Search Results (4,495)

Background/Objectives: Goupi plaster (GP) is a traditional black plaster composed of a biphasic fibrous–oil matrix containing multiple bioactive compounds, and it has been widely used for the treatment of musculoskeletal disorders. Representative active compounds include sinomenine, osthole, cinnamaldehyde, and imperatorin, which exhibit anti-inflammatory and analgesic effects. However, due to its heterogeneous matrix structure and multi-component nature, the pharmaceutical delivery behavior of GP remains difficult to evaluate using conventional methods. Therefore, this study aimed to establish an integrated structure–release–permeation–pharmacokinetic evaluation framework to systematically characterize the transdermal delivery behavior of GP. Methods: GP was evaluated using multi-level analysis, including microstructural imaging (FESEM), in vitro release, ex vivo skin permeation, and in vivo dual-site microdialysis. Four representative bioactive compounds (sinomenine, osthole, cinnamaldehyde, and imperatorin) were selected as marker compounds. Release data were fitted to kinetic models, and structure–release relationships were examined using the Higuchi release constant (k_h). Skin-barrier alterations were assessed by attenuated total reflectance–Fourier transform infrared spectroscopy (ATR–FTIR) and differential scanning calorimetry (DSC). Local concentrations in subcutaneous (SC) and intra-articular (IA) compartments were measured by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) to explore potential in vitro–in vivo correlation (IVIVC). Results: FESEM revealed a fibrous–oil network structure. GP exhibited sustained, diffusion-dominated release, with k_h = 0.9908–0.9977 and Korsmeyer–Peppas (K–P) release exponents (n) = 0.61–0.66, differing from active pharmaceutical ingredient (API) controls. Fiber area fraction and fiber length density showed negative correlations with k_h (r = −0.91 to −0.99); ex vivo permeation profiles varied among compounds, and ATR–FTIR and DSC analyses showed moderate changes in skin-barrier properties. Dual-site microdialysis demonstrated sustained local exposure, and a positive relationship was observed between in vitro release and in vivo concentrations. Conclusions: This study establishes an integrated structure–release–permeation–pharmacokinetic evaluation framework for traditional black plaster systems. The observed IVIVC is descriptive rather than predictive, reflecting a trend-level association under the current experimental conditions. These findings highlight the importance of integrating in vitro release, skin permeation, and local pharmacokinetics for understanding drug delivery behavior in complex transdermal matrix systems, and provide a methodological basis for quality consistency evaluation of traditional black plaster formulations. Full article

Introduction: Distress during pediatric magnetic resonance imaging (MRI) neuroimaging can compromise scan quality and negatively impact children’s experiences. This review aimed to systematically synthesize biomarkers and psychological factors associated with distress in children, adolescents, and young adults undergoing neuroimaging. Methods: This systematic review was conducted according to PRISMA and AMSTAR-2 guidelines and preregistered in OSF. A systematic search was performed in six electronic databases, including observational articles published between 2000 and 2025 that assessed distress during MRI and functional MRI (fMRI). Data extraction and risk of bias assessment (QUIPS tool) were performed independently by two reviewers. Results: Ten studies (n = 558) examining distress during neuroimaging were included in this review. Distress was assessed through subjective self- and parent-reports, objective physiological measures, and qualitative interviews. Overall, distress levels were low to moderate; most participants tolerated scans well, though younger age, male sex, parental anxiety, procedure length, and chronic illness were associated with greater discomfort. Noise, immobility, and boredom emerged as the most frequent triggers, while strategies such as distraction, age-appropriate information, and reducing waiting times were perceived as helpful. Among participants with cancer, scan-related anxiety was closely linked to fear of recurrence and perceived stress. Risk of bias across studies was moderate to high, particularly in domains of attrition and statistical reporting. Conclusions: Distress during scanning is driven by anticipatory and parental anxiety, procedure length, and chronic illness. Biomarkers (e.g., cortisol, blood pressure) showed inconsistent links with subjective distress, highlighting the need for integrated measures. Full article

