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摘要： To evaluate the effect of deep margin elevation (DME) with four different injectable restorative materials on the three-year clinical performance of nanoceramic-resin CAD/CAM onlay restorations. Sixty-four adult patients with subgingival, broad Class II carious molars indicated for onlay restorations were randomly assigned to four groups according to the injectable DME materials used: highly viscous glass ionomer (HVGI), high-filled injectable composite resin (ICR), resin-modified glass ionomer (RMGI), and bioactive ionic resin (BAIR). The proximal subgingival margins were repositioned to a supragingival level using the assigned materials; subsequently, all groups received nanoceramic-resin CAD/CAM onlay restorations. Gingival health and restorative performance were assessed over a three-year evaluation period using the Gingival Index (GI) and modified World Dental Federation (FDI) criteria. The collected data were statistically analyzed. All restorations were evaluated with a 100% recall rate and a 100% survival rate. For GI, assessment of scores across various evaluation periods within each group revealed a significant increase over time. Nevertheless, no significant differences were found among the tested groups at each evaluation period. Additionally, FDI evaluation criteria showed no significant differences. After three years, nanoceramic-resin CAD/CAM onlay restorations following deep margin elevation with injectable restorative materials demonstrated acceptable clinical behavior, with only a slight increase in gingival bleeding. Injectable restorative materials can provide adequate clinical performance as deep margin elevation materials for molars restored with nanoceramic-resin CAD/CAM onlays.

关键词： CAD/CAM   Glossectomy   Palatal augmentation prostheses (PAPs)   Tongue cancer   Tongue pressure  

摘要： PURPOSE:This prospective study evaluated the impact of computer-aided design/manufacturing (CAD/CAM) palatal augmentation prostheses (PAPs) rehabilitation on maximum tongue pressure in tongue cancer patients underwent glossectomy. METHODS:Twelve patients (7 men, 5 women; mean age 53.2 years) with T1N0M0 or T2N0M0 tongue cancer that had undergone a type IIIa glossectomy without radiotherapy or chemotherapy were enrolled. All the participants had intact palates for good retention of PAP. Six weeks postoperatively, maxillary arches were scanned with an intraoral scanner; PAPs were digitally designed to lower the palatal vault utilizing CAD and then were fabricated from biocompatible PMMA via 3D printing and delivered. Patients wore the PAP daily and performed tongue-PAP contact exercises for at least four hours per day over six months. Maximum tongue pressure was measured using the Iowa Oral Performance Instrument before PAP delivery and after six months of rehabilitation. Statistical analysis was conducted using ANOVA with p < 0.05 considered significant. RESULTS:Mean maximum tongue pressure increased from 16.93 ± 13.16 kPa pre-rehabilitation to 26.93 ± 15.60 kPa post-rehabilitation (median: 11.9 to 21.9 kPa). ANOVA showed a significant improvement (F = 5.31, p = 0.0038). CONCLUSION:CAD/CAM PAP rehabilitation significantly improved the maximum tongue pressure in glossectomy patients, suggesting potential benefits for oral-phase swallowing efficiency. Early postoperative delivery through a digital workflow minimized tongue-palate distance, enabling prompt exercises, and accelerating functional recovery. This approach represents an innovative rehabilitation strategy. Integration of pressure sensors into PAPs could enable real-time monitoring of tongue activity, expanding their role as dynamic training devices.

关键词： Art detection   Artificial intelligence   Computer vision   Content authenticity   Feature extraction  

摘要： This study presents a strong framework for the detection of artificial intelligence-generated artwork using digital imaging and deep learning-based transformers models, which helps in art community to distinguish the authenticity of human-created art from highly fascinated machine-generated content. Art holds reflection of deep cultural, historical, and social significance, however, due to rapid advancements in artificial intelligence, particularly in generative adversarial networks and diffusion models, have enabled the production of visually creating artworks that blur the boundaries between originality and synthesis. Traditional methods relying on conventional features and statistical analysis are increasingly lower performance against such advance transformation, highlighting the need for more advanced detection mechanisms. To address this challenge, the proposed approach employs Distillation with No Labels (DINO) v2, a self-distilled transformer model that excels in extracting discriminative features by capturing both global structures and fine-grained visual cues. The model was trained and evaluated on a balanced dataset of real and AI-generated art images, with results benchmarked against strong baselines. Experimental findings demonstrate that the proposed framework achieves 99.01% accuracy, 95.29% precision, 94.58% recall, 94.93% F1-score, and an AUC of 99%, outperforming all baselines with superior generalization. Furthermore, interpretability methods along with statistical validation based on log of p-values, confirmed that predictions are both dependable and transparent.

关键词： Artificial intelligence   Chest radiograph   Computer aided detection   Infectious pulmonary disease   Initial stage of pandemic  

摘要： To investigate the potential application of existing artificial intelligence (AI) software in diagnosing COVID-19 (coronavirus disease 2019) and other pneumonia-related radiographic findings with the unprecedented challenge by COVID-19 pandemic, leveraging pre-trained commercial AI software originally designed for detecting pulmonary nodules in chest radiographs, we assessed its performance using public dataset comprising chest radiographs collected during the early stages of pandemic. The software demonstrated promising results, achieving a sensitivity of 86.83% and a specificity of 59.59% in detecting pneumonia features, including those caused by COVID-19. Considering only posterior-anterior (PA) radiographs, AI software exhibited improved performance, with sensitivity 89.16% and specificity 67.41%. The findings suggest the AI CAD software could serve as a triage for initial screening of infectious pulmonary diseases, especially in regions with limited access to advanced medical resources. This study underscores the importance of promptly adapting existing AI CAD to address urgent healthcare needs during infectious disease outbreaks.

