Search Results (791)

Aims: Coronary heart disease (CHD) associated with rheumatoid arthritis (RA) is a primary driver of mortality in RA patients. In this study, we sought to establish a combined rat model of CHD and RA by integrating cardiac ischemia/reperfusion (I/R), high-fat diet (HFD), and intradermal administration of bovine type II collagen emulsified in complete Freund’s adjuvant. The aim of constructing this model is to investigate and analyze the pathogenesis of RA-induced CHD under the modulation of HFD and cardiac I/R exposure. Methods and Results: Sixty-four male Sprague–Dawley rats were randomly categorized into eight groups (n = 8 per group): control, I/R, HFD, collagen-induced arthritis (CIA), I/R + CIA, HFD + CIA, I/R + HFD, and I/R + HFD + CIA groups (n = 8 per group). We applied Synchrotron radiation-based X-ray micro-computed tomography (micro-CT) to observe the structural changes within the model over time. To further elucidate molecular mechanisms, transcriptome RNA-seq analysis was carried out to identify key signaling pathways, with particular emphasis on the homeostasis of Toll-like receptor 4 (TLR4)/Myd88 signaling in the ischemic myocardium. Furthermore, we conducted in vivo shRNA-mediated knockdown of polymerase I and transcription release factor (PTRF) and evaluated the co-localization of PTRF and TLR4 through immunofluorescence experiments. It is worth mentioning that our rat model of RA-induced (CHD) under a high-fat diet effectively manifested the relevant pathological features that align with the Traditional Chinese Medicine (TCM) definition of “bi” syndrome. The results indicate that the combined stimulation of HFD and CIA significantly elevated cardiac injury markers (CK-MB, LDH, CRP, and c-TNT) and was accompanied by a more severe expansion of the infarct area and increased cardiomyocyte apoptosis compared to the I/R group alone. In addition, the histopathological evaluation revealed significantly aggravated myocardial inflammation and fibrosis deposition, accompanied by extensive areas of tissue damage, further indicating a state of heightened inflammation and severe cardiac degenerative changes. Consistently, myocardial tissues from rats in the I/R + CIA + HFD group exhibited robust activation of the TLR4/MyD88 signaling pathway and a pronounced elevation in the p-JNK/JNK ratio. Moreover, pronounced co-localization between PTRF and TLR4 was evident in small vessels surrounding the infarcted myocardium. Importantly, AAV-mediated knockdown of PTRF attenuated the HFD- and CIA-induced exacerbation of myocardial injury in I/R rats. Conclusions: We successfully established a rat model of CHD with rheumatic syndrome using I/R in combination with RA and HFD. The present findings suggest that the PTRF-related TLR4/MyD88-JNK signaling pathway may act as an important regulatory mechanism underlying myocardial injury aggravated by combined HFD and CIA stimulation. Full article

Polyetheretherketone (PEEK) is widely employed in orthopedic applications due to its bone-mimetic mechanical properties and excellent biocompatibility, establishing it as a promising candidate for bone repair and regeneration. However, the investigation of structural integrity and microstructural features of PEEK implants has remained limited owing to its low atomic number (Z < 8) and hierarchical microstructure. This study presents a comparative characterization of PEEK implants by integrating laboratory X-ray computed tomography (CT) and synchrotron X-ray phase-contrast tomography (SXCT). Laboratory X-ray CT, operating on absorption contrast mechanisms, demonstrates adequate capacity for visualizing macroscopic defects and pore architectures, but exhibits limitations in resolving subtle density variations. In contrast, SXCT, employing phase-contrast imaging, significantly enhances discrimination of ultra-low-contrast features, including interconnected pore networks and localized density gradients. This work elucidates the complementary advantages and inherent limitations of both the imaging modalities for characterizing PEEK-based biomaterials. Furthermore, it demonstrates the potential extensibility of this comparative approach to other polymer composites, offering a methodology to investigate material–tissue interactions in orthopedic research and advancing the development of next-generation implantable devices. Full article

