Search Results (46)

This study focuses on porous polyimide (PPI) lubricating materials for high-speed aerospace bearings. Based on their real microstructure, three-dimensional digital model reconstruction and mesoscale seepage characteristics were investigated. First, a sequence of two-dimensional slice images of PPI was obtained using micro-focus X-ray computed tomography (CT). Through image filtering, threshold segmentation, and three-dimensional reconstruction, a highly faithful digital model of the pore structure was constructed, and a quantified pore-network model was further extracted. Second, a multiple-relaxation-time lattice Boltzmann model based on the D3Q27 discrete scheme was established, and its accuracy and stability in complex boundaries and pressure-driven flows were verified using classic benchmark cases. Subsequently, the validated numerical model was applied to the reconstructed PPI pore structure to simulate and systematically analyze the single-phase seepage behavior of lubricating oil. The results show that the lubricant seepage exhibits a strong “preferential flow path” effect, with most of the flow transported through a small number of large-size throats. A clear quantitative relationship exists between the microscopic flow field structure—including velocity distribution, flow paths, and pressure gradient—and the pore-topology features, such as throat-size distribution, connectivity, and tortuosity. This verifies the mesoscale mechanism that “structure governs flow.” The complete technical chain established in this work—“real-structure reconstruction–numerical model validation–seepage mechanism analysis”—provides a reliable theoretical and numerical tool for gaining deeper insight into the lubricant transport behavior in porous polyimide and offers guidance for the microstructural design and optimization of this material. Full article

Each year, around 23 million miscarriages occur worldwide, which have a substantial emotional impact on parents, and impose significant societal costs. While medical care accounts for most expenses, work productivity loss contributes significantly. Addressing underlying causes of miscarriage could improve parents’ mental health and potentially their economic impact. In most countries, investigations into miscarriage causes are only recommended after recurrent cases, focusing mainly on maternal factors. Fetal and placental tissue are rarely examined, as current guidelines do not advise routine genetic analyses of pregnancy tissue, because the impact of further clinical decision making and individual prognosis is unclear. However, this leaves over 90% of all miscarriage cases unexplained and highlights the need for alternative methods. We therefore conducted a narrative review on genetic analysis, autopsy, and imaging of products of conception (POC). Karyotyping, QF-PCR, SNP array, and aCGH were reviewed in different research settings, with QF-PCR being the most cost-effective, while obtaining the highest technical success rate. Karyotyping, historically being considered the gold standard for POC examination, was the least promising. Post-mortem imaging techniques including post-mortem ultrasound (PMUS), ultra-high-field magnetic resonance imaging (UHF-MRI), and microfocus computed tomography (micro-CT) show promising diagnostic capabilities in miscarriages, with micro-CT achieving the highest cost-effective performance. In conclusion, current guidelines do not recommend diagnostic testing for most cases, leaving the majority unexplained. Although genetic and imaging techniques show promising diagnostic potential, they should not yet be implemented in routine clinical care and require thorough evaluation within research settings—assessing not only diagnostic and psychosocial outcomes but also economic implications. Full article

