The information held by visual representations is typically opaque to information processing systems and able to be interpreted only by human viewers. We introduce the Graphics Descriptor Ontology (GDO) to serve as an ontology for domain-independent annotation and description of graphics and their elements. Our goal is to represent information about graphics that corresponds to what a human observer could conclude from viewing a graphic or that would help to inform a viewer about a graphic. This work builds upon ontological modeling of information content entities and uses theories and vocabularies from the fields of semiotics, visual arts, technical communication, and computer graphics. We define a graphic as a spatial composition composed of graphical marks. The three types of graphical marks are line mark, point mark, and region mark. We present an approach to representing roles and qualities for information content entities, including graphical marks and graphics. Anatomical graphics serve as our use cases, and we provide an anatomy extension for the GDO to model anatomy-specific content. We introduce our work as an illustrated ontology available through a web browser, accompanied by over 100 explanatory graphics.
Generative artificial intelligence (AI) has emerged as a transformative tool for creating high-quality visual materials in medical research and education. In pediatric neurosurgery, where ethical and legal constraints limit the use of real patient photographs, AI-assisted illustrations offer significant potential. However, concerns regarding clinical accuracy, intellectual property, and the protection of vulnerable pediatric patients necessitate rigorous oversight. We present a human-in-the-loop workflow that integrates generative AI with vector-based digital editing to produce scientifically accurate and ethically grounded medical illustrations. We reviewed current AI usage policies from major medical journals, including the International Committee of Medical Journal Editors (ICMJE) and the Journal of Korean Neurosurgical Society (JKNS). To demonstrate practical application, we developed illustrative examples for conditions such as sacral dimple, Crouzon syndrome, and Down syndrome using clinician-led sketches and AI-assisted refinement. Vector-based workflows facilitate the transformation of AI-generated raster drafts into editable, high-resolution graphics, allowing clinicians to correct "hallucinations" and ensure anatomical precision. While most journals prohibit listing AI as an author, they permit its use for conceptual figures provided there is transparent disclosure of the tools and prompts used. Our proposed workflow emphasizes that AI should function as a "constrained assistant" rather than an autonomous creator, ensuring that the final output remains non-identifiable and respectful of pediatric patients' dignity. Generative AI tools can significantly enhance visualization in pediatric neurosurgery when governed by strict ethical and technical safeguards. Adherence to journal policies and the maintenance of human-directed validation are essential to uphold scientific integrity and patient privacy in the era of AI-assisted publishing.
Dense reconstruction and differentiable rendering are fundamental tightly connected operations in 3D vision and computer graphics. Recent neural implicit representations demonstrate compelling advantages in reconstruction fidelity and differentiability over conventional discrete representations such as meshes, point clouds, and voxels. However, many neural implicit models, such as neural radiance fields (NeRF) and signed distance function (SDF) networks, are inefficient in rendering due to the need to perform multiple queries along each camera ray. Moreover, NeRF and Gaussian Splatting methods offer impressive photometric reconstruction but often require careful supervision to achieve accurate geometric reconstruction. To address these challenges, we propose a novel representation called signed directional distance function (SDDF). Unlike SDF and similar to NeRF, SDDF has a position and viewing direction as input. Like SDF and unlike NeRF, SDDF directly provides distance to the observed surface rather than integrating along the view ray. As a result, SDDF achieves accurate geometric reconstruction and efficient differentiable directional distance prediction. To learn and predict scene-level SDDF efficiently, we develop a differentiable hybrid representation that combines explicit ellipsoid priors and implicit neural residuals. This allows the model to handle distance discontinuities around obstacle boundaries effectively while preserving the ability for dense high f idelity distance prediction. Through extensive evaluation against state-of-the-art representations, we show that SDDF achieves (i) competitive SDDF prediction accuracy, (ii) faster prediction speed than SDF and NeRF, and (iii) superior geometric consistency compared to NeRF and Gaussian Splatting.
