Search Results (5,167)

Background: Automated interpretation of 12-lead electrocardiograms (ECGs) remains challenging because multiple abnormalities may coexist and appear in selected leads or brief waveform segments. We developed a compact and interpretable framework for five-superclass multi-label ECG diagnosis. Methods: We evaluated PTB-XL records using the official fold protocol, with folds 1–8 for training, fold 9 for validation monitoring and class-specific threshold selection, and fold 10 for independent internal testing. We then evaluated the frozen 1.33-million-parameter InceptionTime–CNN–BiGRU–Transformer model and validation-derived thresholds on 15,931 Ningbo ECGs without retraining, recalibration, or external threshold adjustment. We also examined calibration, demographic subgroups, computational efficiency, and complementary ECG-domain attribution methods. Results: Macro-AUROC reached 90.83% on PTB-XL fold 10 and 88.85% on Ningbo, indicating generally consistent diagnostic ranking with a modest reduction during external evaluation. Sensitivity analysis showed that CNN-only outperformed the frozen primary model on five of six endpoints. Attribution analyses highlighted qualitatively plausible lead and temporal patterns in representative examples, while calibration and subgroup analyses further characterized model behavior under dataset shift. Conclusions: Our framework integrates leakage-aware development, threshold-controlled testing, frozen external validation, and multimethod interpretability. These findings support its further prospective, locally calibrated evaluation as a potential aid for multi-label ECG interpretation. Full article

Background/ Objectives: Selecting an intraoral scanner (IOS) has evolved into a complex clinical decision that extends beyond technical performance alone. This study aimed to investigate the clinical, technical, educational, and organizational factors influencing intraoral scanner selection among dentists, evaluate awareness of ISO specifications and evidence-based criteria, and propose a conceptual framework to support evidence-based technology selection. Methods: A cross-sectional questionnaire-based study was conducted among dentists practicing in Greece. A total of 86 questionnaires were returned from 271 invited participants (response rate: 31.73%). Valid sample sizes varied across analyses according to item applicability and analyzable responses. The questionnaire assessed demographic and professional characteristics, intraoral scanner use, selection criteria, awareness of ISO specifications, educational background, and evidence-related factors. Composite indices were developed to evaluate the principal decision-making dimensions. Results: Technical and clinical performance-related criteria received the highest importance ratings in intraoral scanner selection. Although participants generally reported good perceived knowledge of intraoral scanners, only 35.7% reported awareness of ISO specifications. Dentists familiar with ISO standards assigned significantly greater importance to specification-related and safety-related criteria, suggesting an association between reported ISO awareness and greater emphasis on specification- and safety-related selection criteria. Continuing professional education represented the predominant source of knowledge acquisition. Based on the integration of these findings, the EBIOS Pilot Framework (Evidence-Based Intraoral Scanner Selection Framework) is proposed as a preliminary conceptual synthesis of factors potentially relevant to evidence-informed intraoral scanner selection. Conclusions: The findings suggest that intraoral scanner selection may be understood as a multidimensional decision-making process involving technical performance, scientific evidence, professional education, and standardized quality criteria. The proposed EBIOS Pilot Framework provides a preliminary conceptual basis for future validation and refinement in larger, independent populations. Full article