In this work, we propose ARTEMIS, a novel and highly interpretable deep learning pipeline for the automatic classification of Chest X-ray (CXR) and Computed Tomography (CT) images into different categories related to important clinical outcomes: COVID-19 infection, Community-Acquired Pneumonia (CAP) cases, and Normal cases. Unlike existing models based on the static feature enhancement step, ARTEMIS proposes a learnable preprocessing component that dynamically adapts the image contrast and sharpness in training mode, facilitating adaptive optimization. Our hybrid network combines EfficientNet-B0 backbone with built-in SE attention with the optional lightweight Transformer encoder block to jointly learn local radiological features and global relationships between pixels. Comprehensive experiments have been conducted on five different datasets, which comprise four publicly available ones and one novel CT dataset annotated by radiologists, including X-ray and CT modalities. Experimental results show strong robustness and generalization with macro F1-scores greater than 96% on public datasets and 99.39% accuracy on our new CT dataset. To interpret the decision-making process, Grad-CAM++ is employed to generate class-discriminative saliency maps; the highlighted regions are systematically validated against established radiological criteria by a board-certified radiologist, confirming that model decisions are grounded in clinically meaningful pulmonary findings rather than imaging artifacts. Full article

Spinal infections in general, and infectious spondylodiscitis in particular, are increasingly diagnosed in the Western world, in recent decades. This rise in incidence is associated with an ageing population and with an increased availability of accurate diagnostic modalities. Even so, due to the non-specific nature of clinical manifestations, and of the implicated blood and serum markers, there is a risk of underdiagnosis or misdiagnosis of the disease in its initial stages. Ionizing radiation methods, such as plain radiography (X-ray) and computed tomography (CT), are also not reliable in the early stages of the diseases, and the golden standard of imagistic diagnosis, magnetic resonance imaging (MRI), is not always available or requested. Still, MRI remains the most reliable method in most cases where there is a need for differential diagnosis with other pathologies, namely Andersson lesions, destructive spondyloarthropathy, erosive osteochondritis, micro-crystalline spondylitis, Modic 1 lesion, Charcot spinal arthropathy, osteoporotic fractures, SAPHO syndrome with spinal involvement, and Schmorl’s nodes. Infectious spondylodiscitis is caused by bacteria, and, less frequently, by fungi. Rare cases of parasitic causes have also been reported in the literature. Infectious spondylodiscitis of bacterial causes may be pyogenic, more frequently caused by Staphylococcus spp. or Streptococcus spp., or granulomatous, usually caused by Mycobacterium tuberculosis complex (MTBC) or from classical brucellosis. In all these cases, therapy may be conservative, with antibiotics, or surgical, when the former fails or in patients with significant spinal instability or other neurological manifestations. There are various surgical approaches, each with its own drawbacks, and usually used according to the preference of the attending physician. Even in cases of surgical treatment, antibiotic administration is prolonged, and it is important for a proper scheme to be selected based on antimicrobial susceptibility testing. However, given that in many cases, the causative agent cannot be identified, empirical treatment must be initiated. Finally, newer approaches, including the incorporation of antimicrobial substances, may offer better solutions for improving treatment and rehabilitation outcomes. Full article

Caking represents a critical stability challenge for whey permeate powders (WPPs), frequently developing during storage and handling due to moisture-driven structural transformations within the powder bed. This study investigated the physical, morphological, and microstructural characteristics associated with caking in a limited set of industrial WPPs. Five commercial WPP samples differing in production date and storage conditions were characterized in terms of dry matter content, water activity (a_w), particle size distribution (PSD), bulk density, porosity, color, and X-ray micro-computed tomography (micro-CT). Dry matter contents were similar among samples (97.74–98.20% w.b.); however, significant differences were observed in a_w, bulk density, porosity, and PSD between the caked sample (WPP2) and the free-flowing powders. WPP2 exhibited the highest a_w (0.261), the lowest bulk density (676 kg/m³), the highest porosity (0.569), and a distinctly coarser PSD. In addition, WPP2 showed the highest yellowness index (44.45), suggesting altered light-scattering behavior associated with structural changes. Micro-CT analysis revealed the presence of enlarged particle clusters and extensive particle–particle solid bridging in WPP2, accompanied by a heterogeneous pore distribution and reduced void connectivity, indicating consolidation of the powder bed. The integrated analytical approach demonstrates the potential of combining conventional measurements with micro-CT to provide detailed insight into the relationships between moisture-related properties and internal powder structure. Full article

