Rheumatoid arthritis(RA)is an autoimmune disease characterized by synovitis,synovial cell proliferation,neo-vascularization,and bone and cartilage destruction.Its pathogenesis is complex and has not yet been fully elucidated.A variety of cells,cytokines and signaling pathways are involved in the pathogenesis of RA.Hypoxia-inducing factor(HIF)and oxygen-sensing signaling pathway(PHD-HIF-VHL)are closely related to the occurrence and development of RA,and play an important role in syno-vial cell proliferation,inflammatory response and cartilage destruction.In this study,the research progress of oxygen sensing signaling pathway in RA was described from the aspects of the mechanism of oxygen sensing signaling pathway and its involvement in the patho-genesis of RA,in order to provide ideas and theoretical basis for the research of anti-RA drugs by targeting important molecules of oxy-gen sensing signaling pathway.

Objective To compare the prognostic differences between simple and complex segmentectomies. Methods We conducted a retrospective cohort analysis of patients with solid pulmonary nodules (≤2 cm) who underwent segmentectomy. Recurrence-free survival (RFS) and local recurrence rates were evaluated. Results We included57 patients undergoing complex segmentectomy and 53 patients undergoing simple segmentectomy. Among patients who did not receive adjuvant therapy, those in the complex group had a significantly lower five-year RFS than those in the simple group (69.86% vs. 85.97%, P=0.04). Furthermore, the local recurrence rate was significantly higher in the complex group (18.75% vs. 4.65%, P=0.003) than in the simple group. Conclusion For solid pulmonary nodules (≤2 cm), complex segmentectomy is associated with inferior local control and worse RFS than simple segmentectomy.

The diagnosis of Meige's syndrome predominantly relies on the clinical assessment by physicians.Given the complexity and similarity of its symptoms to other neurological disorders,the diagnosis is crucial for both doctors and patients.Herein a detection dataset for Meige's syndrome is compiled from video recordings of 31 patients,and an automated diagnostic system for Meige's syndrome(MS-Net)applicable to untrimmed videos is developed.The system utilizes RetinaNet and UNet3+to construct temporal detection and segmentation branches for multi-scale feature extraction and temporal segmentation,obtains probability vectors for detection windows and the probability of disease onset per frame via the decoding of temporal detection and segmentation branches,and finally generates a refined probability for each window by processing the probability predictions from both branches using a multi-layer perceptron.The model performance is optimized using additional loss functions and data augmentation techniques,operating on features interpretable by clinical physicians.MS-Net can assist in the diagnosis of Meige's syndrome,improving the accuracy,convenience,and efficiency of the early diagnosis.The comparison of MS-Net with other state-of-the-art networks indicates that MS-Net achieves comparable performance in terms of average precision while utilizing interpretable features required in clinical practice.

The research on medical image segmentation is of great significance in advancing efficient and accurate automated image processing techniques.To address the problem of inaccurate segmentation results caused by significant variations in organ tissue shapes and blurred boundaries present in medical images,a novel network named MFMANet is proposed.Built upon a"U"-shaped architecture,the network integrates multi-scale information fusion modules and multi-attention modules.Specifically,multi-scale information modules capture multi-scale information in the shallow layers of the network to bridge the semantic gap between encoder and decoder features,thereby enhancing the network's ability to handle large variations in organ sizes.Regarding the issue of blurred boundaries,multi-attention mechanism utilizes Swin Transformer as the deep encoder-decoder network,employing channel and spatial attention instead of traditional skip connections to achieve finer feature extraction.Experimental results on the ACDC and Synapse public datasets show that the proposed method achieves improvements of 1.51%and 1.29%in Dice similarity coefficient as compared with MTUNet,fully demonstrating its effectiveness in enhancing segmentation network accuracy.

The diagnosis of Meige's syndrome predominantly relies on the clinical assessment by physicians.Given the complexity and similarity of its symptoms to other neurological disorders,the diagnosis is crucial for both doctors and patients.Herein a detection dataset for Meige's syndrome is compiled from video recordings of 31 patients,and an automated diagnostic system for Meige's syndrome(MS-Net)applicable to untrimmed videos is developed.The system utilizes RetinaNet and UNet3+to construct temporal detection and segmentation branches for multi-scale feature extraction and temporal segmentation,obtains probability vectors for detection windows and the probability of disease onset per frame via the decoding of temporal detection and segmentation branches,and finally generates a refined probability for each window by processing the probability predictions from both branches using a multi-layer perceptron.The model performance is optimized using additional loss functions and data augmentation techniques,operating on features interpretable by clinical physicians.MS-Net can assist in the diagnosis of Meige's syndrome,improving the accuracy,convenience,and efficiency of the early diagnosis.The comparison of MS-Net with other state-of-the-art networks indicates that MS-Net achieves comparable performance in terms of average precision while utilizing interpretable features required in clinical practice.

Rheumatoid arthritis(RA)is an autoimmune disease characterized by synovitis,synovial cell proliferation,neo-vascularization,and bone and cartilage destruction.Its pathogenesis is complex and has not yet been fully elucidated.A variety of cells,cytokines and signaling pathways are involved in the pathogenesis of RA.Hypoxia-inducing factor(HIF)and oxygen-sensing signaling pathway(PHD-HIF-VHL)are closely related to the occurrence and development of RA,and play an important role in syno-vial cell proliferation,inflammatory response and cartilage destruction.In this study,the research progress of oxygen sensing signaling pathway in RA was described from the aspects of the mechanism of oxygen sensing signaling pathway and its involvement in the patho-genesis of RA,in order to provide ideas and theoretical basis for the research of anti-RA drugs by targeting important molecules of oxy-gen sensing signaling pathway.

