Explore the vast range of titles available.

Accurate segmentation of 3D dental structures is essential for oral diagnosis, orthodontic planning, and digital dentistry. With the rapid advancement of 3D scanning and modeling technologies, high-resolution dental data have become increasingly common. However, existing approaches still struggle to process such high-resolution data efficiently. Current models often suffer from excessive parameter counts, slow inference, high computational overhead, and substantial GPU memory usage. These limitations compel many studies to downsample the input data to reduce training and inference costs—an operation that inevitably diminishes critical geometric details, blurs tooth boundaries, and compromises both fine-grained structural accuracy and model robustness. To address these challenges, this study proposes DiffusionNet++, an end-to-end segmentation framework capable of operating directly on raw high-resolution dental data. Building upon the standard DiffusionNet architecture, our method introduces a normal-enhanced multi-feature input strategy together with a lightweight SE channel-attention mechanism, enabling the model to effectively exploit local directional cues, curvature variations, and other higher-order geometric attributes while adaptively emphasizing discriminative feature channels. Experimental results demonstrate that the coordinates + normal feature configuration consistently delivers the best performance. DiffusionNet++ achieves substantial improvements in overall accuracy (OA), mean Intersection over Union (mIoU), and individual class IoU across all data types, while maintaining strong robustness and generalization on challenging cases, such as missing teeth and partially scanned data. Qualitative visualizations further corroborate these findings, showing superior boundary consistency, finer structural preservation, and enhanced recovery of incomplete regions. Overall, DiffusionNet++ offers an efficient, stable, and highly accurate solution for high-resolution 3D tooth segmentation, providing a powerful foundation for automated digital dentistry research and real-world clinical applications.

With the goal of automatic sea ice mapping during the summer sea ice melt cycle, this study involved designing a fully automatic sea ice segmentation method based on a deep learning semantic segmentation network applicable to summer SAR images, which achieved high accuracy and the fully automatic extraction of sea ice segmentation during the summer ice melt cycle by optimizing the process, improving the pixel-level semantic segmentation network, and introducing high-resolution sea ice concentration features. Firstly, a convolution-based, high-resolution sea ice concentration calculation method is proposed and was applied to the deep learning task. Secondly, the proposed DF-UHRNet network was improved upon by designing high- and low-level fusion modules, introducing an attention mechanism, and reducing the number of convolution layers and other operations, and it can effectively fuse high- and low-scale semantic features and global contextual information based on reducing the overall number of network parameters, enabling it to achieve pixel-level classification. The results show that this method meets the needs associated with the automatic mapping and high-precision classification of thin ice, one-year ice, open water, and multi-year ice and effectively reduces the model size.

Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST-UNet) model based on the UNet model. In particular, in the encoding phase of ST-UNet, we use parallel convolution with different kernel sizes to efficiently extract spatial features, and use ConvLSTM to capture temporal features based on the utilization of spatial features. Atrous Spatial Pyramid Pooling (ASPP) module is placed at the bottleneck of the network to further incorporate the multi-scale features, allowing the spatial features to be fully utilized. The final prediction is then generated in the decoding stage using parallel convolution with different kernel sizes similar to the encoding stage. We conducted a series of experiments on the Bohai Sea and Yellow Sea SST data set, as well as the South China Sea SST data set, using SST data from the past 35 days to predict SST data for 1, 3, and 7 days in the future. The model was trained using data spanning from 2010 to 2021, with data from 2022 being utilized to assess the model’s predictive performance. The experimental results show that the model proposed in this research paper achieves excellent results at different prediction scales in both sea areas, and the model consistently outperforms other methods. Specifically, in the Bohai Sea and Yellow Sea sea areas, when the prediction scales are 1, 3, and 7 days, the MAE of ST-UNet outperforms the best results of the other three compared models by 17%, 12%, and 2%, and the MSE by 16%, 18%, and 9%, respectively. In the South China Sea, when the prediction ranges are 1, 3, and 7 days, the MAE of ST-UNet is 27%, 18%, and 3% higher than the best of the other three compared models, and the MSE is 46%, 39%, and 16% higher, respectively. Our results highlight the effectiveness of the ST-UNet model in capturing spatial correlations and accurately predicting SST. The proposed model is expected to improve marine hydrographic studies.