Classifying benign and malignant lung nodules from computed tomography (CT) images remains difficult because lung nodules can be hard to classify, and unimodal models cannot capture complementary diagnostic information. Despite the success of deep learning, existing methods rely only on image information and miss semantic information that can be obtained from an expert radiologist’s knowledge. Hence, the authors propose a new multimodal lung nodule classification model in this study, named the Graph-Guided Fusion Network (R-GGFN), that combines three-dimensional (3D) CT image features and structured radiologist annotations. The proposed architecture consists of three models. A 3D ResNet-18 network for image feature extraction, an MLP network for encoding semantic information, and a Graph Attention Network (GAT) for capturing inter-nodule relationships and fusing multimodal information with the graph. We add a tabular skip connection to preserve discriminative semantic features and use focal loss to address imbalance during training. To prevent data leakage, we partitioned the publicly available LIDC-IDRI dataset at the patient level. Experimental results on a held-out patient-level test set, accessed only once after model selection was finalized, show that the proposed R-GGFN achieves an accuracy of 85.21%, an AUROC of 0.9147, a PR-AUC of 0.9213, and an F1-score of 0.8609. Among all unimodal and multimodal baselines internally evaluated, R-GGFN achieved the best value on every reported metric, including accuracy, AUROC, PR-AUC, F1-score, Precision, sensitivity, and specificity. Furthermore, the proposed approach enhances model transparency by combining explainable AI techniques (e.g., 3D Grad-CAM, SHAP, and graph visualization) to explain the model at the image, feature, and graph levels. The results show that the graph-guided multimodal fusion method can fully leverage complementary image and semantic information, improving diagnostic accuracy and interpretability. The framework proposed here is a good and understandable computer-aided diagnosis decision-support system for lung cancer and a step towards future external dataset validation. Full article

Solar ultraviolet (UV) radiation is a major contributor to skin damage, making the regular use of broad-spectrum sunscreens essential for effective photoprotection. However, the long-term efficacy of sunscreen formulations is limited by the photoinstability of some organic UV filters, particularly avobenzone. Licuri oil (Syagrus coronata) is a naturally derived Brazilian palm oil that represents a promising alternative to medium-chain triglycerides (MCTs) as an oily core for polymeric nanocapsules. Therefore, this study aimed to develop Eudragit^® RS 100-based cationic nanocapsules using licuri oil for avobenzone encapsulation and to incorporate them into hyaluronic acid (HA) or xanthan gum (XG) hydrogels. The nanocapsules ranged from 125 to 158 nm, with a polydispersity index (PDI) of less than 0.2, positive zeta potential (+11 to +13 mV), and encapsulation efficiency above 98%. After 48 h of UVA exposure, the nanocapsule formulations retained 51% and 52% of their initial avobenzone content for the licuri oil- and MCT-based systems, respectively, compared with only 10% for free avobenzone, indicating a marked improvement in photostability following nanoencapsulation. Additionally, licuri oil nanocapsules exhibited increased antioxidant activity compared to MCT-based nanocapsules in both DPPH and ABTS assays. The nanocapsule suspensions were classified as non- to slightly irritating in the Hen’s Egg Test–Chorioallantoic Membrane (HET-CAM) assay. After incorporation into the hydrogels, the resulting formulations exhibited pseudoplastic and thixotropic behavior, with HA-based hydrogels providing higher UV absorption than XG-based hydrogels. Furthermore, no transdermal permeation of avobenzone was observed from either the HA- or XG-based hydrogel formulations, and the HA hydrogel containing licuri oil nanocapsules showed higher stratum corneum retention. These findings demonstrate the potential of licuri oil as an effective alternative to MCTs as the oily core of polymeric nanocapsules for topical formulations containing encapsulated avobenzone. Full article

This in vitro study evaluated the effects of restorative material type, thickness, curing mode, and post-irradiation time on the degree of conversion (DC) of a dual-cure resin cement. Specimens of VITA Suprinity (VS), VITA Enamic (VE), Lava Ultimate (LU), and Saremco Print Crowntec (SC) were prepared at thicknesses of 1.0 and 1.5 mm. A dual-cure resin cement was placed beneath each specimen and light-cured through the specimen using a VALO X curing light in Standard Power (SP) or Xtra Power (XP) mode (n = 8). DC was measured by FT-IR/ATR spectroscopy at days 1 and 10. The normality and homogeneity of variances were assessed using the Shapiro–Wilk and Levene tests, and data were analyzed using mixed-design repeated-measures ANOVA and Tukey HSD tests (α = 0.05). Material type and thickness significantly affected DC (p < 0.001), whereas curing mode did not (p = 0.110). Post-irradiation time significantly affected DC (p < 0.001), and a significant material–thickness interaction was found (p = 0.043). Within the limitations of this study, DC was affected by material type, thickness, and post-irradiation time, but not by curing mode. Full article