关键词： Breast cancer   Convolutional neural networks   Transformers   Accuracy   Mammography   Histopathology   Magnetic resonance imaging   Deep learning   Computer architecture   Visualization  

摘要： This study aimed to design and evaluate a fusion deep learning architecture (SwinCNN + OE) for robust and interpretable breast cancer classification using histopathological images. The proposed model combines the local feature extraction strengths of EfficientNet-E0 with the global representation power of the Swin Transformer, enhanced by Outlier Exposure (OE), and energy-based Out of Distribution Detection (OOD) scoring to improve safety and real-world adaptability. The proposed model (SwinCNN+OE) exhibited consistently high performance across all primary classification metrics, achieving an overall accuracy of 97.83% and precision, recall, and F1-score of 97.88%, 97.64%, and 97.68%, respectively. Extensive experiments on a real-world histopathology dataset demonstrated the model’s superior diagnostic performance, achieving an overall accuracy of 96.36%, a macro-averaged F1-score of 96.35%, and a mean AUC score of 99.49%. The model also achieved perfect detection (AUC = 1.00) of unknown samples, illustrating its ability to identify out of distribution cases using energy-based scoring. Furthermore, Grad-CAM heatmaps confirmed that the model consistently focused on biologically meaningful regions such as densely packed nuclei and irregular tissue structures. Integrating Grad-CAM for interpretability and energy-based OOD detection contributes significantly to the trustworthiness and safety of Artificial Intelligence (AI) applications in clinical pathology.

摘要： Pancreatic ductal adenocarcinoma (PDAC), the most prevalent form of pancreatic cancer, has a 5-year survival rate of 9 %. PDX1 is an important transcription factor for embryonic development of the pancreas, pancreatic endocrine lineage differentiation, and maintenance of mature pancreatic β-cells. In PDAC patients, PDX1 expression is downregulated in tumor cells compared to adjacent non-tumoral tissue. On the other hand, a higher PDX1 expression has been shown to improve the overall survival rate in PDAC patients. Therefore, our aim was to analyze the role of PDX1 on the phenotype of PDAC cells. To induce PDX1 expression, PANC-1 cells, derived from a human PDAC tumor, were treated with BRD7552 (a PDX1 inducer). Overexpression of PDX1 was confirmed by Western Blot analysis and no cytotoxic effects were observed by MTT reduction or Trypan Blue exclusion assays. Cell confluence assay and BrdU incorporation assay showed a significant reduction in proliferation rate in treated cells, while no differences were observed on the proportion of Ki67⁺ and PH3⁺ cells by immunostaining. In addition, cell cycle analysis by propidium iodide staining followed by flow cytometry showed an arrest in the G1 phase of the cell cycle of treated cells. Additionally, treated cells showed a significant reduction in migration rate. Furthermore, to assess the in ovo effects of BRD7552, treated PANC-1 cells were implanted onto the chorioallantoic membrane (CAM) of chick embryos and tumor size was measured at different time points, revealing a significant reduction in tumor growth of treated cells. Therefore, in this study we observed that a pharmacological induction of PDX1 in PANC-1 cells affects cell cycle, reduces proliferation rate and inhibits migratory potential in vitro. These findings were corroborated in ovo, where PDX1 overexpression diminished tumor growth of PANC-1 cells. In conclusion, the overexpression of PDX1 induced by BRD7552 dri

关键词： Mandibular reconstruction   Finite element analysis   Magnesium   CAD/CAM miniplates   Shape-optimization   Biomechanics  

摘要： Magnesium CAD/CAM miniplates are a promising alternative to titanium plates for mandibular reconstruction. However, gas formation is an inherent part of the magnesium degradation process, and thus, the quantity of magnesium used in fixation scenarios should be limited. Previous studies described several strategies to limit material volume, such as plate thickness reduction and shape-optimization. In particular, shape-optimization has been described as a strategy to limit material volume while maintaining mechanical integrity.

关键词： CAD/CAM技术   ABS材料   高锰钢辙叉   铸造木模   数控加工  

摘要： 介绍了CAD/CAM新技术及ABS新材料在高锰钢辙叉铸造用木模中的应用。通过分析传统木模制作过程存在的加工周期长、成品精度低、使用耐久性差等问题,探讨了采用CAD/CAM新技术及ABS新材料制作高锰钢辙叉铸造木模的具体应用。研究表明,CAD/CAM技术显著提高了木模的设计精度和生产效率,减少了人工误差,缩短了生产周期。文中还通过实际案例分析,验证了CAD/CAM新技术及ABS新材料在提升产品质量和生产效率方面的显著效果。

关键词： Coronary microvascular dysfunction   Coronary flow reserve   Microvascular resistance reserve   Index of microcirculatory resistance   Aging   ANOCA   Angina with no obstructive coronary arteries   CAD   Coronary artery disease   CFR   Coronary flow reserve   CMDR   Coronary Microvascular Disease Registry   CMD   Coronary microvascular dysfunction   IMR   Index of microcirculatory resistance   MRR   Microvascular resistance reserve   PCI   Percutaneous coronary intervention  

摘要： Coronary microvascular dysfunction (CMD) contributes to angina without obstructive coronary artery disease (ANOCA). Although aging is known to impact cardiovascular health, its effect on coronary microvascular function remains unclear. This study examined the relationship between age and microvascular function in patients with ANOCA.