Background: Cone Beam Computed Tomography (CBCT) is widely used in image-guided radiotherapy because it provides on-board volumetric imaging at relatively low doses, but its clinical utility for synthetic CT (sCT) generation remains limited by noise, scatter, artifacts, and reduced Hounsfield Unit (HU) fidelity. Conditional diffusion models (CDMs) have recently emerged as a promising alternative to earlier deep learning approaches because their iterative denoising process may better preserve anatomical structure and model uncertainty. Objective: This systematic review evaluates the use of conditional diffusion models for CBCT-to-CT synthesis, with particular attention to architectural strategies, reported quantitative outcomes, and potential clinical relevance. A systematic search was conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2013 and 2024. Eleven studies met the eligibility criteria and were analyzed to address three questions: (1) Which conditional diffusion strategies have been used? (2) What outcomes have been reported? and (3) What clinical implications have been discussed? Results: Across the included studies, CDMs frequently showed promising image quality performance, especially when incorporating anatomical priors, spatial-frequency guidance, hierarchical refinement, or latent representations. However, the evidence base remains small and highly heterogeneous with respect to anatomy, dimensionality, supervision strategy, and evaluation metrics, limiting the strength of direct comparative claims. The reviewed literature suggests that conditional diffusion models are a promising direction for CBCT-to-CT synthesis, but stronger dose-aware validation, standardized reporting, and broader multicenter evaluation are still needed before routine clinical deployment. This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO), under registration number CRD42024619240. Full article

Introduction: Computed Tomography (CT) brain imaging provides high-resolution anatomical detail but involves relatively higher radiation doses, necessitating dose monitoring and optimisation. Diagnostic reference levels (DRLs) are recommended dose indicators for optimising radiation exposure without compromising diagnostic image quality; however, national DRLs for CT brain imaging have not yet been established in South Africa. This article presents a protocol for establishing local DRLs for non-contrast- (non-CE) and contrast-enhanced (CE) adult CT brain examinations at an academic hospital in Johannesburg, South Africa. Materials and Methods: The research site is at a single hospital in Johannesburg, South Africa. The research design for this study is retrospective. A sample of 197 adult CT brain examinations (63 non-CE, 34 CE, and 100 combined non-CE and CE examinations) performed between 1 January and 31 December 2024 will be used to develop local DRLs. The 64-slice CT scanner of choice for data collection is the Siemens SOMATOM Definition AS. The population defined for this study is individuals aged 18–70 years. The preferred contrast media used for CT brain examination at the research site is 40 mL of Omnipaque 350. The scan range for CT brain is from the base of the skull (foramen magnum) to the vertex, ensuring full coverage of intracranial structures. Dose metrics, including the volumetric CT dose index (CTDIvol) and dose–length product (DLP), will be extracted from archived dose reports. Local DRLs will be established as the 75th percentile values of CTDIvol and DLP for each protocol group. Descriptive statistics (mean, median, and interquartile range) will be used to summarise the data demographics. The effective dose will be estimated by applying a head-specific conversion coefficient to the DLP values. Results: As this is a study protocol, results are not yet available. Local DRLs will be reported as the mean, median, and 75th percentile values of the DLP and CTDIvol for non-CE, CE, and for both non-CE and CE CT brain examinations. The effective dose will be estimated by applying a head-specific dose conversion coefficient (k-factor) to the mean DLP values. Expected Outcomes: This study is expected to establish local DRLs for adult CT brain examinations, providing baseline data for dose optimisation and supporting the future development of national DRLs in South Africa. Conclusions: Establishing local DRLs will support the optimisation of the radiation dose in CT brain imaging to keep the dose as low as reasonably achievable. The DRLs developed for this study will contribute to national and international efforts toward optimising radiation dose during diagnostic X-ray imaging investigations. Full article

