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Background: Idiopathic pulmonary fibrosis (IPF), a chronic, progressive lung disease characterized by interstitial remodeling and tissue destruction, affects people worldwide and places a great burden on society. Cellular senescence is thought to be involved in the mechanisms and development of IPF. The aim of this study was to predictively investigate subtypes of IPF according to cellular senescence-related genes and their correlation with the outcome of patients with IPF, providing possible treatment and management options for disease control. Methods: Gene expression profiles and follow-up data were obtained from the GEO database. Senescence-related genes were obtained from the CSGene database and analyzed their correlation with the outcome of IPF. A consensus cluster was constructed to classify the samples based on correlated genes. The GSVA and WGCNA packages in R were used to calculate the immune-related enriched fractions and construct gene expression modules, respectively. Metascape and the clusterProfiler package in R were used to enrich gene functions. The ConnectivityMap was used to probe suitable drugs for potential treatment. Results: A total of 99 cellular senescence-related genes were associated with IPF prognosis. Patients with IPF were divided into two subtypes with significant prognostic differences. Subtype S2 was characterized by enhanced fibrotic progression and infection, leading to acute exacerbation of IPF and poor prognosis. Finally, five cellular senescence-related genes, TYMS, HJURP, UBE2C, BIRC5, and KIF2C, were identified as potential biomarkers in poor prognostic patients with IPF. Conclusion: The study findings indicate that cellular senescence-related genes can be used to distinguish the prognosis of patients with IPF. Among them, five genes can be used as candidate biomarkers to predict patients with a poor prognostic subtype for which anti-fibrosis and anti-infection treatments could be suitable. [ABSTRACT FROM AUTHOR]

Background: Automatic segmentation of lesion, organ, and tissue from the medical image is an important part of medical image analysis, which are useful for improving the accuracy of disease diagnosis and clinical analysis. For skin melanomas lesions, the contrast ratio between lesions and surrounding skin is low and there are many irregular shapes, uneven distribution, and local and boundary features. Moreover, some hair covering the lesions destroys the local context. Polyp characteristics such as shape, size, and appearance vary at different development stages. Early polyps with small sizes have no distinctive features and could be easily mistaken for other intestinal structures, such as wrinkles and folds. Imaging positions and illumination conditions would alter polyps’ appearance and lead to no visible transitions between polyps and surrounding tissue. It remains a challenging task to accurately segment the skin lesions and polyps due to the high variability in the location, shape, size, color, and texture of the target object.Developing a robust and accurate segmentation method for medical images is necessary. Purpose: To achieve better segmentation performance while dealing with the difficulties above, a U-shape network based on the encoder and decoder structure is proposed to enhance the segmentation performance in target regions. Methods: In this paper,a novel deep network of the encoder-decoder model that combines HarDNet, dual attention (DA),and reverse attention (RA) is proposed. First, HarDNet68 is employed to extract the backbone features while improving the inference speed and computational efficiency. Second, the DA block is adopted to capture the global feature dependency in spatial and channel dimensions, and enrich the contextual information on local features. At last, three RA blocks are exploited to fuse and refine the boundary features to obtain the final segmentation results. Results: Extensive experiments are conducted on a skin lesion dataset which consists of ISIC2016, ISIC2017, and ISIC 2018, and a polyp dataset which consists of several public datasets, that is, Kvasir, CVC-ClinicDB, CVC-ColonDB, ETIS, Endosece. The proposed method outperforms some state-of-art segmentation models on the ISIC2018,ISIC2017,and ISIC2016 datasets, with Jaccard’s indexes of 0.846, 0.881, and 0.894,mean Dice coefficients of 0.907, 0.929, and 03939, precisions of 0.908, 0.977, and 0.968, and accuracies of 0.953, 0.975, and 0.972. Additionally, the proposed method also performs better than some state-of-art segmentation models on the Kvasir, CVC-ClinicDB, CVC-ColonDB, ETIS, and Endosece datasets, with mean Dice coefficients of 0.907, 0.935, 0.716, 0.667, and 0.887, mean intersection over union coefficients of 0.850, 0.885, 0.644, 0.595, and 0.821, structural similarity measures of 0.918, 0.953, 0.823, 0.807, and 0.933, enhanced alignment measures of 0.952, 0.983, 0.850, 0.817,and 0.957, mean absolute errors of 0.026, 0.007, 0.037, 0.030,and 0.009. Conclusions: The proposed deep network could improve lesion segmentation performance in polyp and skin lesion images. The quantitative and qualitative results show that the proposed method can effectively handle the challenging task of segmentation while revealing the great potential for clinical application. [ABSTRACT FROM AUTHOR]

This study presents a microelectromechanical systems S-springs vibration ring gyroscope (MSVRG), which is driven by electrostatic force and detected by capacitance. First, a ring resonator structure with eight S-shaped symmetrical supporting springs is developed, and the capacitor electrodes are designed according to the vibration characteristics of the ring resonator. Then, a precise equivalent stiffness model of MSVRG is established based on the vibration mechanics and the Cassette theorem, which can be employed for any other ring gyroscope with differently shaped springs. Moreover, the mode resonant frequency error of between experiments, finite-element analysis (FEA) and theory calculation, is 10.54% and 3.76%, respectively. After that, the process of MSVRG structure is introduced and the structure is manufactured, and the theory calculation and FEA value with processed parameters have 1.19% and 4.59% difference with resonant frequency tested value, respectively. Finally, the static performance of the fabricated MSVRG is tested, and the bias instability is about 0.0119 deg/s and angle random walk is about 0.0359 deg/s1/2 at room temperature. [ABSTRACT FROM AUTHOR]