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.
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.
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.
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]
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The multifunctional potential of Angelica decursiva ethanol extract (Ad-EE) was evaluated to address the safety and efficacy limitations of conventional whitening agents. Experimental results indicate that Ad-EE suppresses melanin secretion in B16F10 cells in a dose-dependent manner. Furthermore, under 20 mJ/cm2 UVB irradiation, Ad-EE reduces the mRNA and protein expression levels of Tyrosinase and TRP1, thereby mitigating both hormonal and environmental triggers. Comprehensive chemical profiling using gas chromatography-mass spectrometry (GC-MS) and high-resolution liquid chromatography-mass spectrometry (HR- LC-MS) revealed a complex phytochemical matrix extending beyond simple volatile compounds. Molecular docking analysis demonstrated that key metabolites, including 1- methylinosine and adhyperforin, possess high binding affinities for the TRP1 catalytic pocket. Ad-EE also exhibits significant antioxidant and anti-inflammatory activities. Multiple assays, including ABTS and FRAP, showed a dose-dependent increase in radical scavenging and ferric-reducing capacities. In HaCaT and RAW264.7 cells, Ad-EE improved cellular redox status by upregulating endogenous antioxidant genes, including HO-1 and NQO-1, while suppressing inflammatory mediators. By simultaneously inhibiting key enzymatic pathways and enhancing cellular defense signaling, Ad-EE disrupts melanogenesis in response to diverse stimuli. These findings provide a strong molecular basis for considering Ad-EE as a versatile, natural candidate for advanced cosmeceutical applications. Angelica decursiva ethanol extract (Ad-EE) reduces melanin production showing suppression of tyrosinase, TRP1, and TRP2, induces the expression of anti-oxidative genes such as HO-1, and inhibits LPS-induced inflammatory response via down-regulation of Src activity.
Many Nigerian adolescents lack knowledge about ideal oral hygiene practices, which has contributed to the high prevalence of poor oral health among them. Delivering oral hygiene education using innovative methods, such as board games associated with having fun while also learning, would help increase their understanding and adherence to these practices. A board game operates on the principle that knowledge is acquired and retained through repetition and interaction with peers. This paper highlights the development of a culturally tailored board game based on the Health Belief Model (HBM) and validated for promoting oral hygiene among adolescents. To report how a board game on oral hygiene education for adolescents was developed and validated in southwestern Nigeria. A Research and Development (R&D) framework, incorporating Design-Based Research (DBR) principles, was used to develop a board game containing oral hygiene messages. The messages were adapted from the World Health Organisation's (WHO) promoting Oral Health in Africa manual. This was based on the HBM constructs and tailored to fit the African context. Over a period of three months, the development of the oral hygiene education board game involved five community oral health professionals, a paediatric dentist, and a psychologist specialising in adolescent health from the University of Ibadan. Students of the Faculty of Dentistry of the University of Ibadan, a graphic designer, and an artist also contributed to the project. The board game was developed using English, the official language of Nigeria. In the validation of this tool, the ease of use, duration of play, number of players, and its relevance to this age bracket's daily activities were largely considered. A 20 by 20 inches stainless steel framed board game with an acrylic surface containing 100 small boxes, featuring black-themed oral hygiene graphical illustrations and oral hygiene messages inserted in some boxes, were developed. In addition, 10 cards of size 8.5 cm by 5.4 cm containing oral hygiene questions on one side and the answers on the other side, as well as five colour-coded laminated player identification cards, were also created. Two dice and a plastic cup for throwing the dice were procured. The oral hygiene messages, questions and answers focus on enhancing adolescents' knowledge, attitudes and practices regarding optimal oral hygiene measures in Southwestern Nigeria. Oral hygiene messages, questions and answers were modified accordingly to ensure they were age appropriate and effective for promoting oral hygiene education through a board game. The board game was designed to be colourful to increase its appeal and encourage play. The development of the board game was informed by the need for context-specific, age-appropriate tools to enhance oral hygiene education among adolescents. The design stages integrated culturally relevant content, simple language, and familiar visual elements to improve accessibility and relatability. Interactive components were incorporated to promote peer-to-peer learning and active engagement. The board game was structured for ease of implementation in school-based and community settings. While not yet evaluated through formal intervention, its design features suggest potential to support improved oral health awareness and behaviour among adolescents, particularly in low and middle-income contexts.