Achieving the coexistence of high energy density and fast-charging capability remains a fundamental challenge for lithium-ion batteries. Increasing electrode thickness and compaction density enhances energy density but simultaneously alters the pore structure and restricts lithium-ion transport, leading to concentration polarization, increased resistance, and lithium plating. In this work, we employ X-ray computed tomography (X-CT) and 3D reconstruction to establish quantitative relationships between particle size, compaction density, and key structural parameters (porosity, tortuosity, effective proportion of lithium-ion flux (f_eff)). Then, an electrochemical model is used to link the liquid-phase kinetic parameters (ionic conductivity (k₀) and liquid-phase diffusion coefficient), as corrected by the effective proportion of lithium-ion flux f_eff, to polarization and lithium-plating behavior, and the maximum current density without lithium plating under various fabrication conditions is finally determined. Results show that small-particle electrodes exhibit superior rate capability at moderate compaction levels, but suffer from rapidly increasing tortuosity and reduced transport efficiency under high compaction and large thickness. Moreover, a double-layer gradient electrode design effectively integrates the advantages of both large- and small-particle architectures, enabling high-rate operation without lithium plating. The double-layer gradient electrode (ρ = 1.6 g/cm³) exhibited ~50% higher performance at 1.5 C compared to the small-particle anode and enabled 2 C charging without lithium plating. This study offers a robust structural design strategy for optimizing thick-electrode architectures toward high-energy, fast-charging LIBs. Full article

Bacterial infections can delay wound healing and represent serious medical problems both in the hospital and community settings, especially skin wound infections caused by Staphylococcus aureus. In this work, a gelatin hydrogel modified with photo-cross-linkable methacrylamide groups at 10% concentration (GelMA10%), enriched with titanium dioxide nanoparticles (TiO₂NPs), and loaded with Neomycin sulphate was developed with the aim to realize a tissue for wound care with improved mechanical and antimicrobial properties. TiO₂ nanocomposite GelMA films with two concentrations of TiO₂NPs were characterized to assess physicochemical, structural and mechanical properties by scanning electron microscopy equipped with an energy-dispersive X-ray spectrometer (SEM/EDX), micro-computed tomography (micro-CT) and X-ray photoelectron spectroscopy (XPS). TiO₂ nanocomposite GelMA films showed a more compact structure, reduced pore sizes and a higher compressive modulus at the increasing concentration of TiO₂NPs. They were able to absorb and retain water for a prolonged time; however, no significant differences in the swelling degree at the increasing concentration of TiO₂NPs were observed. In vitro drug release and antibacterial activity against Staphylococcus aureus of TiO₂ nanocomposite GelMA film enriched with higher concentrations of TiO₂NPs, identified as a suitable candidate for wound healing, were investigated. Both GelMA10% and TiO₂ nanocomposite GelMA films loaded with drug exhibited a strong antibacterial action, whereas GelMA10% containing only TiO₂NPs did not show any antimicrobial properties. Full article

Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis. Full article

Fused deposition modeling (FDM)-based three-dimensional (3D) fabrication offers a viable approach to manufacturing highly customized carbon fiber-reinforced polyether ether ketone (CFR-PEEK) components with complex geometries. However, the mechanical properties and surface roughness of FDM-fabricated parts are strongly influenced by processing parameters, particularly layer thickness. This study investigates the influence of layer thickness (0.1 mm and 0.2 mm) on the surface roughness, crystallinity, mechanical properties, and morphological characteristics of FDM-printed 10% CFR-PEEK specimens. The specimens were characterized using mechanical testing, differential scanning calorimetry (DSC), confocal laser microscopy, X-ray micro-computed tomography (µCT), and scanning electron microscopy (SEM). The results show that specimens printed with a 0.2 mm layer thickness exhibit higher crystallinity and ball indentation hardness while also showing increased surface roughness and porosity, with µCT analysis revealing larger and more spatially clustered voids near the sub-perimeter regions. In contrast, specimens printed with a 0.1 mm layer thickness demonstrate higher tensile strength, elastic modulus, elongation at break, and compressive stress. SEM fractography further indicates improved interlayer bonding and a relatively cohesive fracture surface in specimens printed with a 0.1 mm layer thickness. These findings demonstrate clear layer-thickness-dependent processing–structure–property relationships in FDM-printed CFR-PEEK composites and provide guidance for optimizing printing parameters to achieve improved mechanical performance. Full article

Identifying primary ceramic forming techniques is often problematic when surface traces are altered or erased by secondary shaping on the potter’s wheel, particularly in vessels combining hand-building and wheel use. This study aims to develop a quantitative, non-destructive method to distinguish wheel-throwing and wheel-coiling techniques by analyzing internal fabric features. Experimental replicas of Middle Minoan handleless conical cups (18th cent. BC), produced using wheel-throwing-off-the-hump and wheel-coiling techniques, were investigated using X-ray micro-computed tomography (µCT). Macropores were segmented from complete 3D µCT datasets and their shape preferred orientation was quantitatively assessed through ellipsoid fitting, orientation distribution functions, and pole figure analysis. The results reveal systematic and reproducible differences between the two forming techniques: wheel-coiled vessels show predominantly horizontal pore elongation, expressed as equatorial girdle textures and vertically clustered short axes, whereas wheel-thrown vessels display inclined pore orientations, forming displaced girdles and ring-like short-axis distributions. These contrasting orientation patterns reflect distinct deformation fields imposed during vessel shaping. The study demonstrates that quantitative 3D analysis of pore orientation in whole vessels provides reliable criteria for identifying ceramic forming techniques and confirms previous qualitative observations. This approach offers a robust framework for technological analysis of ceramics and can be applied to both complete vessels and suitably oriented fragments. Full article

The automated production of large-scale labeled datasets from concrete X-ray computed tomography (CT) images is a fundamental prerequisite for training and validating deep learning-based segmentation models. However, existing methods either require extensive manual annotation or rely on domain-specific deep learning models that themselves demand labeled data—a circular dependency. This paper presents a parameter-driven three-class segmentation framework that automatically classifies each pixel in a concrete CT slice into one of three material phases: void (air pores and cracks), coarse aggregate, and mortar matrix, generating annotation masks suitable for large-scale dataset production without manual labeling. The proposed method combines: (1) fixed-threshold void detection calibrated to concrete CT grayscale characteristics; (2) adaptive percentile-based initial segmentation responsive to image-specific statistics; (3) multi-criteria connected component scoring based on area, shape descriptors (circularity, solidity, compactness, extent, aspect ratio), intensity distribution, and boundary gradient; (4) material science-informed size constraints aligned with concrete phase volume fractions; and (5) a material continuity enforcement module that applies topological hole-filling and conditional convex-hull consolidation to eliminate internal contamination within accepted aggregate regions, reducing boundary roughness by 7.6% and recovering misclassified boundary pixels. All parameters are centralized in a configuration file, enabling reproducible batch processing of 224 × 224 pixel CT slices at 0.07–1.12 s per image. Evaluated on 1007 224 × 224 concrete CT patches cropped from 200 representative scan frames, the framework produces three-class segmentation masks with physically consistent void fractions (mean 3.2%), aggregate fractions (mean 32.4%), and mortar fractions (mean 64.4%), all within ranges reported in the concrete CT literature (used as a dataset-scale QC screen, not a validation metric). Primary outputs and the archived image–mask pairs for this work are provided as an 8-bit patch archive. For pixel-wise validation, we report IoU, Dice, and pixel accuracy on an independently labeled subset that can be unambiguously paired with the released predictions: averaged over 57 matched patches, mean pixel accuracy is 88.6%, macro-mean IoU is 74.7%, and macro-mean Dice is 84.9%. The framework provides a fully automated annotation pipeline for dataset production, eliminating manual labeling costs for concrete CT image collections. The generated datasets are suitable for training semantic segmentation networks such as U-Net and its variants. Full article