Novel brominated flame retardants (NBFRs), including 1,2-bis(2,4,6-tribromophenoxy)ethane (BTBPE), are emerging endocrine-disrupting chemicals, though their direct effects on female gamete maturation remain insufficiently characterized. In this study, we used a refined zebrafish oocyte in vitro maturation (IVM) model integrating germinal vesicle breakdown (GVBD) assessment with real-time, image-based oocyte diameter quantification. The workflow incorporated donor-condition optimization and diameter-based quality control during sorting. Oocytes from donors 4 to 5 months post-fertilization (mpf) showed more consistent diameter dynamics at the dish level than those from donors 3 to 4 mpf. Mixed-sex co-housing was associated with higher GVBD and larger Δdiameter than separated housing, although this comparison should be considered preliminary. Under DHP induction, BTBPE (1–1000 nM) consistently suppressed GVBD and attenuated maturation-associated diameter increases, with a non-monotonic-like response pattern. These findings indicate that BTBPE impairs oocyte maturation competence in vitro and supports real-time morphometric tracking as a practical QC component for zebrafish IVM workflows. Full article

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows. Full article

Background: Clinical target volume (CTV) delineation in glioblastoma remains debated, particularly in the era of modern chemoradiation and image-guided radiotherapy. Whether reduced CTV margins can preserve oncological outcomes without increasing marginal or out-of-field failures remains uncertain. We evaluated the association of the gross tumor volume (GTV)-to-CTV margin with survival, patterns of failure, and its interaction with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. Materials and Methods: We retrospectively analyzed a single-center cohort of patients with glioblastoma treated with conventionally fractionated chemoradiation (58–60 Gy in 29–33 fractions). Patients were categorized into two predefined margin groups: <1.5 cm and 1.5 cm. The primary endpoint was overall survival (OS); secondary endpoints included progression-free survival (PFS) and patterns of failure. Survival was assessed using Kaplan–Meier estimates and Cox regression, including an interaction term with MGMT status. Results: Among 102 eligible patients, 95 were included in the margin-based OS analysis. Reduced margins (<1.5 cm; applied range 1.0–1.4 cm) were not associated with worse OS, either overall or within MGMT subgroups. No significant differences were observed in PFS or recurrence patterns, with overlapping distributions and no increase in marginal or out-of-field recurrences. MGMT methylation and gross total resection were independently associated with improved survival, while no statistically significant interaction between margin and MGMT status was detected. Conclusions: In this retrospective exploratory cohort, reduced GTV-to-CTV margins were not associated with a clear signal of worse survival or less favorable recurrence patterns. These findings are consistent with the oncological adequacy of margins around 15 mm and justify cautious prospective evaluation of whether further reduction can be achieved safely, including formal assessment of toxicity, neurocognitive outcomes, and quality of life. Full article

Despite its efficiency, the structural integrity and geometric accuracy of artificial intelligence (AI)-generated imagery used in environmental psychology experiments have not been sufficiently validated. This study investigated the methodological validity and substitutability of generative AI-generated 360° images as experimental stimuli for indoor environmental research. Using a three-stage framework, we generated base panoramas with controlled structural parameters, integrated greenery via AI-based inpainting, and conducted multifaceted validation through objective quality metrics and expert assessments. Quantitative results confirmed high technical integrity, indicating that structural distortions at panoramic stitching points were effectively minimized. Furthermore, the AI-generated stimuli maintained stable visual quality across varying greenery densities. Expert evaluations confirmed that the AI-driven approach significantly outperforms conventional 3D modeling, particularly in terms of presence and realism. By achieving high usability and spatial integrity scores, we established a novel standard for employing generative AI to create high-fidelity virtual environments for architectural and psychological research. Full article