Background Diffuse large B cell lymphoma (DLBCL) is a more common non-Hodgkin lymphoma (NHL). This study aims to explore the prognostic value of PIM kinase family in DLBCL and its relationship with the immune microenvironment, to provide a certain reference for the prognosis and treatment of DLBCL. Methods The prognostic value of PIM kinase family in DLBCL from the data set GSE10846 was verified through survival analysis and cox regression analysis. Mutations in PIM kinase family and its relationship with immune cell infiltration were explored with online cBioPortal, TIMER database, and single-gene GSEA analysis. Finally, the expression of PIM kinase family in tissues from DLBCL clinical samples was validated through immunohistochemical staining. Results The proteins of PIM kinase family were highly expressed in DLBCL patients, which are good prognostic factors for DLBCL patients. Then, PIM1-3 proteins were positively correlated with the immune infiltration of B cells, whose types of mutations also showed different degrees of correlation with B cells. PIM kinase family proteins also showed a high correlation with PDL1. In addition, PIM kinase family was also associated with the commonly mutated genes in DLBCL, such as MYD88, MYC, and BTK. Conclusion PIM kinase family may be a potential therapeutic target for DLBCL patients.

Immune-mediated hepatic injury plays a key role in the initiation and pathogenesis of diverse liver diseases. However, treatment choice for immune-mediated hepatic injury remains limited. Corilagin, a natural ellagitannin extracted from various traditional Chinese medicines, has been demonstrated to exhibit multiple pharmacological activities, such as anti-inflammatory, anti-tumor, and hepatoprotective properties. The present study aimed to investigate the effects of corilagin on immune-mediated hepatic injury using a murine model of concanavalin A (Con A)-induced hepatitis, which is well-characterized to study acute immune-mediated hepatitis. Herein, mice were administered corilagin (25 mg/kg) intraperitoneally twice at 12 h intervals, and 1 h later, the mice were challenged with Con A (20 mg/kg body weight); serum and liver samples were collected after 12 h. The results showed that corilagin significantly increased the survival of mice and reduced serum alanine transaminase (ALT) and aspartate aminotransferase (AST) levels. In addition, corilagin markedly improved histopathological damage, hepatocyte apoptosis, and oxidative stress in the liver. The activation of M1 macrophages in the hepatic mononuclear cells was also significantly reduced compared with that in the control group. The expression of M1 macrophage-associated proinflammatory cytokines and genes, including interleukin (IL)-6, IL-12, and inducible nitric oxide synthase (iNOS), was also decreased after corilagin treatment. Finally, the results demonstrated that corilagin regulated macrophage polarization by modulating the mitogen-activated protein kinases (MAPK), nuclear factor (NF)-κB, and interferon regulatory factor (IRF) signaling pathways. Thus, the findings indicate that corilagin protects mice from Con A-induced immune-mediated hepatic injury by limiting M1 macrophage activation via the MAPK, NF-κB, and IRF signaling pathways, suggesting corilagin as a possible treatment choice for immune-mediated hepatic injury.

PLA2G16 is a member of the phospholipase A2 family that catalyzes the generation of lysophosphatidic acids (LPAs) and free fatty acids (FFAs) from phosphatidic acid. Previously, PLA2G16 was found to be a host factor for picornaviruses. Here, we discovered that the Foot-and-Mouth Disease Virus (FMDV) infection led to an elevation in PLA2G16 transcription. We established PLA2G16 overexpression and knockdown cell lines in PK-15 cells to investigate the potential role of PLA2G16 in FMDV infection. Our findings revealed that during FMDV infection, PLA2G16-overexpressing cells had increased levels of phosphorylated STAT1 and the interferon-stimulating factors ISG15 and ISG56. In PLA2G16-overexpressing cells, p-STAT1 was observed at higher levels and earlier than in wild-type cells. Subsequent research demonstrated that PLA2G16 specifically promoted an antiviral innate immune response against FMDV. The host could detect the early release of FMDV viral nucleic acid in PLA2G16-overexpressing cells and trigger the interferon signaling pathway. Additionally, we discovered that the supernatants of PLA2G16-overexpressing cells stimulated the production of higher levels of ISG56 and phosphorylated STAT1. This suggests that PLA2G16-overexpressing cells can activate the innate immune pathway of uninfected cells after FMDV infection.