Background/Objectives: Root canal treatment (RCT) is a common dental procedure performed to preserve teeth by removing infected or at-risk pulp tissue caused by caries, trauma, or other pulpal conditions. A successful outcome, among others, depends on accurate identification of the root canal anatomy, planning a suitable therapeutic strategy, and ensuring a bacteria-tight root canal filling. Despite advances in dental techniques, there remains limited integration of computational methods to support key stages of treatment. This review aims to provide a comprehensive overview of computational methods applied throughout the full workflow of RCT, examining their potential to support clinical decision-making, improve treatment planning and outcome assessment, and help bridge the interdisciplinary gap between dentistry and computational research. Methods: A comprehensive literature review was conducted to identify and analyze computational methods applied to different stages of RCT, including root canal segmentation, morphological analysis, treatment planning, quality evaluation, follow-up, and prognosis prediction. In addition, a taxonomy based on application was developed to categorize these methods based on their function within the treatment process. Insights from the authors’ own research experience were also incorporated to highlight implementation challenges and practical considerations. Results: The review identified a wide range of computational methods aimed at enhancing the consistency and efficiency of RCT. Key findings include the use of advanced image processing for segmentation, image analysis for diagnosis and treatment planning, machine learning for morphological classification, and predictive modeling for outcome estimation. While some methods demonstrate high sensitivity and specificity in diagnostic and planning tasks, many remain in experimental stages and lack clinical integration. There is also a noticeable absence of advanced computational techniques for micro-computed tomography and morphological analysis. Conclusions: Computational methods offer significant potential to improve decision-making and outcomes in RCT. However, greater focus on clinical translation and development of cross-modality methodology is needed. The proposed taxonomy provides a structured framework for organizing existing methods and identifying future research directions tailored to specific phases of treatment. This review serves as a resource for both dental professionals, computer scientists and researchers seeking to bridge the gap between clinical practice and computational innovation. Full article

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous malignancy, with various outcomes despite significant advances in therapeutic options. Current conventional prognostic tools, e.g., the International Prognostic Index (IPI), lack sufficient precision at an individual patient level. However, artificial intelligence (AI), including machine learning (ML) and deep learning (DL), can enable specialists to navigate complex datasets, with the final aim of improving prognostic models for DLBCL. Objectives: This scoping review aims to systematically map the current literature regarding the use of AI/ML techniques in DLBCL outcome prediction and risk stratification. We categorized studies by data modality and computational approach to identify key trends, knowledge gaps, and opportunities for their translation into current practice. Methods: We conducted a structured search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases through July 2025 using terms related to DLBCL, prognosis, and AI/ML. Eligible studies included original papers applying AI/ML to predict survival outcomes, classify risk groups, or identify prognostic subtypes. Studies were categorized based on input modality: clinical, positron emission tomography/computed tomography (PET/CT) imaging, histopathology, transcriptomics, genomics, circulating tumor DNA (ctDNA), and multi-omics data. Narrative synthesis was performed in line with PRISMA-ScR guidelines. Results: From the 215 records screened, 91 studies met the inclusion criteria. Group-wise we report the following categories: clinical risk features (n = 8), PET/CT imaging (n = 30), CT (n = 1), digital pathology (n = 3), conventional histopathology (n = 2), gene expression profiling (n = 19), specific mutational signatures (n = 18), ctDNA (n = 3), microRNA (n = 2), and multi-omics integration (n = 5). The most common techniques reported amongst the papers included ensemble learning, convolutional neural networks (CNNs), and LASSO-based Cox models. Several AI techniques demonstrated superior predictive performance over IPI, with area under the curve (AUC) values frequently exceeding 0.80. Multi-omics models and ctDNA-based predictors showed strong potential for clinical translation, a perspective worth considering in further studies. Conclusions: AI/ML methods are increasingly used in DLBCL to improve prognostic accuracy by leveraging data types with diverse inputs. These approaches allow an enhanced stratification, superior to traditional indices, and support the early identification of high-risk patients, earlier guidance for therapy tailoring, and early trial enrollment for flagged cases. Future investigations should focus on external validation and improvement of model interpretability, with tangible perspectives of integration into real-world workflows and translation from bench to bedside. Full article

