Capsular contracture is a common complication following breast implant surgery and is primarily associated with peri-implant fibrotic responses. This study evaluated the effects of an acellular dermal matrix (ADM) powder-coated breast implant on capsular contracture-related outcomes using a rabbit model. Non-textured, smooth-surface breast implants coated with ADM powder were implanted into the subpectoral pocket, and peri-implant tissues were harvested 12 weeks after implantation. Capsule thickness was assessed using hematoxylin and eosin (H&E) staining, while fibrotic changes were evaluated by measuring collagen density in Masson’s trichrome (MT)-stained sections. Immunohistochemical analysis was performed to examine the expression of α-smooth muscle actin (α-SMA) and transforming growth factor-β (TGF-β). Compared with non-textured smooth (NTS) surface breast implants, ADM powder-coated implants demonstrated reduced capsule thickness and collagen density, together with decreased expression of α-SMA and TGF-β. These results suggest that ADM powder coating may attenuate peri-implant fibrotic responses and serve as a feasible approach for reducing capsular contracture.

Artificial intelligence (AI) is increasingly utilized in the medical field, primarily for diagnostic purposes. Although AI has demonstrated efficacy in pneumothorax detection using chest X-rays (CXR), it has yet to be applied for decision-making regarding subsequent treatment. This study aims to develop and evaluate an AI-based system capable of predicting the necessity of chest tube drainage (CTD) in spontaneous pneumothorax patients based on CXR and clinical data. A two-stage AI model was developed: (1) segmentation and quantification of pneumothorax size from CXR using deep learning and (2) prediction of CTD necessity using machine learning models integrating pneumothorax size and patient clinical parameters. The AI model was trained using CXR images and clinical information from 163 pneumothorax patients. Model performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and inference time. The AI model demonstrated high segmentation accuracy for pneumothorax (DSC, 76.95%; MAE, 5.06%). In predicting CTD necessity, the AUROC for the AI model incorporating pneumothorax ratio and clinical data was 89.68 (95% CI, 78.57-98.02), outperforming models without pneumothorax ratio (AUROC, 84.13) or with pneumothorax ratio alone (AUROC, 71.03). The sensitivity and specificity of the optimized AI model were 80.95% and 100%, respectively. The mean inference time was 0.75 ± 0.06s, demonstrating potential for real-time clinical application. This study presents an AI-based clinical decision support system capable of accurately predicting the need for CTD in spontaneous pneumothorax patients. By integrating AI-driven pneumothorax quantification and clinical parameters, the model improves decision-making efficiency and accuracy. Future studies with larger datasets and prospective validation are warranted to further refine and validate this approach.

Implant surface modification techniques have shifted from simple mechanical modifications to sophisticated strategies aimed at modulating biological responses at the bone–implant interface. This study aimed to compare sandblasted, large-grit, acid-etched (SLA) and apatite-coated dental implant surfaces, focusing on their biological, immunological, and mechanical performance. Surface morphology and wettability were assessed by field emission scanning electron microscopy and liquid spreading tests, respectively. In vitro assays evaluated osteoblast adhesion, alkaline phosphatase (ALP) staining, and mineralization. In vivo performance was examined using rat femoral condyle loosening and calvarial defect models to assess early bone formation, macrophage polarization, and vascular endothelial growth factor (VEGF) expression. A beagle mandibular tooth extraction model was used to measure removal torque (RT) and bone-to-implant contact (BIC). The apatite-coated surface exhibited a uniform nanostructured apatite layer with superior wettability compared to the SLA surface. In vitro, apatite-coated surface significantly enhanced osteoblast adhesion and mineralization (p < 0.05). In vivo, apatite-coated surface promoted peri-implant bone formation, accelerated the shift from M1 to M2 macrophages, and increased VEGF expression. In the beagle model, apatite-coated implants demonstrated higher RT and BIC at all time points. Apatite-coated on dental implants enhances osseointegration through combined biological, mechanical, and immunomodulatory effects, promoting rapid bone healing and stable implant fixation.

Ti-xMo-2Fe alloys with high specific strength were designed by adding Mo and Fe as β-stabilizing elements. The influence of cold swaging on the martensitic transformations in Ti-xMo-2Fe (x = 3.4, 5, 9.2 wt.%) alloys was investigated. In these alloys, appropriate chemical compositions promote a stress-induced phase transformation from the β phase to orthorhombic α″ martensite, which improves elongation while maintaining high strength. As the Mo content increases from 3.4 to 5 wt.%, the amount of β-stabilizing elements increases and the β stability is enhanced, thereby altering the phase transformation mechanism. In the Ti-9.2Mo-2Fe alloy, both α″ martensite and a very hard ω phase were identified by X-ray diffraction and transmission electron microscopy. The hard and brittle ω phase causes premature brittle fracture prior to macroscopic yielding. Among the investigated alloys, the Ti-5Mo-2Fe alloy exhibits the best overall combination of high tensile strength, elongation to failure, and high fatigue strength.

