cisplatin significantly reduced the expression of fibronectin, vimentin, P-AKT, P-PI3K, P-JNK, P-ERK, Sma2, and Smad3 induced by TGF-β1 (p < 0.05), and increased the expression of E-cadherin (p < 0.05), respectively. In vivo, resveratrol enhanced tumor growth inhibition and reduced body weight loss and kidney function impairment by cisplatin in MDA231 xenografts, and significantly reduced the expressions of P-AKT, P-PI3K, Smad2, Smad3, P-JNK, P-ERK, and NF-κB in tumor tissues (p < 0.05). These results indicated that resveratrol combined with cisplatin inhibits the viability of breast cancer MDA231 cells synergistically, and inhibits MDA231 cells invasion and migration through Epithelial-mesenchymal transition (EMT) approach, and resveratrol enhanced anti-tumor effect and reduced side of cisplatin in MDA231 xenografts. The mechanism may be involved in the regulations of PI3K/AKT, JNK, ERK and NF-κB expressions.
Journal Article
MicroRNAs as potential biomarkers for the diagnosis of glioma: A systematic review and meta‐analysis
Glioma is the most common central nervous system tumor and associated with poor prognosis. Identifying effective diagnostic biomarkers for glioma is particularly important in order to guide optimizing treatment. MicroRNAs (miRNAs) have drawn much attention because of their diagnostic value in diverse cancers, including glioma. We summarized studies to identify the potential diagnostic values of miRNAs in glioma patients. We included articles reporting miRNAs for differentiation of glioma patients from controls. We calculated sensitivities, specificities, and area under the curves (AUC) of individual miRNA and miRNA panels. We found that overall sensitivity, specificity, and AUC of miRNAs in diagnosis of glioma were 85% (95% confidence interval [CI]: 0.81‐0.89), 90% (95% CI 0.85‐0.93), and 93% (95% CI 0.91‐0.95), respectively. Meta‐regression analysis showed that the detection of miRNAs expression in cerebrospinal fluid (CSF) and brain tissue largely improved the diagnostic accuracy. Likewise, panels of multiple miRNAs could enhance the pooled sensitivity. Moreover, AUC of miR‐21 was 0.88, with 86% sensitivity and 94% specificity. This study demonstrated that miRNAs could function as potential diagnosis markers in glioma. Detection of miRNAs in CSF and brain tissue displays high accuracy in the diagnosis of glioma. Review and meta‐analysis to access diagnostic values of miRNAs in glioma. Overall miRNAs have an outstanding diagnostic accuracy of glioma. miRNAs detected in CSF and brain tissue largely improve the diagnostic accuracy. Panels of multiple miRNAs could enhance the pooled sensitivity. MiR‐21 has a high sensitivity and specificity in detection of glioma.
Journal Article
Quantitative evaluation of DNA damage and mutation rate by atmospheric and room-temperature plasma (ARTP) and conventional mutagenesis
DNA damage is the dominant source of mutation, which is the driving force of evolution. Therefore, it is important to quantitatively analyze the DNA damage caused by different mutagenesis methods, the subsequent mutation rates, and their relationship. Atmospheric and room temperature plasma (ARTP) mutagenesis has been used for the mutation breeding of more than 40 microorganisms. However, ARTP mutagenesis has not been quantitatively compared with conventional mutation methods. In this study, the umu test using a flow-cytometric analysis was developed to quantify the DNA damage in individual viable cells using Salmonella typhimurium NM2009 as the model strain and to determine the mutation rate. The newly developed method was used to evaluate four different mutagenesis systems: a new ARTP tool, ultraviolet radiation, 4-nitroquinoline-1-oxide (4-NQO), and N-methyl-N’-nitro-N-nitrosoguanidine (MNNG) mutagenesis. The mutation rate was proportional to the corresponding SOS response induced by DNA damage. ARTP caused greater DNA damage to individual living cells than the other conventional mutagenesis methods, and the mutation rate was also higher. By quantitatively comparing the DNA damage and consequent mutation rate after different types of mutagenesis, we have shown that ARTP is a potentially powerful mutagenesis tool with which to improve the characteristics of microbial cell factories.
Journal Article
Direct radical functionalization of native sugars
Naturally occurring (native) sugars and carbohydrates contain numerous hydroxyl groups of similar reactivity
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,
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. Chemists, therefore, rely typically on laborious, multi-step protecting-group strategies
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to convert these renewable feedstocks into reagents (glycosyl donors) to make glycans. The direct transformation of native sugars to complex saccharides remains a notable challenge. Here we describe a photoinduced approach to achieve site- and stereoselective chemical glycosylation from widely available native sugar building blocks, which through homolytic (one-electron) chemistry bypasses unnecessary hydroxyl group masking and manipulation. This process is reminiscent of nature in its regiocontrolled generation of a transient glycosyl donor, followed by radical-based cross-coupling with electrophiles on activation with light. Through selective anomeric functionalization of mono- and oligosaccharides, this protecting-group-free ‘cap and glycosylate’ approach offers straightforward access to a wide array of metabolically robust glycosyl compounds. Owing to its biocompatibility, the method was extended to the direct post-translational glycosylation of proteins.
A radical-based method for functionalizing native sugars shows a way to remove typical protecting-group manipulations.
Journal Article