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关键词： Dental implant   Cement-retained   CAD/CAM   Remnant cement   Implant abutment  

摘要： Purpose: To investigate the remnant cement in the subgingival regions (RCS) of abutments fabricated with different CAD/CAM parameters, including margin curvature radius, convergence angle, top fillet radius, and subgingival abutment shape. Methods: Twelve experimental groups were fabricated to test the following parameters: abutment margin curvature radii of 0.8 mm, 1.2 mm, and 1.6 mm;convergence angles of 4 degrees, 8 degrees, and 12 degrees;top fillet radii of 0.6 mm, 1.0 mm, and 1.4 mm;and subgingival abutment shapes of straight, S-curve, and concave. A constant amount of cement was injected into each crown. Vertical and continuous loads were applied to each specimen using a universal machine. RCS values were measured using a 3D intraoral scanner and an analytical balance. Statistical analysis was performed using one-way analysis of variance followed by post hoc Tukey test (alpha = .05). Results: Regarding margin curvature radius, RCS values were significantly higher in the order of 1.6 mm, 1.2 mm, and 0.8 mm (P < 0.001). For convergence angle, higher RCS values were observed at 12 degrees > 8 degrees > 4 degrees(P < 0.001). Among subgingival abutment shapes, concave yielded the highest RCS values, followed by S-curve and straight (P < 0.001). No significant differences in RCS values were found among the groups with different top fillet radii (P > 0.05). Conclusions: Within the limitations of this study, a smaller margin curvature radius, smaller convergence angle, and straight subgingival shape were associated with reduced RCS length, surface area, and weight.

关键词： Crack initiation   Fatigue failure evolution   Hydraulic motor   Variable-curvature cam ring  

摘要： The variable-curvature cam ring serves as the critical component governing the service life of the hydraulic motor, which can achieve ultra-high torque and is widely used in heavy-load machinery. The fatigue failure of cam ring will lead to damage and shutdown of hydraulic motor, potentially leading to severe safety accidents. However, there is still a lack of a deep understanding about failure evolution behaviors and mechanisms of variable-curvature cam ring. For that, this study systematically investigates the multi-scale failure characteristics of the cam ring under heavy-load conditions at different time intervals. The detailed fatigue failure behaviors are revealed about morphology, microstructure, and nanoscale defects. The results reflect that the inner dead point of cam ring is the most obvious failure position due to its vast stress and poor surface morphology. Moreover, the cracks typically expand in an intergranular manner during fatigue failure process. Yet, the residual austenite serves as a turning point for the crack to transition from intergranular expansion to intragranular expansion. Furthermore, the interaction between dislocation behaviors and carbide formation becomes the key to the origin of cracks. Within a relatively short damage time, the rearranged dislocation contacts with the tips of carbides, resulting in the formation of highly refined micro-cracks. When the cumulative damage time is longer, the interwoven cracks formed by the networked dislocations surrounding the carbides completely divide the carbide interior, causing the fragmentation of the carbides. Moreover, reducing roughness in inner dead point and stress concentration areas, including residual austenite and carbides, is meaningful.

关键词： Chemical activation  

摘要： Surface-enhanced Raman spectroscopy (SERS) has been widely applied for non-invasive, rapid measurement-based cancer detection; while its performance is highly dependent on sufficient training data for machine learning models. However, although large cancer cohorts are available in high-level research institutions, stringent privacy regulations and the sensitive nature of patient data limit cross-institutional sharing, constraining most SERS cancer detection studies to small sample sizes. This scarcity of training data results in model overfitting and insufficient generalization capability. To address these challenges, this study proposes CAM-Diffusion, a method that integrates Gradient-Weighted Class Activation Mapping (Grad-CAM) with conditional diffusion models to generate high-quality synthetic SERS spectra and provide interpretable localization of key features. The 1D-Grad-CAM module identifies discriminative Raman shift regions that contribute significantly to cancer classification through gradient-weighted mechanisms and constructs masks to guide the conditional diffusion process. CAM-Diffusion utilizes mask-guided cross-attention mechanisms to improve feature learning in key diagnostic regions through differential allocation of attention weights. Using a serum SERS dataset comprising four cancer types, two benign diseases, and healthy controls, five-fold cross-validation experiments under limited training data scenarios demonstrate that CAM-Diffusion achieved consistent performance improvements across eight classifiers, with accuracy gains ranging from 2.29% to 7.82%. In conclusion, CAM-Diffusion identifies key spectral regions, generates high-quality SERS spectral data, and substantially improves classifier performance through interpretability-guided synthesis.