Background: Chemical hair dyes provide effective and durable coloration but are frequently associated with hair damage and irritation. Therefore, this study aimed to develop herbal-based hair dye products that combine effective coloration with low irritation potential. Methods: The combination of Lawsonia inermis, Clitoria ternatea, and Indigofera tinctoria was incorporated into different formulations (solution, shampoo, and conditioner) and evaluated for physicochemical properties, accelerated stability, and hair dyeing performance. The effects of herbal mixture concentration (10, 20, and 30% w/w) were further investigated for hair dyeing, foaming properties, washing fastness, and color stability under ambient natural-light conditions. The most suitable shampoo was evaluated for irritation potential using the hen’s egg test–chorioallantoic membrane (HET-CAM) assay, hair morphology by scanning electron microscopy, and hair chemical characteristics using Fourier-transform infrared (FT-IR) spectroscopy. Results: Herbal hair dye formulations were successfully developed. Although the solution achieved the greatest hair dyeing performance, the shampoo provided more practical convenience. Increasing the herbal mixture concentration significantly enhanced dyeing performance, with the 30% w/w formulation producing the greatest dyeing performance, with stable color maintained after five washing cycles and excellent resistance to light-induced fading. Interestingly, the herbal shampoo exhibited significantly lower irritation potential than the chemical hair dye shampoo (irritation score: 4.9 ± 0.4 vs. 14.2 ± 0.4, p < 0.05) and better-preserved hair cuticle integrity. FT-IR analysis showed retention of major keratin-associated bands, indicating no complete disruption of the fundamental keratin structure. Conclusions: The herbal hair dye shampoo offers a promising alternative to conventional hair dye, providing effective and durable hair coloration with favorable preservation of hair surface morphology and lower irritation potential. Full article

Melanoma continues to be one of the most challenging diseases in oncology, given its aggressiveness and increased resistance to existing therapies. Therefore, the identification of novel therapeutic strategies is of considerable interest, with medicinal plants representing a promising source of bioactive compounds. In the present study, the phytochemical profile and the in vitro biological activity of a lyophilized aqueous leaf extract of Eriobotrya japonica (Thunb.) Lindl. cultivated in Romania were investigated. The extract was characterized by determining the total phenolic content, antioxidant capacity, and polyphenolic composition using UHPLC-MS/MS. Its cytotoxic potential and cytocompatibility were evaluated at concentrations of 50, 100, 200, 300, 400, and 500 µg/mL in two human melanoma cell lines (RPMI 7951 and A375) and in the human keratinocyte cell line HaCaT. Cell viability and morphological alterations were assessed in all three cell lines. Subsequently, the study focused on assessing the effects of the extract on melanoma cells by evaluating lysosome integrity and the intracellular production of reactive oxygen species (ROS). The impact on mitochondrial membrane potential, as well as on mitochondrial and nuclear morphology, was also determined. Finally, the HET-CAM test was carried out to assess the extract’s preliminary irritant potential at 500 µg/mL. The results showed that the extract presented a high content of phenolic compounds and high antioxidant capacity. The most abundant compounds in the extract were chlorogenic acid, rutin, 3,5-dihydroxybenzoic acid/3,4-dihydroxybenzoic acid, and p-coumaric acid/trans-p-coumaric acid. With regard to biological activity, the treatment induced low cytotoxicity in HaCaT cells including at 500 µg/mL. At the same time, it produced a dose-dependent reduction in the viability of melanoma cells, accompanied by impaired lysosomal integrity. It was observed that the extract increased ROS levels and mitochondrial membrane depolarization, thereby inducing morphological changes at the mitochondrial and nuclear levels. However, across all performed assays, the most pronounced effects were observed starting at a concentration of 300 µg/mL. The HET-CAM assay classified the extract as non-irritant at 500 µg/mL. These findings suggest that Eriobotrya japonica leaf extract exerts antimelanoma effects associated with oxidative stress and mitochondrial dysfunction while maintaining a favorable preliminary safety profile. Full article