Sarcopenia, defined as the progressive loss of skeletal muscle mass and strength, is increasingly recognized as a significant concern in patients with chronic kidney disease (CKD) and particularly in kidney transplant recipients (KTx-ps). This review explores the complex interplay of pathophysiological mechanisms, prevalence, and management strategies of sarcopenia in the context of kidney transplantation. CKD contributes to sarcopenia through systemic inflammation, malnutrition, uremic toxin accumulation, and metabolic imbalances, all of which persist or are exacerbated after transplantation due to immunosuppressive therapies especially corticosteroids. Notably, the post-transplant period may introduce additional risks, such as altered body composition and reduced physical activity, further aggravating muscle wasting. Sarcopenia affects approximately 26% of KTx-ps, leading to adverse outcomes including decreased quality of life, increased risk of infection, frailty, delayed recovery, and graft loss. The diagnosis remains challenging due to variability in assessment tools and a lack of standardized criteria. Management strategies must be multifactorial, including personalized nutritional support, targeted physical activity, and, where appropriate, pharmacological interventions. Early identification through imaging and functional testing is critical, especially in older patients and those with prolonged dialysis vintage. Emerging therapies, such as myostatin inhibitors, offer promise but require further validation. Additionally, early steroid withdrawal may mitigate muscle loss without compromising graft survival in selected patients. This review underscores the need for heightened awareness and standardized protocols to identify and manage sarcopenia in kidney transplantation, ultimately improving long-term outcomes and patient-centered care. Full article

There are numerous benefits associated with creating digital copies of anatomical structures, which can be used during patient diagnosis. Such models can be used not only for visualization, but also in order to assess the condition of the patient. As advances in both medical imaging and 3D graphics are made, it is necessary to determine areas of application of the known reconstruction algorithms. Specifically, it is crucial to find advantages and disadvantages of known approaches to mesh generation, depending on the properties of the object and compare the quality of their results. In order to provide reliable ground-truth data, three 3D models with features resembling those identified in anatomical structures have been created. Based on these meshes, sets of CT-like DICOM images have been generated. Five different reconstruction approaches were proposed: using 3D occupancy information directly, two ways of obtaining point clouds and two methods that utilize Signed Distance Field. A neural network architecture for the SDF upsampling has also been presented. The obtained results justify the popularity of the Marching Cubes algorithm, as it produced accurate reconstructions most reliably. However, for certain scenarios, promising alternatives have been found. The presented outcomes make it clear that the approach to reconstruction must be tailored to the specific problem. Full article

This study presents a comparative evaluation of three modern surveying techniques—UAV photogrammetry, static tripod-based LiDAR scanning, and handheld mobile LiDAR—applied in the context of historic monument restoration. The focus is on analysing workflow efficiency, data accuracy, and adaptability to complex architectural features, including interior wall paintings, which are integral to the monument’s heritage value. Particular attention is given to how each technique captures surface texture, color fidelity, and material deterioration. The study also examines performance around intricate architectural elements such as vaulted ceilings, apses, cornices, columns, and carved stone portals, where occlusions, tight clearances, and fine ornamentation challenge coverage and resolution. By evaluating the strengths and limitations of each approach, the research highlights methodological considerations relevant for conservation professionals. The results indicate that the Static TLS is the most demanding workflow, requiring complex total station integration for control and station points. It produced the highest data density, with acquisition rates of one million points per second, making it the most hardware-intensive and difficult to manipulate. UAV photogrammetry provided a balanced middle-ground; it required minimal physical effort during acquisition and produced datasets that were significantly easier to manage. Handheld SLAM LiDAR emerged as the most productive solution for rapid coverage. While the handheld scanner’s image quality was lower than the photogrammetry, it still provided enough detail for the structural assessment and documentation needed. Although the point cloud lacked the extreme geometric detail provided by the TLS, the FARO Connect software made georeferencing and data manipulation significantly more efficient. Full article

Background/Objectives: This study aimed to compare AI-generated educational material with faculty-authored content in Dental Prostheses Technology, evaluating perceived clarity, accuracy, structure, usefulness, and overall instructional quality across different age and professional groups. Methods: An analytical cross-sectional study was conducted using two versions of the first three chapters of a prosthodontics textbook: the original faculty-authored text and a reformulated version generated by ChatGPT 5.2 (OpenAI). Images were removed and formatting standardized to ensure a text-only comparison. An anonymized online questionnaire based on a five-point Likert scale assessed clarity, accuracy, readability, usefulness and structure. To reduce potential bias, participants were unaware of the authorship of the evaluated materials (human-authored vs. AI-generated). A total of 130 participants independently reviewed both documents. Data were analyzed using Wilcoxon signed-rank, Mann–Whitney U, and Friedman tests. Results: Both materials received favorable evaluations across all dimensions. The AI-generated version demonstrated a statistically significant advantage in clarity (Z = −2.107, p = 0.035; r = 0.19), while no significant differences were observed for structure, accuracy, readability, or usefulness. Generational differences emerged: younger participants valued improved clarity but reported reduced usefulness, mid-career participants showed the greatest improvement in perceived accuracy, and senior professionals reported substantial gains in usefulness and readability. Conclusions: AI-generated educational material demonstrates pedagogical equivalence to faculty-authored content, with clarity representing its principal advantage. Large language models may serve as effective complementary tools in dental education, particularly for restructuring complex content. Full article