关键词： 3D printing   Biocompatibility   Cytotoxicity   Oxidative stress   Inflammatory responses   CAD/CAM composite blocks   CAD/CAM zirconia   Resin composite  

摘要： This study evaluated the in vitro cytotoxicity, oxidative stress, and inflammatory responses of three 3D printed dental resins compared with CAD-CAM zirconia, CAD-CAM composite blocks, and direct resin composites, using human gingival fibroblasts.

关键词： Coronary inflammation   Vulnerable plaque   Cardiac events   Systemic inflammation   Atherosclerosis   Coronary artery disease   ACS   Acute coronary syndromes   CAD   Coronary artery disease   CCTA   Coronary computed tomography angiography   CV   Cardiovascular   GLP-1 RA   Glucagon-like peptide-1 receptor agonist   hsCRP   High-sensitivity C-reactive protein   IL-1β   Interleukin-1 beta   IL-6   Interleukin-6   NIRF   Near-infrared fluorescence   OCT   Optical coherence tomography   PCSK9i   Proprotein convertase subtilisin/kexin type 9 inhibitor   PET   Positron emission tomography   SGLT2i   Sodium–glucose cotransporter-2 inhibitor  

摘要： Coronary inflammation is now widely recognized as a key driver of atherosclerotic plaque initiation, progression, and destabilization. Histopathological studies and advanced imaging have demonstrated the central role of local inflammation, including extensive macrophage infiltration, cytokine activation, and perivascular adipose tissue, in promoting plaque vulnerability and acute coronary syndromes. Among systemic biomarkers, high-sensitivity C-reactive protein remains the most widely used, although its limited specificity underscores the need for more targeted inflammatory markers such as interleukin-6. Invasive intracoronary imaging with optical coherence tomography can visualize macrophage accumulation and quantify local inflammatory burden. Hybrid molecular imaging with OCT-NIRF, novel PET tracers and peri-coronary adipose tissue attenuation can provide emerging opportunities to further characterize vascular inflammation and residual risk. Anti-inflammatory therapies represent a major opportunity to improve outcomes beyond lipid lowering. Among patients with coronary artery disease, the use of colchicine has demonstrated significant risk reduction in adverse events, though with mixed results. Important randomized trials are underway to investigate the efficacy of novel monoclonal antibodies targeting interleukin pathways. In addition, GLP-1 receptor agonists, SGLT2 inhibitors, and PCSK9 inhibitors have been described to exhibit favourable vascular anti-inflammatory effects in experimental and in-vivo studies. In this comprehensive review, we provide an updated reappraisal of the available evidence unravelling how accurate identification of high-risk patients through multimodal imaging and biomarker profiling, combined with selective anti-inflammatory therapy, can represent a promising strategy for further reducing residual cardiovascular risk.

关键词： SAM   Segmentation   Ultrasound images   Weakly supervised learning  

摘要： Ultrasound imaging is vital for the early detection of breast cancer, where accurate lesion segmentation supports clinical diagnosis and treatment planning. However, existing deep learning-based methods rely on pixel-level annotations, which are costly and labor-intensive to obtain. This study presents a weakly supervised framework for breast lesion segmentation in ultrasound images. The framework combines morphological enhancement with Class Activation Map (CAM)-guided lesion localization and utilizes the Segment Anything Model (SAM) for refined segmentation without pixel-level labels. By adopting a lightweight region synthesis strategy and relying solely on SAM inference, the proposed approach substantially reduces model complexity and computational cost while maintaining high segmentation accuracy. Experimental results on the BUSI dataset show that our method achieves a Dice coefficient of 0.7063 under five-fold cross-validation and outperforms several fully supervised models in Hausdorff distance metrics. These results demonstrate that the proposed framework effectively balances segmentation accuracy, computational efficiency, and annotation cost, offering a practical and low-complexity solution for breast ultrasound analysis. The code for this study is available at: https://***/YueXin18/MorSeg-CAM-SAM-Segmentation.

