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Abstract Background Ferroptosis is known to play a crucial role in diabetic osteopathy. However, key genes and molecular mechanisms remain largely unclear. This study aimed to identify a crucial ferroptosis-related differentially expressed gene (FR-DEG) in diabetic osteopathy and investigate its potential mechanism. Methods We identified fibronectin type III domain-containing protein 5 (FNDC5)/irisin as an essential FR-DEG in diabetic osteopathy using the Ferroptosis Database (FerrDb) and GSE189112 dataset. Initially, a diabetic mouse model was induced by intraperitoneal injection of streptozotocin (STZ), followed by intraperitoneal injection of irisin. MC3T3-E1 cells treated with high glucose (HG) were used as an in vitro model. FNDC5 overexpression plasmid was used to explore underlying mechanisms in vitro experiments. Femurs were collected for micro-CT scan, histomorphometry, and immunohistochemical analysis. Peripheral serum was collected for ELISA analysis. Cell viability was assessed using a CCK-8 kit. The levels of glutathione (GSH), malondialdehyde (MDA), iron, reactive oxygen species (ROS), and lipid ROS were detected by the corresponding kits. Mitochondria ultrastructure was observed through transmission electron microscopy (TEM). Finally, mRNA and protein expressions were examined by quantitative real‐time PCR (qRT‐PCR) and western blot analysis. Results The expression of FNDC5 was found to be significantly decreased in both in vivo and in vitro models. Treatment with irisin significantly suppressed ferroptosis and improved bone loss. This was demonstrated by reduced lipid peroxidation and iron overload, increased antioxidant capability, as well as the inhibition of the ferroptosis pathway in bone tissues. Furthermore, in vitro studies demonstrated that FNDC5 overexpression significantly improved HG-induced ferroptosis and promoted osteogenesis. Mechanistic investigations revealed that FNDC5 overexpression mitigated ferroptosis in osteoblasts by inhibiting the eukaryotic initiation factor 2 alpha (eIF2α)/activated transcription factor 4 (ATF4)/C/EBP-homologous protein (CHOP) pathway. Conclusions Collectively, our study uncovered the important role of FNDC5/irisin in regulating ferroptosis of diabetic osteopathy, which might be a potential therapeutic target.

The drastic increase of forest fire occurrence, which in recent years has posed severe threat and damage worldwide to the natural environment and human society, necessitates smoke detection of the early forest fire. First, a semantic segmentation method based on multiple color spaces feature fusion is put forward for forest fire smoke detection. Considering that smoke images in different color spaces may contain varied and distinctive smoke features which are beneficial for improving the detection ability of a model, the proposed model integrates the function of multi-scale and multi-type self-adaptive weighted feature fusion with attention augmentation to extract the enriched and complementary fused features of smoke, utilizing smoke images from multi-color spaces as inputs. Second, the model is trained and evaluated on part of the FIgLib dataset containing high-quality smoke images from watchtowers in the forests, incorporating various smoke types and complex background conditions, with a satisfactory smoke segmentation result for forest fire detection. Finally, the optimal color space combination and the fusion strategy for the model is determined through elaborate and extensive experiments with a superior segmentation result of 86.14 IoU of smoke obtained.

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.

BackgroundOsteoporosis is one of the most common systemic metabolic bone diseases, especially in postmenopausal women. Circular RNA (circRNA) has been implicated in various human diseases. However, the potential role of circRNAs in postmenopausal osteoporosis (PMOP) remains largely unknown. The study aims to identify potential biomarkers and further understand the mechanism of PMOP by constructing a circRNA-associated ceRNA network.MethodsThe PMOP-related datasets GSE161361, GSE64433, and GSE56116 were downloaded from the Gene Expression Omnibus (GEO) database and were used to obtain differentially expressed genes (DEGs). Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to determine possible relevant functions of differentially expressed messenger RNAs (mRNAs). The TRRUST database was used to predict differential transcription factor (TF)-mRNA regulatory pairs. Afterwards, combined CircBank and miRTarBase, circRNA-miRNA as well as miRNA-TF pairs were constructed. Then, a circRNA-miRNA-TF-mRNA network was established. Next, the correlation of mRNAs, TFs, and PMOP was verified by the Comparative Toxicogenomics Database. And expression levels of key genes, including circRNAs, miRNAs, TFs, and mRNAs in the ceRNA network were further validated by quantitative real-time PCR (qRT-PCR). Furthermore, to screen out signaling pathways related to key mRNAs of the ceRNA network, Gene Set Enrichment Analysis (GSEA) was performed.ResultsA total of 1201 DE mRNAs, 44 DE miRNAs, and 1613 DE circRNAs associated with PMOP were obtained. GO function annotation showed DE mRNAs were mainly related to inflammatory responses. KEGG analysis revealed DE mRNAs were mainly enriched in osteoclast differentiation, rheumatoid arthritis, hematopoietic cell lineage, and cytokine-cytokine receptor interaction pathways. We first identified 26 TFs and their target mRNAs. Combining DE miRNAs, miRNA-TF/mRNA pairs were obtained. Combining DE circRNAs, we constructed the ceRNA network contained 6 circRNAs, 4 miRNAs, 4 TFs, and 12 mRNAs. The expression levels of most genes detected by qRT-PCR were generally consistent with the microarray results. Combined with the qRT-PCR validation results, we eventually identified the ceRNA network that contained 4 circRNAs, 3 miRNAs, 3 TFs, and 9 mRNAs. The GSEA revealed that 9 mRNAs participate in many important signaling pathways, such as “olfactory transduction”, “T cell receptor signaling pathway”, and “neuroactive ligand-receptor interaction”. These pathways have been reported to the occurrence and development of PMOP. To sum up, key mRNAs in the ceRNA network may participate in the development of osteoporosis by regulating related signal pathways.ConclusionsA circRNA-associated ceRNA network containing TFs was established for PMOP. The study may help further explore the molecular mechanisms and may serve as potential biomarkers or therapeutic targets for PMOP.

Background Stress‐induced cell premature senescence participates in a variety of tissue and organ remodeling by secreting such proteins as proinflammatory cytokines, chemokines, and growth factors. However, the role of cardiomyocyte senescence in heart remodeling after acute myocardial infarction has not been thoroughly elucidated to date. Therefore, we sought to clarify the impact of premature myocardial senescence on postinfarction heart function. Methods and Results Senescence markers, including p16INK4a, p21CIP1/WAF1, and SA‐β‐gal staining, were analyzed in several heart disease models by immunostaining. Both postinfarction mouse hearts and ischemic human myocardium demonstrated increased senescence markers. Additionally, senescence‐related secretory phenotype was activated after acute myocardial infarction, which upregulated senescence‐related secretory phenotype factors, including CCN family member 1 (CCN1), interleukin‐1α, tumor necrosis factor α, and monocyte chemoattractant protein‐1. In vivo, a tail vein injection of AAV9‐Gata4‐shRNA significantly attenuated senescence‐related secretory phenotype secretion and aggravated postinfarction heart dysfunction. Furthermore, among activated senescence‐related secretory phenotype factors, CCN1 administration reduced myofibroblast viability in vitro and rescued the deleterious effect of AAV9‐Gata4‐shRNA in vivo. Conclusions Myocardial premature senescence was observed in the ischemic hearts and improved postinfarction heart function, partly through the GATA‐binding factor 4‐CCN1 pathway.

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]