Plant secondary metabolites possess remarkable pharmacological properties and play a vital role in the treatment of diverse diseases. Due to their restricted biosynthesis, various strategies are required to boost their production. Although, nanotechnology offers a revolutionary technique to modulate plant metabolism, the molecular study underlaying silver nanoparticles (AgNPs) as nanoelicitors still remain poorly elucidated. In this study, AgNPs were biosynthesized using Catharanthus leaf extract and characterized by using different analytical techniques. The elicitation potential of AgNPs was evaluated in Ocimum tenuiflorum treated with five concentrations (20, 40, 60, 80, and 100 ppm). From the obtained results, 60 ppm was found the most effective concentration, that significantly elevating the accumulation of key metabolites and their associated gene expression. HPLC quantification indicated considerable enhancement in the content of both eugenol (57.89 µg/ml) and rosmarinic acid (50.40 µg/ml) in Tulsi leaves after treatment with AgNPs in comparison to the controls where the amount recorded was only 13.47 and 7.39 µg/ml, respectively. Furthermore, qRT-PCR analysis highlighted a notable increase in expression of biosynthetic pathway genes including EGS (6.40-fold), RAS (5.47-fold), CAD (4.71-fold), and 4CL (2.88-fold) in comparison to control. These experimental results establish, for the first time, a mechanistic link between AgNPs-induced nano-elicitation and expression of secondary metabolite pathways genes in O. tenuiflorum. The study thus bridges a critical knowledge gap and emphasizes the potential of green-synthesized AgNPs as efficient nanoelicitors to increase the high-value phytochemical production through nano-biotechnological approaches. Biosynthesis of AgNPs and its role as nanoelicitor to elevate secondary metabolites production in Ocimum as well as upregulation of related metabolic genes.
Recent advancements in radiance fields, particularly with the emergence of Gaussian splatting, have highlighted their significant potential for 3D scene reconstruction and novel view synthesis. However, existing methods encounter substantial challenges when addressing dynamic environments, especially in complex urban settings with both rigid and non-rigid participants. To tackle these challenges, we propose a geometry-aware framework that integrates Gaussian primitives with a template mesh to effectively represent dynamic objects. This integration facilitates the efficient and accurate reconstruction of urban scenes, ensuring that the geometric integrity of dynamic elements is maintained. We first decompose the scene into a dynamic scene graph and fit the template vertices to observations to construct topologically consistent 3D models. Then, we build Gaussian radiance fields for dynamic nodes based on the template meshes, optimizing the vertex offset of dynamic participants to align with their geometric surfaces. We further project the appearance attributes into the 2D texture space based on topological relationships preserved in the Gaussians, enabling finer reconstruction of small-scale details and smoother appearance generalization on unseen surfaces. To validate the effectiveness of our proposed method, we conduct extensive evaluations on the Waymo Open Dataset (Ettinger et al., 2021) and the KITTI Dataset (Geiger et al., 2013). Our results demonstrate superior performance compared to mainstream dynamic reconstruction methods. We believe our work establishes a foundation for more realistic and geometrically complete urban scene reconstruction.
The present study plays a crucial role in enhancing the safety and perceived quality of life for users of bone-anchored prostheses. It focuses on developing an innovative protective component using various metallic materials to identify and mitigate potential risks during use, thereby reducing the likelihood of sudden fracture and maintaining the system's structural integrity. The protective element is manufactured from Ti6Al4V alloy, while the safety pin is made from ductile cast iron. This combination allows controlled fracture of the protective element without complete separation of the prosthesis, thereby reducing the risk of falls. To optimise the numerical analysis, a 3D model of the prosthesis and its protective component was created using SolidWorks software. Loading conditions were simulated to reflect two critical phases of the gait cycle: heel strike and toe-off. The analysis revealed that the highest stress occurred during the toe-off phase, reaching 248 MPa, with a safety factor of 1.6, demonstrating the design's ability to prevent sudden failure. Tensile testing showed that ductile cast iron is a suitable material for the safety component. Although Ti6Al4V alloy surpasses it in tensile strength, ductile cast iron's lower strength ensures a controlled and less catastrophic failure under excessive loading. Numerical results confirmed a high safety factor for the protective system, indicating improved reliability and mechanical load resistance. This study presents a novel approach aimed at improving the safety of bone-anchored prostheses by minimising injury risks due to mechanical overload, ultimately enhancing user comfort and confidence.