Uropathogenic Escherichia coli (UPEC) is the leading cause of urinary tract infections (UTIs), inducing acute pyelonephritis and may result in permanent renal scarring and failure. Alpha-hemolysin (HlyA), a key UPEC toxin, causes serious tissue damage; however, the mechanism through which HlyA induces kidney injury remains unclear. In the present study, granulocyte-macrophage colony-stimulating factor (GM-CSF) secreted by renal epithelial cells was upregulated by HlyA in vitro and in vivo, which induced M1 macrophage accumulation in kidney, and ADAM10 was found involved in HlyA-induced GM-CSF. Macrophage elimination or GM-CSF neutralization protected against acute kidney injury in mice, and increased GM-CSF was detected in urine of patients infected by hlyA-positive UPEC. In addition, HlyA was found to promote UPEC invasion into renal epithelial cells by interacting with Nectin-2 in vitro. However, HlyA did not affect bacterial titers during acute kidney infections, and HlyA-induced invasion did not contribute to GM-CSF upregulation in vitro, which indicate that HlyA-induced GM-CSF is independent of bacteria invasion. The role of GM-CSF in HlyA-mediated kidney injury may lead to novel strategies to treat acute pyelonephritis.

The Sentinel-3A satellite, equipped with Synthetic Aperture Radar (SAR) Altimeter (SRAL) instrument to derive sea surface height, significant wave height and surface wind speed over the global ocean, was launched on 16 February 2016. The assessment of data quality and the system performance of the altimeter are very important to data application. In this article, Sentinel-3A SRAL data quality is assessed and altimetry system performance is estimated by verifying data availability and monitoring the parameters of altimeter and radiometer through the global statistical analyses of Sentinel-3A Non-Time-Critical (NTC) Marine Level 2 products during the period from 13 March 2016 to 25 February 2019, in comparison with self-crossovers and cross-calibration with the Jason-3 mission. The global statistical analyses and the comparisons at self-crossovers show that Sentinel-3A SRAL data and performance are stable and have no trend over time, and the total cycle average root mean square errors (RMSEs) of sea surface height (SSH) differences at self-crossovers is about 5.4 cm. The comparisons at the dual-crossovers show the consistency of the observation between Sentinel-3A SRAL and Jason-3, and indicate that the systemic bias of SSH is about 2.96 cm. In general, it can be concluded that Sentinel-3A SRAL has good and stable data quality and system performance for operational ocean forecasting and scientific research.

Group B coxsackieviruses (CVBs) are the main cause of virus-induced myocarditis. CVBs use coxsackievirus and adenovirus receptor (CAR) for infection and targeting CAR has been shown to ameliorate CVBs-induced myocarditis. Ligand-of-Numb protein X1 (LNX1) is an E3 ubiquitin ligase that was shown to interact with CAR. However, the precise effect of LNX1 on CAR and the roles of LNX1 on CVBs-induced myocarditis remain unknown. In the present study, we generated mice deficient in LNX1 in the heart and evaluated the symptoms of myocarditis after CVB3 infection. We also monitored the expression and ubiquitination of CAR in LNX1-deficient cardiomyocytes after CVBs infection. We found that CVBs infection decreased CAR expression while promoted the expression of LNX1. Mice with deficiency of LNX1 in the heart had normal myocardial development while had deteriorated myocarditis symptoms after CVB3 infection. In LNX1-deficient cardiomyocytes, decreased ubiquitination of CAR and upregulation of CAR were observed after CVB3 infection. In summary, LNX1 controls CVB3-induced myocarditis by regulating the expression of CAR.

This study was designed based on a cross-sectional investigation conducted Shanghai, China. Demographic characteristics, diaper utilization, Activities of Daily Living (ADL) and emotion were collected by Unified Needs Assessment Form for Elderly Care Questionnaire. Cognition function was assessed by Mini-mental State Examination (MMSE) scale. Multivariate logistic regression was used for statistical analysis. The diaper utilization rate was 31.2%. Female, higher level of education, poorer ADL and cognition, more severe incontinence and financial dependence on others were facilitating factors for diaper usage ( P  < 0.05). The possibility of using diaper differed according to the intimacy of caregivers. Among incontinent individuals with relatively good ADL and cognition level, diaper utilization can significantly decrease the risk of going out only once a month (OR: 2.63 vs 4.05), and going out less than once a month (OR: 5.32 vs 6.53). Incontinence people who going out at least once a week had a lower risk of some negative emotion. Significantly, diaper utilization further decreased this risk. In conclusion, for incontinence elderly people with relatively independent ability, proper use of diaper may improve the frequency of outdoor activity and emotion. Nevertheless, diaper utilization should be decided based on elderly people’s own will.