In the context of soil management, the porous structure present in these systems plays a relevant role due to its capacity to store and transport water, nutrients, gases, and provide root fixation. A detailed and precise analysis of these structures can assist specialists in determining specific agricultural solutions and management practices for each soil, depending on the characteristics of its porous structure. In this regard, this study presents a hybrid method for segmenting porous structures in micro computed tomography (micro CT) images of carbonate rocks, with a focus on applications in agricultural soil analysis and management. Initially, preprocessing steps such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram specification are applied in order to improve image contrast and uniformity. Subsequently, a UNet convolutional neural network is employed to identify pore contours, followed by the application of two geostatistical approaches, ordinary kriging and Universal Kriging, with the purpose of completing segmentation through the interpolation of unclassified regions. The proposed approach was evaluated using the dataset “16 Brazilian Pre Salt Carbonates”, which includes high-resolution micro CT images. The results show that the integration of UNet with ordinary kriging achieved superior performance, with 79.2% IoU, 93.3% precision, 81.7% recall, and 87.1% F1 Score. This method enables detailed analyses of pore distribution and the porous structure of soils and rocks, supporting a better understanding of inherent characteristics such as permeability, porosity, and nutrient retention in soil, thus contributing to more assisted agricultural planning and more efficient soil use strategies. Full article

Background: The phenomenon of pink teeth represents a notable observation in forensic science, although its interpretation remains complex and not directly attributable to a specific cause of death. Methods: This systematic review provides an updated and comprehensive overview of the morphological and histological mechanisms associated with this finding, with a focus on hemoglobin diffusion and pigment accumulation during putrefaction rather than on detailed biochemical pathways. Results: Environmental conditions, especially high humidity and moderate temperatures, are identified as key facilitators. The synthesis of the available evidence, including case reports, observational series, and experimental studies, confirms that pink discoloration is primarily linked to postmortem hemoglobin diffusion following erythrocyte breakdown and release of heme groups into dentinal structures. This process occurs more frequently under conditions that preserve hemoglobin and facilitate its migration into dental tissues. Importantly, pink teeth have been documented across a wide spectrum of postmortem scenarios, such as hanging, drowning, carbon monoxide poisoning, and prolonged exposure to humid environments, indicating that their presence is neither pathognomonic nor exclusively associated with a specific cause of death. Assessment methods include semi-quantitative visual scoring systems (e.g., SPTC and SPTR), spectrophotometric assays, and histochemical analyses for hemoglobin derivatives. Recent advances in digital forensics, particularly micro-computed tomography and artificial intelligence–based segmentation, may further support the objective evaluation of chromatic dental changes. Conclusions: This review underscores the need for standardized approaches to the identification, classification, and analysis, both qualitative and quantitative, of pink teeth in medico-legal practice. Although not diagnostic in isolation, their systematic study enhances our understanding of decomposition processes and contributes supplementary interpretive data in forensic investigations. Full article

Continuous SiC fiber-reinforced SiC matrix composites (SiC/SiC), as structural heat protection integrated materials, are often used in parts for large-area heat protection and sharp leading edges, and there are a variety of low-velocity impact events in their service. In this paper, a drop hammer impact test was conducted using narrow strip samples to simulate the low-velocity impact damage process of sharp-edged components. During the test, different impact energies and impact times were set to focus on investigating the low-velocity impact damage characteristics of 2.5D SiC/SiC composites. To further analyze the damage mechanism, computed tomography (CT) was used to observe the crack propagation paths and distribution states of the composites before and after impact, while scanning electron microscopy (SEM) was employed to characterize the differences in the micro-morphology of their fracture surfaces. The results show that the in-plane impact behavior of a 2.5D needled SiC/SiC composite strip samples differs from the conventional three-stage pattern. In addition to the three stages observed in the energy–time curve—namely in the quasi-linear elastic region, the severe load drop region, and the rebound stage after peak impact energy—a plateau stage appears when the impact energy is 1 J. During the impact process, interlayer load transfer is achieved through the connection of needled fibers, which continuously provide significant structural support, with obvious fiber pull-out and debonding phenomena. When the samples are subjected to two impacts, damage accumulation occurs inside the material. Under conditions with the same total energy, multiple impacts cause more severe damage to the material compared to a single impact. Full article