The research aims to leverage machine learning techniques to better understand the diagnosis of myofascial pelvic pain syndrome (MPPS) and to develop useful tools for clinical practice. This study retrospectively analyzed clinical data from female patients. Between January 2021 and December 2024, 1,204 MPPS cases and 1,217 healthy women from the Pelvic Floor Rehabilitation Center of Zhengzhou University's Third Affiliated Hospital were enrolled. After screening, 1,136 MPPS patients and 1,136 healthy controls were selected. Using Python 3.9, we developed prediction models with 10 machine learning algorithms: logistic regression, support vector machine (SVM), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), categorical boosting (CatBoost), k-nearest neighbors (KNN), and backpropagation (BP). Five-fold cross-validation was used to prevent overfitting. The models' performance was evaluated using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC-ROC) to assess each algorithm's diagnostic value for MPPS. The top four models in terms of AUC, ranked from highest to lowest, were RF, CatBoost, XGBoost, and LightGBM. The top four models in terms of accuracy, ranked from highest to lowest, were CatBoost, RF, XGBoost, and LightGBM. Moreover, the top four models in terms of area under the decision curve (AUDC), ranked from highest to lowest, were CatBoost, LightGBM, XGBoost, and RF. Furthermore, we created a web-based graphical user interface (GUI) for MPPS prediction. It can be packaged for cross-platform use, thereby streamlining diagnosis and improving accessibility for healthcare providers. In conclusion, this study compared 10 machine learning algorithms for diagnosing myofascial pelvic pain syndrome. The CatBoost model showed superior performance in terms of accuracy and clinical utility. In addition, a cross-platform web-based GUI was developed, streamlining diagnosis for healthcare providers and potentially improving patient outcomes.
Stress urinary incontinence (SUI) is one of the most prevalent pelvic floor disorders in women, and it is pathologically linked to collagen metabolism imbalance. Current treatments face limitations, including suboptimal efficacy and invasiveness. radiofrequency therapy (RF) therapy has emerged as a promising non-invasive approach, yet its precise mechanisms, particularly concerning collagen remodeling, remain inadequately elucidated.This study aimed to investigate the effects of RF on vaginal collagen metabolism in SUI rats and elucidate its underlying molecular mechanisms. An SUI rat model was created via vaginal dilation and bilateral ovariectomy. Rats were divided into Sham, SUI, SUI-No-RF, and SUI-RF (n = 7/group). The SUI-RF group received RF treatment (500 kHz, 2 W, 40 °C–42 °C) every 5 days for 3 sessions. At week 3 post-modeling, urodynamic measurements, Masson’s trichrome staining, immunofluorescence, Transmission Electron Microscopy (TEM), Western blot, and quantitative real-time polymerase chain reaction ( qPCR) were performed. Compared to the Sham group, SUI rats exhibited significantly reduced bladder leak point pressure (BLPP) and abdominal leak point pressure (ALPP) (p < 0.001). RF treatment markedly increased BLPP and ALPP in the SUI-RF group versus SUI-No-RF (p < 0.001), restoring urinary continence. Vaginal tissue in the SUI-RF group exhibited collagen volume fraction, elevated type III Collagen expression, and restored fibril alignment (p < 0.05). Mechanistically, RF treatment activated the Transforming Growth Factor-β1 (TGF-β1)/Smad2 pathway (increased TGF-β1 expression and Smad2 phosphorylation), upregulated tissue inhibitor of metalloproteinase 1 (TIMP1), and suppressed matrix metalloproteinase 9 (MMP9) expression (p < 0.05). RF therapy restores vaginal collagen architecture and continence function in SUI rats by activating the TGF-β1/Smad2 pathway and bidirectionally modulating the MMP9/TIMP1 equilibrium. These findings elucidate a molecular mechanism for RF therapy and support its further development as a non-invasive treatment for SUI. Proposed Mechanism of Radiofrequency (RF) Therapy for Stress Urinary Incontinence (SUI). Vaginal RF treatment delivers controlled thermal energy to the tissue. This stimulus is proposed to activate transforming growth factor-beta (TGF-β) signaling. TGF-β binding to its receptor leads to phosphorylation of Smad2 (P-Smad2) and its translocation into the nucleus. Within the nucleus, the activated Smad complex regulates gene expression, promoting the synthesis of type III collagen while inhibiting degradation pathways. This restoration of collagen architecture strengthens pelvic floor support, thereby improving urethral closure function and alleviating SUI. Created with BioRender.com [Image: see text] The online version contains supplementary material available at 10.1007/s10103-026-04879-4.