Multi-branch networks combine backbones whose inductive biases are complementary, but their fusion modules set the branch weights from feature statistics alone and are therefore blind to what a pixel represents. We supervise fusion in label space instead. Ground-truth labels are converted into per-pixel routing targets, which are imposed on the branch weights through a Kullback–Leibler divergence term. Each pixel is thus routed towards the branch suited to its category: a convolutional branch for fine-grained boundaries, a state-space branch for large homogeneous regions, and a windowed-attention branch for intermediate-scale context. The mechanism is instantiated in HRFNet, a three-branch encoder with branch-specific dilation rates. HRFNet attains 76.85% and 79.88% mean intersection over union (mIoU) on Cityscapes and CamVid, averaged over five seeds, and 58.18% and 46.74% on the 59-class PASCAL Context and the 150-class ADE20K benchmarks, exceeding every baseline retrained under an identical budget. Ablations locate the gain in the routing rather than in added capacity: routed fusion adds 2.29% mIoU over average fusion, of which 1.53% comes from the label-space target itself, for 3.8M parameters and 8.8% of the network’s operations. All nine ablation contrasts remain significant after Holm–Bonferroni correction, and the gain persists in every two-branch configuration. Full article

Background: Hypertensive retinopathy (HR) is an early marker of hypertension-related end-organ damage, and multi-grade classification is limited by overlap between the early stages. No prior study has reported edge-deployable HR grading from optical coherence tomography (OCT). We assess patient-level–validated four-grade HR classification from en-face OCT with on-device inference. Methods: MobileViT-XXS and EfficientNetV2-B0 were evaluated on 478 en-face OCT images from 221 patients. To avoid leakage, all partitions were patient-level: stratified group 5-fold cross-validation with a patient-disjoint test set. Each model was evaluated as a 5-fold soft-voting ensemble, with inference profiled on an NVIDIA Jetson Orin Nano. Accuracy, weighted F1, quadratic-weighted kappa, and ROC–AUC were reported; the models were compared by McNemar’s test, and clinical utility by referable-HR (Grade ≥ 2) triage. Results: MobileViT-XXS achieved 84.4% accuracy, ROC–AUC 0.944, and quadratic-weighted kappa 0.916, significantly outperforming EfficientNetV2-B0 (McNemar p = 0.013). Mild-grade overlap limited four-class accuracy, but referable-HR triage reached 95.6% accuracy, 95.2% sensitivity, and 95.8% negative predictive value. MobileViT-XXS required only 3.83 MB versus 23.70 MB, and on-device TensorRT FP16 five-fold ensemble inference achieved 10.4 ms (96 FPS) at 8.9 W. Conclusions: Lightweight models enable feasible, power-efficient point-of-care HR triage from en-face OCT; broader multi-center validation is warranted. Full article

Poly(methyl methacrylate) (PMMA) is one of the most widely used polymeric biomaterials in prosthodontics and digital dentistry because of their clinical reliability and compatibility with computer-aided design/computer-aided manufacturing (CAD/CAM). Its widespread use raises questions regarding material consumption, manufacturing waste, recyclability, and circularity. A critical narrative review supported by a structured literature search was conducted. PubMed, Scilit, OpenAlex, and ScienceDirect were searched for English-language literature published from January 2000 to June 2026. Targeted Google Scholar searches, cross-referencing, standards, and official technical sources supplemented the search. Evidence was organized according to its directness to dental PMMA and synthesized thematically. Prepolymerized CAD/CAM PMMA provides consistent material quality and generally improved mechanical performance compared with conventionally processed PMMA; however, subtractive manufacturing generates disc remnants, milling particles, and polishing residues. Mechanical recycling and depolymerization demonstrate technical recovery potential, although evidence specific to heterogeneous dental waste streams, environmental performance, and clinical-grade reuse remains limited. Technical recyclability should not be automatically equated with a viable circular economy or a net environmental benefit. Future research should quantify dental PMMA waste, establish effective collection and recovery pathways, and integrate life-cycle assessments with clinical performance and safety standards. Full article