With the extensive application of 3D-printed composites across multiple industries, the investigation into their structural reliability under complex loading conditions has become a critical research focus. This study comprehensively employs acoustic emission (AE) monitoring, digital image correlation (DIC) measurement, and micro-computed tomography (Micro-CT) visualization techniques to explore the progressive damage behavior of 3D-printed sandwich-structured composites reinforced with continuous carbon fiber sheets under three-point bending. Mechanical tests show that increasing the fiber content of face sheets from 10% to 20% enhances average bending strength by 56%, while low fiber content compromises stiffness and load-bearing capacity. AE analysis categorizes damage modes into matrix cracking (<50 kHz), debonding/delamination (50–150 kHz), and fiber breakage (>150 kHz) using k-means clustering algorithms. DIC measurement reveals significant structural deformation processes during damage progression. The AE-DIC-Micro-CT combination demonstrates an initial undamaged state, followed by damage initiation and propagation in the subsequent stages. This integrated approach provides an effective method for damage assessment, guiding the design and reliability improvement of 3D-printed composites. Full article

Recent studies primarily focus on how the fiber content and curing age influence the pore structure and strength of concrete. However, The interfacial bonding mechanism in basalt-fiber-reinforced concrete hydration remains unclear. The lack of a long-term performance-prediction model and insufficient research on multi-field coupling effects form key knowledge gaps, hindering the systematic optimal design and wider engineering applications of such materials. By integrating X-ray computed tomography (CT) with the watershed algorithm, this study proposes an innovative gray scale threshold method for pore quantification, enabling a quantitative analysis of pore structure evolution and its correlation with mechanical properties in basalt-fiber-reinforced concrete (BFRC) and normal concrete (NC). The results show the following: (1) Mechanical Enhancement: the incorporation of 0.2% basalt fiber by volume demonstrates significant enhancement in the mechanical performance index. At 28 days, BFRC exhibits compressive and splitting tensile strengths of 50.78 MPa and 4.07 MPa, surpassing NC by 19.88% and 43.3%, respectively. The early strength reduction in BFRC (13.13 MPa vs. 22.81 MPa for NC at 3 days) is attributed to fiber-induced interference through physical obstruction of cement particle hydration pathways, which diminishes as hydration progresses. (2) Porosity Reduction: BFRC demonstrates a 64.83% lower porosity (5.13%) than NC (11.66%) at 28 days, with microscopic analysis revealing a 77.5% proportion of harmless pores (<1.104 × 10⁷ μm³) in BFRC versus 67.6% in NC, driven by densified interfacial transition zones (ITZs). (3) Predictive Modeling: a two dimensional strength-porosity model and a three-dimensional age-dependent model are developed. The proposed multi-factor model demonstrates exceptional predictive capability (R² = 0.9994), establishing a quantitative relationship between pore micro structure and mechanical performance. The innovative pore extraction method and mathematical modeling approach offer valuable insights into the micro-structural evolution mechanism of fiber concrete. Full article

Micro-computed tomography assessment of osteochondral microstructural properties of the distal femur and proximal tibia was comprehensively conducted to compare adult patients with knee rheumatoid arthritis (RA) and primary knee osteoarthritis (KOA), with special focus on the effects of knee malalignment. This study encompassed 402 bone samples divided into three groups: the RA group [patients who were subjected to total knee arthroplasty (TKA) due to RA, n = 23, age: 61 ± 10 years], the KOA group [individuals subjected to TKA due to KOA, n = 24, age: 71 ± 9 years] and the control group [sex-matched cadavers without degenerative knee diseases, n = 20, age: 67 ± 11 years]. Our data revealed that the RA, KOA, and control groups differ significantly in osteochondral microstructural properties depending on the knee alignment. Specifically, increasing femoral and tibial cortical porosity, coupled with thinner articular cartilage, were noted in the RA and KOA groups, compared to the controls. Furthermore, larger femoral and tibial cortical pores, lower tibial and femoral subchondral trabecular bone fraction, and thinner tibial articular cartilage were noted in the RA group in comparison to the KOA group, implying that the medial-to-lateral load distribution in the knee joint could be most affected in these patients. Our data illustrated that the thinnest cartilage, a thicker and less porous cortex, along with lower trabecular bone volume, were present in the lateral femoral and tibial condyles of RA individuals with valgus knee alignment. Observed subchondral trabecular microarchitectural alterations could be morphological factors contributing to different effects of surgical treatment and variable implant stability in individuals with RA, warranting further research. Full article