Nutraceuticals represent promising strategies for preventing, delaying and addressing premature aging of the skin, especially as women advance in years (particularly after 30 years of age, when estrogen levels begin to decline, and remarkably after menopause when estrogen production ceases from the ovaries). This review is part of a larger project, and we present this companion review, which provides a detailed examination of the literature beyond polyphenols and/or phytoestrogens for estrogen-deficient skin. This narrative review covers the top-selling nutraceutical, collagen, along with the antioxidants, curcumin and glutathione, in women between 30 to over 65 years of age regarding their effectiveness in enhancing dermal health parameters. There were 23 clinical studies published between 2020 and 2025 that support collagen as an effective nutraceutical treatment. These studies showed improvement in various skin attributes, but investigations are lacking on collagen's effectiveness on scalp hair and nail health, which warrants further examination. Curcumin and glutathione, while these remain popular nutraceutical applications to improve skin health, had only a few clinical studies published; thus, more studies are needed to establish optimal dosing regimens and identify which combination approaches provide the most meaningful dermal benefits, especially in aging women. Trends and future directions in nutraceutical skin applications include the use of collagen (where many clinical studies have been reported), along with antioxidants and bioactives. Therefore, nutraceutical skin applications using collagen and antioxidants such as curcumin and glutathione demonstrate hopeful results for dermal antiaging effects, especially in estrogen-deficient skin. However, more investigations are warranted to expand their applications to meet the evolving dermatological challenges. Finally, there is a need to balance the psychological and ethical considerations in aesthetic medicine, distinguishing between objective beauty and subjective attractiveness, while emphasizing the importance of a patient's self-confidence in ethical practice.Graphical Abstract available for this article.
The paper is an analytical study of a low-pass electrical model of nonlinear type in a fractional perspective, in which the classical derivative is generalized to the Katugampola fractional operator. Precise traveling-wave solutions are built based on an extended Riccati-Bernoulli sub-ODE scheme together with a Bäcklund transformation. The families of obtained solutions contain bright and dark kink type structures. These solutions have a dynamical behavior that is demonstrated with the help of detailed 3D and 2D visualizations. The 3D plots reveal how sensitive the integer-order parameter is to the waveform whereas the 2D plots show how sensitive the waveform is to the changes in the fractional order (α). To deeper examine the qualitative dynamics, a hamiltonian formulation is created and phase-portrait diagrams are plotted. These unveil the local and global organization of the nonlinear flow underlying. Besides, chaotic behavior is also studied by analyzing sensitivity to initial conditions by determining the largest Lyapunov exponent [Formula: see text]. The findings validate the occurrence of regular, quasi-periodic and chaotic regimes in the parameter space. The entire process of analytical calculations and visualization is implemented in MATLAB, which provides the numerical accuracy of calculations and high-resolution graphical confirmation of fractions solutions. The results illustrate the presence of significant enrichment of the dynamical behavior of the nonlinear electrical model by the fractional extension. It also offers a practical and efficient model to study intricate waves phenomena in the systems of the fractional-order.
Odontogenic maxillary sinusitis (OMS) can develop from apical periodontitis (AP) of maxillary posterior teeth, known as maxillary sinusitis of endodontic origin(MSEO). The treatment of OMS remains controversial. Endodontic microsurgery (EMS) plays a crucial role for treatment of AP when root canal treatment and retreatment fails. With multidisciplinary management, EMS alnoe may be effective for MSEO . Ten cases of MSEO undergoing EMS are reported at the First Affiliated Hospital of Wenzhou Medical University between 2022 and 2023. Clinical and radio-graphic evaluations at 24-month follow-ups demonstrated periapical healing in all cases. With multidisciplinary management, dental treatment alone may be a feasible approach for OMS(MSEO).