Automated corrosion classification is crucial for industrial inspection. However, existing methods face severe class imbalance and strict computational constraints on edge devices. This study proposes a stepwise self-training framework for six-category corrosion classification. Leveraging a Vision Transformer and XGBoost classifier, this study proposes a class-aware proportional screening strategy that expands a small, labelled dataset with 19,964 unlabeled images to create a more balanced training set. Progressive training and knowledge distillation are then integrated to train a lightweight MobileNetV2 student model. The optimal fine-tuned model achieves a peak classification accuracy of 87.52%, improving upon the baseline MobileNetV2 by over 5%. Grad-CAM analysis confirms accurate focus on corroded regions, while mask-based fine-tuning demonstrates robustness, maintaining over 83% accuracy under 10% visual interference. This approach minimizes computational overhead without sacrificing accuracy, thereby offering a practical solution for real-world monitoring on resource-constrained devices. Full article

Background/Objectives: L1 cell adhesion molecule (L1CAM) has been implicated in colorectal cancer progression, but its cellular source and immune microenvironmental context remain incompletely defined. This study aimed to characterize the expression pattern, cellular localization and immune-associated features of L1CAM in colorectal cancer using public bulk and single-cell datasets. Methods: TCGA, GTEx and CPTAC/UALCAN datasets were used to assess L1CAM expression, protein abundance and clinical associations across cancers, with a focus on colorectal cancer. Single-cell RNA sequencing data from GSE178341 were analyzed to identify L1CAM-expressing cell populations, characterize transcriptional programs and evaluate sample-level associations with immune-cell composition and T/NK cytotoxicity signatures. Candidate ligand-receptor interactions and conceptual mod el-based sensitivity analyses were used to explore stromal-immune communication axes. Supplementary external single-cell datasets were analyzed exploratorily. Results: In colorectal cancer, L1CAM showed tumor-normal expression differences and was associated with progression-free interval, but not overall survival. Single-cell analysis detected L1CAM mainly in rare fibroblast and epithelial subsets rather than T/NK cells. L1CAM-positive fibroblasts showed neural-like, wound-response and matrix-remodeling transcriptional features. In the main cohort, L1CAM-Fib+ samples showed lower T/NK cytotoxicity transcriptional signatures, whereas supplementary external datasets did not reproduce the same direction of association. Candidate communication analyses nominated Galectin, HLA-E, TGFB and extracellular matrix-related axes. Conclusions: These findings suggest that L1CAM-positive fibroblast-associated stromal states provide a dataset-specific exploratory context for interpreting L1CAM-associated immune features in colorectal cancer. The results should be considered hypothesis-generating and require spatial and functional validation. Full article

Coastal humid-heat and photochemical-oxidant episodes are commonly studied through concentration estimation, leaving it unclear whether satellite observations improve warning decisions under explicit false-alarm constraints. This study introduces CoAST-EWS Japan, a six-station, validation-locked hindcast benchmark across Osaka Bay and Tokyo Bay. Models were developed using June–July 2023 data, calibrated and thresholded on August 2023 predictions, and retrospectively evaluated on June–August 2025 station-hour observations. The strong non-satellite route combines recent ground history, ERA5 meteorology, CAMS composition, and static station geometry. Adding previous-day MODIS thermal context to an otherwise identical XGBoost route increased average precision from 0.3153 to 0.3429, reduced the Brier score from 0.05032 to 0.04874, and improved recall/F1 under a validation-locked budget of 0.5 false alarms per station-day (FPDs) from 0.1864/0.2511 to 0.2402/0.3042. Japan-local calendar-day intervals supported the improvements in Brier score, recall, and F1. In a dimension-matched comparison using the same 18 MODIS variables, previous-day context increased average precision over the same-day route by 0.0378 (95% CI: 0.0144–0.0603), demonstrating that the timing advantage was not attributable to a larger satellite feature set. The MODIS increment was strongest during high-heat issue times and in Osaka Bay, and its ranking value was reproduced by a 36 h Temporal FLOW model. Matched spatial controls identified distance-based coastal context as the most stable 24 h graph component, while wind-aligned information operated as a complementary route. These results establish latency-aware MODIS thermal context as a measurable decision input for neighborhood-scale coastal compound warning. Strict station-level localization, cross-bay transfer, and forecast-consistent deployment define the next validation frontier. Full article