关键词： PiM   Commodity DRAM   PuM   CAM   Genome classification  

摘要： Processing using memory (PuM) leverages analog properties of memory infrastructure to implement logic and arithmetic operations. Commodity Off-The-Shelf (COTS) DRAM is particularly attractive for PuM because it requires no device modification, thereby preserving the ubiquity, availability, and cost advantages of modern DRAM while enabling massive column-level parallelism. We propose CADM (Content-Addressable DRAM), that enables exact and approximate (similarity) search in- and using- unmodified COTS DRAM. CADM targets genome classification, which is one of the most important applications in bioinformatics. Specifically, rapid and accurate detection of bacterial pathogens is critical for effective clinical decision-making, particularly in life-threatening conditions such as sepsis, where early identification of the causative agent significantly improves patient outcomes. We implement CADM in commercial DDR4 and show that it can achieve up to 185$\times$ higher throughput and 73$\times$ energy savings compared to CPU-run state-of-the-art classifier Kraken2. Using approximate search, CADM can achieve 9$\times$ higher $F_1$ score when matching relatively short ($<$32 DNA bases) ambiguous and erroneous $k$-mers.

关键词： FAI syndrome   Squat   Hip joint   Biomechanics   Kinematics   Kinetics  

摘要： Femoroacetabular impingement syndrome (FAIS) is a movement-related disorder and causing hip and/or groin pain in young active adults. Movement patterns in individuals with FAIS may be modifiable through conservative management. Identifying biomechanical differences between cohorts with asymptomatic cam morphology (FAIM) and FAIS could reveal relevant treatment targets. This study compared lower limb kinematics and kinetics between FAIS, FAIM, and healthy controls during double- (DLS) and single-leg squats (SLS).

关键词： Channel-wise joint disentanglement representation learning   Computer-Aided Diagnosis   Gradient-guided disentanglement   Gramian-based Contrastive Learning Disentanglement   Super-resolution ultrasound  

摘要： B-mode ultrasound (BUS) is widely used in breast cancer diagnosis, while the emerging super-resolution ultrasound (SRUS) provides microvascular information with high spatial resolution, which has shown great potential in improving breast cancer diagnosis. However, as a new ultrasound modality, its diagnosis remains highly dependent on the clinical experience of sonologists, highlighting the need for reliable computer-aided diagnosis (CAD) approaches. In this work, a novel dual-branch network with a Channel-Wise Joint Disentanglement Representation Learning (CW-JDRL) method is proposed for the multimodal ultrasound-based CAD of breast cancer, where one branch processes BUS and the other analyzes multimodal SRUS data. The CW-JDRL is implemented on the SRUS branch by grouping the final-layer network channels to capture both common and specific properties. It consists of two modules, namely Gradient-guided Disentanglement (GD) module and Gramian-based Contrastive Learning Disentanglement (GCLD) module. The former disentangles with gradient guidance to encourage consistency among common channels and distinctiveness among specific ones, and the latter disentangles common and specific representations by integrating them into a unified contrastive objective. Extensive experiments on a multicenter SRUS dataset demonstrate that the proposed dual-branch network with CW-JDRL achieves superior performance over the compared algorithms and maintains robust generalizability to external data. It suggests not only the effectiveness of SRUS for diagnosis of breast cancer, but also the potential of the proposed CAD model in clinical practice. The codes are publicly available at https://***/Zyh-AIUltra/CW-JDRL#

关键词： Erythrosine   Nanoemulsion   Low-energy method   Photodynamic therapy   HET-CAM  

摘要： Erythrosine, a naturally derived dye, is extensively employed in the food and pharmaceutical industries; however, its low stability in aqueous media limits broader technological applications. Therefore, the development of erythrosine-loaded nanoemulsions has emerged as a strategy to overcome this limitation. This study aimed to develop and characterize erythrosine-loaded nanoemulsions, as well as to evaluate their ex vivo biocompatibility and performance. Nanoemulsions were formulated using different surfactants, and the formation region was analyzed using a pseudo-ternary phase diagram. Physicochemical, structural, and photophysical characterizations were performed, along with an evaluation of the erythrosine release profile. The nanoemulsion was developed using a low-energy method with erythrosine incorporated into the organic phase and a high association rate. The formulation remained stable for 90 days under various environmental conditions, with an estimated shelf life of 2.4 months. Morphological characterization confirmed the formation of homogeneous nanoemulsions. To optimize the application, the formulation was gelled using 23% Pluronic® F-127. Rheological and texture analyses showed results consistent with nanostructured materials described in the literature. The release profile of erythrosine from the gelled nanoemulsion exhibited sustained release following Higuchi kinetics, a characteristic model for gel-based release systems. The HET-CAM assay indicated a biocompatible range and supported further investigation of the nanoemulsion system in antiangiogenic studies. These findings highlight nanoemulsions as a promising strategy to enhance the technological application of erythrosine.