The research on medical image segmentation is of great significance in advancing efficient and accurate automated image processing techniques.To address the problem of inaccurate segmentation results caused by significant variations in organ tissue shapes and blurred boundaries present in medical images,a novel network named MFMANet is proposed.Built upon a"U"-shaped architecture,the network integrates multi-scale information fusion modules and multi-attention modules.Specifically,multi-scale information modules capture multi-scale information in the shallow layers of the network to bridge the semantic gap between encoder and decoder features,thereby enhancing the network's ability to handle large variations in organ sizes.Regarding the issue of blurred boundaries,multi-attention mechanism utilizes Swin Transformer as the deep encoder-decoder network,employing channel and spatial attention instead of traditional skip connections to achieve finer feature extraction.Experimental results on the ACDC and Synapse public datasets show that the proposed method achieves improvements of 1.51%and 1.29%in Dice similarity coefficient as compared with MTUNet,fully demonstrating its effectiveness in enhancing segmentation network accuracy.

Given that low-dose computed tomography significantly amplifies image noise due to the mitigation of radiation exposure,which degrades image quality and lowers the precision of clinical diagnoses,a novel model incorporating convolutional neural network and Transformer is established,in which an intra-patch feature extraction module is used to effectively preserve tiny details in the image.A double attention Transformer is constructed by incorporating a multiple-input channel attention module into the self-attention for tackling the problem of incorrect restoration of texture details during denoising using Swin Transformer.AAPM dataset is used for testing,and the results demonstrate that the proposed algorithm not only surpasses the existing algorithms in denoising performance,but also excels in preserving tiny details in the image.

A U-Net incorporating improved Transformer and convolutional channel attention module is designed for biventricular segmentation in MRI image.By replacing the high-level convolution of U-Net with the improved Transformer,the global feature information can be effectively extracted to cope with the challenge of poor segmentation performance due to the complex morphological variation of the right ventricle.The improved Transformer incorporates a fixed window attention for position localization in the self-attention module,and aggregates the output feature map for reducing the feature map size;and the network learning capability is improved by increasing network depth through the adjustment of multilayer perceptron.To solve the problem of unsatisfactory segmentation performance caused by blurred tissue edges,a feature aggregation module is used for the fusion of multi-level underlying features,and a convolutional channel attention module is adopted to rescale the underlying features to achieve adaptive learning of feature weights.In addition,a plug-and-play feature enhancement module is integrated to improve the segmentation performance which is affected by feature loss due to channel decay in the codec structure,which guarantees the spatial information while increasing the proportion of useful channel information.The test on the ACDC dataset shows that the proposed method has higher biventricular segmentation accuracy,especially for the right ventricle segmentation.Compared with other methods,the proposed method improves the DSC coefficient by at least 2.83%,proving its effectiveness in biventricular segmentation.

Methods:Cultured human alveolar epithelial A549 cells were assigned into LPS group and blank control group. LPS group was stimulated with LPS and adenosine triphosphate to induce pyroptosis and inflammation. A549 cells were divided into 4 groups: miR-20a mimics group, mimics-negative control (NC) group, inhibitor group and inhibitor-NC group. MiRNA-20a mimics, mimics-NC, inhibitor, and inhibitor-NC were transfected respectively into A549 cells, and after 24 h, the cells were collected to verify transfection efficiency by qPCR. MiRNA-20a mimics and the constructed TLR4-3'UTR double luciferase reporter plasmid were co-transfected into A549 cells, and luciferase activity was analyzed. MiRNA-20a mimics/inhibitors were transfected into A549 cells, and then the cells were stimulated by LPS for 8 h followed by adenosine triphosphate for 30 min. QPCR, Western Blot and ELISA were used to detect the expression of GSDMD, inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) and Signaling molecules (TLR4、NF-κB) in A549 cells at mRNA level and protein level. Immunofluorescence was used to detect the expression of TLR4 in the A549 cells and NF-κB in the nucleus of A549 cells after transfecting with miRNA-20a mimics/inhibitor.Results:The mRNA and protein expression of pyroptosis marker molecule (GSDMD) and inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) in A549 cells stimulated with LPS were significantly higher than those in the blank control group, and the differences were statistically significant ( P<0.05). The expression of miRNA-20 in the mimics group was significantly higher than that in the mimic-NC group ( P<0.05), while the expression of miRNA-20a in the inhibitor group was lower than that in the inhibitor-NC group ( P<0.01). The double luciferase reporter gene experiment showed that the relative fluorescence value of the co-transfection group for TLR4-3'UTR-WT and miRNA-20a mimics was significantly lower than the co-transfection group for TLR4-3'UTR-WT and miRNA-20a mimics-NC ( P<0.05). The mRNA and protein levels of pyroptosis marker molecule (GSDMD) , inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) and signaling molecules (TLR4, NF-κB) were decreased in the mimics group compared to the mimics-NC group, and increased in inhibitor group compared to inhibitor-NC group. Conclusions:miRNA-20a may inhibit LPS-induced pyroptosis and inflammation of A549 cells via TLR4/NF-κB signal pathway.Objetive:To explore the potential role of miRNA-20a in lipopolysaccharide (LPS) induced pyroptosis and inflamation of human alveolar epithelial A549 cells and its regulation mechanisim.