In this paper, we present a novel, high-resolution method, referred to as HRTR, for localizing underground objects. HRTR is based on a combination of the Time Reversal (TR) and Multiple Signal Classification (MUSIC) algorithms, and can be readily integrated with conventional ground-penetrating radar (GPR) systems without requiring any additional hardware. The proposed method offers significant advantages, particularly in achieving higher resolution, which enhances the ability to distinguish ground surface reflections and detect shallowly buried objects-challenges often encountered with conventional methods. The theoretical foundation of the proposed method is validated through numerical simulations using gprMax, as well as through experimental measurements from laboratory and field tests. The performance of HRTR is compared with conventional GPR methods, focusing on resolution improvements. Both simulations and experimental results demonstrate that HRTR produces clearer, sharper images with enhanced resolution. Unlike classical TR-MUSIC, the proposed HRTR method can be applied directly to conventional GPR measurements without the need for additional hardware or intensive computation. Moreover, it operates with just one antenna in monostatic mode or two in bistatic mode, avoiding the multiple-antenna requirement of TR-MUSIC. Furthermore, the proposed method enables the detection of deeply buried objects by using low-frequency signals for greater penetration while preserving spatial resolution. A graphical user interface and accompanying Python source code were developed and made publicly available on GitHub to facilitate the application of the proposed method to GPR A- and B-scans.
Two-dimensional (2D) semiconductors enable atomically thin channels and attractive electrostatics, but practical scaling increasingly hinges on gate-dielectric integration rather than channel performance. A key challenge is forming high-quality dielectrics on chemically inert, dangling-bond-free 2D surfaces while pushing equivalent oxide thickness to the sub-nanometer regime without excessive leakage, traps, or electrical breakdown. This review addresses the materials and process physics that govern dielectric formation in 2D devices, with an emphasis on atomic layer deposition nucleation, surface pretreatment and functionalization, and the use of seed and buffer layers for conformal high-κ oxides. The roles of layered insulators, such as hexagonal boron nitride, are discussed in terms of interface quality, electrostatic scaling limits, and transport limitations. The impact of dielectrics and processing on leakage mechanisms, defect generation, device-to-device variability, and reliability metrics, including time-dependent dielectric breakdown, bias-temperature instability, hysteresis, and threshold-voltage drift, is examined. Finally, we highlight van der Waals dry integration and dielectric transfer approaches that reduce process-induced damage and support wafer-scale uniformity, as well as opportunities for mixed-dimensional and 3D stacked architectures across logic, memory, and emerging functional systems. [Image: see text]
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Depression affects 28.4% of middle-aged and older adults globally, exacerbating functional decline, mortality, and healthcare burdens. Physical activity mitigates depression through neurobiological and psychosocial pathways, although efficacy varies significantly by gender. Current evidence lacks clarity regarding optimal physical activity intensity and interventions. This study aimed to identify the physical activity patterns most strongly associated with depression and to derive potential intervention targets for middle-aged and older Chinese adults. The data from 3739 participants in the 2020 China Health and Retirement Longitudinal Study were analyzed. Gaussian graphical models were adopted to recognize core and bridge symptoms within physical activity-depression networks across genders. Subsequently, computer-simulated interventions were conducted to determine the optimal targets for reducing depressive symptoms by gender. Both male and female networks identified "depressed" as the central symptom. However, the bridge symptoms differed: males exhibited a bridge role for "days with moderate physical activity per week", whereas females showed this for "duration of moderate physical activity per day". Network comparison test revealed significant gender differences in edge weights (p = 0.007), with 14 edges being statistically significant. Simulation interventions consistently pinpointed "depressed" and "stuck" as effective targets for intervention across genders. Moderate physical activity most strongly correlated with depression. For men, interventions might prioritize increasing the regularity per week with moderate physical activity to prevent depression, whereas for women, focusing on the duration per day of such activity could be a promising target.