Background: In dental implants, micro-gaps at the fixation–abutment interface can cause peri-implantitis and/or loosening or loss of the fixation screw; therefore, three-dimensional imaging is widely used to examine different types of connections. In the present study, we focus on the analysis on biconometric connections to detect and (possibly) measure the presence of micro-gaps in the as-positioned state and after repeated loading and unloading. Methods: Seven biconometric dental implants were characterized using micro-computed tomography (micro-CT). In two specimens (group 1), the cap was inserted, and only the apical portion was imaged, to evaluate the cap–abutment connection; in the remaining five specimens (group 2), the fixture–abutment connection was analyzed. Two implants in group 2 were also subjected to load tests to verify whether stresses could induce the formation of micro-gaps as a consequence of preload loss. Results: Micro-CT analysis showed the absence of micro-gaps greater than 10 µm in both cap–abutment and abutment–fixture connections. This was verified, in the fixture–abutment connection, even after mechanical loading and unloading. The results were reproducible in all the investigated samples in the different experimental conditions. Conclusions: In the human force range during chewing, the conical connection showed a high level of resistance to micro-gap formation at the implant–abutment interface. The absence of micro-gaps, as demonstrated here, provides encouraging preliminary data regarding the stability of the biconometric connections, which will be further verified in follow-up studies on a larger sample size. Full article

Background: Osteoporosis is a bone remodeling disease characterized by an imbalance between bone formation and resorption, leading to bone fragility. Current treatments focus on bone resorption suppression but often have adverse effects. This study aimed to explore the potential of sericin, a silkworm-derived protein, as a dual-action therapeutic agent that enhances bone formation through its component L-serine and inhibits bone resorption via D-serine, which is derived from L-serine by the action of serine racemase. Methods: Cellular experiments were conducted to evaluate the effects of L-serine on osteoblast differentiation and D-serine on osteoclast inhibition. Serum levels of D-serine were measured following sericin administration in an osteoporosis animal model. μ-CT analysis assessed trabecular and cortical bone quality, and bone-related protein expression was analyzed using immunoprecipitation-based high-performance liquid chromatography (IP-HPLC). Results: L-serine significantly upregulated osteogenic markers, including alkaline phosphatase (ALP), Runx2, osterix, and Col1a1, in osteoblasts (p < 0.05). D-serine inhibited osteoclast activation by suppressing cathepsin K expression (p < 0.001). Sericin feeding elevated serum D-serine levels (p < 0.001) and upregulated bone-related proteins such as BMP-2, osterix, and Runx2. Micro-computed tomography (μ-CT) analysis revealed significant improvements in trabecular bone parameters in the OVX-sericin group, including increased trabecular bone volume (Tb.BV/TV; p < 0.05) and reduced trabecular separation (Tb.Sp; p < 0.05), compared to the OVX and OVX-amino acid groups. Cortical bone parameters, including cortical bone volume (Ct.BV/TV) and cortical area (Ct.Ar), did not significantly differ among OVX groups, but all were lower than in the sham group (p < 0.05). Conclusions: This study demonstrates that sericin modulates bone metabolism by enhancing osteoblast activity through L-serine and inhibiting osteoclastogenesis via D-serine. Sericin supplementation improved trabecular bone mass in an osteoporosis model, highlighting its potential for bone health. Full article