Burkitt lymphoma is a highly aggressive mature B-cell non-Hodgkin lymphoma characterized by MYC dysregulation and a high proliferative index. Although the sporadic form commonly presents with abdominal involvement, maxillofacial manifestations in children may mimic odontogenic infections, leading to delayed diagnosis. We report a nine-year-old boy with a rapidly progressive unilateral mandibular swelling initially treated as a dental abscess without improvement. Imaging revealed an ill-defined osteolytic mandibular lesion with cortical erosion and soft tissue extension, raising suspicion for malignancy. Histopathology demonstrated monomorphic medium-sized atypical lymphoid cells with high mitotic activity. Immunohistochemistry confirmed B-cell lineage (CD20, CD10, BCL6, c-MYC) with a Ki-67 index of 95%, consistent with Burkitt lymphoma. The patient was treated with rituximab-based multi-agent chemotherapy and showed a favorable response. This case highlights the importance of early radiologic-pathologic correlation and prompt biopsy in atypical pediatric mandibular swellings to enable timely diagnosis and management.
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.
Our research endeavored to formulate a patient-specific prognostic algorithm and elucidate the interconnection between critical genetic polymorphisms at PTPN1 loci and the predisposition to small vessel pathologies in Han Chinese subjects presenting with T2DM. From January 1, 2019, to June 30, 2024, a total of 3,847 patients with T2DM were enrolled in this cross-sectional case-control study. They were grouped into four groups by means of fundus examination and renal function assessment: the T2DM alone group (T2DM group), the T2DM combined with diabetic retinopathy (DR) group (T2DM + DR group), the T2DM combined with diabetic nephropathy (DN) group (T2DM + DN group), and the T2DM combined with DR + DN group (T2DM + DR +DN group). The genotypes of four SNP loci (rs968289, rs6067484, rs2206521, rs754118) of the PTPN1 gene were detected by PCR-RFLP. To evaluate the association between SNP loci and microvascular complications, multivariate logistic regression analysis was employed, followed by LASSO regression for variable selection to develop a nomogram prediction model. The rs968289-GG genotype demonstrated a statistically significant link to the risk of DR (adjusted OR = 1.47, 95%CI: 1.15-1.88, P = 0.002); the rs6067484-CC genotype exhibited a significant relationship with the risk of DN (adjusted OR = 1.58, 95%CI: 1.21-2.06, P < 0.001); The rs2206521-AA genotype significantly correlated with the risk of DR + DN co-morbidity (adjusted OR = 1.69, 95%CI: 1.28-2.24, P < 0.001). The column-line graphical model constructed based on nine independent predictors had AUCs of 0.823 and 0.808 in the training and validation sets, with sensitivity and specificity of 76.4%/78.9% and 74.2%/80.1%, respectively. Significant associations were observed between specific genotypic variants at the PTPN1 gene's rs968289, rs6067484 and rs2206521 loci and microvascular complication risk in Chinese Han T2DM patients. The column-line graph prediction model integrating genetic markers and clinical indicators has good discriminative ability and clinical utility, providing an important tool for individualized risk assessment and precise prevention of diabetic microvascular complications.
While antiseizure medications (ASMs) exhibit the ability to influence the immune system, the specific immunosuppressive consequences in neonates remain insufficiently delineated. Levetiracetam is regarded a safe and effective second-line treatment following phenobarbital, particularly in cases where first-line agents are ineffective or result in adverse reactions in neonatal seizures. We present a neonate who developed B-cell lymphopenia due to levetiracetam administration, which constitutes a previously unrecognized adverse effect of the medication in neonates. To our knowledge, this case marks the initial documented occurrence of B-cell lymphopenia associated with levetiracetam in a neonate.
Diffuse large B-cell lymphoma (DLBCL) exhibits significant heterogeneity, with therapy resistance in Activated B-cell-like (ABC) and relapsed Germinal Center B-cell-like (GCB) subtypes being major challenges, with underlying drivers poorly understood. We performed single-cell RNA sequencing (scRNA-seq) on de novo ABC-DLBCL, de novo GCB-DLBCL, and relapsed GCB-DLBCL specimens. Analyses included malignant cell subclustering, R-loop scoring, trajectory inference, and cell-cell communication. HMGN2 was functionally validated in vitro. We identified an aggressive, poor-prognosis B-cell subpopulation in de novo ABC and relapsed GCB-DLBCL, defined by significantly reduced R-loop formation.This phenotype was directly associated with the downregulation of High-Mobility Group Nucleosome Binding Domain 2 (HMGN2). Mechanistically, we validated that HMGN2 promotes R-loop formation; its loss derepresses IL4R expression, leading to hyperactivation of the oncogenic PI3K-AKT signaling pathway. Functional validation confirmed that HMGN2 knockdown in DLBCL cell lines enhanced proliferation and clonogenicity, whereas its overexpression was inhibitory. The tumor microenvironment in relapsed GCB tumors exhibited profoundly immunosuppressive features, including functionally impaired CD8+T cells and dominant inhibitory BTLA-TNFRSF14 interactions. Our study identifies a potential HMGN2/R-loop/PI3K-AKT axis that may drive malignant cell-intrinsic fitness while shaping an immune-suppressive microenvironment. These findings position HMGN2 as a candidate regulator of DLBCL progression and a potential prognostic biomarker and therapeutic target to address treatment resistance. [Image: see text] The online version contains supplementary material available at 10.1186/s12967-026-08064-7.