The identification of standing tree species, timber logs, and on-site assessment of their quality and value using images holds significant potential for forestry applications, including inventory management, traceability under EU regulations like the Deforestation Regulation, and market valuation amid growing demands for sustainable practices. This study addresses this by classifying images of timber logs by tree species and market value using the Orange data mining software, which leverages pre-trained convolutional neural networks (Inception v3 and SqueezeNet) to generate embeddings from a dataset of 5549 images collected at a real timber auction in Slovenia, followed by logistic regression image classification. Results show high accuracy for tree species classification (up to 92.6%), but substantially lower accuracy for market value classification (40%–55%), reflecting the greater complexity of value determination from visual features. These findings underscore the promise of deep learning for species identification while indicating the need for further methodological advancements to enhance value classification reliability, which offers the practical impact for operational forestry and bioeconomy value chains. Full article

Pelvic organ prolapse (POP) is a common benign gynecological disorder that substantially affects quality of life, particularly in aging female populations. Current management strategies, including standardized vaginal pessaries and synthetic surgical meshes, are often limited by poor anatomical adaptability, mechanical mismatch with native pelvic tissues, and long-term safety concerns. These limitations have driven increasing interest in personalized and biomechanically compatible therapeutic solutions. Three-dimensional (3D) printing, also known as additive manufacturing, has emerged as a promising bioengineering technology to address these unmet clinical needs. By enabling layer-by-layer fabrication directly from digital models, 3D printing allows for precise control over device geometry, mechanical properties, and material composition, facilitating patient-specific design. This narrative review summarizes recent progress in 3D printing for POP management across three major application domains: (i) next-generation meshes based on biodegradable polymers, elastomeric materials, natural biomaterials, and hydrogel systems; (ii) customized vaginal pessaries tailored to individual pelvic anatomy using imaging-assisted workflows; and (iii) imaging-based pelvic models and prototype devices for surgical planning, education, and exploratory assessment. Overall, existing studies demonstrate that 3D printing enables improved biomechanical compatibility, enhanced tissue integration, and multifunctional device design, including drug delivery capability. Although current evidence is largely pre-clinical or based on pilot studies, additive manufacturing holds strong potential to advance POP management toward safer, personalized, and functionally optimized clinical solutions. Full article

Background/Objectives: Peritoneal metastasis represents the most frequent and prognostically unfavorable metastatic pattern in gastric cancer, largely due to limited sensitivity of conventional imaging, delayed diagnosis, and insufficient response assessment. The aim of this review is to provide an overview of the current evidence on the diagnosis and treatment of gastric cancer with peritoneal metastases and to address current treatment limitations and options. Methods: This review was designed as a narrative review and is based on an extensive literature search in established databases. Results: Systemic chemotherapy remains the cornerstone of palliative treatment, improving the survival and quality of life compared with the best supportive care; however, outcomes in peritoneally metastatic disease remain poor. Advances in molecularly targeted and immune-based therapies have extended survival in selected patient populations, yet favorable molecular profiles are mainly unknown in peritoneal metastases. Staging laparoscopy and semi-quantitative assessment using the Peritoneal Cancer Index (PCI) are therefore essential for accurate diagnosis, prognostication, and treatment selection. Growing evidence from retrospective studies, multi-institutional cohorts, and selected randomized trials suggests that a multimodal approach—combining systemic therapy with intraperitoneal or bidirectional chemotherapy—may improve survival and quality of life. In carefully selected patients whose primary gastric tumor and peritoneal lesions respond to systemic treatment, complete cytoreductive surgery (CRS) followed by hyperthermic intraperitoneal chemotherapy (HIPEC) may further enhance outcomes and, in rare cases, achieve long-term survival. These potential benefits appear to be limited to highly selected patients with a low peritoneal tumor burden (PCI ≤ 6–7), positive cytology, good performance status, controlled extraperitoneal disease, and a high likelihood of achieving complete macroscopic cytoreduction (CC-0). Conclusions: Although the treatment intent in metastatic gastric cancer remains primarily palliative, carefully selected patients with limited peritoneal metastases may benefit from intensified multimodal treatment strategies when managed in specialized centers. Interdisciplinary evaluation, accurate staging, and individualized treatment planning are essential to optimize outcomes in this challenging disease setting. Full article