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Functional bionanomaterials have enabled major advances in nanomedicine through their tunable physicochemical properties, multifunctionality, and ability to interact with biological systems in a controlled manner. Despite sustained progress in materials design and preclinical validation, the clinical translation of bionanomaterials remains limited, with only a small fraction achieving regulatory approval and routine clinical use. While technical performance, safety, manufacturing scalability, and regulatory complexity are widely recognized as key translational barriers, the role of strategic communication in shaping stakeholder understanding, trust, and adoption has received comparatively little attention. In this Perspective, we argue that strategic communication should be considered a core translational enabler for functional bionanomaterials rather than a downstream dissemination activity, as in current trends. We identify recurrent communication gaps between bionanomaterial developers and key stakeholders, including clinicians, patients, regulators, and investors, that contribute to misaligned expectations, heightened risk perception, and delayed clinical adoption. Drawing on examples from nanomedicine translation, we discuss how differences in language, decision-making frameworks, and perceived value across stakeholder groups, can impede progress even for technically mature platforms. Building on insights from translational medicine, behavioral science, and innovation studies, we propose a structured communication framework tailored to the specific challenges of functional bionanomaterials. This framework emphasizes early integration of communication planning, stakeholder-specific narrative framing, visual and digital tools to reduce cognitive complexity, and iterative feedback mechanisms. We further discuss practical approaches for evaluating communication impact using measurable indicators such as stakeholder awareness, trust, regulatory feedback, and clinical uptake. By reframing communication as a bidirectional and designable component of the translational pipeline, this Perspective aims to support more efficient pathways for bringing functional bionanomaterials from the laboratory to the clinic.
Preclinical small animal experiments play an indispensable role in proton therapy research. However, accurate dose calculation poses a significant challenge because of the low beam energy and the requirement for submillimeter spatial resolution. Although the Monte Carlo method offers the necessary precision, its high computational cost hinders efficient implementation. This study aims to develop a GPU-accelerated radiation dose engine for proton radiotherapy (pGARDEN) based on the Monte Carlo method, specifically designed for fast and accurate dose calculation in small animal irradiation. In pGARDEN, we optimized the particle transport algorithm to better align with the GPU architecture. Moreover, various acceleration techniques were implemented to boost computational efficiency. To enhance precision, physical parameters, such as energy cutoffs for proton and electron, were tuned to better suit small animal conditions. The performance of pGARDEN was validated against Geant4 simulations and measurements across various beams and phantoms. To demonstrate its practical utility, pGARDEN was applied to calculate a multi-beam proton treatment plan for a lung tumor-bearing mouse model. Compared to Geant4, the engine achieved a > 1000-fold speedup and a 3D gamma passing rate of > 97% with a strict 1%/0.15 mm criterion in all phantom testing scenarios. The integrated depth dose curves and dose profiles showed good agreement with measurements. In the in vivo validation, the 2D gamma passing rates with a 2%/0.3 mm criterion were 95.52% ± 0.74% for the abdomen and 94.18% ± 1.08% for the thorax. Furthermore, pGARDEN calculated the treatment plan with < 1% statistical uncertainty in 4.3 s on an NVIDIA GeForce RTX 4070 Ti GPU, achieving a 100% 3D gamma passing rate with a 2%/0.3 mm criterion. pGARDEN can calculate proton dose distribution rapidly and accurately at submillimeter resolution for small animal. It provides a valuable tool for supporting small animal proton radiation experiments, such as the investigation of relative biological effectiveness (RBE) and new therapeutic strategies.
Combination therapies with immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) have revolutionized the landscape of cancer treatment, improving the quality of life and overall survival of patients. A deep knowledge of the side effects of ICIs and TKIs combination therapy is mandatory to ensure patient compliance and improve clinical outcomes. Both ICIs and TKIs may cause endocrinopathies such as thyroid dysfunction, adrenal insufficiency, hypophysitis, and diabetes mellitus. To avoid life-threatening conditions and improve patient’s compliance and outcomes, an early diagnosis of endocrine toxicity should be achieved and a multidisciplinary approach involving both endocrinologists and oncologists may be beneficial. This review specifically examines the endocrine adverse events reported in the clinical trials of ICI plus TKI combined treatment, their underlying mechanisms, and practical management guidelines. [Image: see text]