Bovine or porcine xenografts, which are readily available and possess osteoconductivity, are widely used for bone augmentation in clinical practice. The addition of collagen to particulated bone graft material improves handling characteristics and helps maintain graft integrity. These collagenated bone, when collagen is appropriately cross-linked, provide enhanced osteogenic potential and structural stability. However, studies on the histological changes due to the use of collagenated bovine bone for vertical bone augmentation are lacking. Therefore, this study aimed to compare the osteoconductivity and volume stability of two collagenated xenografts—deproteinized bovine bone mineral (DBBM) with crosslinked bovine collagen (DBBM-Cb; A-Oss Collagen) and non-crosslinked porcine collagen (DBBM-NCp; Bio-Oss Collagen)—using rabbit calvarial models of vertical augmentation and critical-sized defects. Surface morphology of the grafts was analyzed using field emission scanning electron microscopy. In vivo bone regeneration was assessed using micro-computed tomography and histological analyses at 3, 5, 6, and 12 weeks following bone grafting in calvarial vertical-augmentation and defect models. Both grafts showed porous and interconnected microarchitecture favorable for osteoconduction. In the augmentation model, DBBM-Cb demonstrated significantly higher bone volume fraction (bone volume/total volume of bone tissue) at 3 weeks and vertical height retention at both 3 and 5 weeks (P < 0.05). In the defect model, DBBM-Cb led to significantly greater defect closure at 12 weeks (P < 0.05). Histological analyses confirmed improved graft integration and bone maturation with DBBM-Cb. DBBM-Cb exhibited superior osteoconductivity, structural stability, and graft volume maintenance compared to DBBM-NCp. These properties support its potential as a more effective biomaterial for vertical bone augmentation.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-30824-6.

Background: Three-dimensional (3D) printed scaffolds have emerged as promising tools for bone regeneration, but the optimal structural design and pore size remain unclear. Polylactic acid (PLA) reinforced with graphene oxide (GO) offers enhanced mechanical and biological performance, yet systematic evaluation of architecture and pore size is limited. Methods: Two scaffold architectures (lattice-type and dode-type) with multiple pore sizes were fabricated using UV-curable PLA/GO resin. Physical accuracy, porosity, and mechanical properties were assessed through compression and fatigue testing. Based on in vitro screening, four pore sizes (930 μm, 690 μm, 558 μm, 562 μm) within the dode-type structure were analyzed. The 558 μm and 562 μm scaffolds, showing distinct fracture thresholds, were further evaluated in rat and rabbit calvarial defect models for inflammation and bone regeneration. Results: In vitro testing revealed that while 930 μm and 690 μm scaffolds exhibited superior compressive strength, the 562 μm scaffold showed a unique critical fracture behavior, and the 558 μm scaffold offered comparable stability with higher resistance to premature failure. In vivo studies confirmed excellent biocompatibility in both groups, with early bone formation favored in the 558 μm scaffold and more continuous and mature bone observed in the 562 μm scaffold at later stages. Conclusions: This stepwise strategy—from structural design to pore size screening and preclinical validation—demonstrates that threshold-level mechanical properties can influence osteogenesis. PLA/GO scaffolds optimized at 558 μm and 562 μm provide a translationally relevant balance between mechanical stability and biological performance for bone tissue engineering.

Deep learning-based tooth segmentation models achieve high accuracy as they grow deeper and more complex; however, their practical application in industrial settings is hindered by high computational and memory demands. To address this, pruning techniques have been employed, but existing methods mainly focus on filter importance and fail to consider redundancy among filters. In this paper, we propose DDGWD, a pruning method that simultaneously incorporates filter importance and redundancy evaluation based on Manhattan distance. Experiments on a tooth segmentation dataset demonstrate that the proposed method effectively reduces computational cost and memory usage compared to conventional importance-based approaches while minimizing performance degradation. This study shows that redundancy-aware pruning can play a crucial role in developing lightweight tooth segmentation models suitable for practical industrial deployment.

Deep learning-based dental image segmentation algorithms have received considerable attention due to their high performance. In particular, hybrid network-based approaches that combine convolutional neural networks (CNNs) with vision transformers demonstrate superior performance compared to single-network models. Although quantization techniques have been explored to enable the deployment of these algorithms in resource-constrained hardware environments, research on quantization for hybrid networks remains limited relative to that for single-network architectures. In this paper, we investigate strategies for efficiently quantizing hybrid networks by analyzing each block’s contribution to overall network performance and its sensitivity to quantization.