Manual fruit thinning is labor-intensive and inefficient, making the development of intelligent visual detection systems a crucial approach for improving the quality and production efficiency of the pear industry. However, in natural orchard environments, young pear fruits are small in size with slender fruit stalks, and their texture and color characteristics are highly similar to those of tender branches. Furthermore, variations in illumination and occlusions caused by branches and leaves make it difficult for existing detection models to simultaneously and accurately identify fruits and fruit stalks, limiting their application in automated thinning equipment. In this study, Yuluxiang pear was selected as the research object, and image data were collected under diverse field conditions, including forward-lighting, backlighting, close-range shooting, long-range shooting and fruit overlapping. A dedicated dataset containing 3057 images was established. Based on the RT-DETR-R18 network, a lightweight and high-precision fruit–stalk synchronous detection model was proposed. Specifically, the backbone network was reconstructed by integrating GCConv with C2f modules to enhance global feature extraction for slender fruit stalks. The bottleneck structure was optimized using GCConvC3 to reduce feature degradation under occlusion conditions, and an additional 4× down-sampling P2 detection head was introduced to improve the detection capability for small targets. To fully validate the model performance and stability, three types of experiments were conducted in this study: ablation experiments, repeated experiments with different random seeds, and comparative experiments. Ablation experiments verified the cumulative performance improvements brought by the introduced modules. Repeated experiments with different random seeds were performed to explore training randomness-induced performance fluctuations, and the results demonstrated that the proposed model maintains stable overall detection accuracy with minor metric fluctuations. Comparative experiments demonstrated that the proposed model achieved a compact parameter size of only 15.97 M, with a precision of 95.0% for young pear fruit detection and an mAP50 of 83.0% for fruit stalk detection, outperforming all comparative models in overall mAP50. The training convergence curves and Grad-CAM++ visualization results further confirmed the stable optimization process and enhanced feature attention capability of the proposed model. By achieving a favorable balance between detection accuracy and model lightweightness, this approach provides effective technical support for the development of intelligent fruit thinning equipment and vision-based systems for smart pear orchards. Full article

Pixel-level annotation of remote sensing imagery is costly, motivating weakly supervised semantic segmentation (WSSS) using only image-level labels. However, class activation maps (CAMs) often highlight only discriminative sub-regions and fail to separate adjacent land-cover regions, particularly in remote sensing scenes characterized by densely co-occurring land-cover classes and substantial variations in object scale. To address these limitations, we propose a three-stage framework that integrates complementary priors from Contrastive Language–Image Pre-training (CLIP), Self-Distillation with No Labels version 2 (DINOv2), and the Segment Anything Model (SAM). First, a lightweight CLIP adapter aligns vision–language priors with remote sensing imagery, while sigmoid-based multi-label decoupled distillation replaces class-competitive distillation with independent class-wise supervision, producing more complete CAMs. Second, DINOv2-guided feature clustering decomposes large merged regions before SAM prompt generation, while Spatial–Semantic Constraints are used to construct confidence-guided point-and-box prompts and reject excessively expanded or semantically inconsistent masks, thereby generating reliable pseudo-labels. Finally, a compact segmentation network is initialized with the weights learned in Stage 1 and retrained using the refined pseudo-labels generated in Stage 2, eliminating the need for foundation models during inference. Experiments on the Potsdam, LoveDA, and DeepGlobe datasets show that the proposed method achieves mean intersection over union (mIoU) scores of 53.16%, 52.66%, and 62.98%, respectively, outperforming state-of-the-art WSSS baselines by 6.55, 1.16, and 1.27 percentage points, respectively. These results demonstrate the effectiveness and generalizability of the proposed framework across diverse remote sensing scenarios under image-level supervision. Full article