Background/Objectives: To determine the optimal reconstruction parameters for accurate visualization of peripheral in-stent restenosis using photon-counting detector CT (PCD-CT), and to evaluate its potential advantages over energy-integrated detector CT (EID-CT). Methods: Endovascular peripheral stents with varying degrees of in-stent restenosis were scanned in a custom-made phantom using EID-CT (Somatom Force) and PCD-CT (Naeotom Alpha) under clinical acquisition protocols. EID-CT images were reconstructed with Bv40 and Bv59 kernels at 512 matrices. PCD-CT data were acquired in standard-resolution (SR) and ultra-high-resolution (UHR) modes. In both modes, images were reconstructed with multiple kernels (Bv40, Bv56 and Bv72) and matrix sizes (512 and 1024 matrix). In SR mode, additional virtual monoenergetic images (40–100 keV) were generated, while UHR mode included only polychromatic reconstructions. Quantitative image quality (noise, contrast, contrast-to-noise ratio [CNR]) was measured, and two blinded readers performed qualitative assessments of restenosis visualization. Results: PCD-CT with SR mode at VMI 40 keV achieved the highest image contrast and CNR, significantly outperforming EID-CT and PCD-CT_UHR under matched conditions (all p < 0.05). The sharper reconstruction kernel further enhanced the image contrast and improved subjective visualization despite increased image noise. Both readers ranked PCD-CT_{SR-Bv72-40keV} at 1024 matrix highest for detecting all degrees of restenosis, with excellent inter-reader agreement (ρ > 0.80). Conclusions: PCD-CT in SR mode at VMI 40 keV, specifically using the Bv72 kernel with a 1024 matrix, optimizes the visualization of peripheral in-stent restenosis. Compared to EID-CT, PCD-CT provides superior image quality and detectability of restenosis. Full article

Accurate distinction between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) remains the key local staging problem in bladder cancer because treatment intensity, timing of radical therapy, and suitability for bladder-preserving strategies all depend on it. Multiparametric magnetic resonance imaging (mpMRI) and the Vesical Imaging-Reporting and Data System (VI-RADS) now provide a standardized imaging framework for local staging and increasingly support MRI-first clinical pathways. Artificial intelligence (AI) has emerged as an additional decision-support layer, but the evidence base remains methodologically uneven. In this structured narrative review, we synthesized peer-reviewed literature from January 2020 to March 2026, while retaining foundational VI-RADS studies from 2018 to 2019, and prioritized guideline documents, meta-analyses, prospective cohorts, multicenter and externally validated AI studies, response-assessment studies, and papers addressing implementation and reporting quality. Current evidence shows that radiomics and deep learning models can achieve high discrimination for MIBC detection on MRI, and that the most plausible incremental value of AI lies in equivocal VI-RADS lesions, reader support outside high-volume expert settings, and multimodal risk stratification. However, most studies remain retrospective, highly selected, segmentation-dependent, and vulnerable to reference-standard bias, domain shift, and poor calibration. This review therefore emphasizes several translational issues that are often underreported: lesion-level versus patient-level inference, the distortive effect of TURBT-based labels, the need to evaluate false-negative consequences in VI-RADS 3 tumors, and the distinction between diagnostic support and broader pathway redesign. We also discuss response assessment, nacVI-RADS, segmentation automation, multicenter and federated infrastructure, workflow ownership, and the limits of imaging-only models in a biologically heterogeneous disease. The most credible near-term role of AI is not autonomous diagnosis, but augmentation of standardized mpMRI and VI-RADS within multidisciplinary care. Future progress will depend on prospective utility studies, site-held-out validation, transparent reporting, and the integration of imaging with molecular and cellular heterogeneity through radiogenomic and multi-omics approaches. Full article