Purpose: To critically evaluate the role or effect of platelet-rich plasma (PRP) in anterior cruciate ligament (ACL) reconstruction in terms of clinical and radiological outcomes. Method: We conducted a systematic search of PubMed, Embase, the Cochrane Library, and Web of Science to identify relevant studies. Clinical outcomes included the Visual Analogue Scale (VAS), International Knee Documentation Committee (IKDC) subjective and objective evaluations, Lysholm score, Tegner score, anterior knee laxity, Knee Injury and Osteoarthritis Outcome Score (KOOS), Kujala score, Victorian Institute of Sport Assessment (VISA) scale, proprioception, isokinetic strength, and physical examination tests (anterior drawer, Lachman, and pivot-shift tests). Radiological outcomes encompassed measures obtained via magnetic resonance imaging (MRI), computed tomography (CT), X-ray, and ultrasound. Statistical significance was defined as a p value < 0.05, and all analyses were performed using R software (version 4.1.3). Results: A total of 23 studies, including 19 randomized controlled trials, met the inclusion criteria, encompassing 1072 patients overall. The meta-analysis showed significant differences between PRP group and non-PRP group with regard to VAS score at 6- and 12-month follow-up, Lysholm score at 6-month follow-up, and Tegner score at 6-month follow-up. Meta-regression showed that the two group differences in VAS score changed significantly with follow-up time (p < 0.01). In terms of radiological findings, about half of the assessments favored PRP to facilitate the graft maturation and integration at 6-month follow-up. Conclusions: PRP application in ACL reconstruction compared with non-PRP, may produce short-term but not long-term clinical outcomes such as VAS score, Lysholm score and Tegner score. While some short-term statistical differences exist, their magnitude and durability do not yet justify routine clinical adoption of PRP in ACL reconstruction. Larger samples and higher-quality studies are needed to support our results and further explore the advantages of PRP in other aspects. Level of evidence: Level II. Full article

To investigate the effects of incorporating nanomaterials—carbon nanotubes (CNTs) and graphene oxide (GO)—on the axial compressive mechanical properties of alkali-activated recycled aggregate concrete (AARAC) after high-temperature exposure, this study designed 51 sets of specimens with recycled coarse aggregate replacement rate, nanomaterial content, and temperature as the main parameters. Compression tests were conducted to analyze the failure mode and strength variation in AARAC specimens after heating. In addition, microscopic tests, including X-ray diffraction, scanning electron microscopy, and computed tomography (CT scanning), were performed to analyze the microstructural characteristics of the post-heated AARAC specimens. The results indicate that as the replacement rate of recycled coarse aggregate increased from 0% to 100%, the residual compressive strength after exposure to 600 °C decreased from 33.6 MPa to 19 MPa. When 0.1 wt% of CNTs is added, the compressive strength of AARAC after exposure to a high temperature of 600 °C increases by approximately 30.4% compared to that of AARAC without nanomaterial addition. When 0.1 wt% of CNTs and 0.05 wt% of GO are added, the compressive strength after exposure to a high temperature of 600 °C increases by approximately 44.3%, while the size of scattered fragments upon failure increased, and the failure mode appeared more complete. Microscopic test results indicate that the high-temperature treatment did not cause significant changes in the main phase composition of AARAC. The synergistic effect of the nanomaterials CNTs and GO can fully utilize their functions as nucleation sites, pore fillers, and crack bridging agents. By strengthening the Interfacial Transition Zone between the recycled coarse aggregate and the cement paste, refining the Matrix Pore Structure, dispersing local thermal stress, and suppressing the propagation of high-temperature cracks, the mechanical properties of AARAC after high-temperature exposure can be effectively maintained. Full article