Accurate prediction of groundwater quality is essential for environmental monitoring and public health protection, particularly in arid regions such as Béchar in southwest Algeria. This study applied a root mean square-based water quality index (RMS-WQI) to evaluate groundwater quality using 621 samples characterized by physicochemical parameters, including pH, electrical conductivity, total dissolved solids, major cations, major anions, and nitrate. Seven supervised machine learning algorithms K-nearest neighbors, artificial neural network (ANN), support vector machine (SVM), ensemble trees (EN), discriminant analysis, Naïve Bayes, and decision trees were trained and optimized in MATLAB using the Classification Learner Toolbox with Bayesian optimization and fivefold cross-validation. Among the tested models, ANN and SVM achieved the highest predictive performance, with accuracies of 99.47 and 97.88%, respectively, along with superior precision, recall, F1-score, and Cohen's Kappa values, indicating strong agreement with observed RMS-WQI classes. Compared to conventional RMS-WQI assessment and previously reported nonoptimized models, the proposed framework demonstrates improved classification accuracy and robustness. Additionally, an operational graphical user interface was developed to facilitate rapid groundwater quality estimation using routine measurements. The findings highlight the effectiveness of optimized ANN and SVM models as reliable decision support tools for groundwater quality management in data-scarce arid environments.
Chitinases from Trichoderma species exhibit strong antifungal activity and high biocontrol potential, yet their industrial utilization has been constrained by low heterologous secretion efficiency and costly purification processes. In this study, the Generally Recognized As Safe yeast Saccharomyces cerevisiae Y2805 was engineered to secrete chitinase Tch36 from a Korean isolate of T. atroviride, using a rice α-amylase signal peptide under a constitutive glyceraldehyde-3-phosphate dehydrogenase (GPD) promoter. When cultivated in glycerol–colloidal chitin medium, the recombinant yeast exhibited approximately a fivefold increase in measurable chitinase activity relative to the SC mock control, whereas the empty-vector control showed only a modest increase. The crude, non-concentrated culture filtrate (approximately 1000 U L⁻1) displayed statistically significant antifungal activity against 12 fungal species, including 9 phytopathogens and 3 opportunistic Aspergillus species. Among the phytopathogens, Fusarium graminearum was one of the most strongly inhibited species, showing approximately 40–50% suppression of colonial growth on solid medium. In liquid culture, microscopy-based germination assays revealed ≥ 63% inhibition of early hyphal elongation in Botrytis cinerea and A. niger. All antifungal effects were evaluated relative to filtrates from the empty-vector control prepared under equivalent dilution conditions. The culture filtrate also enhanced protoplast formation, providing direct evidence of chitinase-associated cell wall weakening. Collectively, these results establish a purification-free, yeast-based heterologous secretion platform capable of producing active Trichoderma chitinase with inhibitory effects on diverse plant- and animal-associated fungi. This strategy has practical potential for biocontrol applications and for sustainable bioprocessing technologies based on microbial chitinase production. [Image: see text] The online version contains supplementary material available at 10.1186/s40643-026-01054-z.
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]
Amaranthus species (particularly Amaranthus cruentus, Amaranthus hypochondriacus, Amaranthus tricolor and Amaranthus caudatus) are traditional underutilised crops with the potential to contribute to sustainable, healthy food systems. We focus on amaranth as a leafy vegetable aiming to develop improved lines for cultivation by smallholder farmers in Sub-Saharan Africa. We demonstrate differences in leaf yield and metabolites relevant to human nutrition across eight amaranth accessions: four A. cruentus and four A. hypochondriacus. These accessions are founders of an inter-specific multi-parent advanced generation inter-cross population. We generated high-quality genome assemblies and annotations for these founder lines and identified sequence and structural variants (SVs) compared with a reference A. cruentus genome. Pangenome analysis (also including A. cruentus, A. hypochondriacus and A. tricolor reference genomes) identified core, dispensable and private gene families. Fifty per cent of gene families were core, highlighting the value, in terms of gene discovery, of sequencing additional accessions and the inclusion of three Amaranthus species. A graphical pangenome was constructed using SVs and demonstrated variation in copy number of genes with a likely role in disease resistance. This inter-specific pangenome will be highly valuable for future research on amaranth and facilitate usage of SVs in trait mapping and causal gene discovery.
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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.
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.