The present study focuses on advancing one of the most popular AM techniques, namely, laser powder bed fusion (LPBF) technology, which has the ability to produce complex geometry parts with minimum material waste but continues to face challenges in minimizing the surface roughness. For this purpose, a novel hybrid manufacturing technology, which applies in a single setup (in-envelope) both LPBF technology and high-speed machining, was examined in this research for the fabrication of tensile specimens with three different surface finish conditions: as-built, hybrid (in-envelope machining) and post-machining (out-of-envelope) on Inconel^® alloy 718, hereafter referred to as IN718. As the application of the IN718 alloy in service is typically specified in the precipitation-hardened condition, three different heat treatments were applied to the tensile specimens based on the most promising thermal cycles identified previously for room-temperature tensile properties by the authors. The as-built (AB) specimens had the highest average surface roughness (R_a) of 5.1 μm ± 1.6 μm, which was a significant improvement (five-fold) on the hybrid (1.0 μm ± 0.2 μm) and post-machined (0.8 μm ± 0.5 μm) surfaces. The influence of this surface roughness on the mechanical properties was studied both at ambient temperature and at 650 °C, which is close to the maximum service temperature of this alloy. Regardless of the surface conditions, the room-temperature mechanical properties of the as-fabricated IN718 specimens were within the range of properties reported for standard wrought IN718 in the annealed condition. Nonetheless, detailed examination of the strain localization behavior during tensile testing using digital image correlation showed that the IN718 specimens with AB surfaces exhibited lower ductility (global and local) relative to the hybrid and post-machined ones, most likely due to the higher surface roughness and near-surface porosity in the former. At 650 °C, even though the mechanical properties of all the heat-treated IN718 specimens surpassed the minimum specifications for the wrought precipitation-hardened IN718, the AB surface condition showed up to 4% lower strength and 33–50% lower ductility compared with the hybrid and PM surface conditions. Microfocus X-ray computed tomography (µXCT) of the fractured specimens revealed the presence of numerous open cracks on the AB surface and a predisposition for the near-surface pores to accelerate rupture, leading to premature failure at lower strains. Full article

In the fabrication of optical polymer-based components, such as diffractive gratings and waveguides, high throughput and high precision are required. The non-destructive evaluation of these complex polymer-based structures is a significant challenge. Different measurement techniques can measure the structure geometry directly or via its functionality indirectly. This study investigates various measurement techniques aimed at assessing these structures from 200 nm up to 20 µm. Environmental scanning electron microscopy (ESEM), white light interferometry (WLI), atomic force microscopy (AFM), micro computed tomography (µCT), optical coherence tomography (OCT), phase contrast microscopy (PCM), and Mueller matrix ellipsometry (MME) are investigated for their practical limits of lateral resolution and aspect ratio. The impact of the specimens’ complexity factors, including structure width and aspect ratio, on measurement quality is discussed. A particular focus of this study is on the suitability of different measurement systems for evaluating undercuts and enclosed structures while considering structure size, slant angle, and cover thickness. The aim is to discuss the specific advantages of the individual measurement systems and their application areas in order to be able to quickly select suitable measurement systems for a non-destructive evaluation of polymer-based micro and nanostructures. Full article

Purpose: The focus of this review is on the imaging techniques used to visualize the meniscal vascular network and arteries in clinical, human ex vivo, and animal model applications. For this purpose, research articles from the past decade that have imaged the vascular network of the meniscus and/or the genicular and popliteal arteries were identified according to established PRISMA statement standards. Methods: Various imaging modalities, including magnetic resonance imaging, micro-computed tomography, and optical and fluorescence microscopy, were included and compared based on the type of visualization, imaging resolution, and range of vessel size detection. These imaging modalities were evaluated based on the outcomes of interest, including diagnostic accuracy in identifying the meniscal vasculature and associated pathologies, clinical applications to guide surgical decisions, and translational applications contributing to the research and development of new therapies and the understanding of meniscal physiology and pathology. Results: The analysis conducted in this study highlights the importance of imaging resolution and visualization type in accurately depicting the complex microvasculature of the meniscus with high precision and detail. Conclusions: This review underscores the necessity for high-resolution 3D imaging techniques to comprehensively understand the meniscal vascular network and enhance surgical approaches and treatment options for meniscal lesions and pathologies. Full article